Method for evaluating fatigue by amino acid concentration in urine

Abstract

The present invention addresses the problem of providing an evaluation method and the like with which it is possible to non-invasively and easily monitor daily changes in fatigue (specifically, body fatigue and the like) associated with exercise in a subject under evaluation (specifically, an exerciser (in particular, a sports player)) using an amino acid concentration in urine, and as a result, the evaluation method and the like can be utilized, for example, in a situation considering measures to take for improving the exercise performance of the subject under evaluation. In the present embodiment, the extent of fatigue in a subject under evaluation is evaluated by: using a concentration value of at least one amino acid among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in urine collected from the subject under evaluation; or using a value calculated from a formula including a variable into which the concentration value is substituted and the concentration value itself.

Description

Method for Evaluating Fatigue Based on Amino Acid Concentration in Urine

[0001] The present invention relates to a method for evaluating fatigue, a calculation method, an evaluation device, a calculation device, an evaluation program, a calculation program, a recording medium, an evaluation system and a terminal device based on urinary amino acid concentration, a method for producing an expression for evaluating the degree of fatigue, and a method for evaluating fatigue using the expression produced by said production method.

[0002] Patent Document 1 discloses a method for monitoring whether a patient complies with a drug maintenance regimen. Patent Document 2 discloses a technique for diagnosing early-stage renal failure by identifying a biomarker for renal failure that can be collected from urine or blood and fluctuates earlier than the glomerular filtration rate and serum creatinine concentration. In Patent Document 3, it is stated in the section of background art that "lactic acid in the body is produced by physical exercise, so the concentration of lactic acid or lactate can be used as an indicator of exercise load and the degree of physical fatigue", and a measurement method is disclosed that enables determining the amount of a blood-derived (artery-derived) substance and the amount of a muscle-derived substance without collecting blood. Patent Document 4 discloses an improved method for searching for biomarkers of urinary metabolites.

[0003] Japanese National Publication of International Patent Application No. 09-508967, Japanese Patent Laid-Open No. 2015-132598, Japanese Patent Laid-Open No. 2018-130403, Japanese Patent Laid-Open No. 2019-105456

[0004] By the way, when athletes carry out excessive daily training, their physical condition may deteriorate due to the accumulation of fatigue and damage, and they may not be able to exert their expected performance. Therefore, objectively evaluating daily physical condition and appropriately adjusting training load, required nutrition and rest is important for improving the performance of athletes.

[0005] However, at present, there is no non-invasive and simple method for evaluating daily changes in physical condition associated with exercise, so the development of a method for athletes to objectively grasp and evaluate their daily physical condition is an issue.

[0006] While imaging diagnostics such as ultrasound or MRI, and physiological evaluations through the analysis of biological samples such as blood or biopsy, exist as methods for assessing physical condition and are highly reliable, they require medical supervision and are invasive, making them difficult for exercisers to adopt on a daily basis. Furthermore, while wearable devices capable of measuring heart rate or activity levels can perform non-invasive measurements, the information they can obtain is limited to changes in heart rate and activity levels during exercise. Therefore, current wearable devices are insufficient for directly evaluating the physical condition that changes due to exercise.

[0007] This invention has been made in view of the above-mentioned circumstances, and aims to provide an evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium, evaluation system, and terminal device that can be used, for example, when considering measures to improve the exercise performance of the subject of evaluation (specifically, exercisers (especially sports athletes)), in a non-invasive and simple manner using urinary amino acid concentration, and can be used in situations such as considering measures to improve the exercise performance of the subject of evaluation. It also aims to provide a method for creating an equation that can be used to evaluate the degree of fatigue.

[0008] To solve the above-mentioned problems and achieve the objective, the evaluation method according to the present invention is characterized by including an evaluation step of evaluating the degree of fatigue in the subject to be evaluated using the concentration value of at least one amino acid from among 21 types of amino acids (Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr) in urine collected from the subject to be evaluated, or using the value of the formula calculated using the formula containing a variable into which the concentration value is substituted and the concentration value.

[0009] In this specification, various amino acids are mainly referred to by their abbreviations, but their full names are as follows: (Abbreviation) (Official name) Ala Alanine Arg Arginine Asn Asparagine Asp Aspartic acid BCAA Branched chain amino acids Cys Cystine Gln Glutamine Glu Glutamic acid and Glutamine Gly Glycine His Histidine Ile Isoleucine Leu Leucine Lys Lysine Met Methionine Phe Phenylanine Pro Proline Ser Serine Thr Threonine Trp Tryptophan Tyr Tyrosine Val Valine

[0010] In the evaluation step, if the concentration value or the value of the formula is lower than or equal to a predetermined value, the degree of fatigue may be evaluated as high, and if the concentration value or the value of the formula is higher than the predetermined value, the degree of fatigue may be evaluated as low.

[0011] Furthermore, the evaluation step may also evaluate the degree of muscle damage or the state of energy metabolism in the subject of evaluation.

[0012] Furthermore, the evaluation results regarding the degree of muscle damage obtained in the evaluation step may correspond to the results obtained by evaluating the degree of muscle damage based on blood myoglobin concentration or blood creatine kinase concentration.

[0013] Furthermore, the evaluation results regarding the state of energy metabolism obtained in the evaluation step may correspond to the results of evaluating the state of energy metabolism based on the total blood ketone body concentration or the blood NEFA (Non-esterified Fatty Acid) concentration.

[0014] Furthermore, in the evaluation step, the value of the formula calculated using the concentration value and one of the following values ​​may be used: the value obtained by dividing the concentration value by the creatinine concentration value obtained from the urine, and the value obtained by dividing the concentration value by the urine specific gravity obtained from the urine.

[0015] Furthermore, in the evaluation step, the value of the formula calculated using the concentration value and the at least one value may be used, which further includes a variable into which at least one of the following values ​​obtained from the subject to be evaluated—a value of a subjective index representing fatigue or pain, a value of the sleep duration of the subject to be evaluated, and a value of the pH of the urine—is substituted.

[0016] Furthermore, in the evaluation step, the value of the formula calculated using the concentration value of at least one micronutrient among phosphorus and chromium in the urine, and the concentration value of at least one amino acid and the concentration value of at least one micronutrient may be used.

[0017] Furthermore, the urine sample may be collected in the morning.

[0018] Furthermore, the urine sample may be collected on the day following the day the exercise subject to evaluation was performed.

[0019] Furthermore, the aforementioned exercise may also be a sport.

[0020] Furthermore, the gender of the person being evaluated may also be male.

[0021] Furthermore, the evaluation step may be performed by a control unit provided in the information processing device.

[0022] Furthermore, the calculation method according to the present invention is characterized by including a calculation step of calculating the value of a formula using the concentration value of at least one of the 21 amino acids in the urine collected from the subject to evaluation, and a formula for evaluating the degree of fatigue which includes a variable into which the concentration value is substituted.

[0023] The calculation step may be performed by a control unit provided in the information processing device.

[0024] Furthermore, the evaluation device according to the present invention is an evaluation device comprising a control unit, wherein the control unit comprises an evaluation means for evaluating the degree of fatigue in the subject to evaluation using the concentration value of at least one of the 21 types of amino acids in the urine collected from the subject to evaluation, or using the value of the formula calculated using the formula containing a variable into which the concentration value is substituted and the concentration value.

[0025] Furthermore, the evaluation device according to the present invention may be connected to a terminal device that provides the concentration value or the value of the formula via a network, and the control unit may further include a data receiving means for receiving the concentration value or the value of the formula transmitted from the terminal device, and a result transmitting means for transmitting the evaluation result obtained by the evaluation means to the terminal device, and the evaluation means may use the concentration value or the value of the formula received by the data receiving means.

[0026] Furthermore, the calculation device according to the present invention is a calculation device comprising a control unit, wherein the control unit comprises a calculation means for calculating the value of a formula, which includes the concentration value of at least one of the 21 amino acids in the urine collected from the object to be evaluated, and a variable into which the concentration value is substituted.

[0027] Furthermore, the evaluation program according to the present invention causes the control unit of the information processing device to function as an evaluation means for evaluating the degree of fatigue in the subject to evaluation, using the concentration value of at least one of the 21 amino acids in the urine collected from the subject to evaluation, or using the value of the formula calculated using the formula containing a variable into which the concentration value is substituted and the concentration value.

[0028] Furthermore, the calculation program according to the present invention causes the control unit of the information processing device to function as a calculation means for calculating the value of an equation for evaluating the degree of fatigue, using the concentration value of at least one of the 21 types of amino acids in the urine collected from the subject to evaluation, and an equation for evaluating the degree of fatigue that includes a variable into which the concentration value is substituted.

[0029] Furthermore, the recording medium according to the present invention is a computer-readable recording medium on which the evaluation program or the calculation program is recorded. Specifically, the recording medium according to the present invention is a non-temporary computer-readable recording medium and is characterized by including programmed instructions for causing an information processing device to execute the evaluation method or the calculation method.

[0030] Furthermore, the evaluation system according to the present invention is an evaluation system configured by connecting an evaluation device equipped with a control unit and a terminal device equipped with a control unit so as to be communicative via a network, wherein the control unit of the terminal device comprises data transmission means for transmitting to the evaluation device the concentration value of at least one of the 21 types of amino acids in the urine collected from the object to be evaluated, or the value of the formula calculated using the formula and a variable into which the concentration value is substituted, and result receiving means for receiving evaluation results regarding the degree of fatigue transmitted from the evaluation device, wherein the control unit of the evaluation device comprises data receiving means for receiving the concentration value or the value of the formula transmitted from the terminal device, evaluation means for evaluating the degree of fatigue in the object to be evaluated using the concentration value or the value of the formula received by the data receiving means, and result transmission means for transmitting the evaluation results obtained by the evaluation means to the terminal device.

[0031] Furthermore, the terminal device according to the present invention is a terminal device equipped with a control unit, wherein the control unit is equipped with a result acquisition means for acquiring evaluation results regarding the degree of fatigue, and the evaluation result is a result of evaluating the degree of fatigue in the subject to evaluation using the concentration value of at least one of the 21 types of amino acids in urine collected from the subject to evaluation, or using the value of the formula calculated using the formula which includes a variable into which the concentration value is substituted and the concentration value.

[0032] Furthermore, the terminal device according to the present invention may be connected to an evaluation device for evaluating the degree of fatigue via a network, the control unit may further include data transmission means for transmitting the concentration value or the value of the formula to the evaluation device, and the result acquisition means may receive the evaluation result transmitted from the evaluation device.

[0033] Furthermore, the method for creating a formula according to the present invention is a method for creating a formula for evaluating the degree of fatigue, and is characterized by comprising the steps of: obtaining the concentration of at least one amino acid from the 21 types of amino acids in urine collected from a subject; obtaining the concentration of any one substance from myoglobin, creatine kinase, total ketone bodies, and NEFA (Non-esterified Fatty Acid) in blood collected from the subject; and creating a formula for determining whether the concentration of any one substance is high or low relative to a predetermined value in the concentration distribution.

[0034] In addition, in the method for creating the formula according to the present invention, the urine and blood may be collected on the morning following the day on which the exercise in question was performed.

[0035] Furthermore, in the method for creating the formula according to the present invention, the urine and blood may be collected on the morning following the most recent rest day of the subject after the day the subject exercises, and the method for creating the formula according to the present invention may further include the step of selecting the combination to be used when creating the formula by excluding combinations of the concentration obtained from the urine and the concentration obtained from the blood that fall outside a predetermined range.

[0036] Furthermore, in the method for creating the formula according to the present invention, the urine and blood may be collected on the morning following the most recent rest day of the subject after the day the subject performed the exercise.

[0037] Furthermore, in the evaluation method according to the present invention, if the value of the formula obtained by substituting the concentration of at least one amino acid from Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in the urine collected from the subject to evaluation on the morning following the most recent rest day after the exercise day into the first formula is lower than a predetermined value, then the value of the formula obtained by substituting the concentration of at least one amino acid in the urine collected from the subject to evaluation on the morning following the most recent exercise day after the rest day into the second formula is used The method includes an evaluation step of evaluating the degree of fatigue in the subject to be evaluated, wherein the first formula is created using urine and blood collected from the subject on the morning following the most recent rest day after the exercise day by a predetermined formula creation method, and the second formula is created using urine and blood collected from the subject on the morning following the most recent exercise day after the rest day by a predetermined formula creation method, wherein the predetermined formula creation method includes the steps of obtaining the concentration of at least one amino acid in the urine, obtaining the concentration of any one of myoglobin, creatine kinase, total ketone bodies, and NEFA (Non-esterified Fatty Acid) in the blood, and creating a formula for determining whether the concentration of any one of the substances is high or low relative to a predetermined value in the concentration distribution.

[0038] According to the present invention, it becomes possible to non-invasively and easily monitor the daily changes in fatigue associated with exercise (specifically, physical fatigue, etc.) in the subject of evaluation (specifically, exercisers (especially sports athletes)) using urinary amino acid concentration. This has the effect of being useful, for example, in situations where measures to improve the exercise performance of the subject of evaluation are being considered. Furthermore, according to the present invention, it is possible to create a formula for evaluating the degree of fatigue.

[0039] Figure 1 is a principle configuration diagram showing the basic principle of the first embodiment. Figure 2 is a principle configuration diagram showing the basic principle of the second embodiment. Figure 3 is a diagram showing an example of the overall configuration of the system. Figure 4 is a diagram showing another example of the overall configuration of the system. Figure 5 is a block diagram showing an example of the configuration of the evaluation device 100 of the system. Figure 6 is a diagram showing an example of the information stored in the concentration data file 106a. Figure 7 is a diagram showing an example of the information stored in the index state information file 106b. Figure 8 is a diagram showing an example of the information stored in the specified index state information file 106c. Figure 9 is a diagram showing an example of the information stored in the formula file 106d1. Figure 10 is a diagram showing an example of the information stored in the evaluation result file 106e. Figure 11 is a block diagram showing the configuration of the evaluation unit 102d. Figure 12 is a block diagram showing an example of the configuration of the client device 200 of the system. Figure 13 is a block diagram showing an example of the configuration of the database device 400 of the system. Figure 14 is a diagram showing 10 logistic regression equations explored in Example 1. Figure 15 is a diagram showing 41 logistic regression equations explored in Example 5.

[0040] The following describes in detail, with reference to the drawings, embodiments of the evaluation method, calculation method, and formula creation method according to the present invention (first embodiment), as well as embodiments of the evaluation apparatus, calculation apparatus, evaluation method, calculation method, evaluation program, calculation program, recording medium, evaluation system, and terminal device according to the present invention (second embodiment). However, the present invention is not limited to these embodiments.

[0041] [First Embodiment] [1-1. Overview of the First Embodiment] Here, an overview of the first embodiment will be described with reference to Figure 1. Figure 1 is a principle configuration diagram showing the basic principle of the first embodiment.

[0042] First, concentration data is obtained from urine collected from the subject of evaluation (for example, an individual such as an animal or a human), including the concentration value of at least one of the 21 types of amino acids (step S11). In other words, concentration data is obtained regarding the concentration value of at least one amino acid selected from the group consisting of the 21 types of amino acids.

[0043] Here, there are no restrictions on the sex of the subject to be evaluated. Urine is preferably collected, for example, in the morning (for example, in a time zone from about 6:00 a.m. to about 9:00 a.m.). Urine is preferably collected, for example, on the day after the day on which the subject to be evaluated performed exercise. Exercise refers to physical activities that are carried out systematically or regularly for the purpose of maintaining or improving health or physical strength, such as sports or fitness. Physical activity refers to all activities accompanied by contraction of skeletal muscles that consume more energy than a resting state. There are no restrictions on the duration of exercise. There are no restrictions on the intensity of exercise.

[0044] Furthermore, in step S11, 1) a concentration value of creatinine in urine, 2) urine specific gravity, 3) a pH value of urine, 4) a concentration value of one or more micronutrients in urine, 5) a value of a subjective index representing fatigue or pain of the evaluation subject, and 6) a sleep time value of the evaluation subject, additional data including at least one value among the foregoing may be further acquired. Examples of the micronutrients include magnesium, iron, calcium, zinc, sodium, phosphorus, potassium, lithium, aluminum, chromium, manganese, cobalt, nickel, copper, selenium, molybdenum, and the like. The value of the subjective index can be acquired only when the evaluation subject is a human, and examples thereof include a value obtained by the evaluation subject subjectively quantifying pain in one or more body parts, a value obtained by the evaluation subject evaluating the subjective intensity of exercise (RPE) of the performed exercise, or a value obtained by the evaluation subject evaluating physical fatigue using a Visual Analog Scale (VAS), and the like. Examples of the body parts include the front of the thigh, the calf, the Achilles tendon, the knee joint, and the like.

[0045] Furthermore, the concentration data obtained in step S11 may be, for example, data measured by a company that performs concentration value measurements. Alternatively, concentration data may be obtained by measuring the concentration value from the collected urine using measurement methods such as ultraviolet detection, fluorescence detection, electrochemical detection, or mass spectrometry in combination with liquid chromatography, gas chromatography, or capillary electrophoresis (Reference 1 "Yanting Song et al., "Recent trends in analytical methods for the determination of amino acids in biological samples", Journal of Pharmaceutical and Biomedical Analysis, Volume 147, 5 January 2018, Pages 35-49." and Reference 2 "Masashi Harada et al., "Simultaneous Analysis of D,L-Amino Acids in Human Urine Using a Chirality-Switchable Biaryl Axial Tag and Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry", Symmetry 2020, 12(6), 913."). Concentration data may be obtained by quantitatively measuring the amount of substances that increase or decrease due to substrate recognition or by measuring spectroscopic values, using molecules such as enzymes, aptamers, or antibodies that react with or bind to the target urinary substance.When using molecules that react with or bind to the target urinary substance, concentration data may be obtained on-site using kits or test strips that can be easily measured with a plate reader or similar device (Reference 3, "Yasuhisa Asano, “Screening and development of enzymes for determination and transformation of amino acids”, Bioscience, Biotechnology, and Biochemistry, Volume 83, Issue 8, 3 August 2019, Pages 1402-1416." and Japanese Patent Publication No. 7630148). When measuring the concentration, acetonitrile or methanol may be added before measurement to remove proteins, or derivatization with a labeling reagent may be performed. Here, the unit of the concentration value may be, for example, molar concentration, weight concentration, or enzyme activity, and may also be obtained by adding, subtracting, multiplying, or dividing these concentrations by an arbitrary constant. Furthermore, the concentration value may be either an absolute value or a relative value.

[0046] Next, using the concentration values ​​included in the concentration data obtained in step S11, the degree of fatigue (specifically, physical fatigue, mental or nervous fatigue, or pathological fatigue) in the subject of evaluation is evaluated (predicted) (step S12). In short, in step S12, information is obtained to evaluate the degree of fatigue in the subject of evaluation. Here, evaluating the degree of physical fatigue means, for example, evaluating the degree of muscle damage or the state of energy metabolism. If the degree of muscle damage is evaluated in step S12, the evaluation result obtained from this evaluation corresponds to the evaluation result based on blood myoglobin concentration or blood creatine kinase concentration, which are known as muscle damage markers. If the state of energy metabolism is evaluated in step S12, the evaluation result obtained from this evaluation corresponds to the evaluation result based on blood total ketone body concentration or blood NEFA (Non-esterified Fatty Acid) concentration, which are known as markers that reflect the state of energy metabolism throughout the body.

[0047] As described above, according to the first embodiment, concentration data of the evaluation target is acquired in step S11, and in step S12, the degree of fatigue in the evaluation target is evaluated using the concentration values included in the acquired concentration data of the evaluation target. Therefore, daily changes in fatigue associated with exercise in the evaluation target (specifically, an exerciser (particularly a sports person)) can be non-invasively and simply monitored using urinary amino acid concentrations, and thus the present invention can be used, for example, in situations where considering measures for improving the exercise performance of the evaluation target.

[0048] It should be noted that, before executing step S12, data such as missing values and outliers may be removed from the data acquired in step S11.

[0049] In addition, when other data is acquired in step S11, in step S12, said evaluation may be performed using the concentration values included in the concentration data and the values included in the other data. For example, when a creatinine concentration value is included in the other data, in step S12, said evaluation may be performed using an amino acid / creatinine ratio value obtained by dividing the concentration value of each amino acid by the creatinine concentration value. Further, for example, when a urine specific gravity is included in the other data, in step S12, said evaluation may be performed using an amino acid / urine specific gravity ratio value obtained by dividing the concentration value of each amino acid by the urine specific gravity. Hereinafter, when the amino acid / creatinine ratio and the amino acid / urine specific gravity ratio are used without distinction, they are simply referred to as "ratio values".

[0050] Furthermore, the concentration value or ratio may be determined to reflect the degree of fatigue in the subject being evaluated. Alternatively, the concentration value or ratio may be transformed using methods such as those listed below, and the transformed value may be determined to reflect the degree of fatigue in the subject being evaluated. In other words, the concentration value or ratio, or the transformed values ​​themselves, may be treated as evaluation results regarding the degree of fatigue in the subject being evaluated. Note that the transformation method will be explained below using the concentration value as the target of transformation, but the same applies to the value of the formula. To ensure that the range of possible concentration values ​​falls within a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or a range from -10.0 to 10.0, etc.), the concentration values ​​may be transformed by, for example, adding, subtracting, multiplying, or dividing them by an arbitrary value, transforming them using a predetermined transformation method (for example, exponential transformation, logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation, etc.), or by combining these calculations on the concentration values. For example, the value of an exponential function with the concentration value as the exponent and Napier's number as the base (specifically, the value of p / (1-p) when the natural logarithm ln(p / (1-p)) is equal to the concentration value, given that the probability p of being evaluated as "high degree of fatigue" according to a predetermined criterion is defined) may be calculated further. Alternatively, the value obtained by dividing the calculated exponential function value by the sum of 1 and the value in question (specifically, the value of the probability p) may be calculated further. Furthermore, the concentration value may be transformed so that the transformed value under specific conditions becomes a specific value. For example, the concentration value may be transformed so that the transformed value is 5.0 when the specificity is 60% and 8.0 when the specificity is 90%, or so that the transformed value is 5.0 when the specificity is 80% and 8.0 when the specificity is 95%. Furthermore, the distribution of the concentration values ​​may be normalized, and the concentration value may be converted into a standard score based on that normalization distribution so that the mean is 50 and the standard deviation is 10, and this process may be performed for each amino acid. The mean and standard deviation in this case can be any real numbers. Furthermore, the various transformations described above may be performed separately for each gender, age, a combination of both, or any other arbitrary condition.Furthermore, the concentration values ​​in this specification may be the concentration values ​​themselves, or they may be values ​​obtained after converting the concentration values ​​(the same applies to ratio values).

[0051] Alternatively, the degree of fatigue in the subject of evaluation may be assessed using the values ​​obtained by converting the concentration values ​​using the conversion method described above (the same applies to the ratio values).

[0052] Alternatively, positional information relating to the location of a predetermined mark on a predetermined ruler, which is visibly displayed on a display device such as a monitor or on a physical medium such as paper, may be generated using the concentration value or the converted value, and it may be determined that the generated positional information reflects the degree of fatigue in the object being evaluated (the same applies to the ratio value). The predetermined ruler is for evaluating the degree of fatigue, and is, for example, a ruler with markings, in which at least the upper and lower limits of the range that the concentration value or the converted value can take, or a part of that range, are indicated (the same applies to the ratio value). The predetermined mark corresponds to the concentration value or the converted value, and is, for example, a circle or a star (the same applies to the ratio value).

[0053] Furthermore, if the concentration value or the converted value is lower than a predetermined value (such as the median, mean ± 1SD, 2SD, 3SD, N-quantile, N-percentile, or a clinically significant cutoff value), the subject may be evaluated as having a high degree of fatigue. If the concentration value or the converted value is equal to or greater than the predetermined value, the subject may be evaluated as having a low degree of fatigue (the same applies to ratio values). In this case, instead of the concentration value or the converted value itself, a standard score (a value obtained by normalizing the distribution of the concentration value or the converted value for each amino acid separately for men and women, and then standardizing the concentration value or the converted value based on that normal distribution so that the mean is 50 and the standard deviation is 10) may be used (the same applies to ratio values). For example, if the standard score is less than the mean - 2SD (standard score < 30), the degree of fatigue may be evaluated as high, and if the standard score is greater than the mean + 2SD (standard score > 70), the degree of fatigue may be evaluated as low.

[0054] Alternatively, the evaluation may be performed by calculating the value of an expression using an expression that includes a variable into which the concentration values ​​contained in the concentration data are substituted, and the concentration values ​​themselves. Specifically, the evaluation may be performed by substituting the concentration values ​​contained in the concentration data into a variable in an expression for evaluating the degree of fatigue and calculating the value of the expression.

[0055] Furthermore, if other data is obtained in step S11, the evaluation may be performed by calculating the value of the expression using an expression that includes a variable to which the concentration value contained in the concentration data is substituted and a variable to which the value contained in the other data is substituted, as well as the concentration value and the value of the expression (the expression conceptually includes an expression that includes a variable to which the ratio value is substituted).

[0056] Furthermore, the value of the calculated formula may be determined to reflect the degree of fatigue in the subject being evaluated. Alternatively, the value of the formula may be transformed using methods such as those listed below, and the transformed value may be determined to reflect the degree of fatigue in the subject being evaluated. In other words, the value of the formula or its transformed value itself may be treated as an evaluation result regarding the degree of fatigue in the subject being evaluated. To ensure that the range of possible values ​​for the formula falls within a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or a range from -10.0 to 10.0, etc.), the value of the formula may be transformed by adding, subtracting, multiplying, or dividing the formula by an arbitrary value, or by transforming the formula using a predetermined transformation method (for example, exponential transformation, logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation, etc.), or by combining these calculations on the formula. For example, the value of an exponential function with the value of the expression as the exponent and Napier's number as the base (specifically, the value of p / (1-p) when the natural logarithm ln(p / (1-p)) is equal to the value of the expression, given that the probability p of being evaluated as "highly fatigued" according to a predetermined criterion is defined) may be calculated further. Alternatively, the value obtained by dividing the calculated exponential function value by the sum of 1 and the value in question (specifically, the value of the probability p) may be calculated further. Furthermore, the value of the expression may be transformed so that the transformed value under specific conditions becomes a specific value. For example, the value of the expression may be transformed so that the transformed value is 5.0 when the specificity is 60% and 8.0 when the specificity is 90%, or so that the transformed value is 5.0 when the specificity is 80% and 8.0 when the specificity is 95%. Furthermore, the value of the expression may be converted into a standard score so that the mean is 50 and the standard deviation is 10. In addition, the various transformations described above may be performed separately for men and women or for different age groups. In this specification, the value of an expression may be the value of the expression itself, or it may be the value after transforming the value of the expression.

[0057] Alternatively, positional information relating to the location of a predetermined mark on a predetermined ruler, which is visibly displayed on a display device such as a monitor or on a physical medium such as paper, may be generated using the value of the formula or the converted value, and it may be determined that the generated positional information reflects the degree of fatigue in the object being evaluated. The predetermined ruler is for evaluating the degree of fatigue in the object being evaluated, and is, for example, a ruler with markings, in which at least the upper and lower limits of the range that the value of the formula or the converted value can take, or a part of that range, are indicated. The predetermined mark corresponds to the value of the formula or the converted value, and is, for example, a circle or a star.

[0058] Furthermore, the degree of fatigue in the subject of evaluation may be qualitatively evaluated. Specifically, the subject of evaluation may be classified into one of several categories defined with at least the degree of fatigue, using "concentration values ​​included in the concentration data and one or more pre-set thresholds" or "an expression including a variable to which the concentration values ​​included in the concentration data are substituted, the concentration values, and one or more pre-set thresholds." Also, if other data is obtained in step S11, the subject of evaluation may be classified into one of several categories defined with at least the degree of fatigue, using "concentration values ​​included in the concentration data, values ​​included in the other data, and one or more pre-set thresholds" or "an expression including a variable to which the concentration values ​​are substituted and a variable in the other data to which the concentration values ​​are substituted (the expression conceptually includes an expression including a variable to which a ratio value is substituted)." Note that the multiple categories may include categories for subjects with a high degree of fatigue, categories for subjects with a low degree of fatigue, and categories for subjects with a moderate degree of fatigue. Furthermore, the multiple categories may include categories for subjects with a high degree of fatigue and categories for subjects with a low degree of fatigue. Alternatively, the concentration value or formula value may be converted using a predetermined method, and the evaluated subject may be classified into one of the multiple categories using the converted value (the same applies to ratio values).

[0059] Furthermore, while there are no specific requirements regarding the form of the formulas used in the aforementioned evaluation, they may, for example, be in the following forms: • Linear models such as multiple regression equations, linear discriminant equations, principal component analysis, and canonical discriminant analysis based on the least squares method • Generalized linear models such as logistic regression and Cox regression based on the maximum likelihood method • Generalized linear mixed models that consider random effects such as individual differences and facility differences in addition to generalized linear models • Formulas created using cluster analysis methods such as K-means, k-nearest neighbors, and hierarchical cluster analysis • Formulas created using Bayesian statistics such as MCMC (Markov chain Monte Carlo), Bayesian networks, hierarchical Bayesian methods, and Gaussian process models • Formulas created using classification methods such as support vector machines and decision trees • RandomForest, GBDT, lightGBM, and Extremely randomized-Tree Formulas created by ensemble learning models that combine multiple models such as Classifer; formulas created based on neural network structures such as deep learning models, Self-Attention, and Transformer; formulas created by methods that do not belong to the above categories, such as fractional formulas; and formulas that are expressed as a sum of formulas of different forms.

[0060] Furthermore, the formula used in the evaluation may be prepared, for example, by the method described in International Publication No. 2004 / 052191, an international application by the present applicant, or by the method described in International Publication No. 2006 / 098192, an international application by the present applicant. Formulas obtained by these methods can be suitably used to evaluate the degree of fatigue, regardless of the units of the concentration value.

[0061] Here, in multiple regression equations, multiple logistic regression equations, canonical discriminant functions, etc., coefficients and constant terms are added to each variable. Preferably, these coefficients and constant terms may be real numbers, more preferably values ​​that fall within the 99% confidence interval of the coefficients and constant terms obtained from the data for the various classifications, and even more preferably values ​​that fall within the 95% confidence interval of the coefficients and constant terms obtained from the data for the various classifications. Furthermore, the values ​​of each coefficient and their confidence intervals may be obtained by multiplying them by a real number, and the values ​​of the constant terms and their confidence intervals may be obtained by adding, subtracting, multiplying, or dividing them by an arbitrary real constant. When using logistic regression equations, linear discriminant equations, multiple regression equations, etc., in evaluation, linear transformations (addition of constants, multiplication by a constant) and monotonically increasing (decreasing) transformations (e.g., log transformation) do not change the evaluation performance of the equation, and the evaluation performance of the transformed equation is equivalent to that of the original equation, so the transformed equation may be used in evaluation.

[0062] Furthermore, a fractional expression is one in which the numerator is expressed as the sum of variables A, B, C, ... and / or the denominator is expressed as the sum of variables a, b, c, ... A fractional expression also includes the sum of fractional expressions α, β, γ, ... (for example, α + β). A fractional expression also includes divided fractional expressions. The variables used in the numerator and denominator may each have appropriate coefficients. The variables used in the numerator and denominator may be repeated. Each fractional expression may also have appropriate coefficients. The values ​​of the coefficients of each variable and the values ​​of the constant term may be real numbers. Furthermore, while the sign of the correlation with the dependent variable is generally reversed between a given fraction and the fraction obtained by swapping the variables in the numerator and denominator of that fraction ("the swapped fraction"), the correlation with the dependent variable is preserved, and therefore the evaluation performance of the formulas can be considered equivalent. Thus, the swapped fraction is also included in the definition of a fraction.

[0063] Furthermore, when evaluating the degree of fatigue, in addition to the urinary concentration value of at least one of the 21 amino acids mentioned above, values ​​relating to other biological information listed below may also be used. In addition, the formula used in the evaluation may further include one or more variables to which values ​​relating to other biological conditions listed below are substituted, in addition to the variable to which the urinary concentration value of at least one of the 21 amino acids mentioned above is substituted. 1. Concentration values ​​of other blood metabolites (sugars, lipids, etc.), proteins, peptides, minerals, vitamins, organic acids, hormones, etc. other than the 21 amino acids mentioned above. 2. Blood test values ​​such as total protein, glycated albumin, insulin resistance index, total cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), GOT (AST), GPT (ALT), glucose (blood glucose level), MCV, MCH, MCHC, platelet count, insulin, urea nitrogen, calcium, serum iron, etc. 3. 4. Values ​​obtained from imaging information such as ultrasound, X-ray, CT, MRI, and endoscopic images.

[0064] Furthermore, the formula used in the evaluation may be created by a method including, for example, the following steps A, B, and C. In this method, urine and blood may be collected on the morning of the day following the exercise day, or on the morning of the day following the most recent rest day of the subject after the exercise day. If the urine and blood are collected on the morning of the day following the most recent rest day of the subject after the exercise day, step C below may be replaced with step C' below. Here, a rest day is a day other than an exercise day. (Step A) Obtain the concentration of at least one of the 21 amino acids in the urine collected from the subject. (Step B) Obtain the concentration of one of myoglobin, creatine kinase, total ketone bodies, and NEFA in the blood collected from the subject. (Step C) Using the combination of the concentration obtained in step A and the concentration obtained in step B, create a formula to determine whether the concentration obtained in step B is high or low relative to a predetermined value (e.g., the median) in the concentration distribution shown. (Step C') By ​​excluding combinations of concentrations obtained in Step A and Step B that fall outside a predetermined range, a combination to be used when creating the formula is selected, and using the selected combination, a formula is created to determine whether the concentration obtained in Step B included in that combination is high or low relative to a predetermined value (e.g., median) in the concentration distribution.

[0065] Furthermore, if the first formula is created using the above-described method with urine and blood collected on the morning following the most recent rest day after the exercise day, and the second formula is created using the above-described method with urine and blood collected on the morning following the most recent exercise day after the rest day, an evaluation method including the following steps X, Y, and Z may be performed. (Step X) Obtain the concentration (concentration A) of at least one amino acid from the 21 types of amino acids in the urine collected from the subject to be evaluated on the morning following the most recent rest day after the exercise day, and the concentration (concentration B) of the same amino acid in the urine collected from the subject to be evaluated on the morning following the most recent exercise day after the rest day. (Step Y) Substitute the concentration A obtained in step X into the first formula and determine whether the value of the formula obtained is higher or lower than a predetermined value (e.g., median). (Step Z) If it is determined to be lower in step Y, use the value of the formula obtained by substituting the concentration B obtained in step X into the second formula to evaluate the degree of fatigue in the subject to be evaluated.

[0066] Furthermore, the evaluation results obtained from the above-mentioned evaluation can be used for feedback-type fatigue management, which involves considering, implementing, and re-evaluating measures to improve athletic performance. In other words, based on the evaluation results obtained in step S12, the following operations can be carried out.

[0067] Based on the evaluation results, at least one intervention selected from rest, nutritional supplementation (protein, amino acids, electrolytes, carbohydrates, etc.), fluid intake, sleep adjustment, training intensity adjustment, stretching, icing, etc., will be presented and implemented. The terminal device may receive the evaluation results and present candidate countermeasures, as well as record the selection and implementation status.

[0068] After the intervention, a urine sample is collected again at a predetermined time (e.g., the following morning), and the degree of fatigue is reassessed using the same evaluation procedure as described above. If necessary, the evaluation using the first / second formula described above may also be applied.

[0069] The degree of improvement is calculated by comparing the concentration values ​​before and after the intervention, or the values ​​in an expression using those concentration values ​​as variables, and the effectiveness is determined based on a predetermined threshold. The training plan, nutrition plan, and rest plan are adjusted according to the evaluation results.

[0070] Furthermore, the client device 200 and evaluation device 100 described in the second embodiment below may be linked via a network 300 to transmit and receive concentration values ​​or formula values, as well as to provide candidate countermeasures, notify re-measurement schedules, and record and compare evaluation results before and after intervention.

[0071] [Second Embodiment] [2-1. Overview of the Second Embodiment] Here, an overview of the second embodiment will be described with reference to Figure 2. Figure 2 is a principle configuration diagram showing the basic principle of the second embodiment. Note that in this description of the second embodiment, explanations that overlap with the first embodiment described above may be omitted. In particular, here, as an example, a case in which the value of the formula or its converted value is used when evaluating the degree of fatigue is described, but for example, the concentration value included in the concentration data, the value or ratio value included in other data, or their converted values ​​(e.g., standard score) may be used.

[0072] The control unit evaluates the degree of fatigue in the subject to evaluation by calculating the value of an expression, which is stored in the memory unit in advance and includes the concentration value contained in the concentration data of the subject to evaluation that was acquired in advance and the variable to which the concentration value is substituted (step S21). This makes it possible to non-invasively and easily monitor the daily changes in fatigue associated with exercise in the subject to evaluation (specifically, exercisers (especially sports athletes)) using urinary amino acid concentration, and can be used, for example, when considering measures to improve the exercise performance of the subject to evaluation.

[0073] The formula used in step S21 may be one created based on the formula creation process (steps 1 to 4) described below. Here, we will explain the outline of the formula creation process. Note that the process described here is merely an example, and the method of creating the formula is not limited to this.

[0074] First, the control unit creates a candidate equation (for example, y = a1x1 + a2x2 + ... + anxn, y: index data, xi: concentration data, ai: constant, i = 1, 2, ..., n) from index state information (which may have missing values ​​or outliers removed beforehand) stored in the memory unit, based on a predetermined equation creation method (step 1). The index state information includes concentration data and index data related to the values ​​of existing indicators related to the degree of fatigue (specifically, blood myoglobin concentration or blood creatine kinase concentration, which are known as muscle damage markers, or blood total ketone body concentration or blood NEFA concentration, which are known as markers that reflect the state of energy metabolism throughout the body).

[0075] In step 1, multiple candidate equations may be created from the index state information by using multiple different equation creation methods (including those related to multivariate analysis such as principal component analysis, discriminant analysis, support vector machines, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, decision trees, k-nearest neighbors, Gaussian process models, RandomForest, GBDT, Extremely randomized-Trees Classifer, and neural networks). Specifically, multiple groups of candidate equations may be created simultaneously and in parallel using multiple different algorithms for the index state information, which is multivariate data consisting of concentration data and index data obtained by analyzing urine and blood samples from many individuals. For example, two different candidate equations may be created by simultaneously performing discriminant analysis and logistic regression analysis using different algorithms. Alternatively, candidate equations may be created by transforming the index state information using candidate equations created by principal component analysis, and then performing discriminant analysis on the transformed index state information. This makes it possible to ultimately create the equation best suited for evaluation.

[0076] Here, the candidate equation created using principal component analysis is a linear equation containing each variable that maximizes the variance of all concentration data. The candidate equation created using discriminant analysis is a higher-order equation (including exponential and logarithmic) containing each variable that minimizes the ratio of the sum of variances within each group to the variance of all concentration data. The candidate equation created using support vector machines is a higher-order equation (including kernel functions) containing each variable that maximizes the boundary between groups. The candidate equation created using multiple regression analysis is a higher-order equation containing each variable that minimizes the sum of distances from all concentration data. The candidate equation created using Cox regression analysis is a linear model including the log hazard ratio, containing each variable and its coefficient that maximizes the likelihood of that model. The candidate equation created using logistic regression analysis is a linear model representing the log odds of probability, containing each variable that maximizes the likelihood of that probability. Furthermore, the k-means method explores the k neighbors of each concentration data point, defines the group to which the data belongs as the most frequent group among the groups to which the neighbors belong, and selects a variable that best matches the group to which the input concentration data belongs with the defined group. Cluster analysis is a method of clustering (grouping) the points that are closest to each other among all the concentration data. Decision trees are a method of assigning a ranking to variables and determining the group of concentration data from the possible patterns of the higher-ranking variables.

[0077] Returning to the explanation of the formula creation process, the control unit verifies (cross-verifies) the candidate formulas created in step 1 based on a predetermined verification method (step 2). Verification of the candidate formulas is performed for each candidate formula created in step 1. In step 2, verification may also be performed with respect to at least one of several indicators, such as the discrimination rate, sensitivity, specificity, information criterion (Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC)), and ROC_AUC (area under the curve of the receiver characteristic curve), based on at least one of several methods, such as the bootstrap method, holdout method, N-fold method, and leave-one-out method. This makes it possible to create candidate formulas with high predictability or robustness that take into account indicator state information and evaluation conditions.

[0078] Here, the discriminant rate is the proportion of evaluation subjects whose true state is negative (e.g., evaluation subjects with a low degree of fatigue) are correctly evaluated as negative, and evaluation subjects whose true state is positive (e.g., evaluation subjects with a high degree of fatigue) are correctly evaluated as positive, according to the evaluation method of this embodiment. Sensitivity is the proportion of evaluation subjects whose true state is positive are correctly evaluated as positive, according to the evaluation method of this embodiment. Specificity is the proportion of evaluation subjects whose true state is negative are correctly evaluated as negative, according to the evaluation method of this embodiment. The Akaike Information Criterion (AIC) is a criterion used in regression analysis and the like to represent how well the observed data matches the statistical model, and the model with the smallest value, defined as "-2 × (maximum log-likelihood of the statistical model) + 2 × (number of free parameters of the statistical model)", is judged to be the best. Furthermore, the Bayesian Information Criterion (BIC) is a model selection criterion derived based on the principles of Bayesian statistics. It is defined as "-2 × (maximum log-likelihood of the statistical model) + (number of free parameters of the statistical model) × ln (sample size)". The model with the smallest value (a model with fewer parameters) is considered the best. Additionally, ROC_AUC is defined as the area under the receiver characteristic curve (ROC), which is a curve created by plotting (x, y) = (1 - specificity, sensitivity) on a two-dimensional coordinate system. The value of ROC_AUC is 1 for perfect discrimination, and the closer this value is to 1, the higher the discriminative ability. Predictability is the average of the discrimination rate, sensitivity, and specificity obtained by repeatedly testing candidate equations. Robustness is the variance of the discrimination rate, sensitivity, and specificity obtained by repeatedly testing candidate equations.

[0079] Returning to the explanation of the formula creation process, the control unit selects a combination of concentration data included in the index state information used when creating the candidate formula by selecting the variables of the candidate formula based on a predetermined variable selection method (step 3). Note that in step 3, the variable selection may be performed for each candidate formula created in step 1. This allows for the appropriate selection of variables for the candidate formula. Then, step 1 is executed again using the index state information including the concentration data selected in step 3. Alternatively, in step 3, the variables of the candidate formula may be selected based on the verification results in step 2, using at least one of the following methods: stepwise method, best path method, nearest neighbor search method, genetic algorithm, and multi-objective optimization. Note that the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index given by the candidate formula. Multi-objective optimization is a method of simultaneously optimizing two or more evaluation indexes (such as the number of features, training performance, verification performance, or computation time).

[0080] Returning to the explanation of the formula creation process, the control unit repeatedly executes steps 1, 2, and 3 described above, and based on the verification results accumulated therein, selects a candidate formula to be used for evaluation from among multiple candidate formulas, thereby creating a formula to be used for evaluation (step 4). Note that the selection of candidate formulas may involve, for example, selecting the optimal or multi-purpose optimal formula from among candidate formulas created using the same formula creation method, or selecting the optimal or multi-purpose optimal formula from all candidate formulas.

[0081] As explained above, the formula creation process systematically (systematizes) the creation of candidate formulas, verification of candidate formulas, and selection of variables for candidate formulas, thereby enabling the creation of a formula best suited to evaluating the degree of fatigue. In other words, the formula creation process uses concentration values ​​in multivariate statistical analysis and combines variable selection methods and cross-validation to select the optimal and robust set of variables, thereby extracting a formula with high evaluation performance.

[0082] [2-2. Configuration of the Second Embodiment] Here, the configuration of the evaluation system according to the second embodiment (hereinafter sometimes referred to as "this system") will be described with reference to Figures 3 to 13. Note that this system is merely an example, and the present invention is not limited thereto. In particular, here, as an example, a case in which the value of an expression or its converted value is used when evaluating the degree of fatigue is described, but for example, concentration values ​​included in the concentration data, values ​​or ratio values ​​included in other data, or converted values ​​thereof (e.g., standard scores) may be used.

[0083] First, the overall configuration of this system will be explained with reference to Figures 3 and 4. Figure 3 is a diagram showing one example of the overall configuration of this system. Figure 4 is a diagram showing another example of the overall configuration of this system. As shown in Figure 3, this system is configured by connecting an evaluation device 100 that evaluates the degree of fatigue in an individual being evaluated and a client device 200 (corresponding to the terminal device of the present invention) that provides concentration data of the individual, so that they can communicate via a network 300.

[0084] In this system, the client device 200 that provides the data used for evaluation and the client device 200 that receives the evaluation results may be separate devices. As shown in Figure 4, in addition to the evaluation device 100 and the client devices 200, the system may also be configured by connecting a database device 400, which stores indicator status information used when creating formulas in the evaluation device 100 and formulas used during evaluation, to the network 300 so that it can communicate.

[0085] Next, the configuration of the evaluation device 100 of this system will be described with reference to Figures 5 to 11. Figure 5 is a block diagram showing an example of the configuration of the evaluation device 100 of this system, conceptually showing only the parts of the configuration that are relevant to the present invention.

[0086] The evaluation device 100 consists of a control unit 102 such as a CPU (Central Processing Unit) that comprehensively controls the evaluation device, a communication interface unit 104 that connects the evaluation device to the network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line, a storage unit 106 that stores various databases, tables, files, etc., and an input / output interface unit 108 that connects to an input device 112 and an output device 114, and each of these units is connected to communicate via any communication path. Here, the evaluation device 100 may be configured in the same housing as various analytical devices (for example, an amino acid analyzer). For example, a small analytical device equipped with a configuration (hardware and software) that calculates (measures) the concentration value of at least one of the 21 types of amino acids in urine and outputs the calculated value (printing or displaying on a monitor, etc.) may further include an evaluation unit 102d, which will be described later, and is characterized in that the results obtained by the evaluation unit 102d are output using the above configuration.

[0087] The communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). In other words, the communication interface unit 104 has the function of communicating data with other terminals via a communication line.

[0088] The input / output interface unit 108 connects to the input device 112 and the output device 114. The output device 114 can be a monitor (including a home television), a speaker, or a printer (in the following, the output device 114 may be referred to as the monitor 114). The input device 112 can be a keyboard, a mouse, a microphone, or a monitor that works in conjunction with a mouse to provide pointing device functionality.

[0089] The memory unit 106 is a storage means, and can use memory devices such as RAM (Random Access Memory) and ROM (Read Only Memory), fixed disk devices such as hard disks, flexible disks, optical disks, etc. The memory unit 106 stores computer programs that cooperate with the OS (Operating System) to give instructions to the CPU and perform various processes. As shown in the figure, the memory unit 106 stores a concentration data file 106a, an index status information file 106b, a specified index status information file 106c, a formula-related information database 106d, and an evaluation result file 106e.

[0090] The concentration data file 106a stores concentration data. Figure 6 shows an example of the information stored in the concentration data file 106a. As shown in Figure 6, the information stored in the concentration data file 106a is structured by relating an individual number for uniquely identifying the individual (sample) being evaluated with concentration data (which may also be a combination of concentration data and other data). Here, in Figure 6, the concentration data is treated as a numerical value, i.e., a continuous scale, but the concentration data may also be a nominal or ordinal scale. If the concentration data is a nominal or ordinal scale, any numerical value assigned to each state may be used in various processes such as evaluation and calculation of formula values. In addition, values ​​related to other biological information (see above) may be combined with the concentration data.

[0091] Returning to Figure 5, the index state information file 106b stores the index state information used when creating the formula. Figure 7 shows an example of the information stored in the index state information file 106b. As shown in Figure 7, the information stored in the index state information file 106b is composed of an interrelated set of individual numbers, index data related to the degree of fatigue, and concentration data (which may be a combination of concentration data and other data). Here, in Figure 7, the index data and concentration data are treated as numerical values ​​(i.e., continuous scale), but the index data and concentration data may also be nominal or ordinal scale. If the index data and concentration data are nominal or ordinal scale, any numerical value assigned to each state may be used in various processes such as evaluation and calculation of formula values.

[0092] Returning to Figure 5, the specified indicator status information file 106c stores the indicator status information specified by the specification unit 102b, which will be described later. Figure 8 shows an example of the information stored in the specified indicator status information file 106c. As shown in Figure 8, the information stored in the specified indicator status information file 106c is composed of the individual number, the specified indicator data, and the specified concentration data (which may be a combination of concentration data and other data) being related to each other.

[0093] Returning to Figure 5, the formula-related information database 106d consists of formula files 106d1 that store the formulas created by the formula creation unit 102c, which will be described later. Formula files 106d1 store the formulas used during evaluation. Figure 9 shows an example of the information stored in formula file 106d1. As shown in Figure 9, the information stored in formula file 106d1 is composed of a relationship between rank, formulas (in Figure 9, such as Fp(Phe,...), Fp(Gly,Leu,Phe), Fk(Gly,Leu,Phe,...), etc.), thresholds corresponding to each formula creation method, and the verification results of each formula (for example, the value of each formula).

[0094] Returning to Figure 5, the evaluation result file 106e stores the evaluation results obtained by the evaluation unit 102d, which will be described later. Figure 10 shows an example of the information stored in the evaluation result file 106e. The information stored in the evaluation result file 106e is configured by relating together an individual number for uniquely identifying the individual (sample) to be evaluated, concentration data of the individual obtained in advance (which may be a combination of concentration data and other data), and evaluation results regarding the degree of fatigue (for example, the value of the formula calculated by the calculation unit 102d1, which will be described later, the value after the formula value has been converted by the conversion unit 102d2, which will be described later, location information generated by the generation unit 102d3, which will be described later, or the classification result obtained by the classification unit 102d4, which will be described later).

[0095] Returning to Figure 5, the control unit 102 has an internal memory for storing control programs such as the OS, programs that define various processing procedures, and required data, and performs various information processing based on these programs. As shown in the figure, the control unit 102 is broadly composed of an acquisition unit 102a, a designation unit 102b, a formula creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f. The control unit 102 also performs data processing on indicator status information transmitted from the database device 400 and concentration data transmitted from the client device 200 (which may also be a combination of concentration data and other data), such as removing data with missing values, removing data with many outliers, and removing variables with many missing values.

[0096] The acquisition unit 102a acquires information (specifically, concentration data (which may be a combination of concentration data and other data), index status information, formulas, etc.). For example, the acquisition unit 102a may acquire information by receiving information (specifically, concentration data (which may be a combination of concentration data and other data), index status information, formulas, etc.) transmitted from a client device 200 or a database device 400 via a network 300 or the like. The acquisition unit 102a may also receive data used for evaluation transmitted from a client device 200 different from the client device 200 to which the evaluation results are sent. Furthermore, for example, if the evaluation device 100 is equipped with a mechanism (including hardware and software) for reading information recorded on a recording medium, the acquisition unit 102a may acquire information by reading information (specifically, concentration data (which may be a combination of concentration data and other data), index status information, formulas, etc.) recorded on the recording medium via the mechanism. The designation section 102b specifies the target index data and concentration data (or a combination of concentration data and other data) to be used when creating the formula.

[0097] The formula creation unit 102c creates a formula based on the index status information acquired by the acquisition unit 102a and the index status information specified by the specification unit 102b. If a formula is already stored in a predetermined storage area of ​​the storage unit 106, the formula creation unit 102c may create a formula by selecting a desired formula from the storage unit 106. Alternatively, the formula creation unit 102c may create a formula by selecting and downloading a desired formula from another computer device (for example, a database device 400) that has formulas already stored in it.

[0098] The evaluation unit 102d evaluates the degree of fatigue in an individual by calculating the value of the formula using a formula obtained in advance (for example, a formula created by the formula creation unit 102c or a formula obtained by the acquisition unit 102a) and the concentration values ​​included in the individual concentration data obtained by the acquisition unit 102a (which may be a combination of the concentration values ​​included in the concentration data and the values ​​included in other data). The evaluation unit 102d may also evaluate the degree of fatigue in an individual using the concentration value, the ratio value, or the converted value thereof (for example, the standard score).

[0099] Here, the configuration of the evaluation unit 102d will be described with reference to Figure 11. Figure 11 is a block diagram showing the configuration of the evaluation unit 102d, conceptually showing only the parts of the configuration that are relevant to the present invention. The evaluation unit 102d further comprises a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.

[0100] The calculation unit 102d1 calculates the value of the expression using an expression that includes a variable to which the concentration value contained in the concentration data is substituted (which may further include a variable to which values ​​contained in other data are substituted) and the concentration value contained in the concentration data (which may be a combination of the concentration value contained in the concentration data and the values ​​contained in other data). The evaluation unit 102d may store the value of the expression calculated by the calculation unit 102d1 as an evaluation result in a predetermined storage area of ​​the evaluation result file 106e.

[0101] The conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1 using, for example, the conversion method described above. The evaluation unit 102d may store the value after conversion by the conversion unit 102d2 as an evaluation result in a predetermined storage area of ​​the evaluation result file 106e. The conversion unit 102d2 may also convert the concentration values ​​included in the concentration data, or the values ​​or ratios included in other data, using, for example, the conversion method described above.

[0102] The generation unit 102d3 generates positional information relating to the position of a predetermined mark on a predetermined ruler that is visible on a display device such as a monitor or a physical medium such as paper, using the value of the formula calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (which may be a concentration value, a ratio value, or the converted value thereof). The evaluation unit 102d may store the positional information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of ​​the evaluation result file 106e.

[0103] The classification unit 102d4 uses the value of the formula calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (which may be a concentration value, a ratio value, or the converted value thereof) to classify the individual into one of several categories defined with at least the degree of fatigue taken into consideration.

[0104] The result output unit 102e outputs the processing results from each processing unit of the control unit 102 (including the evaluation results obtained by the evaluation unit 102d) to the output device 114.

[0105] The transmitting unit 102f transmits evaluation results to the client device 200 that is the source of the individual concentration data, and transmits formulas and evaluation results created by the evaluation device 100 to the database device 400. The transmitting unit 102f may also transmit evaluation results to a client device 200 that is different from the client device 200 that is the source of the data used for evaluation.

[0106] Next, the configuration of the client device 200 of this system will be described with reference to Figure 12. Figure 12 is a block diagram showing an example of the configuration of the client device 200 of this system, and conceptually shows only the part of the configuration that is relevant to the present invention.

[0107] The client device 200 consists of a control unit 210, a ROM 220, an HD (Hard Disk) 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280, and each of these parts is connected to communicate via any communication path. The client device 200 may also be based on an information processing device (for example, an information processing terminal such as a personal computer, workstation, home game console, internet TV, PHS (Personal Handyphone System) terminal, mobile terminal, mobile communication terminal, or PDA (Personal Digital Assistant)) to which peripheral devices such as printers, monitors, and image scanners are connected as needed.

[0108] The input device 250 includes a keyboard, mouse, microphone, etc. The monitor 261, described later, also works in conjunction with the mouse to provide a pointing device function. The output device 260 is an output means that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. Additionally, the output device 260 may be equipped with a speaker or the like. The input / output IF 270 connects to the input device 250 and the output device 260.

[0109] The communication interface 280 connects the client device 200 to the network 300 (or a communication device such as a router) in a communicative manner. In other words, the client device 200 is connected to the network 300 via a communication device (e.g., a modem, TA (Terminal Adapter), router, etc.) and a telephone line or a dedicated line. This allows the client device 200 to access the evaluation device 100 in accordance with a predetermined communication protocol.

[0110] The control unit 210 includes a receiving unit 211 and a transmitting unit 212. The receiving unit 211 receives various information, such as evaluation results, transmitted from the evaluation device 100 via the communication IF 280. The transmitting unit 212 transmits various information, such as individual concentration data, to the evaluation device 100 via the communication IF 280.

[0111] The control unit 210 may implement all or any part of the processing performed by the control unit using a CPU and a program that is interpreted and executed by the CPU. The ROM 220 or HD 230 stores a computer program that works in cooperation with the OS to give instructions to the CPU and perform various processing tasks. This computer program is executed by being loaded into the RAM 240 and works in cooperation with the CPU to constitute the control unit 210. Alternatively, this computer program may be stored on an application program server connected to the client device 200 via any network, and the client device 200 may download all or part of it as needed. Furthermore, all or any part of the processing performed by the control unit 210 may be implemented using hardware such as wired logic.

[0112] Here, the control unit 210 may also include an evaluation unit 210a (including a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) that has functions similar to those of the evaluation unit 102d provided in the evaluation device 100. If the control unit 210 is equipped with an evaluation unit 210a, the evaluation unit 210a may, in accordance with the information contained in the evaluation results transmitted from the evaluation device 100, convert the value of the formula (which may also be a concentration value or a ratio value included in the concentration data) using the conversion unit 210a2, generate position information corresponding to the value of the formula or the converted value (which may also be a concentration value or a ratio value included in the concentration data or a converted value thereof) using the generation unit 210a3, or classify individuals into one of several categories using the value of the formula or the converted value (which may also be a concentration value or a ratio value included in the concentration data or a converted value thereof) using the classification unit 210a4.

[0113] Next, the network 300 of this system will be described with reference to Figures 3 and 4. The network 300 has the function of connecting the evaluation device 100, the client device 200, and the database device 400 so that they can communicate with each other, and is, for example, the Internet, an intranet, or a LAN (Local Area Network) (including both wired and wireless). Network 300 includes VAN (Value-Added Network), personal computer network, public telephone network (including both analog and digital), dedicated line network (including both analog and digital), CATV (Community Antenna TeleVision) network, mobile circuit-switched network or mobile packet-switched network (IMT (International Mobile Telecommunication) 2000 system, GSM (Registered Trademark) (Global System for Mobile Communications) system or PDC (Personal Digital) This may include cellular / PDC-P systems, radio paging networks, local radio networks such as Bluetooth®, PHS networks, and satellite communication networks (including CS (Communication Satellite), BS (Broadcasting Satellite), or ISDB (Integrated Services Digital Broadcasting), etc.).

[0114] Next, the configuration of the database device 400 of this system will be described with reference to Figure 13. Figure 13 is a block diagram showing an example of the configuration of the database device 400 of this system, and conceptually shows only the part of the configuration that is relevant to the present invention.

[0115] The database device 400 has the function of storing indicator status information used when creating formulas in the evaluation device 100 or the database device, formulas created in the evaluation device 100, and evaluation results from the evaluation device 100. As shown in Figure 13, the database device 400 consists of a control unit 402 such as a CPU that comprehensively controls the database device, a communication interface unit 404 that connects the database device to the network 300 so that it can communicate via a communication device such as a router and a wired or wireless communication circuit such as a dedicated line, a storage unit 406 that stores various databases, tables and files (for example, files for web pages), and an input / output interface unit 408 that connects to the input device 412 and output device 414, and each of these units is connected so that it can communicate via any communication path.

[0116] The memory unit 406 is a storage means, and can be a memory device such as RAM or ROM, a fixed disk device such as a hard disk, a flexible disk, or an optical disk. The memory unit 406 stores various programs used for various processes. The communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has the function of communicating data with other terminals via a communication line. The input / output interface unit 408 is connected to the input device 412 and the output device 414. Here, the output device 414 can be a monitor (including a home television), a speaker, or a printer. The input device 412 can be a keyboard, a mouse, a microphone, or a monitor that works in cooperation with a mouse to provide a pointing device function.

[0117] The control unit 402 has an internal memory for storing control programs such as the OS, programs that define various processing procedures, and required data, and performs various information processing based on these programs. As shown in the figure, the control unit 402 is broadly composed of a transmitting unit 402a and a receiving unit 402b. The transmitting unit 402a transmits various information such as indicator status information and formulas to the evaluation device 100. The receiving unit 402b receives various information such as formulas and evaluation results transmitted from the evaluation device 100.

[0118] In this explanation, we have used as an example a case in which the evaluation device 100 performs tasks from receiving concentration data to calculating the value of the formula, classifying individuals into categories, and transmitting the evaluation results, and the client device 200 performs the task of receiving the evaluation results. However, if the client device 200 is equipped with an evaluation unit 210a, the evaluation device 100 only needs to perform the calculation of the value of the formula, and tasks such as converting the value of the formula, generating location information, and classifying individuals into categories may be appropriately divided and performed by the evaluation device 100 and the client device 200. For example, when the client device 200 receives the value of the formula from the evaluation device 100, the evaluation unit 210a may convert the value of the formula in the conversion unit 210a2, generate location information corresponding to the value of the formula or its converted value in the generation unit 210a3, or classify individuals into one of several categories using the value of the formula or its converted value in the classification unit 210a4. Furthermore, if the client device 200 receives a converted value from the evaluation device 100, the evaluation unit 210a may generate location information corresponding to the converted value in the generation unit 210a3, or classify the individual into one of several categories using the converted value in the classification unit 210a4. Also, if the client device 200 receives the value of the formula or its converted value and location information from the evaluation device 100, the evaluation unit 210a may classify the individual into one of several categories using the value of the formula or its converted value in the classification unit 210a4.

[0119] [2-3. Other Embodiments] The evaluation apparatus, calculation apparatus, evaluation method, calculation method, evaluation program, calculation program, recording medium, evaluation system, and terminal device according to the present invention may be implemented in various different embodiments within the scope of the technical idea described in the claims, in addition to the second embodiment described above.

[0120] Furthermore, among the processes described in the second embodiment, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods.

[0121] Furthermore, the processing procedures, control procedures, specific names, information including parameters such as registration data and search conditions for each process, screen examples, and database configuration shown in this specification and in the drawings may be changed at will unless otherwise specified.

[0122] Furthermore, with respect to each device constituting the evaluation system, the components shown in the diagram are functional concepts and do not necessarily need to be physically configured as shown.

[0123] For example, the processing functions of the evaluation device 100, particularly those performed by the control unit 102, may be implemented in whole or in part by a CPU and a program interpreted and executed by the CPU, or they may be implemented as wired logic hardware. The program is recorded on a non-temporary computer-readable recording medium containing programmed instructions for causing the information processing device to execute the evaluation method or calculation method according to the present invention, and is mechanically read by the evaluation device 100 as needed. That is, the storage unit 106, such as ROM or HDD (Hard Disk Drive), records a computer program that works in cooperation with the OS to give instructions to the CPU and perform various processing tasks. This computer program is executed by being loaded into RAM and works in cooperation with the CPU to constitute the control unit.

[0124] Furthermore, this computer program may be stored on an application program server connected to the evaluation device 100 via any network, and it is possible to download all or part of it as needed.

[0125] Furthermore, the evaluation program or calculation program according to the present invention may be stored on a non-temporary computer-readable recording medium, or it may be configured as a program product. Here, "recording medium" refers to memory cards, USB (Universal Serial Bus) memory, SD (Secure Digital) cards, flexible disks, magneto-optical disks, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable and Programmable Read Only Memory) (registered trademark), CD-ROM (Compact Disc Read Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile). This includes any "portable physical media" such as Blu-ray Discs and Blu-ray® Discs.

[0126] Furthermore, "program" refers to a data processing method described in any language or writing method, regardless of its format, such as source code or binary code. Note that "program" is not necessarily limited to a single, monolithic structure; it also includes distributed structures consisting of multiple modules or libraries, and those that work in cooperation with other programs, such as an operating system, to achieve their functions. Regarding the specific configuration and reading procedures for reading the recording medium in each device shown in the embodiments, as well as the installation procedures after reading, well-known configurations and procedures can be used.

[0127] The various databases stored in the storage unit 106 are storage means such as memory devices like RAM and ROM, fixed disk devices like hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and website provision.

[0128] Furthermore, the evaluation device 100 may be configured as an information processing device such as a known personal computer or workstation, or an information processing device to which any peripheral device is connected. Alternatively, the evaluation device 100 may be realized by implementing software (including programs or data, etc.) that implements the evaluation method or calculation method of the present invention on the information processing device.

[0129] Furthermore, the specific forms of distribution and integration of the devices are not limited to those shown in the illustration. The entire or a part of the devices can be configured by functionally or physically distributing and integrating them in any unit according to various additions or functional loads. In other words, the embodiments described above may be implemented in any combination, or selectively implemented.

[0130] Blood and urine samples were collected from 95 athletes belonging to university sports teams on the day after a rest day and the day after an exercise day. An exercise day is a practice day designated by the sports team in which the athletes participate, while a rest day is any day other than an exercise day. Since both the day after a rest day and the day after an exercise day are exercise days, blood and urine samples were collected from each athlete in a fasted state before the start of practice (specifically in the morning).

[0131] Using urine samples collected from each athlete, the urinary amino acid concentrations [µM] for 18 amino acids consisting of Ala, Val, Leu, Ile, Thr, Lys, Gly, His, Glu, Trp, Arg, Phe, Met, Ser, Asn, Gln, Pro, and Tyr were measured by LC-MS / MS.

[0132] Using blood samples collected from each athlete, four fatigue indicators were measured: blood Mb (Myoglobin) concentration [ng / mL], blood CK (Creatine PhosphoKinase) concentration [U / L], blood ketone body (total ketone body) concentration [μmol / L], and blood NEFA (Non-esterified Fatty Acid) concentration [μEq / L].

[0133] For each of the four fatigue indices, the median fatigue index for the day after exercise was calculated using the fatigue indices of 95 athletes on the day after exercise. Then, for each of the four fatigue indices, the calculated median was defined as the baseline value, and fatigue levels were classified based on the fatigue index on the day after rest and the fatigue index on the day after exercise. Athletes with a fatigue index higher than the baseline value on the day after rest were classified as the high-fatigue group, and athletes with a fatigue index below the baseline value on the day after rest were classified as the low-fatigue group. Similarly, athletes with a fatigue index higher than the baseline value on the day after exercise were classified as the high-fatigue group, and athletes with a fatigue index below the baseline value on the day after exercise were classified as the low-fatigue group.

[0134] For each of the four fatigue indicators, a logistic regression equation was searched for to distinguish between high-fatigue and low-fatigue groups using 18 types of urinary amino acid concentrations and classification results, with one variable included in each equation. From the searched logistic regression equations, the one with good discriminative ability was selected based on the ROC_AUC value.

[0135] Figure 14 shows the ROC_AUC values ​​of logistic regression equations with an ROC_AUC value of 0.550 or higher. These logistic regression equations are considered useful for the aforementioned discrimination because of their high ROC_AUC values.

[0136] In Example 2, the classification results based on blood CK concentration obtained in Example 1, the urine sample collected in Example 1, and the concentrations of 18 types of urinary amino acids measured in Example 1 were used.

[0137] The urinary creatinine (Cre) concentration [mg / dL] was measured using urine samples. For each of the 18 urinary amino acid concentrations, a corrected value (hereinafter referred to as "urinary amino acid concentration Cre-corrected value") was calculated by dividing the urinary amino acid concentration by the urinary Cre concentration.

[0138] Using 18 types of urinary amino acid concentration (Cre-corrected values) and classification results, a logistic regression equation was searched for to distinguish between two groups, a high-fatigue group and a low-fatigue group, based on blood CK concentration, with three variables included in the equation. From the searched logistic regression equations, the one with good discriminative ability was selected based on the ROC_AUC value.

[0139] Of the 816 logistic regression equations explored, 518 logistic regression equations with an ROC_AUC value of 0.573 or higher are shown below in [1: Equations selected in Example 2]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0140] Furthermore, the notation "AdjCre_XXX" shown in the following [1: Formula selected in Example 2] refers to the Cre-corrected value of the urinary amino acid concentration for the amino acid "XXX" represented by the three-letter notation.

[0141] In Example 3, the classification results based on blood CK concentration obtained in Example 1, the urine sample collected in Example 1, and the concentrations of 18 types of urinary amino acids measured in Example 1 were used.

[0142] Urine specific gravity (SG) was measured using urine samples. For each of the 18 different urinary amino acid concentrations, a corrected value (hereinafter referred to as "urinary amino acid concentration SG corrected value") was calculated by dividing the urinary amino acid concentration by the SG.

[0143] Using 18 types of urinary amino acid concentration SG-corrected values ​​and classification results, a logistic regression equation was searched for to distinguish between two groups, a high-fatigue group and a low-fatigue group, based on blood CK concentration, with three variables included in the equation. From the searched logistic regression equations, a logistic regression equation with good discriminative ability was selected based on the ROC_AUC value.

[0144] Of the 816 logistic regression equations explored, 579 with an ROC_AUC value of 0.569 or higher are shown below in [2: Equations selected in Example 3]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0145] Furthermore, the notation "AdjSG_XXX" shown in [2: Formula selected in Example 3] below refers to the SG correction value for urinary amino acid concentration for the amino acid "XXX" represented by the three-letter notation.

[0146] In Example 4, the classification results based on the blood CK concentration obtained in Example 1 and the Cre-corrected values ​​for the 18 types of urinary amino acid concentrations calculated in Example 2 were used.

[0147] Each athlete performed a subjective assessment of pain in four body parts: the front of the thigh, calf, Achilles tendon, and knee joint. They also evaluated the perceived exertion (RPE) of the exercises they performed and assessed their physical fatigue using the Visual Analog Scale (VAS). As a result, six different evaluation values ​​were obtained from each athlete.

[0148] Using 24 values ​​consisting of 18 types of urinary amino acid concentration Cre-corrected values ​​and 6 types of evaluation values, and the classification results, a logistic regression equation to distinguish between two groups, a high-fatigue group and a low-fatigue group, based on blood CK concentration, was searched for with three combinations of variables to be included in the equation. From the searched logistic regression equations, a logistic regression equation with good discriminative ability was selected based on the ROC_AUC value.

[0149] Of the 2024 logistic regression equations explored, 466 with an ROC_AUC value of 0.650 or higher are shown below in [3: Equations selected in Example 4]. These logistic regression equations are considered useful for the aforementioned discrimination because of their high ROC_AUC values.

[0150] In addition, the notation "AdjCre_XXX" shown in the following [3: Formula selected in Example 4] means the Cre-corrected value of the urinary amino acid concentration for the amino acid "XXX" represented by three letters, the notation "Pain_Front of Thigh" means the evaluation value of pain in the front of the thigh, the notation "Pain_Calf" means the evaluation value of pain in the calf, the notation "Pain_Achilles Tendon" means the evaluation value of pain in the Achilles tendon, the notation "Pain_Knee Joint" means the evaluation value of pain in the knee joint, the notation "RPE" means the evaluation value by RPE, and the notation "VAS" means the evaluation value of the degree of physical fatigue using VAS.

[0151] In Example 5, in order to focus on fatigue caused by exercise load, the study targeted athletes from the 95 athletes included in Example 1 whose fatigue indicators on the day following a rest day were all below the reference values ​​defined in Example 1. For each of the four fatigue indicators, the athletes were classified into a high-fatigue group if their fatigue indicator on the day following exercise was higher than the reference value, and a low-fatigue group if their fatigue indicator on the day following exercise was below the reference value. In Example 5, the 18 types of urinary amino acid concentrations measured in Example 1 were used for each athlete.

[0152] For each of the four fatigue indicators, a logistic regression equation was searched for to distinguish between high-fatigue and low-fatigue groups using 18 types of urinary amino acid concentrations and classification results, with one variable included in the logistic regression equation. From the searched logistic regression equations, the one with good discriminative ability was selected based on the ROC_AUC value.

[0153] Figure 15 shows the ROC_AUC values ​​of logistic regression equations with an ROC_AUC value of 0.550 or higher. These logistic regression equations are considered useful for the aforementioned discrimination because of their high ROC_AUC values.

[0154] In Example 6, in order to focus on fatigue caused by exercise load, the study targeted athletes from the 95 athletes included in Example 1 whose blood ketone body concentration on the day after a rest day was below the reference value defined in Example 1. Each athlete was then classified into a high-fatigue group and a low-fatigue group using the same method as in Example 5, based on the blood ketone body concentration measured on the day after exercise in Example 1. In Example 6, the Cre-corrected values ​​of the 18 types of urinary amino acid concentrations calculated in Example 2 were used for each athlete.

[0155] Using 18 types of urinary amino acid concentration (Cre-corrected values) and classification results, a logistic regression equation was searched for to distinguish between two groups, a high-fatigue group and a low-fatigue group, based on blood ketone body concentration, with three variables included in the equation. From the searched logistic regression equations, the one with good discriminative ability was selected based on the ROC_AUC value.

[0156] Of the 816 logistic regression equations explored, 206 with an ROC_AUC value of 0.700 or higher are shown below in [4: Equations selected in Example 6]. These logistic regression equations are considered useful for the aforementioned discrimination because of their high ROC_AUC values.

[0157] Furthermore, the notation "AdjCre_XXX" shown in [4: Formula selected in Example 6] below refers to the Cre-corrected value of the urinary amino acid concentration for the amino acid "XXX" represented by the three-letter notation.

[0158] In Example 7, in order to focus on fatigue caused by exercise load, the study targeted athletes from the 95 athletes targeted in Example 1 whose blood NEFA concentration on the day after a rest day was below the reference value defined in Example 1. Each of the target athletes was then classified into a high-fatigue group and a low-fatigue group using the same method as in Example 5, based on the blood NEFA concentration on the day after exercise measured in Example 1. In Example 7, the 18 types of urinary amino acid concentration Cre-corrected values ​​calculated in Example 2 and the 6 types of evaluation values ​​obtained in Example 4 were used for each of the target athletes.

[0159] Using 24 values ​​consisting of 18 types of urinary amino acid concentration Cre-corrected values ​​and 6 types of evaluation values, and the classification results, a logistic regression equation to distinguish between two groups, a high-fatigue group and a low-fatigue group, based on blood NEFA concentration, was searched for with three combinations of variables to be included in the equation. From the searched logistic regression equations, a logistic regression equation with good discriminative ability was selected based on the ROC_AUC value.

[0160] Of the 2024 logistic regression equations explored, 452 logistic regression equations with an ROC_AUC value of 0.700 or higher are shown below in [5: Equations selected in Example 7]. These logistic regression equations are considered useful for the aforementioned discrimination because of their high ROC_AUC values.

[0161] In addition, the notation "AdjCre_XXX" shown in the following [5: Formula selected in Example 7] means the Cre-corrected value of the urinary amino acid concentration for the amino acid "XXX" represented by three letters, the notation "Pain_Front of Thigh" means the evaluation value of pain in the front of the thigh, the notation "Pain_Calf" means the evaluation value of pain in the calf, the notation "Pain_Achilles Tendon" means the evaluation value of pain in the Achilles tendon, the notation "Pain_Knee Joint" means the evaluation value of pain in the knee joint, the notation "RPE" means the evaluation value by RPE, and the notation "VAS" means the evaluation value of the degree of physical fatigue using VAS.

[0162] In Example 8, in order to focus on fatigue caused by exercise load, the study targeted athletes from the 95 athletes targeted in Example 1 whose blood CK concentration on the day after a rest day was below the reference value defined in Example 1. Each of the target athletes was then classified into a high-fatigue group and a low-fatigue group using the same method as in Example 5, based on the blood CK concentration on the day after exercise measured in Example 1. In Example 8, the urine sample collected in Example 1, the urinary Cre concentration measured in Example 2, the Cre-corrected values ​​of 18 types of urinary amino acid concentrations calculated in Example 2, and the six types of evaluation values ​​obtained in Example 4 were used for each of the target athletes.

[0163] Using urine samples, the concentrations of 16 urinary micronutrients—magnesium, iron, calcium, zinc, sodium, phosphorus, potassium, lithium, aluminum, chromium, manganese, cobalt, nickel, copper, selenium, and molybdenum—were measured. For each of the 16 urinary micronutrient concentrations, a corrected value (hereinafter referred to as the "urinary micronutrient concentration Cre-corrected value") was calculated by dividing the urinary micronutrient concentration by the urinary creatinine concentration.

[0164] Using 40 values ​​and classification results consisting of 18 types of urinary amino acid concentration Cre-corrected values, 6 types of evaluation values, and 16 types of urinary micronutrient concentration Cre-corrected values, a logistic regression equation to distinguish between two groups, a high-fatigue group and a low-fatigue group, based on blood CK concentration, was searched for with three combinations of variables to include in the equation. From the searched logistic regression equations, a logistic regression equation with good discriminative ability was selected based on the ROC_AUC value.

[0165] Of the 7140 logistic regression equations explored, 524 logistic regression equations with an ROC_AUC value of 0.700 or higher are shown below in [6: Equations selected in Example 8]. These logistic regression equations are considered useful for the aforementioned discrimination because of their high ROC_AUC values.

[0166] In addition, the notation "AdjCre_XXX" shown in the following [6: Formula selected in Example 8] means the Cre-corrected value of urinary amino acid concentration for the amino acid "XXX" represented by a three-letter notation, "AdjCre_X" means the Cre-corrected value of urinary micronutrient concentration for a micronutrient represented by a single-letter element symbol, "AdjCre_Xx" means the Cre-corrected value of urinary micronutrient concentration for a micronutrient represented by a two-letter element symbol, "Pain_Front of Thigh" means the evaluation value of pain in the front of the thigh, "Pain_Calf" means the evaluation value of pain in the calf, "Pain_Achilles Tendon" means the evaluation value of pain in the Achilles tendon, "Pain_Knee Joint" means the evaluation value of pain in the knee joint, "RPE" means the evaluation value by RPE, and "VAS" means the evaluation value of physical fatigue using VAS.

[0167] In this study, 96 athletes belonging to a different university sports team than in Example 1 were targeted. Blood and urine samples were collected under the same conditions as in Example 1, and the same measurement items as in Example 1 were obtained using the same methods as in Example 1.

[0168] For each of the four fatigue indices, the median fatigue index for the day after exercise was calculated using the fatigue indices of the 96 athletes in Example 9 and the 95 athletes in Example 1, both on the day after exercise. Then, for each of the four fatigue indices, the calculated median was defined as the baseline value, and the fatigue state was classified based on the fatigue index on the day after rest and the fatigue index on the day after exercise.

[0169] In Example 9, in order to focus on fatigue caused by exercise load, the study targeted athletes from the 96 athletes in Example 9 whose blood myoglobin (Mb) concentration on the day following a rest day was below the defined reference value. Each athlete was then classified into two groups based on their blood Mb concentration on the day following exercise: those with a blood Mb concentration higher than the reference value were classified as the high-fatigue group, and those with a blood Mb concentration below the reference value were classified as the low-fatigue group. In Example 9, the concentrations of 18 types of urinary amino acids measured by the same method as in Example 1 were used without correction for each athlete.

[0170] Using the concentrations of 18 types of urinary amino acids (uncorrected) and the classification results, a logistic regression equation was searched for to distinguish between two groups, a high-fatigue group and a low-fatigue group, which were set based on blood Mb concentration, with three variables included in the equation. Specifically, logistic regression equations were created for combinations of selecting three types from the 18 types of urinary amino acids, and the logistic regression equation with good discriminative ability was selected based on the ROC_AUC value of each equation.

[0171] Of the 816 logistic regression equations explored, 444 with an ROC_AUC value of 0.610 or higher are shown below in [7-1: Equations selected in Example 9 (Part 1)]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0172] Furthermore, in Example 9, using 16 types of urinary amino acid concentrations (uncorrected), consisting of the concentrations of 15 urinary amino acids obtained by excluding Ile, Leu, and Val from 18 types of urinary amino acids, and the urinary BCAA concentration calculated by summing the concentrations of Ile, Leu, and Val, and the classification results, a logistic regression equation for distinguishing between two groups, a high-fatigue group and a low-fatigue group, based on blood Mb concentration, was searched for, with three variables included in the equation. Specifically, logistic regression equations were created for combinations of selecting three types from the 16 types of urinary amino acids, and logistic regression equations with good discriminative ability were selected based on the ROC_AUC value of each equation.

[0173] Of the 560 logistic regression equations explored, 48 logistic regression equations that include BCAA as a variable and have an ROC_AUC value of 0.615 or higher are shown below in [7-2: Equations selected in Example 9 (Part 2)]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0174] In addition, the notation "XXX" shown in [7-1: Formula selected in Example 9 (Part 1)] and [7-2: Formula selected in Example 9 (Part 2)] below refers to the urinary amino acid concentration for the amino acid "XXX" represented by a three-letter abbreviation.

[0175] In Example 10, the subjects were athletes from the "96 athletes targeted in Example 9" whose blood myoglobin (Mb) concentration on the day following a rest day was below the reference value defined in Example 9. Each of the subjects was then classified into two groups based on their blood Mb concentration on the day following exercise, as measured in Example 9: athletes with a blood Mb concentration higher than the reference value were classified into the high-fatigue group, and athletes with a blood Mb concentration below the reference value were classified into the low-fatigue group. In Example 10, 18 types of urinary amino acid concentration Cre-corrected values ​​(AdjCre), calculated using the same method as in Example 2, were used for each of the subjects.

[0176] Using 18 types of urinary amino acid concentration (Cre-corrected values) and the classification results, a logistic regression equation was searched for to distinguish between two groups, a high-fatigue group and a low-fatigue group, based on blood Mb concentration, with three variables included in the equation. Specifically, logistic regression equations were created for combinations of selecting three types from the 18 types of urinary amino acid (Cre-corrected values), and the logistic regression equation with good discriminative ability was selected based on the ROC_AUC value of each equation.

[0177] Of the 816 logistic regression equations explored, 741 logistic regression equations with an ROC_AUC value of 0.613 or higher are shown below in [8: Equations selected in Example 10]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0178] Furthermore, the notation "AdjCre_XXX" shown in [8: Formula selected in Example 10] below refers to the Cre-corrected value of the urinary amino acid concentration for the amino acid "XXX" represented by the three-letter notation.

[0179] In Example 11, among the 96 athletes from Example 9, those whose blood Mb concentration on the day after a rest day was below the reference value defined in Example 9 were selected and classified into a high-fatigue group and a low-fatigue group based on their blood Mb concentration on the day after exercise. In Example 11, for each athlete, in addition to 18 types of urinary amino acid concentrations (uncorrected), evaluation values ​​of five types of subjective indicators (pain in four body parts consisting of the front of the thigh, calf, Achilles tendon, and knee joint, and the degree of physical fatigue using Visual Analog Scale (VAS)) were used.

[0180] Using 23 values ​​consisting of 18 types of urinary amino acid concentrations (uncorrected) and evaluation values ​​of 5 types of subjective indicators, as well as the classification results, a logistic regression equation was searched for to distinguish between two groups set based on blood Mb concentration, with three variables included in the equation. Specifically, a three-variable logistic regression equation was created for combinations of selecting three variables from the 23 variables consisting of 18 types of urinary amino acids and 5 types of subjective indicators, and a logistic regression equation with good discriminative ability was selected based on the ROC_AUC value of each equation.

[0181] Of the 1771 logistic regression equations explored, 755 logistic regression equations with an ROC_AUC value of 0.617 or higher are shown below in [9: Equations selected in Example 11]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0182] In addition, the notation "XXX" shown in the following [9: Formula selected in Example 11] refers to the urinary amino acid concentration (uncorrected) for the amino acid "XXX" represented by three letters, the notations "front of thigh," "calf," "Achilles tendon," and "knee joint" refer to the pain evaluation values ​​for each area, and the notation "level of physical fatigue" refers to the level of physical fatigue evaluation value using VAS.

[0183] In Example 12, among the 96 athletes from Example 9, those whose blood Mb concentration on the day after a rest day was below the reference value defined in Example 9 were selected and classified into a high-fatigue group and a low-fatigue group based on their blood Mb concentration on the day after exercise. In Example 12, for each athlete, in addition to 18 types of urinary amino acid concentration Cre-corrected values ​​(AdjCre), evaluation values ​​of five types of subjective indicators obtained in Example 11 (pain [front of thigh / calf / Achilles tendon / knee joint], physical fatigue level) were used.

[0184] Using 23 values ​​consisting of 18 types of urinary amino acid concentrations (Cre-corrected values) and evaluation values ​​of 5 types of subjective indicators, as well as the classification results, a logistic regression equation was searched for to distinguish between two groups based on blood Mb concentration, with three variables included in the equation. Specifically, a three-variable logistic regression equation was created for combinations of selecting three variables from the 23 variables consisting of 18 types of urinary amino acids and 5 types of subjective indicators, and a logistic regression equation with good discriminative ability was selected based on the ROC_AUC value of each equation.

[0185] Of the 1771 logistic regression equations explored, 1496 logistic regression equations with an ROC_AUC value of 0.615 or higher are shown below in [10: Equations selected in Example 12]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0186] Furthermore, the notation "AdjCre_XXX" shown in the following [10: Formula selected in Example 12] refers to the Cre-corrected value of the urinary amino acid concentration for the amino acid "XXX" represented by three letters, the notations "front of thigh," "calf," "Achilles tendon," and "knee joint" refer to the pain evaluation values ​​for each area, and the notation "level of physical fatigue" refers to the evaluation value of the level of physical fatigue using VAS.

[0187] Example 13 aimed to create an algorithm for determining whether the blood Mb concentration on the day after a rest day exceeds a predetermined threshold (the reference value defined in Example 9). Specifically, among the 96 athletes in Example 9, the blood Mb concentration on the day after a rest day was used to classify athletes whose blood Mb concentration was higher than the reference value into the "high group," and athletes whose blood Mb concentration was below the reference value into the "low group." In this explanation, "the day after a rest day" refers to the morning after the most recent rest day following the day of exercise. This example demonstrates that the reference day for fatigue evaluation is not limited to the day after exercise, and that a discrimination algorithm can be constructed using other points in time, such as the day after a rest day, as the reference day.

[0188] Using the concentrations of 18 types of urinary amino acids (uncorrected) and the classification results, a logistic regression equation was searched for to distinguish between the "high group" and the "low group," with three variables included in the equation. Specifically, a logistic regression equation was created for each combination of selecting three amino acids from the 18 types, and the equation with good discriminative ability was selected based on the ROC_AUC value of each equation.

[0189] Of the 816 logistic regression equations explored, 65 logistic regression equations with an ROC_AUC value of 0.704 or higher are shown below in [11: Equations selected in Example 13]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0190] In addition, the notation "XXX" shown in [11: Formula selected in Example 13] below refers to the urinary amino acid concentration (uncorrected) for the amino acid "XXX" represented by the three-letter notation.

[0191] In Example 14, among the 96 athletes from Example 9, those whose blood CK concentration on the day after a rest day was below the reference value defined in Example 9 were selected and classified into a high-fatigue group and a low-fatigue group based on their blood CK concentration on the day after exercise. In Example 14, the concentrations of 18 types of urinary amino acids were used without correction for each athlete.

[0192] Using the concentrations of 18 types of urinary amino acids (uncorrected) and the classification results mentioned above, a logistic regression equation was searched for to distinguish between the "high group" and the "low group," with three variables included in the equation. Specifically, a logistic regression equation was created for each combination of selecting three amino acids from the 18 types, and the equation with good discriminative ability was selected based on the ROC_AUC value of each equation.

[0193] Of the 816 logistic regression equations explored, 654 logistic regression equations with an ROC_AUC value of 0.601 or higher are shown below in [12: Equations selected in Example 14]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0194] In addition, the notation "XXX" shown in [12: Formula selected in Example 14] below refers to the urinary amino acid concentration (uncorrected) for the amino acid "XXX" represented by the three-letter notation.

[0195] In Example 15, among the 96 athletes from Example 9, those whose total blood ketone body concentration on the day after a rest day was below the reference value defined in Example 9 were selected and classified into a high-fatigue group and a low-fatigue group based on their total blood ketone body concentration on the day after exercise. In Example 14, the concentrations of 18 types of urinary amino acids were used without correction for each athlete.

[0196] Using the concentrations of 18 types of urinary amino acids (uncorrected) and the classification results mentioned above, a logistic regression equation was searched for to distinguish between the "high group" and the "low group," with three variables included in the equation. Specifically, a logistic regression equation was created for each combination of selecting three amino acids from the 18 types, and the equation with good discriminative ability was selected based on the ROC_AUC value of each equation.

[0197] Of the 816 logistic regression equations explored, 240 with an ROC_AUC value of 0.603 or higher are shown below in [13: Equations selected in Example 15]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0198] In addition, the notation "XXX" shown in [13: Formula selected in Example 15] below refers to the urinary amino acid concentration (uncorrected) for the amino acid "XXX" represented by the three-letter notation.

[0199] In Example 16, among the 96 athletes targeted in Example 9, those whose blood NEFA concentration on the day after a rest day was below the reference value defined in Example 9 were targeted and classified into a high-fatigue group and a low-fatigue group based on their blood NEFA concentration on the day after exercise. In Example 14, the concentrations of 18 types of urinary amino acids were used without correction for each target athlete.

[0200] Using the concentrations of 18 types of urinary amino acids (uncorrected) and the classification results mentioned above, a logistic regression equation was searched for to distinguish between the "high group" and the "low group," with three variables included in the equation. Specifically, a logistic regression equation was created for each combination of selecting three amino acids from the 18 types, and the equation with good discriminative ability was selected based on the ROC_AUC value of each equation.

[0201] Of the 816 logistic regression equations explored, 63 with an ROC_AUC value of 0.617 or higher are shown below in [14: Equations selected in Example 16]. These logistic regression equations are considered useful for the aforementioned discrimination because they have high ROC_AUC values ​​and the lower limit of the 95% CI of the ROC_AUC value is higher than 0.5.

[0202] In addition, the notation "XXX" shown in [14: Formula selected in Example 16] below refers to the urinary amino acid concentration (uncorrected) for the amino acid "XXX" represented by the three-letter notation.

[0203] As described above, the present invention can be widely implemented in many industrial fields, particularly in fields such as pharmaceuticals, food, and medicine, and is especially useful in the field of bioinformatics for predicting the degree of physical fatigue.

[0204] 100 Evaluation device 102 Control unit 102a Acquisition unit 102b Designation unit 102c Formula creation unit 102d Evaluation unit 102d1 Calculation unit 102d2 Conversion unit 102d3 Generation unit 102d4 Classification unit 102e Result output unit 102f Transmission unit 104 Communication interface unit 106 Storage unit 106a Concentration data file 106b Indicator status information file 106c Designated indicator status information file 106d Formula-related information database 106d1 Formula file 106e Evaluation result file 108 Input / Output interface unit 112 Input device 114 Output device 200 Client device (Terminal device (Information and communication terminal device)) 300 Network 400 Database device

[0205] [1: Formula selected in Example 2] AdjCre_Asn, AdjCre_Met, AdjCre_Ser, 0.652 (0.586, 0.719); AdjCre_Asn, AdjCre_Glu, AdjCre_Gly, 0.644 (0.578, 0.711); AdjCre_Asn, AdjCre_Glu, AdjCre_Ser, 0.643 (0.577, 0.71); AdjCre_Asn, AdjCre_Met, AdjCre_Gly, 0.638 (0.571, 0.705); AdjCre_Ala, AdjCre_Asn, AdjCre_Glu, 0.638 (0.571, 0.706); AdjCre_Arg, AdjCre_Asn, AdjCre_Gly, 0.634 (0.567, 0.702); AdjCre_Ala, AdjCre_Glu, AdjCre_Phe, 0.634 (0.567, 0.701); AdjCre_Asn, AdjCre_Ser, AdjCre_Gly, 0.634 (0.567, 0.702); AdjCre_Ala, AdjCre_Asn, AdjCre_Ser, 0.633 (0.565, 0.701); AdjCre_Asn, AdjCre_Ile, AdjCre_Ser, 0.632 (0.565, 0.7); AdjCre_Arg, AdjCre_Asn, AdjCre_Ser, 0.632 (0.566, 0.699); AdjCre_Glu, AdjCre_His, AdjCre_Gly, 0.631 (0.563, 0.699); AdjCre_Asn, AdjCre_Thr, AdjCre_Gly, 0.631 (0.562, 0.7); AdjCre_Ala, AdjCre_Glu, AdjCre_His, 0.629 (0.56, 0.698); AdjCre_Glu, AdjCre_Phe, AdjCre_Gly, 0.629 (0.562, 0.696); AdjCre_Asn, AdjCre_Ile, AdjCre_Gly, 0.629 (0.561, 0.697); AdjCre_Asn, AdjCre_Lys, AdjCre_Gly, 0.629 (0.56, 0.697); AdjCre_Asn, AdjCre_Leu, AdjCre_Gly, 0.628 (0.561, 0.696); AdjCre_Asn, AdjCre_Leu, AdjCre_Ser, 0.628 (0.561, 0.696); AdjCre_Asn, AdjCre_Pro, AdjCre_Gly, 0.628 (0.559, 0.697); AdjCre_Glu, AdjCre_Trp, AdjCre_Tyr, 0.628 (0.561, 0.695); AdjCre_Asn, AdjCre_Tyr, AdjCre_Gly, 0.628 (0.559, 0.696); AdjCre_Asn, AdjCre_Gln, AdjCre_Ser, 0.628 (0.561, 0.694); AdjCre_Ala, AdjCre_Glu, AdjCre_Trp, 0.628 (0.559, 0.697); AdjCre_Asn, AdjCre_Met, AdjCre_Val, 0.627 (0.561, 0.694); AdjCre_Ala, AdjCre_Asn, AdjCre_Met, 0.627 (0.56, 0.694); AdjCre_Asn, AdjCre_Phe, AdjCre_Gly, 0.627 (0.559, 0.695); AdjCre_Asn, AdjCre_Ser, AdjCre_Val, 0.627 (0.56, 0.695); AdjCre_Asn, AdjCre_His, AdjCre_Gly, 0.627 (0.559, 0.695); AdjCre_Asn, AdjCre_Trp, AdjCre_Gly, 0.627 (0.559, 0.695); AdjCre_Asn, AdjCre_Gln, AdjCre_Gly, 0.627 (0.559, 0.695); AdjCre_Glu, AdjCre_His, AdjCre_Met, 0.626 (0.559, 0.694); AdjCre_Glu, AdjCre_Met, AdjCre_Phe, 0.626 (0.559, 0.693); AdjCre_Asn, AdjCre_Val, AdjCre_Gly, 0.626 (0.558, 0.694); AdjCre_Asn, AdjCre_Ser, AdjCre_Tyr, 0.625 (0.557, 0.693); AdjCre_Ala, AdjCre_Arg, AdjCre_Asn, 0.624 (0.556, 0.692); AdjCre_Asn, AdjCre_Glu, AdjCre_Met, 0.624 (0.556, 0.691); AdjCre_Asn, AdjCre_Ser, AdjCre_Trp, 0.623 (0.556, 0.69); AdjCre_Glu, AdjCre_Met, AdjCre_Trp, 0.623 (0.555, 0.691); AdjCre_Asn, AdjCre_His, AdjCre_Ser, 0.622 (0.555, 0.689); AdjCre_Asn, AdjCre_Pro, AdjCre_Ser, 0.622 (0.552, 0.692); AdjCre_Asn, AdjCre_Gln, AdjCre_Glu, 0.621 (0.554, 0.689); AdjCre_Asn, AdjCre_Gln, AdjCre_Met, 0.621 (0.554, 0.688); AdjCre_Glu, AdjCre_Trp, AdjCre_Gly, 0.621 (0.553, 0.689); AdjCre_Asn, AdjCre_Leu, AdjCre_Met, 0.621 (0.554, 0.687); AdjCre_Asn, AdjCre_Met, AdjCre_Phe, 0.62 (0.554, 0.687); AdjCre_Gln, AdjCre_Glu, AdjCre_His, 0.62 (0.552, 0.688); AdjCre_Asn, AdjCre_Lys, AdjCre_Ser, 0.62 (0.552, 0.688); AdjCre_Asn, AdjCre_Ser, AdjCre_Thr, 0.62 (0.551, 0.688); AdjCre_Glu, AdjCre_Phe, AdjCre_Pro, 0.619 (0.552, 0.687); AdjCre_Asn, AdjCre_Phe, AdjCre_Ser, 0.619 (0.55, 0.688); AdjCre_Glu, AdjCre_Ile, AdjCre_Trp, 0.619 (0.551, 0.686); AdjCre_Asn, AdjCre_Ile, AdjCre_Met, 0.618 (0.552, 0.685); AdjCre_Asn, AdjCre_Glu, AdjCre_Trp, 0.618 (0.551, 0.686); AdjCre_Ala, AdjCre_Asn, AdjCre_Gly, 0.618 (0.55, 0.686); AdjCre_Arg, AdjCre_Asn, AdjCre_Glu, 0.618 (0.55, 0.685); AdjCre_Glu, AdjCre_Pro, AdjCre_Trp, 0.617 (0.549, 0.686); AdjCre_Asn, AdjCre_Met, AdjCre_Thr, 0.617 (0.549, 0.685); AdjCre_Asn, AdjCre_His, AdjCre_Met, 0.617 (0.551, 0.684); AdjCre_Asn, AdjCre_Met, AdjCre_Trp, 0.617 (0.551, 0.684); AdjCre_Asn, AdjCre_Glu, AdjCre_Tyr, 0.617 (0.549, 0.685); AdjCre_Glu, AdjCre_Ile, AdjCre_Phe, 0.617 (0.55, 0.684); AdjCre_Asn, AdjCre_Glu, AdjCre_Phe, 0.617 (0.549, 0.684); AdjCre_Asn, AdjCre_Glu, AdjCre_Lys, 0.617 (0.549, 0.685); AdjCre_Ala, AdjCre_Asn, AdjCre_Thr, 0.616 (0.547, 0.685); AdjCre_Glu, AdjCre_His, AdjCre_Phe, 0.616 (0.549, 0.684); AdjCre_Glu, AdjCre_Phe, AdjCre_Tyr, 0.616 (0.549, 0.683); AdjCre_Glu, AdjCre_His, AdjCre_Trp, 0.616 (0.548, 0.684); AdjCre_Glu, AdjCre_Leu, AdjCre_Phe, 0.616 (0.549, 0.683); AdjCre_Arg, AdjCre_Asn, AdjCre_Gln, 0.616 (0.549, 0.683); AdjCre_Glu, AdjCre_His, AdjCre_Ser, 0.616 (0.548, 0.684); AdjCre_Arg, AdjCre_Asn, AdjCre_Met, 0.616 (0.549, 0.683); AdjCre_Asn, AdjCre_Glu, AdjCre_Ile, 0.616 (0.548, 0.683); AdjCre_Glu, AdjCre_His, AdjCre_Ile, 0.616 (0.548, 0.684); AdjCre_Glu, AdjCre_His, AdjCre_Pro, 0.615 (0.547, 0.684); AdjCre_Ala, AdjCre_His, AdjCre_Met, 0.615 (0.547, 0.684); AdjCre_Glu, AdjCre_Lys, AdjCre_Phe, 0.615 (0.548, 0.683); AdjCre_Glu, AdjCre_Leu, AdjCre_Trp, 0.615 (0.547, 0.683); AdjCre_Glu, AdjCre_His, AdjCre_Leu, 0.615 (0.547, 0.683); AdjCre_Asn, AdjCre_Glu, AdjCre_Val, 0.615 (0.547, 0.683); AdjCre_Asn, AdjCre_Glu, AdjCre_Leu, 0.615 (0.547, 0.683); AdjCre_Asn, AdjCre_Glu, AdjCre_His, 0.615 (0.547, 0.683); AdjCre_Asn, AdjCre_Glu, AdjCre_Pro, 0.615 (0.546, 0.683); AdjCre_Glu, AdjCre_Phe, AdjCre_Trp, 0.614 (0.547, 0.682); AdjCre_Asn, AdjCre_Met, AdjCre_Tyr, 0.614 (0.548, 0.681); AdjCre_Arg, AdjCre_Glu, AdjCre_Phe, 0.614 (0.547, 0.682); AdjCre_Gln, AdjCre_Glu, AdjCre_Trp, 0.614 (0.546, 0.682); AdjCre_Glu, AdjCre_His, AdjCre_Val, 0.614 (0.546, 0.682); AdjCre_Glu, AdjCre_Phe, AdjCre_Val, 0.614 (0.547, 0.682); AdjCre_Gln, AdjCre_Glu, AdjCre_Phe, 0.614 (0.547, 0.681); AdjCre_Arg, AdjCre_Glu, AdjCre_His, 0.614 (0.546, 0.682); AdjCre_Asn, AdjCre_Met, AdjCre_Pro, 0.614 (0.546, 0.682); AdjCre_His, AdjCre_Met, AdjCre_Gly, 0.614 (0.546, 0.682); AdjCre_Glu, AdjCre_His, AdjCre_Tyr, 0.614 (0.545, 0.682); AdjCre_Glu, AdjCre_Trp, AdjCre_Val, 0.613 (0.545, 0.681); AdjCre_Asn, AdjCre_Gln, AdjCre_Trp, 0.613 (0.547, 0.68); AdjCre_Glu, AdjCre_Lys, AdjCre_Trp, 0.613 (0.545, 0.681); AdjCre_Gln, AdjCre_Glu, AdjCre_Gly, 0.613 (0.544, 0.681); AdjCre_His, AdjCre_Pro, AdjCre_Gly, 0.613 (0.543, 0.682); AdjCre_Glu, AdjCre_Ile, AdjCre_Val, 0.612 (0.544, 0.681); AdjCre_Asn, AdjCre_Glu, AdjCre_Thr, 0.612 (0.544, 0.681); AdjCre_Arg, AdjCre_Glu, AdjCre_Trp, 0.612 (0.544, 0.68); AdjCre_Glu, AdjCre_His, AdjCre_Thr, 0.612 (0.543, 0.681); AdjCre_Glu, AdjCre_His, AdjCre_Lys, 0.612 (0.544, 0.681); AdjCre_Ala, AdjCre_Asn, AdjCre_Leu, 0.612 (0.544, 0.68); AdjCre_Glu, AdjCre_Lys, AdjCre_Gly, 0.612 (0.543, 0.681); AdjCre_Glu, AdjCre_Thr, AdjCre_Trp, 0.612 (0.543, 0.68); AdjCre_Glu, AdjCre_Met, AdjCre_Val, 0.612 (0.543, 0.68); AdjCre_Glu, AdjCre_Phe, AdjCre_Thr, 0.612 (0.543, 0.68); AdjCre_Ala, AdjCre_Glu, AdjCre_Lys, 0.612 (0.543, 0.68); AdjCre_Asn, AdjCre_Lys, AdjCre_Met, 0.611 (0.544, 0.679); AdjCre_Ala, AdjCre_Glu, AdjCre_Val, 0.611 (0.542, 0.68); AdjCre_Asn, AdjCre_Gln, AdjCre_Phe, 0.611 (0.545, 0.677); AdjCre_Glu, AdjCre_Leu, AdjCre_Gly, 0.611 (0.542, 0.679); AdjCre_Ala, AdjCre_Gln, AdjCre_Glu, 0.611 (0.542, 0.68); AdjCre_Glu, AdjCre_Phe, AdjCre_Ser, 0.61 (0.543, 0.678); AdjCre_Ala, AdjCre_Asn, AdjCre_Ile, 0.61 (0.542, 0.678); AdjCre_Glu, AdjCre_Ser, AdjCre_Trp, 0.61 (0.542, 0.678); AdjCre_Asn, AdjCre_Gln, AdjCre_His, 0.61 (0.543, 0.677); AdjCre_Glu, AdjCre_Val, AdjCre_Gly, 0.61 (0.541, 0.679); AdjCre_Ala, AdjCre_Glu, AdjCre_Tyr, 0.61 (0.541, 0.679); AdjCre_His, AdjCre_Met, AdjCre_Val, 0.61 (0.54, 0.68); AdjCre_Ala, AdjCre_Glu, AdjCre_Ser, 0.61 (0.541, 0.678); AdjCre_Ala, AdjCre_Asn, AdjCre_Lys, 0.609 (0.54, 0.678); AdjCre_Ala, AdjCre_Glu, AdjCre_Leu, 0.609 (0.541, 0.678); AdjCre_Ala, AdjCre_Asn, AdjCre_Pro, 0.609 (0.539, 0.679); AdjCre_Asn, AdjCre_Gln, AdjCre_Lys, 0.609 (0.542, 0.676); AdjCre_Ala, AdjCre_Asn, AdjCre_Tyr, 0.609 (0.54, 0.677); AdjCre_Asn, AdjCre_Gln, AdjCre_Ile, 0.609 (0.541, 0.676); AdjCre_Glu, AdjCre_Thr, AdjCre_Gly, 0.608 (0.539, 0.677); AdjCre_Glu, AdjCre_Lys, AdjCre_Val, 0.608 (0.539, 0.677); AdjCre_Ala, AdjCre_Asn, AdjCre_Gln, 0.608 (0.539, 0.676); AdjCre_Gln, AdjCre_Glu, AdjCre_Val, 0.608 (0.539, 0.676); AdjCre_Ala, AdjCre_His, AdjCre_Pro, 0.608 (0.537, 0.679); AdjCre_Glu, AdjCre_Ile, AdjCre_Leu, 0.608 (0.539, 0.676); AdjCre_Glu, AdjCre_Leu, AdjCre_Lys, 0.607 (0.538, 0.676); AdjCre_Ala, AdjCre_Glu, AdjCre_Thr, 0.607 (0.538, 0.677); AdjCre_Glu, AdjCre_Met, AdjCre_Gly, 0.607 (0.539, 0.676); AdjCre_Glu, AdjCre_Ser, AdjCre_Gly, 0.607 (0.538, 0.676); AdjCre_Pro, AdjCre_Trp, AdjCre_Tyr, 0.607 (0.537, 0.677); AdjCre_Ala, AdjCre_Asn, AdjCre_His, 0.607 (0.539, 0.676); AdjCre_Ala, AdjCre_Asn, AdjCre_Trp, 0.607 (0.538, 0.676); AdjCre_Gln, AdjCre_His, AdjCre_Pro, 0.607 (0.536, 0.679); AdjCre_Asn, AdjCre_Gln, AdjCre_Leu, 0.607 (0.54, 0.674); AdjCre_Glu, AdjCre_Met, AdjCre_Thr, 0.607 (0.538, 0.676); AdjCre_Glu, AdjCre_Pro, AdjCre_Thr, 0.607 (0.538, 0.676); AdjCre_Asn, AdjCre_Gln, AdjCre_Val, 0.607 (0.54, 0.674); AdjCre_His, AdjCre_Met, AdjCre_Thr, 0.607 (0.537, 0.676); AdjCre_Glu, AdjCre_Leu, AdjCre_Met, 0.607 (0.538, 0.676); AdjCre_Glu, AdjCre_Thr, AdjCre_Val, 0.606 (0.537, 0.676); AdjCre_Glu, AdjCre_Lys, AdjCre_Thr, 0.606 (0.538, 0.675); AdjCre_Glu, AdjCre_Leu, AdjCre_Val, 0.606 (0.537, 0.675); AdjCre_Glu, AdjCre_Ile, AdjCre_Gly, 0.606 (0.538, 0.675); AdjCre_Glu, AdjCre_Tyr, AdjCre_Gly, 0.606 (0.537, 0.675); AdjCre_Glu, AdjCre_Thr, AdjCre_Tyr, 0.606 (0.537, 0.675); AdjCre_Glu, AdjCre_Lys, AdjCre_Met, 0.606 (0.537, 0.675); AdjCre_His, AdjCre_Met, AdjCre_Pro, 0.606 (0.535, 0.676); AdjCre_Ala, AdjCre_Asn, AdjCre_Phe, 0.606 (0.537, 0.674); AdjCre_Gln, AdjCre_Glu, AdjCre_Leu, 0.605 (0.537, 0.674); AdjCre_Glu, AdjCre_Pro, AdjCre_Val, 0.605 (0.536, 0.675); AdjCre_Glu, AdjCre_Met, AdjCre_Tyr, 0.6 05 (0.537, 0.674); AdjCre_Gln, AdjCre_Glu, AdjCre_Thr, 0.605 (0.536, 0.674); AdjCre_Arg, AdjCre_Glu, AdjCre_Val, 0.605 (0.536, 0.674); AdjCre_Glu, AdjCre_Ile, AdjCre_Lys, 0.605 (0.536, 0.674); AdjCre_Ala, AdjCre_Arg, AdjCre_Glu, 0.605 (0.537, 0.673); AdjCre_Arg, AdjCre_Glu, AdjCre_Gly, 0.605 (0.536, 0.674); AdjCre_Ala, AdjCre_Glu, AdjCre_Ile, 0.605 (0.536, 0.674); AdjCre_Ala, AdjCre_Glu, AdjCre_Met, 0.605 (0.536, 0.673); AdjCre_Arg, AdjCre_Glu, AdjCre_Lys, 0.605 (0.536, 0.674); AdjCre_Arg, AdjCre_Glu, AdjCre_Leu, 0.605 (0.536, 0.674); AdjCre_Gln, AdjCre_Glu, AdjCre_Ile, 0.605 (0.537, 0.673); AdjCre_Gln, AdjCre_Glu, AdjCre_Tyr, 0.604 (0.536, 0.673); AdjCre_Glu, AdjCre_Leu, AdjCre_Pro, 0.604 (0.535, 0.674); AdjCre_Asn, AdjCre_Gln, AdjCre_Thr, 0.604 (0.536, 0.672); AdjCre_His, AdjCre_Tyr, AdjCre_Gly, 0.604 (0.536, 0.672); AdjCre_Glu, AdjCre_Leu, AdjCre_Thr, 0.604 (0.535, 0.673); AdjCre_Glu, AdjCre_Tyr, AdjCre_Val, 0.604 (0.535, 0.673); AdjCre_Glu, AdjCre_Pro, AdjCre_Tyr, 0.604 (0.535, 0.673); AdjCre_Gln, AdjCre_Glu, AdjCre_Pro, 0.604 (0.535, 0.673); AdjCre_Glu, AdjCre_Leu, AdjCre_Tyr, 0.604 (0.535, 0.672); AdjCre_Glu, AdjCre_Ile, AdjCre_Thr, 0.604 (0.535, 0.673); AdjCre_Ala, AdjCre_Asn, AdjCre_Val, 0.604 (0.535, 0.673); AdjCre_Arg, AdjCre_Gln, AdjCre_Glu, 0.604 (0.535, 0.672); AdjCre_Glu, AdjCre_Ser, AdjCre_Val, 0.604 (0.535, 0.672); AdjCre_Met, AdjCre_Thr, AdjCre_Val, 0.604 (0.532, 0.675); AdjCre_Gln, AdjCre_Glu, AdjCre_Ser, 0.604 (0.535, 0.672); AdjCre_Glu, AdjCre_Ser, AdjCre_Thr, 0.603 (0.535, 0.672); AdjCre_Asn, AdjCre_Gln, AdjCre_Tyr, 0.603 (0.536, 0.671); AdjCre_Glu, AdjCre_Lys, AdjCre_Pro, 0.603 (0.534, 0.673); AdjCre_Arg, AdjCre_Glu, AdjCre_Tyr, 0.603 (0.535, 0.672); AdjCre_Arg, AdjCre_Glu, AdjCre_Thr, 0.603 (0.534, 0.672); AdjCre_Ala, AdjCre_His, AdjCre_Gly, 0.603 (0.534, 0.672); AdjCre_Gln, AdjCre_Glu, AdjCre_Lys, 0.603 (0.534, 0.672); AdjCre_Glu, AdjCre_Lys, AdjCre_Tyr, 0.603 (0.534, 0.671); AdjCre_Glu, AdjCre_Ile, AdjCre_Tyr, 0.603 (0.534, 0.671); AdjCre_Glu, AdjCre_Leu, AdjCre_Ser, 0.603 (0.534, 0.671); AdjCre_Glu, AdjCre_Ile, AdjCre_Met, 0.602 (0.533, 0.671); AdjCre_Gln, AdjCre_His, AdjCre_Met, 0.602 (0.533, 0.67); AdjCre_Arg, AdjCre_Glu, AdjCre_Ile, 0.602 (0.533, 0.67); AdjCre_Glu, AdjCre_Met, AdjCre_Pro, 0.602 (0.532, 0.671); AdjCre_Ala, AdjCre_Pro, AdjCre_Trp, 0.602 (0.53, 0.673); AdjCre_Met, AdjCre_Thr, AdjCre_Trp, 0.601 (0.53, 0.673); AdjCre_His, AdjCre_Phe, AdjCre_Gly, 0.601 (0.533, 0.67); AdjCre_Glu, AdjCre_Ser, AdjCre_Tyr, 0.601 (0.533, 0.67); AdjCre_Ala, AdjCre_Glu, AdjCre_Pro, 0.601 (0.532, 0.67); AdjCre_Arg, AdjCre_Glu, AdjCre_Ser, 0.601 (0.532, 0.669); AdjCre_Glu, AdjCre_Ile, AdjCre_Ser, 0.601 (0.532, 0.669); AdjCre_His, AdjCre_Pro, AdjCre_Ser, 0.601 (0.529, 0.672); AdjCre_Glu, AdjCre_Ile, AdjCre_Pro, 0.601 (0.531, 0.67); AdjCre_Ala, AdjCre_Glu, AdjCre_Gly, 0.6 (0.532, 0.669); AdjCre_His, AdjCre_Pro, AdjCre_Val, 0.6 (0.529, 0.671); AdjCre_Asn, AdjCre_Gln, AdjCre_Pro, 0.6 (0.53, 0.67); AdjCre_His, AdjCre_Lys, AdjCre_Pro, 0.6 (0.529, 0.671); AdjCre_His, AdjCre_Leu, AdjCre_Met, 0.6 (0.532, 0.669); AdjCre_Gln, AdjCre_Glu, AdjCre_Met, 0.6 (0.531, 0.669); AdjCre_His, AdjCre_Lys, AdjCre_Gly, 0.6 (0.531, 0.669); AdjCre_Glu, AdjCre_Lys, AdjCre_Ser, 0.6 (0.531, 0.669); AdjCre_Arg, AdjCre_Glu, AdjCre_Met, 0.6 (0.531, 0.668); AdjCre_Leu, AdjCre_Lys, AdjCre_Met, 0.599 (0.53, 0.669); AdjCre_Met, AdjCre_Phe, AdjCre_Thr, 0.599 (0.529, 0.67); AdjCre_Leu, AdjCre_Met, AdjCre_Phe, 0.599 (0.529, 0.669); AdjCre_Glu, AdjCre_Pro, AdjCre_Gly, 0.599 (0.53, 0.668); AdjCre_Gln, AdjCre_His, AdjCre_Gly, 0.599 (0.53, 0.667); AdjCre_Lys, AdjCre_Met, AdjCre_Thr, 0.598 (0.528, 0.669); AdjCre_Glu, AdjCre_Met, AdjCre_Ser, 0.598 (0.53, 0.667); AdjCre_His, AdjCre_Lys, AdjCre_Met, 0.598 (0.529, 0.668); AdjCre_Gln, AdjCre_His, AdjCre_Thr, 0.598 (0.528, 0.668); AdjCre_Leu, AdjCre_Met, AdjCre_Thr, 0.598 (0.527, 0.669); AdjCre_Lys, AdjCre_Met, AdjCre_Phe, 0.598 (0.529, 0.667); AdjCre_His, AdjCre_Pro, AdjCre_Tyr, 0.598 (0.527, 0.669); AdjCre_Arg, AdjCre_Glu, AdjCre_Pro, 0.598 (0.529, 0.667); AdjCre_Ile, AdjCre_Met, AdjCre_Thr, 0.598 (0.527, 0.669); AdjCre_His, AdjCre_Pro, AdjCre_Trp, 0.598 (0.527, 0.669); AdjCre_His, AdjCre_Leu, AdjCre_Gly, 0.598 (0.529, 0.667); AdjCre_Met, AdjCre_Phe, AdjCre_Pro, 0.598 (0.529, 0.667); AdjCre_His, AdjCre_Ile, AdjCre_Gly, 0.598 (0.529, 0.666); AdjCre_His, AdjCre_Phe, AdjCre_Pro, 0.597 (0.526, 0.668); AdjCre_Ala, AdjCre_Phe, AdjCre_Pro, 0.597 (0.528, 0.666); AdjCre_Glu, AdjCre_Pro, AdjCre_Ser, 0.597 (0.528, 0.666); AdjCre_His, AdjCre_Ile, AdjCre_Pro, 0.597 (0.526, 0.668); AdjCre_Arg, AdjCre_His, AdjCre_Pro, 0.597 (0.525, 0.668); AdjCre_Ala, AdjCre_His, AdjCre_Thr, 0.597 (0.527, 0.666); AdjCre_His, AdjCre_Leu, AdjCre_Pro, 0.597 (0.525, 0.668); AdjCre_His, AdjCre_Thr, AdjCre_Gly, 0.596 (0.526, 0.667); AdjCre_Arg, AdjCre_His, AdjCre_Gly, 0.596 (0.528, 0.665); AdjCre_Lys, AdjCre_Met, AdjCre_Val, 0.596 (0.526, 0.666); AdjCre_Ala, AdjCre_Arg, AdjCre_His, 0.596 (0.527, 0.665); AdjCre_Lys, AdjCre_Pro, AdjCre_Trp, 0.596 (0.525, 0.667); AdjCre_Asn, AdjCre_Lys, AdjCre_Pro, 0.595 (0.524, 0.666); AdjCre_Arg, AdjCre_Asn, AdjCre_His, 0.595 (0.528, 0.662); AdjCre_Arg, AdjCre_Asn, AdjCre_Ile, 0.595 (0.528, 0.662); AdjCre_His, AdjCre_Ile, AdjCre_Met, 0.595 (0.526, 0.663); AdjCre_Met, AdjCre_Thr, AdjCre_Tyr, 0.594 (0.523, 0.666); AdjCre_His, AdjCre_Val, AdjCre_Gly, 0.594 (0.525, 0.663); AdjCre_His, AdjCre_Ser, AdjCre_Gly, 0.594 (0.525, 0.664); AdjCre_His, AdjCre_Met, AdjCre_Phe, 0.594 (0.526, 0.662); AdjCre_Ala, AdjCre_His, AdjCre_Leu, 0.594 (0.525, 0.662); AdjCre_His, AdjCre_Trp, AdjCre_Gly, 0.594 (0.525, 0.663); AdjCre_Pro, AdjCre_Trp, AdjCre_Gly, 0.594 (0.522, 0.665); AdjCre_Arg, AdjCre_His, AdjCre_Met, 0.593 (0.525, 0.662); AdjCre_Met, AdjCre_Pro, AdjCre_Trp, 0.593 (0.522, 0.664); AdjCre_Arg, AdjCre_Asn, AdjCre_Leu, 0.593 (0.526, 0.66); AdjCre_Asn, AdjCre_Pro, AdjCre_Thr, 0.593 (0.522, 0.664); AdjCre_Asn, AdjCre_Pro, AdjCre_Tyr, 0.593 (0.522, 0.664); AdjCre_Arg, AdjCre_Asn, AdjCre_Val, 0.593 (0.526, 0.66); AdjCre_Ala, AdjCre_His, AdjCre_Lys, 0.593 (0.524, 0.662); AdjCre_Met, AdjCre_Thr, AdjCre_Gly, 0.593 (0.522, 0.664); AdjCre_Asn, AdjCre_Pro, AdjCre_Trp, 0.593 (0.522, 0.663); AdjCre_Gln, AdjCre_His, AdjCre_Tyr, 0.592 (0.524, 0.661); AdjCre_His, AdjCre_Met, AdjCre_Ser, 0.592 (0.524, 0.66); AdjCre_Ala, AdjCre_His, AdjCre_Ile, 0.592 (0.524, 0.661); AdjCre_Lys, AdjCre_Trp, AdjCre_Tyr, 0.592 (0.524, 0.66); AdjCre_Asn, AdjCre_Pro, AdjCre_Val, 0.592 (0.521, 0.663); AdjCre_Asn, AdjCre_Trp, AdjCre_Tyr, 0.592 (0.524, 0.66); AdjCre_Arg, AdjCre_Asn, AdjCre_Tyr, 0.592 (0.524, 0.66); AdjCre_His, AdjCre_Met, AdjCre_Tyr, 0.592 (0.524, 0.66); AdjCre_Ala, AdjCre_Gln, AdjCre_His, 0.592 (0.523, 0.661); AdjCre_Leu, AdjCre_Met, AdjCre_Trp, 0.592 (0.522, 0.662); AdjCre_Asn, AdjCre_Ile, AdjCre_Pro, 0.592 (0.521, 0.662); AdjCre_Asn, AdjCre_Phe, AdjCre_Pro, 0.592 (0.521, 0.662); AdjCre_Gln, AdjCre_His, AdjCre_Trp, 0.592 (0.523, 0.661); AdjCre_Ala, AdjCre_His, AdjCre_Tyr, 0.592 (0.523, 0.66); AdjCre_His, AdjCre_Pro, AdjCre_Thr, 0.591 (0.52, 0.663); AdjCre_Gln, AdjCre_Pro, AdjCre_Trp, 0.591 (0.52, 0.662); AdjCre_Lys, AdjCre_Thr, AdjCre_Gly, 0.591 (0.52, 0.662); AdjCre_Arg, AdjCre_Asn, AdjCre_Pro, 0.591 (0.521, 0.661); AdjCre_Ile, AdjCre_Pro, AdjCre_Trp, 0.591 (0.52, 0.662); AdjCre_Arg, AdjCre_Pro, AdjCre_Trp, 0.591 (0.52, 0.662); AdjCre_Asn, AdjCre_Leu, AdjCre_Pro, 0.591 (0.52, 0.661); AdjCre_Arg, AdjCre_Asn, AdjCre_Phe, 0.591 (0.523, 0.659); AdjCre_Gln, AdjCre_His, AdjCre_Ile, 0.591 (0.522, 0.659); AdjCre_Pro, AdjCre_Trp, AdjCre_Val, 0.591 (0.52, 0.662); AdjCre_His, AdjCre_Met, AdjCre_Trp, 0.591 (0.522, 0.659); AdjCre_Gln, AdjCre_His, AdjCre_Phe, 0.59 (0.521, 0.66); AdjCre_Gln, AdjCre_His, AdjCre_Leu, 0.59 (0.521, 0.659); AdjCre_Asn, AdjCre_His, AdjCre_Pro, 0.59 (0.52, 0.661); AdjCre_Leu, AdjCre_Pro, AdjCre_Trp, 0.59 (0.519, 0.661); AdjCre_Phe, AdjCre_Pro, AdjCre_Trp, 0.59 (0.519, 0.661); AdjCre_Arg, AdjCre_Asn, AdjCre_Trp, 0.59 (0.522, 0.658); AdjCre_Ala, AdjCre_His, AdjCre_Trp, 0.59 (0.52, 0.659); AdjCre_Arg, AdjCre_Asn, AdjCre_Lys, 0.59 (0.521, 0.658); AdjCre_Lys, AdjCre_Pro, AdjCre_Val, 0.59 (0.518, 0.661); AdjCre_Met, AdjCre_Trp, AdjCre_Val, 0.589 (0.518, 0.661); AdjCre_Arg, AdjCre_Gln, AdjCre_His, 0.589 (0.52, 0.659); AdjCre_Ala, AdjCre_His, AdjCre_Phe, 0.589 (0.52, 0.658); AdjCre_Asn, AdjCre_Leu, AdjCre_Trp, 0.589 (0.522, 0.657); AdjCre_Ala, AdjCre_Met, AdjCre_Phe, 0.589 (0.521, 0.657); AdjCre_Gln, AdjCre_His, AdjCre_Lys, 0.589 (0.52, 0.658); AdjCre_Ala, AdjCre_Met, AdjCre_Thr, 0.589 (0.519, 0.658); AdjCre_Asn, AdjCre_Leu, AdjCre_Tyr, 0.589 (0.521, 0.657); AdjCre_Lys, AdjCre_Met, AdjCre_Trp, 0.588 (0.519, 0.658); AdjCre_Ala, AdjCre_His, AdjCre_Ser, 0.588 (0.519, 0.657); AdjCre_Asn, AdjCre_Ile, AdjCre_Val, 0.588 (0.521, 0.656); AdjCre_Asn, AdjCre_His, AdjCre_Val, 0.588 (0.52, 0.656); AdjCre_Pro, AdjCre_Thr, AdjCre_Trp, 0.588 (0.516, 0.659); AdjCre_Arg, AdjCre_Asn, AdjCre_Thr, 0.588 (0.519, 0.657); AdjCre_Gln, AdjCre_Met, AdjCre_Thr, 0.588 (0.517, 0.659); AdjCre_Asn, AdjCre_Ile, AdjCre_Leu, 0.588 (0.52, 0.655); AdjCre_Ile, AdjCre_Lys, AdjCre_Met, 0.588 (0.518, 0.657); AdjCre_Lys, AdjCre_Ser, AdjCre_Gly, 0.588 (0.517, 0.658); AdjCre_Asn, AdjCre_Leu, AdjCre_Val, 0.587 (0.52, 0.655); AdjCre_Gln, AdjCre_Pro, AdjCre_Gly, 0.587 (0.516, 0.659); AdjCre_Arg, AdjCre_Lys, AdjCre_Thr, 0.587 (0.517, 0.657); AdjCre_Ala, AdjCre_Trp, AdjCre_Tyr, 0.587 (0.517, 0.658); AdjCre_Asn, AdjCre_Trp, AdjCre_Val, 0.587 (0.519, 0.655); AdjCre_Ala, AdjCre_Met, AdjCre_Trp, 0.587 (0.516, 0.658); AdjCre_Met, AdjCre_Pro, AdjCre_Val, 0.587 (0.516, 0.658); AdjCre_Lys, AdjCre_Phe, AdjCre_Pro, 0.587 (0.517, 0.657); AdjCre_Lys, AdjCre_Met, AdjCre_Gly, 0.587 (0.518, 0.656); AdjCre_Asn, AdjCre_His, AdjCre_Leu, 0.587 (0.519, 0.655); AdjCre_Leu, AdjCre_Lys, AdjCre_Pro, 0.587 (0.515, 0.658); AdjCre_Ala, AdjCre_His, AdjCre_Val, 0.587 (0.518, 0.656); AdjCre_Asn, AdjCre_Tyr, AdjCre_Val, 0.587 (0.518, 0.655); AdjCre_Arg, AdjCre_Lys, AdjCre_Pro, 0.587 (0.516, 0.657); AdjCre_Met, AdjCre_Ser, AdjCre_Thr, 0.586 (0.516, 0.657); AdjCre_Asn, AdjCre_Leu, AdjCre_Phe, 0.586 (0.518, 0.654); AdjCre_Gln, AdjCre_Thr, AdjCre_Gly, 0.586 (0.515, 0.658); AdjCre_Pro, AdjCre_Ser, AdjCre_Trp, 0.586 (0.515, 0.658); AdjCre_Gln, AdjCre_His, AdjCre_Val, 0.586 (0.517, 0.655); AdjCre_Asn, AdjCre_Leu, AdjCre_Lys, 0.586 (0.518, 0.654); AdjCre_Gln, AdjCre_Lys, AdjCre_Met, 0.586 (0.516, 0.656); AdjCre_Arg, AdjCre_Met, AdjCre_Thr, 0.586 (0.515, 0.657); AdjCre_Asn, AdjCre_Thr, AdjCre_Val, 0.586 (0.517, 0.655); AdjCre_Arg, AdjCre_Phe, AdjCre_Pro, 0.586 (0.516, 0.655); AdjCre_Arg, AdjCre_Lys, AdjCre_Gly, 0.586 (0.517, 0.654); AdjCre_Ser, AdjCre_Thr, AdjCre_Gly, 0.586 (0.514, 0.657); AdjCre_Lys, AdjCre_Met, AdjCre_Pro, 0.585 (0.515, 0.656); AdjCre_Asn, AdjCre_His, AdjCre_Ile, 0.585 (0.518, 0.653); AdjCre_Arg, AdjCre_Lys, AdjCre_Met, 0.585 (0.516, 0.655); AdjCre_Ala, AdjCre_Lys, AdjCre_Ser, 0.585 (0.516, 0.654); AdjCre_Phe, AdjCre_Pro, AdjCre_Gly, 0.585 (0.515, 0.655); AdjCre_Asn, AdjCre_Phe, AdjCre_Val, 0.585 (0.517, 0.653); AdjCre_Arg, AdjCre_Thr, AdjCre_Gly, 0.585 (0.514, 0.656); AdjCre_Ile, AdjCre_Lys, AdjCre_Pro, 0.585 (0.514, 0.656); AdjCre_Ala, AdjCre_Arg, AdjCre_Lys, 0.585 (0.517, 0.653); AdjCre_Asn, AdjCre_Leu, AdjCre_Thr, 0.585 (0.516, 0.654); AdjCre_Lys, AdjCre_Pro, AdjCre_Gly, 0.585 (0.515, 0.655); AdjCre_Ala, AdjCre_Lys, AdjCre_Pro, 0.585 (0.516, 0.653); AdjCre_Lys, AdjCre_Pro, AdjCre_Thr, 0.584 (0.513, 0.656); AdjCre_Asn, AdjCre_Ile, AdjCre_Phe, 0.584 (0.516, 0.652); AdjCre_Ala, AdjCre_Ile, AdjCre_Trp, 0.584 (0.514, 0.654); AdjCre_His, AdjCre_Trp, AdjCre_Tyr, 0.584 (0.517, 0.652); AdjCre_Asn, AdjCre_Ile, AdjCre_Tyr, 0.584 (0.516, 0.652); AdjCre_Met, AdjCre_Phe, AdjCre_Val, 0.584 (0.514, 0.654); AdjCre_Asn, AdjCre_Lys, AdjCre_Val, 0.584 (0.515, 0.652); AdjCre_Met, AdjCre_Phe, AdjCre_Gly, 0.584 (0.515, 0.652); AdjCre_Met, AdjCre_Pro, AdjCre_Thr, 0.584 (0.512, 0.656); AdjCre_Pro, AdjCre_Thr, AdjCre_Gly, 0.584 (0.512, 0.655); AdjCre_Gln, AdjCre_Lys, AdjCre_Thr, 0.584 (0.514, 0.653); AdjCre_Gln, AdjCre_Lys, AdjCre_Pro, 0.584 (0.512, 0.655); AdjCre_Ala, AdjCre_Gln, AdjCre_Met, 0.583 (0.514, 0.653); AdjCre_Ala, AdjCre_Arg, AdjCre_Trp, 0.583 (0.513, 0.654); AdjCre_Gln, AdjCre_His, AdjCre_Ser, 0.583 (0.515, 0.652); AdjCre_Ile, AdjCre_Met, AdjCre_Phe, 0.583 (0.514, 0.652); AdjCre_Thr, AdjCre_Trp, AdjCre_Tyr, 0.583 (0.514, 0.652); AdjCre_Ala, AdjCre_Arg, AdjCre_Thr, 0.583 (0.513, 0.653); AdjCre_Ala, AdjCre_Ser, AdjCre_Thr, 0.583 (0.513, 0.652); AdjCre_Phe, AdjCre_Thr, AdjCre_Gly, 0.583 (0.511, 0.654); AdjCre_Lys, AdjCre_Pro, AdjCre_Tyr, 0.583 (0.511, 0.654); AdjCre_Ala, AdjCre_Lys, AdjCre_Thr, 0.583 (0.513, 0.652); AdjCre_Ile, AdjCre_Phe, AdjCre_Pro, 0.583 (0.513, 0.652); AdjCre_Phe, AdjCre_Pro, AdjCre_Val, 0.582 (0.512, 0.653); AdjCre_Asn, AdjCre_His, AdjCre_Lys, 0.582 (0.514, 0.651); AdjCre_Asn, AdjCre_Lys, AdjCre_Tyr, 0.582 (0.513, 0.652); AdjCre_Gln, AdjCre_Lys, AdjCre_Gly, 0.582 (0.512, 0.652); AdjCre_Ala, AdjCre_Thr, AdjCre_Gly, 0.582 (0.512, 0.652); AdjCre_Leu, AdjCre_Met, AdjCre_Tyr, 0.582 (0.511, 0.653); AdjCre_Ile, AdjCre_Thr, AdjCre_Gly, 0.582 (0.511, 0.653); AdjCre_Ala, AdjCre_Trp, AdjCre_Gly, 0.582 (0.511, 0.653); AdjCre_Asn, AdjCre_Phe, AdjCre_Thr, 0.582 (0.512, 0.652); AdjCre_Ala, AdjCre_Pro, AdjCre_Thr, 0.582 (0.511, 0.653); AdjCre_Gln, AdjCre_Met, AdjCre_Val, 0.582 (0.511, 0.653); AdjCre_Asn, AdjCre_His, AdjCre_Trp, 0.582 (0.514, 0.65); AdjCre_Arg, AdjCre_Gln, AdjCre_Thr, 0.582 (0.511, 0.653); AdjCre_Ala, AdjCre_Thr, AdjCre_Trp, 0.582 (0.51, 0.653); AdjCre_Ala, AdjCre_Lys, AdjCre_Met, 0.582 (0.513, 0.65); AdjCre_Asn, AdjCre_Ile, AdjCre_Lys, 0.582 (0.513, 0.65); AdjCre_Asn, AdjCre_Ile, AdjCre_Thr, 0.581 (0.513, 0.65); AdjCre_Thr, AdjCre_Trp, AdjCre_Gly, 0.581 (0.509, 0.654); AdjCre_Thr, AdjCre_Tyr, AdjCre_Gly, 0.581 (0.509, 0.654); AdjCre_Leu, AdjCre_Phe, AdjCre_Pro, 0.581 (0.511, 0.651); AdjCre_Leu, AdjCre_Thr, AdjCre_Gly, 0.581 (0.51, 0.653); AdjCre_Phe, AdjCre_Pro, AdjCre_Thr, 0.581 (0.511, 0.652); AdjCre_Asn, AdjCre_His, AdjCre_Phe, 0.581 (0.513, 0.65); AdjCre_Ile, AdjCre_Pro, AdjCre_Val, 0.581 (0.51, 0.652); AdjCre_Asn, AdjCre_His, AdjCre_Tyr, 0.581 (0.512, 0.65); AdjCre_Gln, AdjCre_Trp, AdjCre_Tyr, 0.581 (0.512, 0.65); AdjCre_Ile, AdjCre_Trp, AdjCre_Tyr, 0.581 (0.513, 0.649); AdjCre_Arg, AdjCre_Lys, AdjCre_Val, 0.581 (0.512, 0.65); AdjCre_Ala, AdjCre_Lys, AdjCre_Gly, 0.581 (0.513, 0.649); AdjCre_Arg, AdjCre_Leu, AdjCre_Lys, 0.581 (0.511, 0.65); AdjCre_Ala, AdjCre_Phe, AdjCre_Trp, 0.581 (0.51, 0.651); AdjCre_Ala, AdjCre_Lys, AdjCre_Trp, 0.581 (0.511, 0.65); AdjCre_Lys, AdjCre_Met, AdjCre_Tyr, 0.581 (0.511, 0.651); AdjCre_Asn, AdjCre_Phe, AdjCre_Trp, 0.581 (0.512, 0.65); AdjCre_Arg, AdjCre_Lys, AdjCre_Trp, 0.581 (0.511, 0.65); AdjCre_Ala, AdjCre_Ser, AdjCre_Trp, 0.58 (0.51, 0.651); AdjCre_Met, AdjCre_Ser, AdjCre_Val, 0.58 (0.51, 0.651); AdjCre_Gln, AdjCre_Phe, AdjCre_Pro, 0.58 (0.511, 0.65); AdjCre_Met, AdjCre_Trp, AdjCre_Tyr, 0.58 (0.51, 0.651); AdjCre_Ala, AdjCre_Leu, AdjCre_Trp, 0.58 (0.51, 0.651); AdjCre_Asn, AdjCre_Ile, AdjCre_Trp, 0.58 (0.513, 0.648); AdjCre_Ala, AdjCre_Phe, AdjCre_Thr, 0.58 (0.51, 0.65); AdjCre_Met, AdjCre_Phe, AdjCre_Trp, 0.58 (0.51, 0.65); AdjCre_Arg, AdjCre_Met, AdjCre_Trp, 0.58 (0.509, 0.651); AdjCre_Met, AdjCre_Trp, AdjCre_Gly, 0.58 (0.509, 0.651); AdjCre_Leu, AdjCre_Met, AdjCre_Ser, 0.58 (0.509, 0.65); AdjCre_His, AdjCre_Ser, AdjCre_Thr, 0.58 (0.507, 0.652); AdjCre_Thr, AdjCre_Val, AdjCre_Gly, 0.58 (0.508, 0.651); AdjCre_Asn, AdjCre_Phe, AdjCre_Tyr, 0.58 (0.511, 0.648); AdjCre_Trp, AdjCre_Tyr, AdjCre_Val, 0.579 (0.512, 0.647); AdjCre_Ala, AdjCre_Gln, AdjCre_Lys, 0.579 (0.51, 0.649); AdjCre_Lys, AdjCre_Met, AdjCre_Ser, 0.579 (0.51, 0.649); AdjCre_Asn, AdjCre_Thr, AdjCre_Tyr, 0.579 (0.51, 0.649); AdjCre_Ile, AdjCre_Leu, AdjCre_Met, 0.579 (0.509, 0.65); AdjCre_Arg, AdjCre_Gln, AdjCre_Lys, 0.579 (0.51, 0.648); AdjCre_Ala, AdjCre_Gln, AdjCre_Thr, 0.579 (0.509, 0.649); AdjCre_Gln, AdjCre_Leu, AdjCre_Met, 0.579 (0.508, 0.65); AdjCre_Gln, AdjCre_Phe, AdjCre_Thr, 0.579 (0.508, 0.65); AdjCre_Ala, AdjCre_Ile, AdjCre_Thr, 0.579 (0.509, 0.649); AdjCre_Leu, AdjCre_Trp, AdjCre_Tyr, 0.579 (0.511, 0.647); AdjCre_Ala, AdjCre_Gln, AdjCre_Pro, 0.579 (0.507, 0.651); AdjCre_Arg, AdjCre_Met, AdjCre_Phe, 0.579 (0.509, 0.648); AdjCre_Met, AdjCre_Phe, AdjCre_Tyr, 0.579 (0.509, 0.648); AdjCre_Ile, AdjCre_Pro, AdjCre_Thr, 0.578 (0.506, 0.651); AdjCre_Ile, AdjCre_Met, AdjCre_Trp, 0.578 (0.508, 0.649); AdjCre_Ile, AdjCre_Met, AdjCre_Val, 0.578 (0.508, 0.648); AdjCre_Arg, AdjCre_Pro, AdjCre_Val, 0.578 (0.507, 0.65); AdjCre_Gln, AdjCre_Leu, AdjCre_Thr, 0.578 (0.507, 0.649); AdjCre_Met, AdjCre_Phe, AdjCre_Ser, 0.578 (0.509, 0.647); AdjCre_Gln, AdjCre_Thr, AdjCre_Val, 0.578 (0.507, 0.649); AdjCre_Ala, AdjCre_Trp, AdjCre_Val, 0.578 (0.507, 0.649); AdjCre_Asn, AdjCre_His, AdjCre_Thr, 0.578 (0.509, 0.647); AdjCre_Gln, AdjCre_Ile, AdjCre_Thr, 0.578 (0.507, 0.649); AdjCre_Asn, AdjCre_Lys, AdjCre_Phe, 0.578 (0.508, 0.648); AdjCre_Pro, AdjCre_Thr, AdjCre_Val, 0.578 (0.506, 0.65); AdjCre_Gln, AdjCre_Pro, AdjCre_Thr, 0.578 (0.506, 0.65); AdjCre_Arg, AdjCre_Trp, AdjCre_Tyr, 0.578 (0.51, 0.646); AdjCre_Arg, AdjCre_Ile, AdjCre_Lys, 0.578 (0.509, 0.647); AdjCre_Gln, AdjCre_Pro, AdjCre_Val, 0.578 (0.507, 0.649); AdjCre_Arg, AdjCre_Pro, AdjCre_Thr, 0.578 (0.506, 0.65); AdjCre_Leu, AdjCre_Met, AdjCre_Val, 0.578 (0.507, 0.648); AdjCre_Ala, AdjCre_Ile, AdjCre_Lys, 0.578 (0.51, 0.645); AdjCre_Lys, AdjCre_Trp, AdjCre_Gly, 0.578 (0.506, 0.649); AdjCre_Phe, AdjCre_Pro, AdjCre_Tyr, 0.578 (0.507, 0.648); AdjCre_Ala, AdjCre_Gln, AdjCre_Trp, 0.578 (0.507, 0.648); AdjCre_His, AdjCre_Phe, AdjCre_Thr, 0.577 (0.507, 0.648); AdjCre_Ala, AdjCre_Gln, AdjCre_Gly, 0.577 (0.508, 0.647); AdjCre_Met, AdjCre_Tyr, AdjCre_Val, 0.577 (0.506, 0.649); AdjCre_Ala, AdjCre_Met, AdjCre_Ser, 0.577 (0.509, 0.646); AdjCre_Phe, AdjCre_Ser, AdjCre_Gly, 0.577 (0.506, 0.648); AdjCre_Arg, AdjCre_Lys, AdjCre_Tyr, 0.577 (0.508, 0.646); AdjCre_Ala, AdjCre_Leu, AdjCre_Thr, 0.577 (0.507, 0.647); AdjCre_Leu, AdjCre_Pro, AdjCre_Val, 0.577 (0.506, 0.648); AdjCre_Met, AdjCre_Ser, AdjCre_Gly, 0.577 (0.507, 0.647); AdjCre_His, AdjCre_Phe, AdjCre_Ser, 0.577 (0.507, 0.647); AdjCre_Leu, AdjCre_Pro, AdjCre_Thr, 0.577 (0.505, 0.649); AdjCre_Lys, AdjCre_Pro, AdjCre_Ser, 0.577 (0.505, 0.648); AdjCre_Ile, AdjCre_Lys, AdjCre_Gly, 0.577 (0.507, 0.646); AdjCre_Lys, AdjCre_Val, AdjCre_Gly, 0.576 (0.506, 0.647); AdjCre_Trp, AdjCre_Tyr, AdjCre_Gly, 0.576 (0.505, 0.648); AdjCre_Ala, AdjCre_Arg, AdjCre_Phe, 0.576 (0.508, 0.645); AdjCre_Lys, AdjCre_Phe, AdjCre_Gly , 0.576 (0.507, 0.646); AdjCre_Phe, AdjCre_Trp, AdjCre_Tyr, 0.576 (0.507, 0.645); AdjCre_Arg, AdjCre_His, AdjCre_Lys, 0.576 (0.507, 0.646); AdjCre_Gln, AdjCre_Thr, AdjCre_Tyr, 0.576 (0.505, 0.647); AdjCre_Gln, AdjCre_Met, AdjCre_Trp, 0.576 (0.505, 0.647); AdjCre_Ala, AdjCre_Pro, AdjCre_Val, 0.576 (0.505, 0.647); AdjCre_Arg, AdjCre_Lys, AdjCre_Phe, 0.576 (0.507, 0.645); AdjCre_Leu, AdjCre_Lys, AdjCre_Gly, 0.576 (0.506, 0.645); AdjCre_Arg, AdjCre_His, AdjCre_Thr, 0.576 (0.504, 0.647); AdjCre_Gln, AdjCre_Met, AdjCre_Gly, 0.576 (0.504, 0.647); AdjCre_Phe, AdjCre_Pro, AdjCre_Ser, 0.576 (0.506, 0.645); AdjCre_Ala, AdjCre_Ser, AdjCre_Gly, 0.576 (0.506, 0.645); AdjCre_Pro, AdjCre_Thr, AdjCre_Tyr, 0.575 (0.503, 0.648); AdjCre_Ala, AdjCre_Thr, AdjCre_Val, 0.575 (0.505, 0.646); AdjCre_Lys, AdjCre_Ser, AdjCre_Thr, 0.575 (0.504, 0.647); AdjCre_Pro, AdjCre_Ser, AdjCre_Thr, 0.575 (0.503, 0.647); AdjCre_Ala, AdjCre_Phe, AdjCre_Gly, 0.575 (0.507, 0.643); AdjCre_Ala, AdjCre_Phe, AdjCre_Ser, 0.575 (0.506, 0.644); AdjCre_Thr, AdjCre_Trp, AdjCre_Val, 0.575 (0.504, 0.646); AdjCre_Leu, AdjCre_Met, AdjCre_Gly, 0.575 (0.504, 0.646); AdjCre_Met, AdjCre_Ser, AdjCre_Trp, 0.575 (0.504, 0.645); AdjCre_Pro, AdjCre_Tyr, AdjCre_Val, 0.574 (0.503, 0.646); AdjCre_Leu, AdjCre_Met, AdjCre_Pro, 0.574 (0.502, 0.647); AdjCre_Gln, AdjCre_Met, AdjCre_Phe, 0.574 (0.504, 0.644); AdjCre_Met, AdjCre_Pro, AdjCre_Tyr, 0.574 (0.503, 0.645); AdjCre_His, AdjCre_Lys, AdjCre_Phe, 0.574 (0.504, 0.644); AdjCre_Arg, AdjCre_Ile, AdjCre_Thr, 0.574 (0.503, 0.645); AdjCre_His, AdjCre_Thr, AdjCre_Val, 0.574 (0.503, 0.644); AdjCre_Asn, AdjCre_Lys, AdjCre_Trp, 0.574 (0.505, 0.643); AdjCre_Ala, AdjCre_Leu, AdjCre_Lys, 0.574 (0.506, 0.642); AdjCre_Arg, AdjCre_Thr, AdjCre_Val, 0.574 (0.502, 0.645); AdjCre_Ala, AdjCre_Gln, AdjCre_Ser, 0.574 (0.504, 0.643); AdjCre_Arg, AdjCre_His, AdjCre_Tyr, 0.573 (0.504, 0.643); AdjCre_Arg, AdjCre_Leu, AdjCre_Met, 0.573 (0.503, 0.644); AdjCre_Ser, AdjCre_Thr, AdjCre_Val, 0.573 (0.502, 0.645); AdjCre_Ser, AdjCre_Thr, AdjCre_Trp, 0.573 (0.501, 0.646).

[0206] [2: Formula selected in Example 3] AdjSG_Asn, AdjSG_Met, AdjSG_Ser, 0.666 (0.601, 0.731); AdjSG_Asn, AdjSG_Glu, AdjSG_Ser, 0.656 (0.591, 0.722); AdjSG_Asn, AdjSG_Glu, AdjSG_Gly, 0.648 (0.581, 0.715); AdjSG_Asn, AdjSG_Met, AdjSG_Gly, 0.648 (0.581, 0.714); AdjSG_Asn, AdjSG_Ile, AdjSG_Ser, 0.646 (0.58, 0.712); AdjSG_Ala, AdjSG_Asn, AdjSG_Glu, 0.643 (0.575, 0.71); AdjSG_Asn, AdjSG_Leu, AdjSG_Ser, 0.642 (0.575, 0.708); AdjSG_Asn, AdjSG_Ser, AdjSG_Gly, 0.641 (0.575, 0.708); AdjSG_Arg, AdjSG_Asn, AdjSG_Ser, 0.64 (0.574, 0.707); AdjSG_Arg, AdjSG_Asn, AdjSG_Gly, 0.64 (0.574, 0.707); AdjSG_Ala, AdjSG_Asn, AdjSG_Ser, 0.64 (0.573, 0.707); AdjSG_Asn, AdjSG_Ser, AdjSG_Val, 0.639 (0.572, 0.706); AdjSG_Asn, AdjSG_Pro, AdjSG_Ser, 0.638 (0.569, 0.707); AdjSG_Glu, AdjSG_Trp, AdjSG_Tyr, 0.638 (0.571, 0.704); AdjSG_Asn, AdjSG_Gln, AdjSG_Ser, 0.637 (0.571, 0.703); AdjSG_Asn, AdjSG_Ile, AdjSG_Gly, 0.636 (0.568, 0.703); AdjSG_Asn, AdjSG_Ser, AdjSG_Tyr, 0.635 (0.569, 0.702); AdjSG_Ala, AdjSG_Glu, AdjSG_Trp, 0.635 (0.566, 0.704); AdjSG_Ala, AdjSG_Glu, AdjSG_Phe, 0.634 (0.567, 0.701); AdjSG_Asn, AdjSG_Tyr, AdjSG_Gly, 0.634 (0.565, 0.702); AdjSG_Asn, AdjSG_Thr, AdjSG_Gly, 0.634 (0.565, 0.703); AdjSG_Asn, AdjSG_Pro, AdjSG_Gly, 0.633 (0.564, 0.702); AdjSG_Asn, AdjSG_Leu, AdjSG_Gly, 0.633 (0.566, 0.701); AdjSG_Asn, AdjSG_Glu, AdjSG_Met, 0.633 (0.565, 0.7); AdjSG_Ala, AdjSG_Asn, AdjSG_Met, 0.632 (0.565, 0.698); AdjSG_Asn, AdjSG_Gln, AdjSG_Gly, 0.631 (0.564, 0.699); AdjSG_Asn, AdjSG_His, AdjSG_Ser, 0.631 (0.565, 0.698); AdjSG_Asn, AdjSG_Val, AdjSG_Gly, 0.631 (0.563, 0.699); AdjSG_Asn, AdjSG_Lys, AdjSG_Gly, 0.631 (0.563, 0.699); AdjSG_Ala, AdjSG_Glu, AdjSG_His, 0.63 (0.562, 0.699); AdjSG_Asn, AdjSG_Phe, AdjSG_Gly, 0.63 (0.562, 0.698); AdjSG_Glu, AdjSG_His, AdjSG_Gly, 0.63 (0.562, 0.698); AdjSG_Asn, AdjSG_Ser, AdjSG_Trp, 0.63 (0.563, 0.697); AdjSG_Asn, AdjSG_Gln, AdjSG_Met, 0.63 (0.564, 0.696); AdjSG_Asn, AdjSG_Trp, AdjSG_Gly, 0.629 (0.562, 0.697); AdjSG_Ala, AdjSG_Arg, AdjSG_Asn, 0.629 (0.561, 0.697); AdjSG_Asn, AdjSG_Gln, AdjSG_Glu, 0.629 (0.562, 0.696); AdjSG_Asn, AdjSG_Phe, AdjSG_Ser, 0.629 (0.561, 0.697); AdjSG_Asn, AdjSG_Lys, AdjSG_Ser, 0.628 (0.561, 0.696); AdjSG_Glu, AdjSG_Met, AdjSG_Trp, 0.628 (0.56, 0.696); AdjSG_Asn, AdjSG_Ser, AdjSG_Thr, 0.628 (0.56, 0.696); AdjSG_Glu, AdjSG_Met, AdjSG_Phe, 0.627 (0.56, 0.695); AdjSG_Asn, AdjSG_His, AdjSG_Gly, 0.627 (0.559, 0.695); AdjSG_Glu, AdjSG_Pro, AdjSG_Trp, 0.627 (0.559, 0.695); AdjSG_Glu, AdjSG_Phe, AdjSG_Gly, 0.627 (0.559, 0.694); AdjSG_Glu, AdjSG_Phe, AdjSG_Pro, 0.626 (0.559, 0.693); AdjSG_Glu, AdjSG_Trp, AdjSG_Gly, 0.626 (0.558, 0.694); AdjSG_Ala, AdjSG_Asn, AdjSG_Gly, 0.626 (0.558, 0.693); AdjSG_Glu, AdjSG_His, AdjSG_Met, 0.625 (0.557, 0.693); AdjSG_Asn, AdjSG_Met, AdjSG_Val, 0.624 (0.557, 0.691); AdjSG_Glu, AdjSG_Ile, AdjSG_Trp, 0.624 (0.557, 0.691); AdjSG_Asn, AdjSG_Glu, AdjSG_Ile, 0.623 (0.556, 0.691); AdjSG_Gln, AdjSG_Glu, AdjSG_His, 0.623 (0.555, 0.691); AdjSG_Asn, AdjSG_Glu, AdjSG_Trp, 0.623 (0.555, 0.69); AdjSG_Asn, AdjSG_Met, AdjSG_Phe, 0.622 (0.556, 0.689); AdjSG_Asn, AdjSG_Met, AdjSG_Thr, 0.622 (0.555, 0.69); AdjSG_Asn, AdjSG_Glu, AdjSG_Leu, 0.622 (0.554, 0.69); AdjSG_Arg, AdjSG_Asn, AdjSG_Glu, 0.622 (0.554, 0.689); AdjSG_Arg, AdjSG_Asn, AdjSG_Gln, 0.621 (0.555, 0.687); AdjSG_Glu, AdjSG_Ile, AdjSG_Phe, 0.621 (0.554, 0.688); AdjSG_Asn, AdjSG_Glu, AdjSG_Phe, 0.621 (0.553, 0.688); AdjSG_Asn, AdjSG_Glu, AdjSG_Val, 0.621 (0.553, 0.688); AdjSG_Asn, AdjSG_Leu, AdjSG_Met, 0.621 (0.554, 0.687); AdjSG_Glu, AdjSG_Leu, AdjSG_Trp, 0.621 (0.553, 0.688); AdjSG_Asn, AdjSG_Glu, AdjSG_Lys, 0.62 (0.552, 0.688); AdjSG_Arg, AdjSG_Asn, AdjSG_Met, 0.62 (0.554, 0.686); AdjSG_Glu, AdjSG_His, AdjSG_Pro, 0.62 (0.551, 0.689); AdjSG_Ala, AdjSG_Asn, AdjSG_Leu, 0.62 (0.552, 0.688); AdjSG_Asn, AdjSG_Glu, AdjSG_Tyr, 0.62 (0.552, 0.688); AdjSG_Asn, AdjSG_His, AdjSG_Met, 0.619 (0.553, 0.686); AdjSG_Glu, AdjSG_His, AdjSG_Ile, 0.619 (0.551, 0.687); AdjSG_Ala, AdjSG_Asn, AdjSG_Thr, 0.619 (0.55, 0.688); AdjSG_Asn, AdjSG_Ile, AdjSG_Met, 0.619 (0.553, 0.685); AdjSG_Asn, AdjSG_Gln, AdjSG_Trp, 0.619 (0.552, 0.685); AdjSG_Glu, AdjSG_His, AdjSG_Ser, 0.619 (0.551, 0.687); AdjSG_Glu, AdjSG_Trp, AdjSG_Val, 0.618 (0.55, 0.686); AdjSG_Asn, AdjSG_Met, AdjSG_Tyr, 0.618 (0.552, 0.684); AdjSG_Asn, AdjSG_Met, AdjSG_Trp, 0.618 (0.551, 0.685); AdjSG_His, AdjSG_Pro, AdjSG_Gly, 0.618 (0.548, 0.688); AdjSG_Pro, AdjSG_Trp, AdjSG_Tyr, 0.618 (0.549, 0.687); AdjSG_Gln, AdjSG_Glu, AdjSG_Trp, 0.618 (0.55, 0.686); AdjSG_Glu, AdjSG_His, AdjSG_Leu, 0.618 (0.55, 0.686); AdjSG_Asn, AdjSG_Glu, AdjSG_Pro, 0.618 (0.549, 0.686); AdjSG_Glu, AdjSG_Leu, AdjSG_Phe, 0.618 (0.55, 0.685); AdjSG_Glu, AdjSG_Lys, AdjSG_Phe, 0.617 (0.55, 0.685); AdjSG_Asn, AdjSG_Met, AdjSG_Pro, 0.617 (0.549, 0.686); AdjSG_Glu, AdjSG_Thr, AdjSG_Trp, 0.617 (0.549, 0.685); AdjSG_Arg, AdjSG_Glu, AdjSG_Phe, 0.617 (0.55, 0.684); AdjSG_Asn, AdjSG_Glu, AdjSG_His, 0.617 (0.549, 0.685); AdjSG_Ala, AdjSG_Asn, AdjSG_Ile, 0.617 (0.549, 0.685); AdjSG_Gln, AdjSG_Glu, AdjSG_Phe, 0.617 (0.549, 0.684); AdjSG_Asn, AdjSG_Glu, AdjSG_Thr, 0.617 (0.548, 0.685); AdjSG_Asn, AdjSG_Gln, AdjSG_Phe, 0.617 (0.551, 0.683); AdjSG_Glu, AdjSG_His, AdjSG_Trp, 0.617 (0.549, 0.684); AdjSG_Glu, AdjSG_His, AdjSG_Phe, 0.616 (0.549, 0.684); AdjSG_Ala, AdjSG_His, AdjSG_Pro, 0.616 (0.545, 0.687); AdjSG_Glu, AdjSG_Ser, AdjSG_Trp, 0.616 (0.548, 0.684); AdjSG_Glu, AdjSG_Phe, AdjSG_Tyr, 0.616 (0.548, 0.683); AdjSG_Arg, AdjSG_Glu, AdjSG_Trp, 0.616 (0.548, 0.683); AdjSG_Glu, AdjSG_Phe, AdjSG_Trp, 0.616 (0.548, 0.683); AdjSG_Glu, AdjSG_Lys, AdjSG_Trp, 0.616 (0.548, 0.684); AdjSG_Asn, AdjSG_Gln, AdjSG_Ile, 0.615 (0.549, 0.682); AdjSG_Glu, AdjSG_Phe, AdjSG_Val, 0.615 (0.548, 0.682); AdjSG_Asn, AdjSG_Lys, AdjSG_Met, 0.615 (0.547, 0.682); AdjSG_Glu, AdjSG_His, AdjSG_Tyr, 0.615 (0.546, 0.683); AdjSG_Arg, AdjSG_Glu, AdjSG_His, 0.615 (0.546, 0.683); AdjSG_Ala, AdjSG_Asn, AdjSG_Gln, 0.615 (0.547, 0.682); AdjSG_Gln, AdjSG_His, AdjSG_Pro, 0.614 (0.543, 0.685); AdjSG_Asn, AdjSG_Gln, AdjSG_Lys, 0.614 (0.547, 0.681); AdjSG_Glu, AdjSG_His, AdjSG_Val, 0.614 (0.546, 0.682); AdjSG_Asn, AdjSG_Gln, AdjSG_Thr, 0.614 (0.547, 0.681); AdjSG_Glu, AdjSG_His, AdjSG_Lys, 0.613 (0.545, 0.682); AdjSG_His, AdjSG_Met, AdjSG_Gly, 0.613 (0.545, 0.681); AdjSG_Ala, AdjSG_Asn, AdjSG_Lys, 0.613 (0.544, 0.682); AdjSG_Glu, AdjSG_Phe, AdjSG_Ser, 0.613 (0.545, 0.68); AdjSG_Asn, AdjSG_Gln, AdjSG_Leu, 0.613 (0.546, 0.679); AdjSG_Ala, AdjSG_Asn, AdjSG_Pro, 0.613 (0.542, 0.683); AdjSG_Asn, AdjSG_Gln, AdjSG_Val, 0.613 (0.546, 0.679); AdjSG_Ala, AdjSG_His, AdjSG_Met, 0.612 (0.544, 0.68); AdjSG_Ala, AdjSG_Asn, AdjSG_Tyr, 0.612 (0.544, 0.681); AdjSG_Glu, AdjSG_His, AdjSG_Thr, 0.612 (0.543, 0.681); AdjSG_Glu, AdjSG_Phe, AdjSG_Thr, 0.612 (0.544, 0.681); AdjSG_Asn, AdjSG_Gln, AdjSG_His, 0.612 (0.545, 0.679); AdjSG_Ala, AdjSG_Pro, AdjSG_Trp, 0.612 (0.54, 0.683); AdjSG_Glu, AdjSG_Ser, AdjSG_Gly, 0.611 (0.541, 0.68); AdjSG_Asn, AdjSG_Gln, AdjSG_Pro, 0.611 (0.542, 0.68); AdjSG_Ala, AdjSG_Glu, AdjSG_Tyr, 0.611 (0.542, 0.68); AdjSG_Glu, AdjSG_Ile, AdjSG_Leu, 0.611 (0.542, 0.679); AdjSG_Glu, AdjSG_Lys, AdjSG_Gly, 0.611 (0.541, 0.68); AdjSG_Glu, AdjSG_Met, AdjSG_Val, 0.61 (0.541, 0.679); AdjSG_Ala, AdjSG_Glu, AdjSG_Lys, 0.61 (0.541, 0.679); AdjSG_Asn, AdjSG_Gln, AdjSG_Tyr, 0.61 (0.543, 0.677); AdjSG_Ala, AdjSG_Asn, AdjSG_Trp, 0.61 (0.541, 0.679); AdjSG_Glu, AdjSG_Ile, AdjSG_Val, 0.609 (0.541, 0.678); AdjSG_Glu, AdjSG_Leu, AdjSG_Met, 0.609 (0.54, 0.679); AdjSG_Ala, AdjSG_Asn, AdjSG_Val, 0.609 (0.541, 0.678); AdjSG_Ala, AdjSG_Asn, AdjSG_His, 0.609 (0.54, 0.677); AdjSG_Ala, AdjSG_Glu, AdjSG_Leu, 0.608 (0.539, 0.678); AdjSG_Ala, AdjSG_Glu, AdjSG_Ser, 0.608 (0.539, 0.677); AdjSG_His, AdjSG_Pro, AdjSG_Ser, 0.608 (0.537, 0.679); AdjSG_Ala, AdjSG_Gln, AdjSG_Glu, 0.608 (0.539, 0.677); AdjSG_Gln, AdjSG_Glu, AdjSG_Thr, 0.608 (0.538, 0.677); AdjSG_Glu, AdjSG_Ile, AdjSG_Lys, 0.608 (0.538, 0.677); AdjSG_Glu, AdjSG_Lys, AdjSG_Val, 0.608 (0.538, 0.677); AdjSG_Ala, AdjSG_Asn, AdjSG_Phe, 0.607 (0.539, 0.676); AdjSG_Glu, AdjSG_Met, AdjSG_Tyr, 0.607 (0.539, 0.676); AdjSG_His, AdjSG_Met, AdjSG_Pro, 0.607 (0.537, 0.678); AdjSG_Glu, AdjSG_Lys, AdjSG_Pro, 0.607 (0.537, 0.677); AdjSG_Gln, AdjSG_Glu, AdjSG_Ile, 0.607 (0.539, 0.676); AdjSG_Glu, AdjSG_Ile, AdjSG_Tyr, 0.607 (0.539, 0.676); AdjSG_Glu, AdjSG_Pro, AdjSG_Thr, 0.607 (0.537, 0.677); AdjSG_Arg, AdjSG_Glu, AdjSG_Lys, 0.607 (0.538, 0.676); AdjSG_Glu, AdjSG_Leu, AdjSG_Lys, 0.607 (0.538, 0.676); AdjSG_Gln, AdjSG_Glu, AdjSG_Gly, 0.607 (0.537, 0.676); AdjSG_Glu, AdjSG_Lys, AdjSG_Met, 0.607 (0.537, 0.676); AdjSG_Glu, AdjSG_Leu, AdjSG_Gly, 0.606 (0.537, 0.676); AdjSG_Ala, AdjSG_Glu, AdjSG_Val, 0.606 (0.537, 0.676); AdjSG_Gln, AdjSG_His, AdjSG_Met, 0.606 (0.538, 0.675); AdjSG_Glu, AdjSG_Pro, AdjSG_Tyr, 0.606 (0.537, 0.675); AdjSG_Glu, AdjSG_Tyr, AdjSG_Gly, 0.606 (0.537, 0.675); AdjSG_Ala, AdjSG_Phe, AdjSG_Pro, 0.606 (0.537, 0.675); AdjSG_Ala, AdjSG_His, AdjSG_Gly, 0.605 (0.537, 0.674); AdjSG_Glu, AdjSG_Lys, AdjSG_Thr, 0.605 (0.536, 0.675); AdjSG_His, AdjSG_Lys, AdjSG_Pro, 0.605 (0.534, 0.676); AdjSG_Glu, AdjSG_Thr, AdjSG_Tyr, 0.605 (0.536, 0.674); AdjSG_Gln, AdjSG_Glu, AdjSG_Ser, 0.605 (0.536, 0.674); AdjSG_Glu, AdjSG_Lys, AdjSG_Tyr, 0.605 (0.536, 0.674); AdjSG_Gln, AdjSG_Glu, AdjSG_Leu, 0.605 (0.536, 0.674); AdjSG_Glu, AdjSG_Thr, AdjSG_Gly, 0.605 (0.535, 0.675); AdjSG_Glu, AdjSG_Val, AdjSG_Gly, 0.605 (0.535, 0.674); AdjSG_Glu, AdjSG_Ser, AdjSG_Thr, 0.605 (0.535, 0.674); AdjSG_Ala, AdjSG_Glu, AdjSG_Met, 0.605 (0.536, 0.674); AdjSG_His, AdjSG_Met, AdjSG_Thr, 0.605 (0.535, 0.674); AdjSG_Glu, AdjSG_Met, AdjSG_Thr, 0.605 (0.535, 0.674); AdjSG_Arg, AdjSG_Glu, AdjSG_Ser, 0.604 (0.536, 0.673); AdjSG_Ala, AdjSG_Glu, AdjSG_Thr, 0.604 (0.535, 0.674); AdjSG_Glu, AdjSG_Ile, AdjSG_Ser, 0.604 (0.535, 0.673); AdjSG_Gln, AdjSG_Glu, AdjSG_Lys, 0.604 (0.535, 0.673); AdjSG_Glu, AdjSG_Ser, AdjSG_Tyr, 0.604 (0.535, 0.673); AdjSG_Glu, AdjSG_Ile, AdjSG_Thr, 0.604 (0.534, 0.674); AdjSG_His, AdjSG_Tyr, AdjSG_Gly, 0.604 (0.536, 0.672); AdjSG_Met, AdjSG_Thr, AdjSG_Val, 0.604 (0.532, 0.676); AdjSG_Glu, AdjSG_Leu, AdjSG_Ser, 0.604 (0.535, 0.673); AdjSG_Gln, AdjSG_Glu, AdjSG_Met, 0.604 (0.535, 0.672); AdjSG_Met, AdjSG_Phe, AdjSG_Thr, 0.604 (0.533, 0.674); AdjSG_Glu, AdjSG_Lys, AdjSG_Ser, 0.604 (0.534, 0.673); AdjSG_Glu, AdjSG_Leu, AdjSG_Thr, 0.603 (0.534, 0.673); AdjSG_Arg, AdjSG_Glu, AdjSG_Thr, 0.603 (0.534, 0.673); AdjSG_Ala, AdjSG_Glu, AdjSG_Gly, 0.603 (0.535, 0.672); AdjSG_Arg, AdjSG_Glu, AdjSG_Gly, 0.603 (0.534, 0.673); AdjSG_Gln, AdjSG_Glu, AdjSG_Tyr, 0.603 (0.535, 0.672); AdjSG_Met, AdjSG_Thr, AdjSG_Trp, 0.603 (0.532, 0.674); AdjSG_Glu, AdjSG_Leu, AdjSG_Pro, 0.603 (0.533, 0.673); AdjSG_His, AdjSG_Met, AdjSG_Val, 0.603 (0.533, 0.673); AdjSG_Ala, AdjSG_Glu, AdjSG_Ile, 0.603 (0.534, 0.672); AdjSG_Arg, AdjSG_Gln, AdjSG_Glu, 0.603 (0.534, 0.672); AdjSG_Ala, AdjSG_Arg, AdjSG_Glu, 0.603 (0.534, 0.672); AdjSG_Asn, AdjSG_Pro, AdjSG_Trp, 0.603 (0.533, 0.673); AdjSG_Ala, AdjSG_Glu, AdjSG_Pro, 0.603 (0.534, 0.672); AdjSG_His, AdjSG_Pro, AdjSG_Val, 0.603 (0.532, 0.674); AdjSG_Glu, AdjSG_Ile, AdjSG_Gly, 0.603 (0.533, 0.672); AdjSG_Glu, AdjSG_Leu, AdjSG_Tyr, 0.603 (0.534, 0.671); AdjSG_Glu, AdjSG_Leu, AdjSG_Val, 0.602 (0.533, 0.672); AdjSG_Glu, AdjSG_Met, AdjSG_Gly, 0.602 (0.533, 0.672); AdjSG_His, AdjSG_Pro, AdjSG_Trp, 0.602 (0.532, 0.673); AdjSG_Glu, AdjSG_Pro, AdjSG_Val, 0.602 (0.533, 0.672); AdjSG_Gln, AdjSG_Glu, AdjSG_Pro, 0.602 (0.533, 0.672); AdjSG_Arg, AdjSG_Glu, AdjSG_Tyr, 0.602 (0.533, 0.671); AdjSG_Gln, AdjSG_Glu, AdjSG_Val, 0.602 (0.533, 0.671); AdjSG_Pro, AdjSG_Trp, AdjSG_Gly, 0.602 (0.531, 0.673); AdjSG_Glu, AdjSG_Met, AdjSG_Pro, 0.602 (0.532, 0.672); AdjSG_Glu, AdjSG_Ile, AdjSG_Met, 0.602 (0.533, 0.671); AdjSG_Arg, AdjSG_Glu, AdjSG_Leu, 0.602 (0.532, 0.671); AdjSG_Arg, AdjSG_Glu, AdjSG_Met, 0.602 (0.532, 0.671); AdjSG_Glu, AdjSG_Met, AdjSG_Ser, 0.602 (0.533, 0.671); AdjSG_Arg, AdjSG_Glu, AdjSG_Ile, 0.602 (0.532, 0.671); AdjSG_Arg, AdjSG_Glu, AdjSG_Val, 0.602 (0.532, 0.671); AdjSG_Arg, AdjSG_Glu, AdjSG_Pro, 0.602 (0.532, 0.671); AdjSG_Asn, AdjSG_Lys, AdjSG_Pro, 0.601 (0.531, 0.672); AdjSG_His, AdjSG_Ile, AdjSG_Pro, 0.601 (0.531, 0.672); AdjSG_Glu, AdjSG_Tyr, AdjSG_Val, 0.601 (0.533, 0.67); AdjSG_His, AdjSG_Leu, AdjSG_Pro, 0.601 (0.531, 0.672); AdjSG_Glu, AdjSG_Ile, AdjSG_Pro, 0.601 (0.531, 0.671); AdjSG_Gln, AdjSG_His, AdjSG_Gly, 0.601 (0.533, 0.669); AdjSG_His, AdjSG_Pro, AdjSG_Tyr, 0.601 (0.53, 0.672); AdjSG_Ile, AdjSG_Pro, AdjSG_Trp, 0.601 (0.531, 0.671); AdjSG_Arg, AdjSG_His, AdjSG_Pro, 0.601 (0.53, 0.671); AdjSG_Lys, AdjSG_Pro, AdjSG_Trp, 0.601 (0.53, 0.671); AdjSG_Lys, AdjSG_Met, AdjSG_Val, 0.601 (0.531, 0.671); AdjSG_Glu, AdjSG_Ser, AdjSG_Val, 0.601 (0.531, 0.67); AdjSG_His, AdjSG_Pro, AdjSG_Thr, 0.6 (0.529, 0.672); AdjSG_Glu, AdjSG_Pro, AdjSG_Gly, 0.6 (0.531, 0.669); AdjSG_Glu, AdjSG_Pro, AdjSG_Ser, 0.6 (0.53, 0.67); AdjSG_Glu, AdjSG_Thr, AdjSG_Val, 0.6 (0.53, 0.67); AdjSG_Gln, AdjSG_His, AdjSG_Thr, 0.6 (0.53, 0.67); AdjSG_Gln, AdjSG_Pro, AdjSG_Trp, 0.6 (0.53, 0.67); AdjSG_Asn, AdjSG_Pro, AdjSG_Thr, 0.6 (0.529, 0.671); AdjSG_Asn, AdjSG_Ile, AdjSG_Pro, 0.6 (0.53, 0.67); AdjSG_Arg, AdjSG_Pro, AdjSG_Trp, 0.6 (0.529, 0.67); AdjSG_Lys, AdjSG_Met, AdjSG_Phe, 0.6 (0.531, 0.668); AdjSG_Lys, AdjSG_Trp, AdjSG_Tyr, 0.6 (0.532, 0.667); AdjSG_Asn, AdjSG_Leu, AdjSG_Pro, 0.599 (0.529, 0.669); AdjSG_Ala, AdjSG_His, AdjSG_Thr, 0.599 (0.53, 0.669); AdjSG_Met, AdjSG_Phe, AdjSG_Pro, 0.599 (0.53, 0.668); AdjSG_His, AdjSG_Phe, AdjSG_Pro, 0.599 (0.529, 0.669); AdjSG_Asn, AdjSG_Trp, AdjSG_Tyr, 0.599 (0.531, 0.667); AdjSG_Asn, AdjSG_Phe, AdjSG_Pro, 0.599 (0.529, 0.669); AdjSG_His, AdjSG_Thr, AdjSG_Gly, 0.599 (0.528, 0.669); AdjSG_His, AdjSG_Leu, AdjSG_Gly, 0.599 (0.53, 0.667); AdjSG_Leu, AdjSG_Lys, AdjSG_Met, 0.599 (0.529, 0.668); AdjSG_His, AdjSG_Ile, AdjSG_Gly, 0.598 (0.53, 0.667); AdjSG_Leu, AdjSG_Pro, AdjSG_Trp, 0.598 (0.528, 0.668); AdjSG_Ala, AdjSG_Trp, AdjSG_Tyr, 0.598 (0.528, 0.668); AdjSG_Arg, AdjSG_Asn, AdjSG_His, 0.598 (0.531, 0.665); AdjSG_Met, AdjSG_Pro, AdjSG_Trp, 0.598 (0.527, 0.669); AdjSG_Pro, AdjSG_Thr, AdjSG_Trp, 0.598 (0.527, 0.669); AdjSG_Asn, AdjSG_Pro, AdjSG_Val, 0.598 (0.528, 0.668); AdjSG_His, AdjSG_Lys, AdjSG_Gly, 0.598 (0.529, 0.667); AdjSG_Asn, AdjSG_Thr, AdjSG_Val, 0.598 (0.53, 0.666); AdjSG_Pro, AdjSG_Trp, AdjSG_Val, 0.598 (0.527, 0.668); AdjSG_Asn, AdjSG_Pro, AdjSG_Tyr, 0.598 (0.528, 0.668); AdjSG_His, AdjSG_Phe, AdjSG_Gly, 0.598 (0.529, 0.666); AdjSG_Arg, AdjSG_Asn, AdjSG_Pro, 0.597 (0.528, 0.667); AdjSG_Arg, AdjSG_His, AdjSG_Gly, 0.597 (0.528, 0.665); AdjSG_Asn, AdjSG_His, AdjSG_Val, 0.596 (0.529, 0.664); AdjSG_Ala, AdjSG_Arg, AdjSG_His, 0.596 (0.527, 0.665); AdjSG_Ala, AdjSG_His, AdjSG_Ile, 0.596 (0.528, 0.664); AdjSG_His, AdjSG_Lys, AdjSG_Met, 0.596 (0.527, 0.666); AdjSG_Lys, AdjSG_Met, AdjSG_Thr, 0.596 (0.526, 0.667); AdjSG_Asn, AdjSG_His, AdjSG_Pro, 0.596 (0.525, 0.666); AdjSG_Pro, AdjSG_Ser, AdjSG_Trp, 0.596 (0.525, 0.666); AdjSG_Leu, AdjSG_Met, AdjSG_Thr, 0.595 (0.524, 0.667); AdjSG_Asn, AdjSG_His, AdjSG_Leu, 0.595 (0.528, 0.663); AdjSG_His, AdjSG_Val, AdjSG_Gly, 0.595 (0.526, 0.664); AdjSG_Phe, AdjSG_Pro, AdjSG_Trp, 0.595 (0.524, 0.666); AdjSG_Arg, AdjSG_Asn, AdjSG_Leu, 0.595 (0.528, 0.662); AdjSG_Ala, AdjSG_Gln, AdjSG_His, 0.595 (0.526, 0.664); AdjSG_Arg, AdjSG_Asn, AdjSG_Ile, 0.595 (0.528, 0.662); AdjSG_Asn, AdjSG_Leu, AdjSG_Trp, 0.595 (0.528, 0.662); AdjSG_Asn, AdjSG_Leu, AdjSG_Thr, 0.595 (0.526, 0.663); AdjSG_Ala, AdjSG_His, AdjSG_Leu, 0.595 (0.526, 0.663); AdjSG_Met, AdjSG_Thr, AdjSG_Tyr, 0.595 (0.523, 0.666); AdjSG_Asn, AdjSG_His, AdjSG_Ile, 0.595 (0.528, 0.662); AdjSG_Met, AdjSG_Thr, AdjSG_Gly, 0.594 (0.523, 0.665); AdjSG_Arg, AdjSG_Asn, AdjSG_Val, 0.594 (0.527, 0.662); AdjSG_Arg, AdjSG_Gln, AdjSG_His, 0.594 (0.525, 0.663); AdjSG_Ala, AdjSG_His, AdjSG_Lys, 0.594 (0.525, 0.663); AdjSG_Arg, AdjSG_Asn, AdjSG_Tyr, 0.594 (0.526, 0.661); AdjSG_Ile, AdjSG_Met, AdjSG_Thr, 0.594 (0.523, 0.665); AdjSG_Asn, AdjSG_Tyr, AdjSG_Val, 0.594 (0.526, 0.661); AdjSG_His, AdjSG_Leu, AdjSG_Met, 0.594 (0.525, 0.662); AdjSG_Ala, AdjSG_His, AdjSG_Trp, 0.594 (0.524, 0.663); AdjSG_Lys, AdjSG_Met, AdjSG_Trp, 0.593 (0.524, 0.663); AdjSG_Asn, AdjSG_Leu, AdjSG_Val, 0.593 (0.526, 0.66); AdjSG_Asn, AdjSG_Leu, AdjSG_Tyr, 0.593 (0.526, 0.66); AdjSG_Thr, AdjSG_Trp, AdjSG_Tyr, 0.593 (0.525, 0.661); AdjSG_Gln, AdjSG_His, AdjSG_Leu, 0.593 (0.524, 0.661); AdjSG_Lys, AdjSG_Phe, AdjSG_Pro, 0.593 (0.523, 0.662); AdjSG_Met, AdjSG_Trp, AdjSG_Tyr, 0.592 (0.522, 0.663); AdjSG_His, AdjSG_Met, AdjSG_Phe, 0.592 (0.524, 0.661); AdjSG_His, AdjSG_Ser, AdjSG_Gly, 0.592 (0.523, 0.662); AdjSG_Asn, AdjSG_Leu, AdjSG_Phe, 0.592 (0.525, 0.659); AdjSG_Gln, AdjSG_His, AdjSG_Trp, 0.592 (0.523, 0.661); AdjSG_Gln, AdjSG_Met, AdjSG_Thr, 0.592 (0.521, 0.663); AdjSG_Met, AdjSG_Ser, AdjSG_Thr, 0.592 (0.521, 0.662); AdjSG_Gln, AdjSG_His, AdjSG_Tyr, 0.592 (0.524, 0.66); AdjSG_Arg, AdjSG_His, AdjSG_Met, 0.592 (0.523, 0.66); AdjSG_Gln, AdjSG_His, AdjSG_Ile, 0.592 (0.524, 0.659); AdjSG_His, AdjSG_Trp, AdjSG_Gly, 0.592 (0.522, 0.661); AdjSG_Ala, AdjSG_His, AdjSG_Tyr, 0.592 (0.523, 0.66); AdjSG_Asn, AdjSG_Phe, AdjSG_Val, 0.592 (0.524, 0.659); AdjSG_Asn, AdjSG_Ile, AdjSG_Leu, 0.591 (0.524, 0.658); AdjSG_Ala, AdjSG_Met, AdjSG_Thr, 0.591 (0.522, 0.661); AdjSG_Ile, AdjSG_Trp, AdjSG_Tyr, 0.591 (0.523, 0.659); AdjSG_Asn, AdjSG_Trp, AdjSG_Val, 0.591 (0.524, 0.659); AdjSG_Phe, AdjSG_Pro, AdjSG_Gly, 0.591 (0.522, 0.66); AdjSG_Gln, AdjSG_His, AdjSG_Val, 0.591 (0.523, 0.66); AdjSG_Lys, AdjSG_Pro, AdjSG_Val, 0.591 (0.519, 0.663); AdjSG_Gln, AdjSG_His, AdjSG_Phe, 0.591 (0.522, 0.66); AdjSG_Gln, AdjSG_His, AdjSG_Lys, 0.591 (0.522, 0.659); AdjSG_Met, AdjSG_Pro, AdjSG_Thr, 0.591 (0.52, 0.662); AdjSG_Ala, AdjSG_His, AdjSG_Phe, 0.591 (0.522, 0.66); AdjSG_Ile, AdjSG_Lys, AdjSG_Met, 0.591 (0.521, 0.66); AdjSG_Asn, AdjSG_Ile, AdjSG_Val, 0.59 (0.524, 0.657); AdjSG_Arg, AdjSG_Asn, AdjSG_Thr, 0.59 (0.522, 0.659); AdjSG_His, AdjSG_Met, AdjSG_Ser, 0.59 (0.522, 0.659); AdjSG_Ala, AdjSG_His, AdjSG_Val, 0.59 (0.521, 0.659); AdjSG_Arg, AdjSG_Asn, AdjSG_Phe, 0.59 (0.522, 0.658); AdjSG_Asn, AdjSG_Lys, AdjSG_Val, 0.59 (0.522, 0.658); AdjSG_Ala, AdjSG_His, AdjSG_Ser, 0.59 (0.521, 0.659); AdjSG_Asn, AdjSG_Ile, AdjSG_Tyr, 0.59 (0.523, 0.657); AdjSG_His, AdjSG_Ile, AdjSG_Met, 0.59 (0.521, 0.658); AdjSG_Arg, AdjSG_Lys, AdjSG_Met, 0.59 (0.52, 0.659); AdjSG_Arg, AdjSG_Asn, AdjSG_Trp, 0.589 (0.522, 0.657); AdjSG_Leu, AdjSG_Met, AdjSG_Phe, 0.589 (0.52, 0.659); AdjSG_Ala, AdjSG_Thr, AdjSG_Trp, 0.589 (0.518, 0.661); AdjSG_Asn, AdjSG_Ile, AdjSG_Thr, 0.589 (0.521, 0.657); AdjSG_Ala, AdjSG_Met, AdjSG_Trp, 0.589 (0.519, 0.659); AdjSG_Arg, AdjSG_Met, AdjSG_Thr, 0.589 (0.518, 0.66); AdjSG_Arg, AdjSG_Asn, AdjSG_Lys, 0.589 (0.52, 0.658); AdjSG_Ala, AdjSG_Arg, AdjSG_Trp, 0.589 (0.518, 0.659); AdjSG_Asn, AdjSG_His, AdjSG_Tyr, 0.589 (0.521, 0.657); AdjSG_Trp, AdjSG_Tyr, AdjSG_Gly, 0.589 (0.518, 0.659); AdjSG_Gln, AdjSG_Lys, AdjSG_Met, 0.588 (0.518, 0.659); AdjSG_Ile, AdjSG_Phe, AdjSG_Pro, 0.588 (0.52, 0.657); AdjSG_Lys, AdjSG_Met, AdjSG_Gly, 0.588 (0.519, 0.657); AdjSG_Arg, AdjSG_Trp, AdjSG_Tyr, 0.588 (0.519, 0.657); AdjSG_Asn, AdjSG_Leu, AdjSG_Lys, 0.588 (0.52, 0.656); AdjSG_Leu, AdjSG_Lys, AdjSG_Pro, 0.588 (0.517, 0.66); AdjSG_Lys, AdjSG_Met, AdjSG_Pro, 0.588 (0.517, 0.659); AdjSG_Asn, AdjSG_His, AdjSG_Lys, 0.588 (0.52, 0.657); AdjSG_His, AdjSG_Met, AdjSG_Tyr, 0.588 (0.52, 0.656); AdjSG_Ala, AdjSG_Ile, AdjSG_Trp, 0.588 (0.518, 0.658); AdjSG_Asn, AdjSG_Ile, AdjSG_Phe, 0.588 (0.52, 0.655); AdjSG_Leu, AdjSG_Trp, AdjSG_Tyr, 0.588 (0.519, 0.657); AdjSG_His, AdjSG_Trp, AdjSG_Tyr, 0.587 (0.52, 0.655); AdjSG_Ala, AdjSG_Met, AdjSG_Phe, 0.587 (0.519, 0.656); AdjSG_Leu, AdjSG_Met, AdjSG_Trp, 0.587 (0.517, 0.657); AdjSG_Ala, AdjSG_Lys, AdjSG_Pro, 0.587 (0.517, 0.657); AdjSG_Arg, AdjSG_Lys, AdjSG_Pro, 0.587 (0.516, 0.658); AdjSG_Lys, AdjSG_Pro, AdjSG_Gly, 0.587 (0.517, 0.658); AdjSG_His, AdjSG_Met, AdjSG_Trp, 0.587 (0.518, 0.656); AdjSG_Ala, AdjSG_Leu, AdjSG_Trp, 0.587 (0.516, 0.658); AdjSG_Trp, AdjSG_Tyr, AdjSG_Val, 0.587 (0.519, 0.655); AdjSG_Ala, AdjSG_Lys, AdjSG_Trp, 0.587 (0.517, 0.656); AdjSG_Phe, AdjSG_Trp, AdjSG_Tyr, 0.587 (0.518, 0.656); AdjSG_Phe, AdjSG_Pro, AdjSG_Thr, 0.587 (0.517, 0.657); AdjSG_Pro, AdjSG_Thr, AdjSG_Gly, 0.587 (0.515, 0.658); AdjSG_Gln, AdjSG_Trp, AdjSG_Tyr, 0.587 (0.518, 0.656); AdjSG_Asn, AdjSG_His, AdjSG_Phe, 0.586 (0.518, 0.655); AdjSG_Gln, AdjSG_His, AdjSG_Ser, 0.586 (0.518, 0.654); AdjSG_Arg, AdjSG_Phe, AdjSG_Pro, 0.586 (0.517, 0.655); AdjSG_Ala, AdjSG_Arg, AdjSG_Lys, 0.586 (0.518, 0.654); AdjSG_Met, AdjSG_Trp, AdjSG_Val, 0.586 (0.515, 0.657); AdjSG_Asn, AdjSG_His, AdjSG_Thr, 0.586 (0.517, 0.655); AdjSG_Asn, AdjSG_Lys, AdjSG_Tyr, 0.586 (0.517, 0.655); AdjSG_Lys, AdjSG_Ser, AdjSG_Gly, 0.585 (0.515, 0.656); AdjSG_Ala, AdjSG_Pro, AdjSG_Thr, 0.585 (0.515, 0.656); AdjSG_Ala, AdjSG_Arg, AdjSG_Thr, 0.585 (0.515, 0.655); AdjSG_Asn, AdjSG_Phe, AdjSG_Trp, 0.585 (0.516, 0.654); AdjSG_Ile, AdjSG_Lys, AdjSG_Pro, 0.585 (0.514, 0.656); AdjSG_Ser, AdjSG_Thr, AdjSG_Gly, 0.585 (0.513, 0.657); AdjSG_Met, AdjSG_Phe, AdjSG_Gly, 0.585 (0.516, 0.654); AdjSG_Gln, AdjSG_Pro, AdjSG_Gly, 0.585 (0.513, 0.657); AdjSG_Leu, AdjSG_Phe, AdjSG_Pro, 0.585 (0.515, 0.654); AdjSG_Met, AdjSG_Pro, AdjSG_Val, 0.585 (0.513, 0.656); AdjSG_Arg, AdjSG_Pro, AdjSG_Thr, 0.585 (0.513, 0.656); AdjSG_Lys, AdjSG_Met, AdjSG_Tyr, 0.585 (0.515, 0.654); AdjSG_Gln, AdjSG_Lys, AdjSG_Pro, 0.585 (0.513, 0.656); AdjSG_Ala, AdjSG_Lys, AdjSG_Met, 0.585 (0.516, 0.653); AdjSG_Asn, AdjSG_Phe, AdjSG_Tyr, 0.584 (0.516, 0.653); AdjSG_Asn, AdjSG_Thr, AdjSG_Tyr, 0.584 (0.515, 0.654); AdjSG_Lys, AdjSG_Met, AdjSG_Ser, 0.584 (0.515, 0.654); AdjSG_Asn, AdjSG_Ile, AdjSG_Lys, 0.584 (0.516, 0.652); AdjSG_Lys, AdjSG_Pro, AdjSG_Tyr, 0.584 (0.513, 0.655); AdjSG_Thr, AdjSG_Trp, AdjSG_Gly, 0.584 (0.511, 0.657); AdjSG_Gln, AdjSG_Phe, AdjSG_Pro, 0.584 (0.514, 0.653); AdjSG_Asn, AdjSG_Ile, AdjSG_Trp, 0.584 (0.516, 0.651); AdjSG_Ala, AdjSG_Ser, AdjSG_Thr, 0.584 (0.514, 0.653); AdjSG_Arg, AdjSG_Thr, AdjSG_Gly, 0.583 (0.512, 0.655); AdjSG_Lys, AdjSG_Pro, AdjSG_Thr, 0.583 (0.512, 0.655); AdjSG_Ala, AdjSG_Gln, AdjSG_Pro, 0.583 (0.511, 0.655); AdjSG_Met, AdjSG_Ser, AdjSG_Gly, 0.583 (0.513, 0.654); AdjSG_Arg, AdjSG_Lys, AdjSG_Gly, 0.583 (0.514, 0.652); AdjSG_Phe, AdjSG_Pro, AdjSG_Val, 0.583 (0.513, 0.653); AdjSG_Gln, AdjSG_Met, AdjSG_Trp, 0.583 (0.513, 0.653); AdjSG_Lys, AdjSG_Thr, AdjSG_Gly, 0.583 (0.512, 0.654); AdjSG_Ile, AdjSG_Met, AdjSG_Trp, 0.583 (0.513, 0.653); AdjSG_Asn, AdjSG_His, AdjSG_Trp, 0.582 (0.515, 0.65); AdjSG_Met, AdjSG_Ser, AdjSG_Val, 0.582 (0.512, 0.653); AdjSG_Met, AdjSG_Phe, AdjSG_Trp, 0.582 (0.512, 0.653); AdjSG_Met, AdjSG_Trp, AdjSG_Gly, 0.582 (0.512, 0.653); AdjSG_Ile, AdjSG_Pro, AdjSG_Thr, 0.582 (0.511, 0.654); AdjSG_Asn, AdjSG_Phe, AdjSG_Thr, 0.582 (0.512, 0.652); AdjSG_Ile, AdjSG_Pro, AdjSG_Val, 0.582 (0.511, 0.653); AdjSG_Arg, AdjSG_Lys, AdjSG_Thr, 0.582 (0.512, 0.652); AdjSG_Leu, AdjSG_Pro, AdjSG_Thr, 0.582 (0.51, 0.653); AdjSG_Phe, AdjSG_Thr, AdjSG_Gly, 0.582 (0.51, 0.653); AdjSG_Arg, AdjSG_Lys, AdjSG_Trp, 0.582 (0.512, 0.651); AdjSG_Ala, AdjSG_Met, AdjSG_Ser, 0.582 (0.513, 0.65); AdjSG_Pro, AdjSG_Thr, AdjSG_Val, 0.581 (0.51, 0.653); AdjSG_Arg, AdjSG_Met, AdjSG_Trp, 0.581 (0.511, 0.652); AdjSG_Gln, AdjSG_Pro, AdjSG_Thr, 0.581 (0.51, 0.653); AdjSG_Arg, AdjSG_Gln, AdjSG_Thr, 0.581 (0.51, 0.652); AdjSG_Pro, AdjSG_Thr, AdjSG_Tyr, 0.581 (0.509, 0.653); AdjSG_Arg, AdjSG_His, AdjSG_Thr, 0.581 (0.51, 0.652); AdjSG_Met, AdjSG_Phe, AdjSG_Val, 0.581 (0.511, 0.651); AdjSG_Ser, AdjSG_Trp, AdjSG_Tyr, 0.581 (0.513, 0.649); AdjSG_Thr, AdjSG_Trp, AdjSG_Val, 0.581 (0.51, 0.651); AdjSG_Gln, AdjSG_Thr, AdjSG_Gly, 0.581 (0.509, 0.652); AdjSG_Ala, AdjSG_Ser, AdjSG_Trp, 0.581 (0.51, 0.651); AdjSG_Ile, AdjSG_Thr, AdjSG_Gly, 0.581 (0.509, 0.652); AdjSG_Ala, AdjSG_Gln, AdjSG_Met, 0.58 (0.511, 0.65); AdjSG_Ile, AdjSG_Trp, AdjSG_Gly, 0.58 (0.51, 0.651); AdjSG_Leu, AdjSG_Met, AdjSG_Ser, 0.58 (0.51, 0.651); AdjSG_Ala, AdjSG_Arg, AdjSG_Phe, 0.58 (0.512, 0.648); AdjSG_Met, AdjSG_Phe, AdjSG_Ser, 0.58 (0.511, 0.649); AdjSG_Arg, AdjSG_Met, AdjSG_Phe, 0.58 (0.51, 0.65); AdjSG_Gln, AdjSG_Ile, AdjSG_Thr, 0.58 (0.509, 0.65); AdjSG_Phe, AdjSG_Pro, AdjSG_Ser, 0.58 (0.51, 0.649); AdjSG_Arg, AdjSG_Ile, AdjSG_Lys, 0.58 (0.511, 0.648); AdjSG_Arg, AdjSG_Gln, AdjSG_Lys, 0.579 (0.511, 0.648); AdjSG_Ala, AdjSG_Phe, AdjSG_Thr, 0.579 (0.509, 0.65); AdjSG_Gln, AdjSG_Leu, AdjSG_Thr, 0.579 (0.508, 0.65); AdjSG_Lys, AdjSG_Pro, AdjSG_Ser, 0.579 (0.508, 0.651); AdjSG_Ala, AdjSG_Trp, AdjSG_Val, 0.579 (0.508, 0.65); AdjSG_Ile, AdjSG_Met, AdjSG_Phe, 0.579 (0.51, 0.649); AdjSG_Arg, AdjSG_Lys, AdjSG_Phe, 0.579 (0.511, 0.648); AdjSG_Arg, AdjSG_Leu, AdjSG_Lys, 0.579 (0.51, 0.648); AdjSG_Leu, AdjSG_Thr, AdjSG_Gly, 0.579 (0.508, 0.651); AdjSG_Ala, AdjSG_Trp, AdjSG_Gly, 0.579 (0.508, 0.65); AdjSG_Arg, AdjSG_Pro, AdjSG_Val, 0.579 (0.508, 0.651); AdjSG_Ala, AdjSG_Lys, AdjSG_Ser, 0.579 (0.51, 0.648); AdjSG_Met, AdjSG_Ser, AdjSG_Trp, 0.579 (0.509, 0.649); AdjSG_Ala, AdjSG_Pro, AdjSG_Val, 0.579 (0.508, 0.65); AdjSG_Ala, AdjSG_Phe, AdjSG_Ser, 0.579 (0.51, 0.648); AdjSG_Ala, AdjSG_Pro, AdjSG_Ser, 0.579 (0.508, 0.65); AdjSG_Pro, AdjSG_Ser, AdjSG_Thr, 0.579 (0.507, 0.65); AdjSG_Pro, AdjSG_Ser, AdjSG_Gly, 0.579 (0.507, 0.65); AdjSG_Ala, AdjSG_Thr, AdjSG_Gly, 0.579 (0.508, 0.649); AdjSG_Ala, AdjSG_Lys, AdjSG_Tyr, 0.579 (0.51, 0.647); AdjSG_Ile, AdjSG_Met, AdjSG_Val, 0.579 (0.508, 0.649); AdjSG_Asn, AdjSG_Lys, AdjSG_Phe, 0.579 (0.509, 0.648); AdjSG_Arg, AdjSG_Lys, AdjSG_Val, 0.578 (0.509, 0.648); AdjSG_Gln, AdjSG_Leu, AdjSG_Met, 0.578 (0.507, 0.649); AdjSG_Ile, AdjSG_Lys, AdjSG_Gly, 0.578 (0.509, 0.647); AdjSG_Phe, AdjSG_Pro, AdjSG_Tyr, 0.578 (0.508, 0.648); AdjSG_Gln, AdjSG_Lys, AdjSG_Thr, 0.578 (0.508, 0.648); AdjSG_Lys, AdjSG_Trp, AdjSG_Gly, 0.578 (0.508, 0.649); AdjSG_Gln, AdjSG_Lys, AdjSG_Gly, 0.578 (0.508, 0.648); AdjSG_Ala, AdjSG_Phe, AdjSG_Trp, 0.578 (0.507, 0.649); AdjSG_Met, AdjSG_Pro, AdjSG_Tyr, 0.578 (0.507, 0.648); AdjSG_Gln, AdjSG_Thr, AdjSG_Val, 0.578 (0.507, 0.649); AdjSG_Thr, AdjSG_Val, AdjSG_Gly, 0.578 (0.506, 0.649); AdjSG_Arg, AdjSG_Thr, AdjSG_Trp, 0.578 (0.506, 0.65); AdjSG_Ala, AdjSG_Gln, AdjSG_Lys, 0.578 (0.508, 0.647); AdjSG_Ala, AdjSG_Ile, AdjSG_Lys, 0.578 (0.51, 0.645); AdjSG_Ala, AdjSG_Lys, AdjSG_Phe, 0.578 (0.51, 0.645); AdjSG_Ala, AdjSG_Lys, AdjSG_Gly, 0.578 (0.509, 0.646); AdjSG_Leu, AdjSG_Met, AdjSG_Tyr, 0.578 (0.507, 0.648); AdjSG_Leu, AdjSG_Pro, AdjSG_Val, 0.577 (0.506, 0.649); AdjSG_Ala, AdjSG_Gln, AdjSG_Trp, 0.577 (0.506, 0.648); AdjSG_Leu, AdjSG_Thr, AdjSG_Trp, 0.577 (0.506, 0.648); AdjSG_Arg, AdjSG_Lys, AdjSG_Tyr, 0.577 (0.508, 0.646); AdjSG_Ala, AdjSG_Lys, AdjSG_Thr, 0.577 (0.507, 0.646); AdjSG_Gln, AdjSG_Met, AdjSG_Val, 0.577 (0.505, 0.649); AdjSG_Ala, AdjSG_Ile, AdjSG_Phe, 0.577 (0.509, 0.645); AdjSG_Phe, AdjSG_Ser, AdjSG_Gly, 0.577 (0.506, 0.648); AdjSG_Thr, AdjSG_Tyr, AdjSG_Gly, 0.577 (0.505, 0.649); AdjSG_Arg, AdjSG_Ile, AdjSG_Thr, 0.577 (0.506, 0.647); AdjSG_Ile, AdjSG_Leu, AdjSG_Met, 0.577 (0.506, 0.647); AdjSG_Ala, AdjSG_Ser, AdjSG_Gly, 0.577 (0.507, 0.646); AdjSG_Ala, AdjSG_Ile, AdjSG_Thr, 0.577 (0.507, 0.646); AdjSG_His, AdjSG_Ser, AdjSG_Thr, 0.577 (0.504, 0.649); AdjSG_Ala, AdjSG_Pro, AdjSG_Tyr, 0.577 (0.505, 0.648); AdjSG_Met, AdjSG_Phe, AdjSG_Tyr, 0.576 (0.507, 0.646); AdjSG_Ala, AdjSG_Arg, AdjSG_Ser, 0.576 (0.508, 0.645); AdjSG_Ala, AdjSG_Gln, AdjSG_Phe, 0.576 (0.507, 0.646); AdjSG_Ala, AdjSG_Gln, AdjSG_Thr, 0.576 (0.506, 0.646); AdjSG_Gln, AdjSG_Ile, AdjSG_Trp, 0.576 (0.507, 0.645); AdjSG_Gln, AdjSG_Met, AdjSG_Phe, 0.576 (0.506, 0.646); AdjSG_Gln, AdjSG_Met, AdjSG_Gly, 0.576 (0.505, 0.647); AdjSG_Ala, AdjSG_Leu, AdjSG_Thr, 0.576 (0.506, 0.646); AdjSG_Gln, AdjSG_Thr, AdjSG_Trp, 0.576 (0.504, 0.648); AdjSG_Ala, AdjSG_Leu, AdjSG_Lys, 0.576 (0.508, 0.644); AdjSG_Arg, AdjSG_His, AdjSG_Lys, 0.576 (0.506, 0.645); AdjSG_Arg, AdjSG _Phe, AdjSG_Thr, 0.575 (0.504, 0.647); AdjSG_Arg, AdjSG_Thr, AdjSG_Val, 0.575 (0.504, 0.647); AdjSG_Ile, AdjSG_Thr, AdjSG_Trp, 0.575 (0.505, 0.645); AdjSG_Arg, AdjSG_Ser, AdjSG_Thr, 0.575 (0.504, 0.647); AdjSG_Gln, AdjSG_Pro, AdjSG_Val, 0.575 (0.503, 0.647); AdjSG_Arg, AdjSG_Leu, AdjSG_Thr, 0.575 (0.504, 0.646); AdjSG_Ser, AdjSG_Trp, AdjSG_Gly, 0.575 (0.504, 0.646); AdjSG_Ala, AdjSG_Thr, AdjSG_Tyr, 0.575 (0.504, 0.646); AdjSG_Arg, AdjSG_Thr, AdjSG_Tyr, 0.575 (0.503, 0.647); AdjSG_Gln, AdjSG_Met, AdjSG_Pro, 0.575 (0.504, 0.646); AdjSG_His, AdjSG_Thr, AdjSG_Val, 0.574 (0.504, 0.645); AdjSG_Pro, AdjSG_Val, AdjSG_Gly, 0.574 (0.503, 0.646); AdjSG_Lys, AdjSG_Phe, AdjSG_Gly, 0.574 (0.506, 0.643); AdjSG_Gln, AdjSG_Phe, AdjSG_Thr, 0.574 (0.504, 0.645); AdjSG_Ile, AdjSG_Leu, AdjSG_Thr, 0.574 (0.503, 0.644); AdjSG_Arg, AdjSG_Ser, AdjSG_Gly, 0.574 (0.503, 0.644); AdjSG_Leu, AdjSG_Trp, AdjSG_Gly, 0.573 (0.502, 0.645); AdjSG_Pro, AdjSG_Tyr, AdjSG_Val, 0.573 (0.502, 0.645); AdjSG_Leu, AdjSG_Thr, AdjSG_Tyr, 0.573 (0.502, 0.644); AdjSG_Leu, AdjSG_Met, AdjSG_Pro, 0.573 (0.501, 0.646); AdjSG_Gln, AdjSG_Thr, AdjSG_Tyr, 0.573 (0.502, 0.644); AdjSG_Leu, AdjSG_Ser, AdjSG_Thr, 0.573 (0.502, 0.644); AdjSG_Ala, AdjSG_Gln, AdjSG_Gly, 0.573 (0.503, 0.643); AdjSG_Arg, AdjSG_Lys, AdjSG_Ser, 0.573 (0.504, 0.643); AdjSG_Thr, AdjSG_Tyr, AdjSG_Val, 0.573 (0.502, 0.644); AdjSG_Gln, AdjSG_Ile, AdjSG_Gly, 0.573 (0.503, 0.643); AdjSG_Gln, AdjSG_Ser, AdjSG_Thr, 0.573 (0.503, 0.643); AdjSG_Ile, AdjSG_Ser, AdjSG_Thr, 0.573 (0.503, 0.643); AdjSG_Leu, AdjSG_Thr, AdjSG_Val, 0.573 (0.502, 0.644); AdjSG_Ser, AdjSG_Thr, AdjSG_Val, 0.573 (0.502, 0.644); AdjSG_Ala, AdjSG_Ile, AdjSG_Ser, 0.573 (0.504, 0.642); AdjSG_Ala, AdjSG_Phe, AdjSG_Gly, 0.573 (0.504, 0.641); AdjSG_His, AdjSG_Leu, AdjSG_Thr, 0.572 (0.502, 0.643); AdjSG_Leu, AdjSG_Lys, AdjSG_Gly, 0.572 (0.503, 0.642); AdjSG_Lys, AdjSG_Ser, AdjSG_Thr, 0.572 (0.501, 0.644); AdjSG_Met, AdjSG_Tyr, AdjSG_Val, 0.572 (0.5, 0.644); AdjSG_Leu, AdjSG_Met, AdjSG_Gly, 0.572 (0.501, 0.644); AdjSG_Ile, AdjSG_Thr, AdjSG_Val, 0.572 (0.502, 0.642); AdjSG_Leu, AdjSG_Met, AdjSG_Val, 0.572 (0.501, 0.643); AdjSG_Gln, AdjSG_Ile, AdjSG_Lys, 0.572 (0.503, 0.641); AdjSG_Ile, AdjSG_Ser, AdjSG_Gly, 0.572 (0.501, 0.642); AdjSG_Arg, AdjSG_His, AdjSG_Tyr, 0.571 (0.503, 0.64); AdjSG_Ala, AdjSG_Gln, AdjSG_Ser, 0.571 (0.502, 0.641); AdjSG_Gln, AdjSG_Leu, AdjSG_Lys, 0.571 (0.502, 0.64); AdjSG_Ala, AdjSG_Leu, AdjSG_Ser, 0.571 (0.502, 0.641); AdjSG_Ile, AdjSG_Phe, AdjSG_Gly, 0.571 (0.502, 0.64); AdjSG_Gln, AdjSG_Lys, AdjSG_Ser, 0.571 (0.502, 0.64); AdjSG_Gln, AdjSG_Lys, AdjSG_Tyr, 0.571 (0.502, 0.64); AdjSG_Lys, AdjSG_Val, AdjSG_Gly, 0.571 (0.501, 0.641); AdjSG_Ala, AdjSG_Thr, AdjSG_Val, 0.571 (0.501, 0.641); AdjSG_Gln, AdjSG_Ser, AdjSG_Gly, 0.571 (0.5, 0.642); AdjSG_Ala, AdjSG_Arg, AdjSG_Gln, 0.571 (0.501, 0.641); AdjSG_Arg, AdjSG_His, AdjSG_Val, 0.571 (0.502, 0.64); AdjSG_Gln, AdjSG_Leu, AdjSG_Pro, 0.571 (0.499, 0.643); AdjSG_Phe, AdjSG_Trp, AdjSG_Gly, 0.571 (0.499, 0.643); AdjSG_Trp, AdjSG_Val, AdjSG_Gly, 0.571 (0.499, 0.642); AdjSG_Pro, AdjSG_Ser, AdjSG_Val, 0.571 (0.499, 0.642); AdjSG_Gln, AdjSG_Lys, AdjSG_Val, 0.571 (0.501, 0.64); AdjSG_Gln, AdjSG_Lys, AdjSG_Trp, 0.571 (0.501, 0.64); AdjSG_Leu, AdjSG_Lys, AdjSG_Thr, 0.57 (0.5, 0.641); AdjSG_Arg, AdjSG_His, AdjSG_Ser, 0.57 (0.501, 0.64); AdjSG_Lys, AdjSG_Thr, AdjSG_Val, 0.57 (0.5, 0.641); AdjSG_Ile, AdjSG_Leu, AdjSG_Pro, 0.57 (0.499, 0.641); AdjSG_Asn, AdjSG_Thr, AdjSG_Trp, 0.57 (0.5, 0.64); AdjSG_Ile, AdjSG_Pro, AdjSG_Tyr, 0.57 (0.499, 0.641); AdjSG_Leu, AdjSG_Phe, AdjSG_Thr, 0.57 (0.499, 0.64); AdjSG_His, AdjSG_Phe, AdjSG_Thr, 0.57 (0.499, 0.641); AdjSG_His, AdjSG_Phe, AdjSG_Ser, 0.57 (0.5, 0.64); AdjSG_Gln, AdjSG_Trp, AdjSG_Gly, 0.57 (0.498, 0.641); AdjSG_Asn, AdjSG_Lys, AdjSG_Trp, 0.57 (0.5, 0.639); AdjSG_Asn, AdjSG_Lys, AdjSG_Thr, 0.57 (0.499, 0.64); AdjSG_Arg, AdjSG_His, AdjSG_Leu, 0.569 (0.501, 0.638); AdjSG_L.Arg, AdjSG_L.Ile, AdjSG_L.Trp, 0.569 (0.501, 0.638).

[0207] [3: Formulas selected in Example 4] Pain_Achilles Tendon, VAS, AdjCre_Glu, 0.701 (0.63, 0.771); RPE, AdjCre_Ala, AdjCre_Asn, 0.697 (0.628, 0.765); VAS, AdjCre_Ala, AdjCre_Asn, 0.696 (0.627, 0.765); Pain_Achilles Tendon, VAS, AdjCre_Ala, 0.695 (0.625, 0.766); VAS, AdjCre_Asn, AdjCre_Gly, 0.695 (0.625, 0.766); Pain_Achilles Tendon, RPE, AdjCre_Glu, 0.695 (0.625, 0.765); RPE, AdjCre_Ala, AdjCre_His, 0.695 (0.626, 0.764); RPE, AdjCre_Asn, AdjCre_Glu, 0.692 (0.622, 0.762); Pain_Achilles Tendon, VAS, AdjCre_Pro, 0.691 (0.619, 0.763); RPE, AdjCre_Asn, AdjCre_Gly, 0.69 (0.621, 0.76); Pain_Achilles Tendon, VAS, RPE, 0.69 (0.62, 0.759); RPE, AdjCre_Glu, AdjCre_Trp, 0.69 (0.619, 0.76); VAS, AdjCre_Asn, AdjCre_Glu, 0.689 (0.619, 0.759); Pain_Achilles Tendon, RPE, AdjCre_Lys, 0.689 (0.619, 0.759); VAS, AdjCre_Ala, AdjCre_His, 0.687 (0.618, 0.757); RPE, AdjCre_His, AdjCre_Gly, 0.687 (0.618, 0.757); VAS, AdjCre_Asn, AdjCre_Met, 0.687 (0.618, 0.756); RPE, AdjCre_Glu, AdjCre_His, 0.687 (0.616, 0.757); Pain_Achilles Tendon, RPE, AdjCre_Thr, 0.685 (0.614, 0.755); RPE, AdjCre_Glu, AdjCre_Tyr, 0.684 (0.614, 0.755); Pain_Achilles Tendon, VAS, AdjCre_Met, 0.684 (0.612, 0.755); Pain_Achilles Tendon, VAS, AdjCre_Lys, 0.684 (0.611, 0.756); RPE, AdjCre_Ser, AdjCre_Gly, 0.684 (0.613, 0.754); Pain_Calf, VAS, AdjCre_Glu, 0.683 (0.612, 0.755); RPE, AdjCre_Lys, AdjCre_Gly, 0.683 (0.612, 0.754); Pain_Achilles Tendon, RPE, AdjCre_Ala, 0.682 (0.613, 0.752); RPE, AdjCre_Glu, AdjCre_Lys, 0.682 (0.611, 0.754); RPE, AdjCre_Glu, AdjCre_Thr, 0.682 (0.611, 0.753); VAS, AdjCre_Glu, AdjCre_His, 0.682 (0.611, 0.752); Pain_Achilles Tendon, VAS, AdjCre_Thr, 0.682 (0.608, 0.755); RPE, AdjCre_Thr, AdjCre_Gly, 0.682 (0.61, 0.753); VAS, AdjCre_Glu, AdjCre_Trp, 0.682 (0.61, 0.753); RPE, AdjCre_Ala, AdjCre_Trp, 0.681 (0.611, 0.752); RPE, AdjCre_Ala, AdjCre_Lys, 0.681 (0.61, 0.752); Pain_Achilles Tendon, RPE, AdjCre_Pro, 0.681 (0.61, 0.752); Pain_Achilles Tendon, VAS, AdjCre_Gly, 0.681 (0.609, 0.753); RPE, AdjCre_Asn, AdjCre_Met, 0.681 (0.611, 0.75); VAS, AdjCre_Glu, AdjCre_Lys, 0.68 (0.609, 0.752); RPE, AdjCre_Ala, AdjCre_Ser, 0.68 (0.61, 0.75); RPE, AdjCre_Gln, AdjCre_Glu, 0.68 (0.609, 0.751); Pain_Knee Joint, VAS, AdjCre_Glu, 0.68 (0.609, 0.751); RPE, AdjCre_Glu, AdjCre_Phe, 0.68 (0.609, 0.751); RPE, AdjCre_Ala, AdjCre_Thr, 0.68 (0.609, 0.751); RPE, AdjCre_Glu, AdjCre_Pro, 0.68 (0.608, 0.751); RPE, AdjCre_Ala, AdjCre_Tyr, 0.68 (0.61, 0.75); Pain_Achilles Tendon, RPE, AdjCre_Met, 0.68 (0.61, 0.749); RPE, AdjCre_Ala, AdjCre_Gln, 0.68 (0.609, 0.75); RPE, AdjCre_Glu, AdjCre_Val, 0.679 (0.609, 0.75); Pain_Achilles Tendon, VAS, AdjCre_Phe, 0.679 (0.607, 0.751); Pain_Achilles Tendon, RPE, AdjCre_Gly, 0.679 (0.609, 0.749); VAS, RPE, AdjCre_Glu, 0.679 (0.608, 0.75); Pain_Front of Thigh, VAS, AdjCre_Glu, 0.679 (0.607, 0.752); VAS, AdjCre_His, AdjCre_Gly, 0.679 (0.608, 0.751); VAS, AdjCre_Asn, AdjCre_Pro, 0.679 (0.608, 0.751); RPE, AdjCre_Glu, AdjCre_Ser, 0.679 (0.608, 0.75); RPE, AdjCre_Trp, AdjCre_Gly, 0.679 (0.609, 0.749); VAS, AdjCre_Glu, AdjCre_Val, 0.679 (0.608, 0.75); Pain_Achilles tendon, RPE, AdjCre_Arg, 0.679 (0.609, 0.749); VAS, AdjCre_Glu, AdjCre_Leu, 0.679 (0.607, 0.75); Pain_Knee joint, RPE, AdjCre_Glu, 0.678 (0.607, 0.75); Pain_Achilles Tendon, RPE, AdjCre_Val, 0.678 (0.608, 0.748); Pain_Achilles Tendon, RPE, AdjCre_Ile, 0.678 (0.608, 0.748); RPE, AdjCre_Lys, AdjCre_Pro, 0.678 (0.606, 0.75); RPE, AdjCre_Gln, AdjCre_Gly, 0.678 (0.608, 0.748); Pain_Achilles Tendon, RPE, AdjCre_Leu, 0.678 (0.608, 0.748); Pain_Achilles Tendon, RPE, AdjCre_Phe, 0.678 (0.608, 0.747); Pain_Calf, Pain_Achilles tendon, RPE, 0.678 (0.608, 0.747); RPE, AdjCre_Lys, AdjCre_Met, 0.677 (0.607, 0.748); Pain_Front of thigh, Pain_Achilles tendon, RPE, 0.677 (0.608, 0.747); RPE, AdjCre_His, AdjCre_Met, 0.677 (0.608, 0.747); RPE, AdjCre_Glu, AdjCre_Ile, 0.677 (0.606, 0.748); RPE, AdjCre_Glu, AdjCre_Leu, 0.677 (0.606, 0.748); Pain_Achilles tendon, RPE, AdjCre_Tyr, 0.677 (0.607, 0.747); Pain_Achilles tendon, RPE, AdjCre_Asn, 0.677 (0.607, 0.747); Pain_calf, VAS, RPE, 0.677 (0.607, 0.747); Pain_Achilles tendon, Pain_knee joint, RPE, 0.677 (0.607, 0.747); Pain_calf, RPE, AdjCre_Glu, 0.677 (0.606, 0.748); VAS, RPE, AdjCre_Lys, 0.677 (0.606, 0.748); Pain_Achilles tendon, RPE, AdjCre_Gln, 0.677 (0.607, 0.747); Pain_Knee Joint, VAS, RPE, 0.676 (0.607, 0.746); Pain_Achilles Tendon, RPE, AdjCre_Ser, 0.676 (0.606, 0.746); RPE, AdjCre_Met, AdjCre_Trp, 0.676 (0.606, 0.747); VAS, AdjCre_Glu, AdjCre_Tyr, 0.676 (0.604, 0.748); RPE, AdjCre_Arg, AdjCre_Lys, 0.676 (0.605, 0.747); VAS, AdjCre_Gln, AdjCre_Glu, 0.676 (0.605, 0.747); VAS, AdjCre_Ala, AdjCre_Trp, 0.676 (0.603, 0.749); VAS, AdjCre_Glu, AdjCre_Ser, 0.676 (0.605, 0.747); Pain_Front of Thigh, Pain_Achilles Tendon, VAS, 0.676 (0.604, 0.748); Pain_Achilles Tendon, RPE, AdjCre_His, 0.676 (0.606, 0.746); VAS, AdjCre_Ser, AdjCre_Gly, 0.676 (0.604, 0.748); RPE, AdjCre_Arg, AdjCre_Glu, 0.676 (0.604, 0.747); RPE, AdjCre_Tyr, AdjCre_Gly, 0.676 (0.606, 0.746); Pain_Achilles Tendon, VAS, AdjCre_Asn, 0.676 (0.604, 0.748); VAS, RPE, AdjCre_Gly, 0.676 (0.605, 0.746); Pain_Knee Joint, VAS, AdjCre_Ala, 0.676 (0.604, 0.747); VAS, RPE, AdjCre_Ala, 0.675 (0.605, 0.746); Pain_Achilles Tendon, RPE, AdjCre_Trp, 0.675 (0.605, 0.745); Pain_Front of Thigh, RPE, AdjCre_Glu, 0.675 (0.604, 0.747); VAS, AdjCre_His, AdjCre_Pro, 0.675 (0.604, 0.746); RPE, AdjCre_Ala, AdjCre_Glu, 0.675 (0.604, 0.747); Pain_Front of thigh, VAS, RPE, 0.675 (0.605, 0.745); RPE, AdjCre_Phe, AdjCre_Trp, 0.675 (0.605, 0.745); Pain_Achilles tendon, Pain_Knee joint, VAS, 0.675 (0.603, 0.747); VAS, AdjCre_Ala, AdjCre_Ser, 0.675 (0.604, 0.746); VAS, AdjCre_Asn, AdjCre_Phe, 0.675 (0.604, 0.746); Pain_Achilles tendon, VAS, AdjCre_Ile, 0.675 (0.602, 0.747); VAS, AdjCre_Ala, AdjCre_Lys, 0.675 (0.603, 0.747); VAS, AdjCre_Glu, AdjCre_Thr, 0.675 (0.603, 0.747); Pain_Achilles Tendon, VAS, AdjCre_Tyr, 0.674 (0.602, 0.747); VAS, AdjCre_Asn, AdjCre_Gln, 0.674 (0.603, 0.746); VAS, AdjCre_His, AdjCre_Met, 0.674 (0.605, 0.744); Pain_Calf, Pain_Achilles Tendon, VAS, 0.674 (0.602, 0.747); RPE, AdjCre_Glu, AdjCre_Met, 0.674 (0.603, 0.746); Pain_Achilles Tendon, VAS, AdjCre_Val, 0.674 (0.602, 0.747); RPE, AdjCre_Met, AdjCre_Thr, 0.674 (0.603, 0.745); Pain_Achilles Tendon, VAS, AdjCre_Trp, 0.674 (0.602, 0.746); RPE, AdjCre_Met, AdjCre_Tyr, 0.674 (0.603, 0.744); Pain_Achilles Tendon, VAS, AdjCre_Arg, 0.674 (0.602, 0.746); RPE, AdjCre_Glu, AdjCre_Gly, 0.674 (0.602, 0.745); RPE, AdjCre_Ala, AdjCre_Phe, 0.674 (0.603, 0.744); Pain_Achilles tendon, VAS, AdjCre_Gln, 0.674 (0.601, 0.746); RPE, AdjCre_Asn, AdjCre_Phe, 0.673 (0.603, 0.744); VAS, RPE, AdjCre_Thr, 0.673 (0.602, 0.745); VAS, AdjCre_Ala, AdjCre_Thr, 0.673 (0.601, 0.746); VAS, AdjCre_Ala, AdjCre_Glu, 0.673 (0.601, 0.745); RPE, AdjCre_Val, AdjCre_Gly, 0.673 (0.603, 0.743); Achilles tendon pain, VAS, AdjCre_Leu, 0.673 (0.601, 0.745); RPE, AdjCre_Lys, AdjCre_Phe, 0.673 (0.602, 0.744); VAS, RPE, AdjCre_Asn, 0.673 (0.603, 0.743); RPE, AdjCre_Ile, AdjCre_Lys, 0.673 (0.602, 0.744); Achilles tendon pain, VAS, AdjCre_His, 0.673 (0.6, 0.745); VAS, AdjCre_Lys, AdjCre_Pro, 0.673 (0.6, 0.745); VAS, AdjCre_Pro, AdjCre_Trp, 0.673 (0.6, 0.745); RPE, AdjCre_Asn, AdjCre_Pro, 0.673 (0.601, 0.744); VAS, AdjCre_Glu, AdjCre_Phe, 0.673 (0.601, 0.744); VAS, AdjCre_Glu, AdjCre_Ile, 0.673 (0.601, 0.744); RPE, AdjCre_Met, AdjCre_Val, 0.673 (0.603, 0.743); RPE, AdjCre_Asn, AdjCre_Gln, 0.672 (0.602, 0.743); RPE, AdjCre_Gln, AdjCre_Lys, 0.672 (0.601, 0.744); RPE, AdjCre_Ala, AdjCre_Val, 0.672 (0.602, 0.743); RPE, AdjCre_Asn, AdjCre_Ser, 0.672 (0.602, 0.742); RPE, AdjCre_Ala, AdjCre_Leu, 0.672 (0.602, 0.743); VAS, RPE, AdjCre_Pro, 0.672 (0.6, 0.744); Pain_Achilles Tendon, VAS, AdjCre_Ser, 0.672 (0.599, 0.745); RPE, AdjCre_Lys, AdjCre_Tyr, 0.672 (0.601, 0.743); Pain_Knee Joint, RPE, AdjCre_Ala, 0.672 (0.601, 0.742); RPE, AdjCre_His, AdjCre_Phe, 0.672 (0.602, 0.742); VAS, AdjCre_Thr, AdjCre_Gly, 0.672 (0.599, 0.745); RPE, AdjCre_Leu, AdjCre_Lys, 0.672 (0.6, 0.743); RPE, AdjCre_His, AdjCre_Pro, 0.671 (0.6, 0.743); Pain_Knee_Joint, RPE, AdjCre_Lys, 0.671 (0.6, 0.742); RPE, AdjCre_Asn, AdjCre_Leu, 0.671 (0.601, 0.741); RPE, AdjCre_Phe, AdjCre_Thr, 0.671 (0.599, 0.742); RPE, AdjCre_Asn, AdjCre_Lys, 0.671 (0.6, 0.742); RPE, AdjCre_Lys, AdjCre_Thr, 0.671 (0.599, 0.742); VAS, AdjCre_Asn, AdjCre_Tyr, 0.671 (0.6, 0.741); Pain_Front of Thigh, RPE, AdjCre_Ala, 0.671 (0.6, 0.741); RPE, AdjCre_Gln, AdjCre_His, 0.67 (0.6, 0.741); RPE, AdjCre_Pro, AdjCre_Thr, 0.67 (0.598, 0.743); VAS, AdjCre_Arg, AdjCre_Glu, 0.67 (0.598, 0.742); Pain_Calf, VAS, AdjCre_Ala, 0.67 (0.598, 0.743); RPE, AdjCre_Ala, AdjCre_Gly, 0.67 (0.599, 0.741); VAS, AdjCre_Ala, AdjCre_Leu, 0.67 (0.598, 0.742); Pain_Front of Thigh, VAS, AdjCre_Ala, 0.67 (0.597, 0.743); RPE, AdjCre_Asn, AdjCre_Ile, 0.67 (0.6, 0.74); RPE, AdjCre_Phe, AdjCre_Gly, 0.67 (0.599, 0.741); Pain_Front of Thigh, RPE, AdjCre_Lys, 0.67 (0.599, 0.741); Pain_Knee Joint, RPE, AdjCre_Gly, 0.67 (0.599, 0.74); RPE, AdjCre_Lys, AdjCre_Val, 0.67 (0.598, 0.741); RPE, AdjCre_Ile, AdjCre_Thr, 0.67 (0.598, 0.741); RPE, AdjCre_Met, AdjCre_Gly, 0.67 (0.599, 0.74); VAS, RPE, AdjCre_Ser, 0.669 (0.599, 0.74); RPE, AdjCre_Pro, AdjCre_Trp, 0.669 (0.597, 0.742); RPE, AdjCre_Leu, AdjCre_Gly, 0.669 (0.599, 0.74); RPE, AdjCre_Arg, AdjCre_Gly, 0.669 (0.599, 0.74); VAS, AdjCre_Ala, AdjCre_Tyr, 0.669 (0.596, 0.743); RPE, AdjCre_Lys, AdjCre_Trp, 0.669 (0.59 8, 0.741); VAS, AdjCre_Ala, AdjCre_Val, 0.669 (0.597, 0.741); VAS, RPE, AdjCre_Trp, 0.669 (0.599, 0.74); RPE, AdjCre_Asn, AdjCre_Val, 0.669 (0.599, 0.739); RPE, AdjCre_Ile, AdjCre_Gly, 0.669 (0.599, 0.74); VAS, AdjCre_Glu, AdjCre_Pro, 0.669 (0.596, 0.742); VAS, AdjCre_Asn, AdjCre_Ser, 0.669 (0.598, 0.74); RPE, AdjCre_Leu, AdjCre_Met, 0.669 (0.599, VAS, RPE, AdjCre_Phe, 0.669 (0.599, 0.739); RPE, AdjCre_Pro, AdjCre_Ser, 0.669 (0.597, 0.741); RPE, AdjCre_Arg, AdjCre_Asn, 0.669 (0.599, 0.739); VAS, AdjCre_Lys, AdjCre_Gly, 0.669 (0.596, 0.742); RPE, AdjCre_Ala, AdjCre_Ile, 0.669 (0.598, 0.739); RPE, AdjCre_Met, AdjCre_Ser, 0.669 (0.599, 0.739); RPE, AdjCre_Lys, AdjCre_Ser, 0.669 (0.598, 0.74); RPE, AdjCre_Asn, AdjCre_Tyr, 0.669 (0.598, 0.739); RPE, AdjCre_Ile, AdjCre_Trp, 0.669 (0.598, 0.739); VAS, RPE, AdjCre_Met, 0.669 (0.598, 0.739); Pain_Front of Thigh, RPE, AdjCre_Thr, 0.669 (0.597, 0.74); RPE, AdjCre_Arg, AdjCre_Thr, 0.669 (0.597, 0.74); VAS, AdjCre_Leu, AdjCre_Met, 0.669 (0.598, 0.739); Pain_Front of Thigh, RPE, AdjCre_Gly, 0.668 (0.598, 0.739); RPE, AdjCre_Gln, AdjCre_Met, 0.668 (0.598, 0.739); RPE, AdjCre_Ala, AdjCre_Arg, 0.668 (0.597, 0.739); RPE, AdjCre_Asn, AdjCre_Trp, 0.668 (0.598, 0.738); RPE, AdjCre_Ser, AdjCre_Thr, 0.668 (0.597, 0.739); RPE, AdjCre_His, AdjCre_Lys, 0.668 (0.597, 0.74); VAS, AdjCre_Asn, AdjCre_Leu, 0.668 (0.596, 0.74); RPE, AdjCre_Ala, AdjCre_Pro, 0.668 (0.596, 0.74); VAS, RPE, AdjCre_Gln, 0.668 (0.597, 0.738); VAS, RPE, AdjCre_His, 0.668 (0.597, 0.738); VAS, AdjCre_Pro, AdjCre_Val, 0.668 (0.595, 0.74); Pain_Calf, RPE, AdjCre_Gly, 0.668 (0.597, 0.738); RPE, AdjCre_Pro, AdjCre_Gly, 0.668 (0.596, 0.739); Pain_Knee Joint, RPE, AdjCre_Thr, 0.668 (0.596, 0.739); RPE, AdjCre_Thr, AdjCre_Tyr, 0.668 (0.596, 0.739); Pain_Knee Joint, RPE, AdjCre_Asn, 0.668 (0.597, 0.738); RPE, AdjCre_Ala, AdjCre_Met, 0.667 (0.597, 0.738); Pain_Knee Joint, VAS, AdjCre_Pro, 0.667 (0.595, 0.74); VAS, RPE, AdjCre_Leu, 0.667 (0.597, 0.738); VAS, AdjCre_Ala, AdjCre_Gln, 0.667 (0.596, 0.739); VAS, RPE, AdjCre_Ile, 0.667 (0.597, 0.738); Pain_Calf, RPE, AdjCre_Lys, 0.667 (0.596, 0.738); VAS, AdjCre_Glu, AdjCre_Gly, 0.667 (0.595, 0.74); VAS, AdjCre_Pro, AdjCre_Thr, 0.667 (0.594, 0.741); RPE, AdjCre_Leu, AdjCre_Thr, 0.667 (0.596, 0.739); RPE, AdjCre_Thr, AdjCre_Trp, 0.667 (0.596, 0.739); RPE, AdjCre_Pro, AdjCre_Tyr, 0.667 (0.595, 0.739); VAS, RPE, AdjCre_Tyr, 0.667 (0.596, 0.738); RPE, AdjCre_Pro, AdjCre_Val, 0.667 (0.595, 0.739); VAS, AdjCre_Pro, AdjCre_Ser, 0.667 (0.594, 0.739); VAS, AdjCre_His, AdjCre_Phe, 0.667 (0.596, 0.738); Pain_Front of Thigh, Pain_Knee Joint, RPE, 0.667 (0.597, 0.737); Pain_Calf, VAS, AdjCre_Pro, 0.667 (0.593, 0.74); VAS, AdjCre_Pro, AdjCre_Tyr, 0.667 (0.594, 0.74); VAS, AdjCre_Arg, AdjCre_Asn, 0.667 (0.595, 0.739); RPE, AdjCre_Leu, AdjCre_Pro, 0.667 (0.595, 0.738); VAS, AdjCre_Met, AdjCre_Ser, 0.666 (0.596, 0.737); VAS, AdjCre_Leu, AdjCre_Pro, 0.666 (0.594, 0.738); RPE, AdjCre_Asn, AdjCre_Thr, 0.666 (0.595, 0.738); RPE, AdjCre_His, AdjCre_Thr, 0.666 (0.595, 0.738); RPE, AdjCre_Leu, AdjCre_Trp, 0.666 (0.596, 0.737); RPE, AdjCre_Thr, AdjCre_Val, 0.666 (0.595, 0.738); VAS, RPE, AdjCre_Arg, 0.666 (0.596, 0.737); Pain_Front of thigh, RPE, AdjCre_Pro, 0.666 (0.594, 0.738); Pain_Calf, RPE, AdjCre_Ala, 0.666 (0.596, 0.737); VAS, RPE, AdjCre_Val, 0.666 (0.596, 0.737); RPE, AdjCre_Trp, AdjCre_Val, 0.666 (0.595, 0.737); Pain_Knee Joint, RPE, AdjCre_Pro, 0.666 (0.594, 0.738); RPE, AdjCre_Phe, AdjCre_Ser, 0.666 (0.595, 0.736); VAS, AdjCre_Gln, AdjCre_Gly, 0.666 (0.593, 0.738); Pain_Front of Thigh, VAS, AdjCre_Pro, 0.665 (0.591, 0.74); RPE, AdjCre_Ile, AdjCre_Ser, 0.665 (0.595, 0.736); VAS, AdjCre_Glu, AdjCre_Met, 0.665 (0.593, 0.738); RPE, AdjCre_Gln, AdjCre_Thr, 0.665 (0.594, 0.737); VAS, AdjCre_Asn, AdjCre_Ile, 0.665 (0.594, 0.737); VAS, AdjCre_Asn, AdjCre_Trp, 0.665 (0.594, 0.737); Pain_Front of Thigh, RPE, AdjCre_Trp, 0.665 (0.595, 0.736); RPE, AdjCre_Ile, AdjCre_Val, 0.665 (0.594, 0.736); Pain_Knee_Joint, RPE, AdjCre_Met, 0.665 (0.594, 0.735); RPE, AdjCre_Phe, AdjCre_Tyr, 0.665 (0.594, 0.735); RPE, AdjCre_Gln, AdjCre_Pro, 0.665 (0.593, 0.737); VAS, AdjCre_Asn, AdjCre_Val, 0.665 (0.593, 0.737); VAS, AdjCre_Gln, AdjCre_Pro, 0.665 (0.592, 0.737); Pain_Front of Thigh, RPE, AdjCre_Asn, 0.665 (0.594, 0.735); Pain_Knee Joint, RPE, AdjCre_Trp, 0.665 (0.594, 0.735); RPE, AdjCre_Asn, AdjCre_His, 0.665 (0.594, 0.735); RPE, AdjCre_Gln, AdjCre_Trp, 0.665 (0.594, 0.735); RPE, AdjCre_Trp, AdjCre_Tyr, 0.664 (0.594, 0.735); Pain_Knee Joint, RPE, AdjCre_Ser, 0.664 (0.594, 0.735); RPE, AdjCre_His, AdjCre_Leu, 0.664 (0.593, 0.735); Pain_Front of Thigh, RPE, AdjCre_Ile, 0.664 (0.593, 0.735); RPE, AdjCre_Arg, AdjCre_His, 0.664 (0.593, 0.735); RPE, AdjCre_Ser, AdjCre_Trp, 0.664 (0.593, 0.735); RPE, AdjCre_Ser, AdjCre_Val, 0.664 (0.593, 0.735); Pain_Calf, RPE, AdjCre_Pro, 0.664 (0.592, 0.736); RPE, AdjCre_Phe, AdjCre_Pro, 0.664 (0.592, 0.736); Pain_Knee Joint, VAS, AdjCre_Gly, 0.664 (0.592, 0.736); VAS, AdjCre_Asn, AdjCre_Thr, 0.664 (0.591, 0.737); RPE, AdjCre_His, AdjCre_Trp, 0.664 (0.593, 0.734); RPE, AdjCre_Ile, AdjCre_Tyr, 0.664 (0.593, 0.735); RPE, AdjCre_Leu, AdjCre_Tyr, 0.664 (0.593, 0.735); RPE, AdjCre_Tyr, AdjCre_Val, 0.664 (0.593, 0.734); Pain_Calf, Pain_Knee Joint, RPE, 0.664 (0.593, 0.734); VAS, AdjCre_Ala, AdjCre_Pro, 0.664 (0.59, 0.737); Pain_Front of Thigh, RPE, AdjCre_Ser, 0.664 (0.593, 0.734); RPE, AdjCre_Leu, AdjCre_Ser, 0.664 (0.593, 0.734); RPE, AdjCre_Arg, AdjCre_Pro, 0.664 (0.592, 0.736); RPE, AdjCre_Met, AdjCre_Phe, 0.664 (0.593, 0.734); VAS, AdjCre_Met, AdjCre_Thr, 0.664 (0.592, 0.735); VAS, AdjCre_Ala, AdjCre_Ile, 0.664 (0.591, 0.736); VAS, AdjCre_Met, AdjCre_Val, 0.663 (0.592, 0.735); Pain_Calf, RPE, AdjCre_Thr, 0.663 (0.592, 0.735); VAS, AdjCre_Ala, AdjCre_Phe, 0.663 (0.591, 0.736); Pain_Front of Thigh, RPE, AdjCre_His, 0.663 (0.593, 0.734); Pain_Front of Thigh, RPE, AdjCre_Leu, 0.663 (0.592, 0.734); Pain_Knee Joint, RPE, AdjCre_His, 0.663 (0.593, 0.734); RPE, AdjCre_His, AdjCre_Val, 0.663 (0.592, 0.734); Pain_Front of Thigh, RPE, AdjCre_Met, 0.663 (0.592, 0.734); RPE, AdjCre_Arg, AdjCre_Ser, 0.663 (0.592, 0.734); RPE, AdjCre_Arg, AdjCre_Trp, 0.663 (0.592, 0.734); RPE, AdjCre_Ile, AdjCre_Pro, 0.663 (0.591, 0.735); RPE, AdjCre_Ile, AdjCre_Leu, 0.663 (0.592, 0.734); VAS, AdjCre_Asn, AdjCre_His, 0.663 (0.591, 0.735); VAS, AdjCre_Asn, AdjCre_Lys, 0.663 (0.59, 0.736); RPE, AdjCre_Ser, AdjCre_Tyr, 0.663 (0.592, 0.733); RPE, AdjCre_His, AdjCre_Ile, 0.663 (0.592, 0.733); Pain_Knee Joint, RPE, AdjCre_Tyr, 0.663 (0.592, 0.733); Pain_Front of Thigh, Pain_Calf, RPE, 0.662 (0.592, 0.733); Pain_Front of Thigh, RPE, AdjCre_Tyr, 0.662 (0.592, 0.733); RPE, AdjCre_Arg, AdjCre_Tyr, 0.662 (0.592, 0.733); RPE, AdjCre_His, AdjCre_Ser, 0.662 (0.592, 0.733); Pain_Front of Thigh, RPE, AdjCre_Phe, 0.662 (0.592, 0.733); Pain_Calf, VAS, AdjCre_Asn, 0.662 (0.59, 0.735); RPE, AdjCre_Gln, AdjCre_Ser, 0.662 (0.591, 0.733); VAS, AdjCre_Ala, AdjCre_Arg, 0.662 (0.589, 0.734); VAS, AdjCre_Phe, AdjCre_Pro, 0.662 (0.589, 0.735); Pain_Knee Joint, RPE, AdjCre_Ile, 0.662 (0.591, 0.733); RPE, AdjCre_Phe, AdjCre_Val, 0.662 (0.591, 0.733); VAS, AdjCre_Met, AdjCre_Trp, 0.662 (0.589, 0.735); RPE, AdjCre_Met, AdjCre_Pro, 0.662 (0.59, 0.734); Pain_Front of Thigh, RPE, AdjCre_Val, 0.662 (0.591, 0.732); RPE, AdjCre_Leu, AdjCre_Val, 0.662 (0.591, 0.732); RPE, AdjCre_Gln, AdjCre_Ile, 0.662 (0.591, 0.732); RPE, AdjCre_His, AdjCre_Tyr, 0.662 (0.591, 0.732); Pain_Calf, RPE, AdjCre_Met, 0.662 (0.591, 0.732); VAS, AdjCre_Ala, AdjCre_Met, 0.662 (0.589, 0.734); VAS, AdjCre_Leu, AdjCre_Gly, 0.662 (0.589, 0.734); Pain_Front of Thigh, RPE, AdjCre_Arg, 0.661 (0.591, 0.732); RPE, AdjCre_Gln, AdjCre_Tyr, 0.661 (0.591, 0.732); Pain_Calf, RPE, AdjCre_Asn, 0.661 (0.591, 0.732); VAS, AdjCre_Lys, AdjCre_Met, 0.661 (0.589, 0.733); VAS, AdjCre_Lys, AdjCre_Phe, 0.661 (0.589, 0.734); RPE, AdjCre_Ile, AdjCre_Met, 0.661 (0.59, 0.732); RPE, AdjCre_Ile, AdjCre_Phe, 0.661 (0.59, 0.732); Pain_Knee Joint, VAS, AdjCre_Asn, 0.661 (0.59, 0.732); RPE, AdjCre_Arg, AdjCre_Ile, 0.661 (0.59, 0.732); Pain_Front of Thigh, VAS, AdjCre_Gly, 0.661 (0.587, 0.735); VAS, AdjCre_Met, AdjCre_Tyr, 0.661 (0.588, 0.734); RPE, AdjCre_Arg, AdjCre_Met, 0.661 (0.59, 0.732); VAS, AdjCre_Trp, AdjCre_Gly, 0.661 (0.587, 0.734); Pain_Knee Joint, VAS, AdjCre_Lys, 0.661 (0.589, 0.733); Pain_Front of Thigh, VAS, AdjCre_Met, 0.661 (0.587, 0.734); Pain_Knee Joint, RPE, AdjCre_Val, 0.661 (0.59, 0.731); RPE, AdjCre_Gln, AdjCre_Phe, 0.661 (0.59, 0.731); RPE, AdjCre_Arg, AdjCre_Phe, 0.66 (0.59, 0.731); Pain_Calf, VAS, AdjCre_Thr, 0.66 (0.586, 0.734); Pain_Front of Thigh, RPE, AdjCre_Gln, 0.66 (0.59, 0.731); VAS, AdjCre_Ala, AdjCre_Gly, 0.66 (0.588, 0.733); Pain_Calf, RPE, AdjCre_Ile, 0.66 (0.589, 0.731); RPE, AdjCre_Arg, AdjCre_Val, 0.66 (0.589, 0.731); RPE, AdjCre_Leu, AdjCre_Phe, 0.66 (0.59, 0.731); Pain_Calf, RPE, AdjCre_His, 0.66 (0.589, 0.731); Pain_Knee Joint, RPE, AdjCre_Arg, 0.66 (0.589, 0.731); Pain_Knee Joint, RPE, AdjCre_Leu, 0.66 (0.589, 0.731); VAS, AdjCre_Ile, AdjCre_Pro, 0.66 (0.587, 0.733); Pain_Calf, RPE, AdjCre_Ser, 0.66 (0.589, 0.731); Pain_Calf, RPE, AdjCre_Trp, 0 .66 (0.589, 0.731); Pain_Calf, VAS, AdjCre_Gly, 0.66 (0.586, 0.733); Pain_Calf, RPE, AdjCre_Tyr, 0.66 (0.589, 0.73); Pain_Knee Joint, RPE, AdjCre_Gln, 0.66 (0.589, 0.73); Pain_Knee Joint, RPE, AdjCre_Phe, 0.66 (0.589, 0.731); Pain_Calf, VAS, AdjCre_Lys, 0.66 (0.586, 0.733); Pain_Calf, VAS, AdjCre_Met, 0.66 (0.586, 0.733); Pain_Front of Thigh, VAS, AdjCre_Lys, 0.659 (0.586, 0.733); RPE, AdjCre_Arg, AdjCre_Leu, 0.659 (0.588, 0.73); VAS, AdjCre_Arg, AdjCre_Lys, 0.659 (0.587, 0.731); VAS, AdjCre_Phe, AdjCre_Thr, 0.659 (0.586, 0.732); Pain_Front of Thigh, VAS, AdjCre_Asn, 0.659 (0.586, 0.732); VAS, AdjCre_Phe, AdjCre_Ser, 0.659 (0.587, 0.731); VAS, AdjCre_Tyr, AdjCre_Gly, 0.659 (0.585, 0.733); Pain_Front of Thigh, VAS, AdjCre_Thr, 0.659 (0.585, 0.733); Pain_Calf, RPE, AdjCre_Val, 0.659 (0.588, 0.73); Pain_Knee Joint, VAS, AdjCre_Thr, 0.659 (0.586, 0.731); Pain_Calf, RPE, AdjCre_Leu, 0.659 (0.588, 0.729); RPE, AdjCre_Gln, AdjCre_Val, 0.659 (0.588, 0.729); VAS, AdjCre_Lys, AdjCre_Thr, 0.658 (0.585, 0.732); VAS, AdjCre_Val, AdjCre_Gly, 0.658 (0.585, 0.731); Pain_Calf, Pain_Knee Joint, VAS, 0.658 (0.586, 0.731); Pain_Calf, RPE, AdjCre_Phe, 0.658 (0.587, 0.729); Pain_Calf, VAS, AdjCre_His, 0.658 (0.585, 0.731); VAS, AdjCre_Gln, AdjCre_Thr, 0.658 (0.584, 0.731); Pain_Calf, VAS, AdjCre_Ser, 0.658 (0.585, 0.731); Pain_Calf, RPE, AdjCre_Arg, 0.658 (0.587, 0.729); VAS, AdjCre_Gln, AdjCre_Ser, 0.658 (0.585, 0.73); VAS, AdjCre_Pro, AdjCre_Gly, 0.658 (0.584, 0.731); RPE, AdjCre_Gln, AdjCre_Leu, 0.657 (0.586, 0.728); Pain_knee joint, VAS, AdjCre_Met, 0.657 (0.585, 0.729); VAS, AdjCre_Ile, AdjCre_Lys, 0.657 (0.585, 0.729); VAS, AdjCre_Lys, AdjCre_Tyr, 0.657 (0.585, 0.729); RPE, AdjCre_Arg, AdjCre_Gln, 0.657 (0.586, 0.728); Pain_Front of Thigh, VAS, AdjCre_Phe, 0.657 (0.583, 0.731); VAS, AdjCre_Arg, AdjCre_Pro, 0.657 (0.584, 0.73); Pain_Front of Thigh, Pain_Calf, VAS, 0.657 (0.584, 0.73); VAS, AdjCre_Phe, AdjCre_Trp, 0.657 (0.583, 0.73); Pain_Front of Thigh, VAS, AdjCre_Ser, 0.657 (0.584, 0.73); Pain_Calf, VAS, AdjCre_Arg, 0.657 (0.584, 0.73); Pain_Calf, VAS, AdjCre_Phe, 0.657 (0.583, 0.73); Pain_Calf, VAS, AdjCre_Tyr, 0.657 (0.584, 0.73); VAS, AdjCre_Arg, AdjCre_Ser, 0.656 (0.584, 0.729); VAS, AdjCre_Arg, AdjCre_Gly, 0.656 (0.583, 0.729); VAS, AdjCre_Leu, AdjCre_Lys, 0.656 (0.584, 0.729); Pain_Calf, RPE, AdjCre_Gln, 0.656 (0.585, 0.727); Pain_Calf, VAS, AdjCre_Leu, 0.656 (0.583, 0.73); Pain_Knee Joint, VAS, AdjCre_Ser, 0.656 (0.585, 0.728); VAS, AdjCre_Gln, AdjCre_Met, 0.656 (0.585, 0.728); Pain_Front of Thigh, Pain_Knee Joint, VAS, 0.656 (0.583, 0.729); Pain_Calf, VAS, AdjCre_Ile, 0.656 (0.583, 0.729); VAS, AdjCre_Thr, AdjCre_Tyr, 0.656 (0.583, 0.729); Pain_Calf, VAS, AdjCre_Trp, 0.656 (0.583, 0.729); VAS, AdjCre_Gln, AdjCre_Lys, 0.656 (0.583, 0.729); VAS, AdjCre_Ser, AdjCre_Tyr, 0.656 (0.584, 0.728); VAS, AdjCre_Ile, AdjCre_Thr, 0.656 (0.583, 0.729); VAS, AdjCre_Ser, AdjCre_Thr, 0.656 (0.582, 0.729); Pain_Calf, VAS, AdjCre_Val, 0.656 (0.582, 0.729); VAS, AdjCre_Ser, AdjCre_Val, 0.656 (0.583, 0.728); Pain_Calf, VAS, AdjCre_Gln, 0.656 (0.582, 0.729); VAS, AdjCre_Gln, AdjCre_His, 0.656 (0.584, 0.727); VAS, AdjCre_Leu, AdjCre_Ser, 0.655 (0.583, 0.728); VAS, AdjCre_Met, AdjCre_Pro, 0.655 (0.582, 0.729); Pain_Front of Thigh, VAS, AdjCre_Ile, 0.655 (0.582, 0.729); VAS, AdjCre_Ile, AdjCre_Ser, 0.655 (0.583, 0.728); Pain_Front of Thigh, VAS, AdjCre_His, 0.655 (0.582, 0.728); VAS, AdjCre_Phe, AdjCre_Tyr, 0.655 (0.582, 0.728); VAS, AdjCre_Leu, AdjCre_Thr, 0.655 (0.582, 0.728); VAS, AdjCre_Leu, AdjCre_Phe, 0.655 (0.583, 0.727); VAS, AdjCre_His, AdjCre_Ser, 0.655 (0.582, 0.727); VAS, AdjCre_His, AdjCre_Tyr, 0.655 (0.583, 0.726); VAS, AdjCre_Ser, AdjCre_Trp, 0.655 (0.582, 0.727); Pain_knee joint, VAS, AdjCre_Gln, 0.655 (0.582, 0.727); Pain_Knee joint, VAS, AdjCre_His, 0.654 (0.583, 0.726); VAS, AdjCre_Lys, AdjCre_Val, 0.654 (0.582, 0.727); VAS, AdjCre_Thr, AdjCre_Val, 0.654 (0.581, 0.727); Pain_Front of thigh, VAS, AdjCre_Arg, 0.654 (0.581, 0.728); VAS, AdjCre_Phe, AdjCre_Val, 0.654 (0.582, 0.726); Pain_Front of thigh, VAS, AdjCre_Trp, 0.654 (0.581, 0.728); Pain_Front of thigh, VAS, AdjCre_Val, 0.654 (0.581, 0.728); Pain_Front of Thigh, VAS, AdjCre_Gln, 0.654 (0.58, 0.728); VAS, AdjCre_His, AdjCre_Thr, 0.654 (0.581, 0.728); VAS, AdjCre_Lys, AdjCre_Trp, 0.654 (0.581, 0.726); Pain_Front of Thigh, VAS, AdjCre_Leu, 0.654 (0.58, 0.727); Pain_Front of Thigh, VAS, AdjCre_Tyr, 0.654 (0.58, 0.727); Pain_Knee Joint, VAS, AdjCre_Phe, 0.654 (0.581, 0.726); Pain_Knee joint, VAS, AdjCre_Trp, 0.654 (0.582, 0.726); VAS, AdjCre_Lys, AdjCre_Ser, 0.653 (0.581, 0.726); Pain_Knee_Joint, VAS, AdjCre_Ile, 0.653 (0.581, 0.726); Pain_Knee_Joint, VAS, AdjCre_Leu, 0.653 (0.581, 0.726); VAS, AdjCre_Arg, AdjCre_Thr, 0.653 (0.58, 0.727); Pain_Knee_Joint, VAS, AdjCre_Val, 0.653 (0.581, 0.725); VAS, AdjCre_Thr, AdjCre_Trp, 0.653 (0.58, 0.726); Pain_Knee Joint, VAS, AdjCre_Tyr, 0.653 (0.581, 0.725); VAS, AdjCre_Ile, AdjCre_Gly, 0.653 (0.58, 0.726); VAS, AdjCre_His, AdjCre_Ile, 0.653 (0.581, 0.725); Pain_Knee Joint, VAS, AdjCre_Arg, 0.653 (0.581, 0.725); VAS, AdjCre_His, AdjCre_Leu, 0.653 (0.581, 0.725); VAS, AdjCre_His, AdjCre_Lys, 0.652 (0.579, 0.725); AdjCre_Asn, AdjCre_Met, AdjCre_Ser, 0.652 (0.586, 0.719); VAS, AdjCre_His, AdjCre_Trp, 0.652 (0.58, 0.724); VAS, AdjCre_Arg, AdjCre_His, 0.652 (0.579, 0.724); VAS, AdjCre_His, AdjCre_Val, 0.652 (0.579, 0.724); VAS, AdjCre_Phe, AdjCre_Gly, 0.652 (0.578, 0.725); VAS, AdjCre_Arg, AdjCre_Met, 0.651 (0.579, 0.723); VAS, AdjCre_Gln, AdjCre_Phe, 0.651 (0.579, 0.723); VAS, AdjCre_Ile, AdjCre_Met, 0.651 (0.579, 0.724); VAS, AdjCre_Ile, AdjCre_Leu, 0.651 (0.579, 0.723); VAS, AdjCre_Ile, AdjCre_Phe, 0.65 (0.578, 0.723); VAS, AdjCre_Met, AdjCre_Gly, 0.65 (0.577, 0.723); VAS, AdjCre_Ile, AdjCre_Trp, 0.65 (0.578, 0.723); VAS, AdjCre_Met, AdjCre_Phe, 0.65 (0.578, 0.722); VAS, AdjCre_Arg, AdjCre_Phe, 0.65 (0.577, 0.722).

[0208] [4: Formula selected in Example 6] AdjCre_Arg, AdjCre_Trp, AdjCre_Tyr, 0.815 (0.713, 0.917); AdjCre_Trp, AdjCre_Tyr, AdjCre_Val, 0.788 (0.684, 0.892); AdjCre_Pro, AdjCre_Thr, AdjCre_Tyr, 0.785 (0.675, 0.894); AdjCre_Glu, AdjCre_Trp, AdjCre_Tyr, 0.784 (0.672, 0.897); AdjCre_Ala, AdjCre_Glu, AdjCre_Tyr, 0.783 (0.672, 0.894); AdjCre_Met, AdjCre_Trp, AdjCre_Tyr, 0.779 (0.673, 0.885); AdjCre_Ile, AdjCre_Tyr, AdjCre_Val, 0.777 (0.671, 0.884); AdjCre_Pro, AdjCre_Trp, AdjCre_Tyr, 0.776 (0.665, 0.888); AdjCre_Arg, AdjCre_Pro, AdjCre_Tyr, 0.772 (0.659, 0.886); AdjCre_Pro, AdjCre_Ser, AdjCre_Tyr, 0.768 (0.656, 0.881); AdjCre_Arg, AdjCre_Glu, AdjCre_Tyr, 0.767 (0.656, 0.878); AdjCre_Ala, AdjCre_Trp, AdjCre_Tyr, 0.766 (0.653, 0.878); AdjCre_Glu, AdjCre_Pro, AdjCre_Tyr, 0.763 (0.645, 0.88); AdjCre_Glu, AdjCre_Lys, AdjCre_Tyr, 0.758 (0.642, 0.875); AdjCre_Asn, AdjCre_Pro, AdjCre_Tyr, 0.758 (0.642, 0.874); AdjCre_Leu, AdjCre_Trp, AdjCre_Tyr, 0.757 (0.644, 0.87); AdjCre_Glu, AdjCre_Thr, AdjCre_Tyr, 0.755 (0.641, 0.869); AdjCre_Ile, AdjCre_Pro, AdjCre_Tyr, 0.753 (0.638, 0.869); AdjCre_Pro, AdjCre_Tyr, AdjCre_Val, 0.752 (0.639, 0.864); AdjCre_Ala, AdjCre_Tyr, AdjCre_Val, 0.752 (0.64, 0.863); AdjCre_Trp, AdjCre_Tyr, AdjCre_Gly, 0.75 (0.637, 0.863); AdjCre_Asn, AdjCre_Glu, AdjCre_Tyr, 0.749 (0.633, 0.866); AdjCre_Ala, AdjCre_Arg, AdjCre_Tyr, 0.748 (0.637, 0.86); AdjCre_Glu, AdjCre_Ser, AdjCre_Tyr, 0.748 (0.634, 0.863); AdjCre_Ala, AdjCre_Pro, AdjCre_Tyr, 0.747 (0.63, 0.864); AdjCre_Gln, AdjCre_Pro, AdjCre_Tyr, 0.747 (0.631, 0.863); AdjCre_Glu, AdjCre_Ile, AdjCre_Tyr, 0.747 (0.63, 0.864); AdjCre_Glu, AdjCre_Tyr, AdjCre_Val, 0.747 (0.634, 0.86); AdjCre_Glu, AdjCre_His, AdjCre_Thr, 0.747 (0.631, 0.863); AdjCre_Lys, AdjCre_Trp, AdjCre_Tyr, 0.746 (0.629, 0.863); AdjCre_Gln, AdjCre_Glu, AdjCre_Tyr, 0.745 (0.629, 0.861); AdjCre_His, AdjCre_Pro, AdjCre_Tyr, 0.745 (0.628, 0.862); AdjCre_His, AdjCre_Ile, AdjCre_Val, 0.745 (0.631, 0.858); AdjCre_Phe, AdjCre_Trp, AdjCre_Tyr, 0.745 (0.63, 0.859); AdjCre_Met, AdjCre_Pro, AdjCre_Tyr, 0.744 (0.63, 0.859); AdjCre_Glu, AdjCre_His, AdjCre_Tyr, 0.743 (0.625, 0.861); AdjCre_Ala, AdjCre_Glu, AdjCre_His, 0.741 (0.622, 0.86); AdjCre_Phe, AdjCre_Pro, AdjCre_Tyr, 0.741 (0.625, 0.857); AdjCre_Glu, AdjCre_Leu, AdjCre_Tyr, 0.74 (0.626, 0.855); AdjCre_Pro, AdjCre_Tyr, AdjCre_Gly, 0.74 (0.623, 0.858); AdjCre_Glu, AdjCre_Ile, AdjCre_Thr, 0.738 (0.619, 0.858); AdjCre_Glu, AdjCre_Phe, AdjCre_Tyr, 0.738 (0.621, 0.855); AdjCre_Met, AdjCre_Thr, AdjCre_Tyr, 0.738 (0.622, 0.854); AdjCre_Lys, AdjCre_Pro, AdjCre_Tyr, 0.736 (0.617, 0.856); AdjCre_Gln, AdjCre_Glu, AdjCre_Thr, 0.736 (0.618, 0.854); AdjCre_Glu, AdjCre_Ile, AdjCre_Pro, 0.736 (0.612, 0.859); AdjCre_His, AdjCre_Pro, AdjCre_Thr, 0.735 (0.618, 0.853); AdjCre_Asn, AdjCre_Glu, AdjCre_Thr, 0.734 (0.616, 0.853); AdjCre_Glu, AdjCre_Leu, AdjCre_Thr, 0.734 (0.614, 0.855); AdjCre_Leu, AdjCre_Pro, AdjCre_Tyr, 0.734 (0.616, 0.852); AdjCre_Ala, AdjCre_Arg, AdjCre_His, 0.731 (0.615, 0.847); AdjCre_Gln, AdjCre_Trp, AdjCre_Tyr, 0.731 (0.615, 0.848); AdjCre_Phe, AdjCre_Tyr, AdjCre_Val, 0.731 (0.616, 0.846); AdjCre_Ala, AdjCre_Leu, AdjCre_Tyr, 0.73 (0.616, 0.845); AdjCre_Asn, AdjCre_Ile, AdjCre_Val, 0.73 (0.613, 0.848); AdjCre_Asn, AdjCre_Pro, AdjCre_Thr, 0.73 (0.612, 0.849); AdjCre_Glu, AdjCre_Ile, AdjCre_Val, 0.73 (0.614, 0.846); AdjCre_Pro, AdjCre_Thr, AdjCre_Trp, 0.729 (0.61, 0.848); AdjCre_Glu, AdjCre_Met, AdjCre_Tyr, 0.729 (0.613, 0.845); AdjCre_Ile, AdjCre_Pro, AdjCre_Ser, 0.729 (0.61, 0.848); AdjCre_Ala, AdjCre_Phe, AdjCre_Tyr, 0.727 (0.609, 0.846); AdjCre_Ile, AdjCre_Ser, AdjCre_Val, 0.727 (0.611, 0.844); AdjCre_Glu, AdjCre_Pro, AdjCre_Trp, 0.727 (0.603, 0.85); AdjCre_Ile, AdjCre_Leu, AdjCre_Val, 0.727 (0.611, 0.842); AdjCre_Glu, AdjCre_His, AdjCre_Pro, 0.726 (0.604, 0.848); AdjCre_Glu, AdjCre_Phe, AdjCre_Thr, 0.725 (0.606, 0.845); AdjCre_Glu, AdjCre_Tyr, AdjCre_Gly, 0.725 (0.607, 0.843); AdjCre_Thr, AdjCre_Trp, AdjCre_Tyr, 0.725 (0.606, 0.844); AdjCre_Glu, AdjCre_Ile, AdjCre_Lys, 0.725 (0.601, 0.849); AdjCre_His, AdjCre_Pro, AdjCre_Ser, 0.724 (0.604, 0.844); AdjCre_Asn, AdjCre_Pro, AdjCre_Ser, 0.723 (0.602, 0.843); AdjCre_Glu, AdjCre_His, AdjCre_Ser, 0.723 (0.603, 0.842); AdjCre_Ile, AdjCre_Phe, AdjCre_Pro, 0.723 (0.602, 0.844); AdjCre_Ile, AdjCre_Pro, AdjCre_Val, 0.723 (0.605, 0.841); AdjCre_Ala, AdjCre_Met, AdjCre_Tyr, 0.723 (0.606, 0.839); AdjCre_Arg, AdjCre_Met, AdjCre_Tyr, 0.723 (0.605, 0.841); AdjCre_Ile, AdjCre_Trp, AdjCre_Val, 0.723 (0.604, 0.841); AdjCre_Glu, AdjCre_Lys, AdjCre_Phe, 0.722 (0.599, 0.846); AdjCre_Ile, AdjCre_Pro, AdjCre_Thr, 0.722 (0.602, 0.842); AdjCre_Ile, AdjCre_Met, AdjCre_Pro, 0.721 (0.602, 0.841); AdjCre_Ile, AdjCre_Lys, AdjCre_Val, 0.72 (0.602, 0.839); AdjCre_Gln, AdjCre_Glu, AdjCre_His, 0.72 (0.601, 0.84); AdjCre_Ala, AdjCre_His, AdjCre_Met, 0.72 (0.603, 0.838); AdjCre_Ile, AdjCre_Met, AdjCre_Tyr, 0.72 (0.601, 0.839); AdjCre_Met, AdjCre_Phe, AdjCre_Tyr, 0.72 (0.604, 0.837); AdjCre_Asn, AdjCre_Glu, AdjCre_His, 0.72 (0.598, 0.841); AdjCre_Arg, AdjCre_Leu, AdjCre_Val, 0.72 (0.602, 0.837); AdjCre_Leu, AdjCre_Met, AdjCre_Tyr, 0.72 (0.602, 0.837); AdjCre_Asn, AdjCre_Met, AdjCre_Tyr, 0.719 (0.601, 0.837); AdjCre_Thr, AdjCre_Tyr, AdjCre_Val, 0.719 (0.601, 0.836); AdjCre_His, AdjCre_Met, AdjCre_Tyr, 0.718 (0.6, 0.836); AdjCre_Ala, AdjCre_His, AdjCre_Pro, 0.718 (0.597, 0.839); AdjCre_Ala, AdjCre_His, AdjCre_Thr, 0.718 (0.599, 0.837); AdjCre_Ile, AdjCre_Pro, AdjCre_Gly, 0.718 (0.596, 0.839); AdjCre_Arg, AdjCre_Phe, AdjCre_Tyr, 0.717 (0.6, 0.835); AdjCre_Ala, AdjCre_Glu, AdjCre_Ile, 0.717 (0.594, 0.84); AdjCre_Glu, AdjCre_His, AdjCre_Ile, 0.717 (0.594, 0.84); AdjCre_Glu, AdjCre_Phe, AdjCre_Pro, 0.717 (0.594, 0.841); AdjCre_Ile, AdjCre_Thr, AdjCre_Val, 0.717 (0.599, 0.836); AdjCre_Glu, AdjCre_Thr, AdjCre_Trp, 0.717 (0.597, 0.837); AdjCre_Arg, AdjCre_Glu, AdjCre_Lys, 0.717 (0.595, 0.839); AdjCre_Ile, AdjCre_Lys, AdjCre_Pro, 0.717 (0.594, 0.84); AdjCre_Ala, AdjCre_Ile, AdjCre_Pro, 0.716 (0.596, 0.837); AdjCre_Arg, AdjCre_Asn, AdjCre_Glu, 0.716 (0.596, 0.837); AdjCre_Asn, AdjCre_Glu, AdjCre_Ser, 0.716 (0.595, 0.838); AdjCre_Asn, AdjCre_Ile, AdjCre_Pro, 0.716 (0.594, 0.839); AdjCre_Gln, AdjCre_Ile, AdjCre_Pro, 0.716 (0.595, 0.837); AdjCre_Ile, AdjCre_Pro, AdjCre_Trp, 0.716 (0.594, 0.839); AdjCre_Glu, AdjCre_Ile, AdjCre_Ser, 0.716 (0.59, 0.841); AdjCre_Arg, AdjCre_Lys, AdjCre_Tyr, 0.715 (0.594, 0.837); AdjCre_Glu, AdjCre_Met, AdjCre_Thr, 0.715 (0.593, 0.838); AdjCre_Glu, AdjCre_Lys, AdjCre_Thr, 0.715 (0.593, 0.838); AdjCre_Gln, AdjCre_Met, AdjCre_Tyr, 0.715 (0.597, 0.833); AdjCre_Leu, AdjCre_Tyr, AdjCre_Val, 0.715 (0.597, 0.833); AdjCre_Met, AdjCre_Tyr, AdjCre_Val, 0.715 (0.597, 0.832); AdjCre_Gln, AdjCre_Glu, AdjCre_Ile, 0.715 (0.591, 0.839); AdjCre_Glu, AdjCre_Ile, AdjCre_Trp, 0.715 (0.591, 0.838); AdjCre_His, AdjCre_Ile, AdjCre_Pro, 0.715 (0.592, 0.838); AdjCre_Glu, AdjCre_Ser, AdjCre_Thr, 0.715 (0.593, 0.836); AdjCre_Asn, AdjCre_Glu, AdjCre_Ile, 0.714 (0.59, 0.838); AdjCre_Glu, AdjCre_Ile, AdjCre_Phe, 0.714 (0.59, 0.838); AdjCre_Glu, AdjCre_Leu, AdjCre_Phe, 0.714 (0.59, 0.838); AdjCre_Arg, AdjCre_Leu, AdjCre_Tyr, 0.713 (0.594, 0.832); AdjCre_Glu, AdjCre_Leu, AdjCre_Pro, 0.713 (0.588, 0.839); AdjCre_Ile, AdjCre_Leu, AdjCre_Pro, 0.713 (0.592, 0.835); AdjCre_Lys, AdjCre_Met, AdjCre_Tyr, 0.713 (0.593, 0.833); AdjCre_Glu, AdjCre_Lys, AdjCre_Trp, 0.713 (0.588, 0.838); AdjCre_Arg, AdjCre_Asn, AdjCre_Tyr, 0.713 (0.592, 0.833); AdjCre_Arg, AdjCre_Ile, AdjCre_Tyr, 0.713 (0.593, 0.832); AdjCre_Gln, AdjCre_Tyr, AdjCre_Val, 0.713 (0.595, 0.83); AdjCre_Arg, AdjCre_Glu, AdjCre_Ile, 0.712 (0.59, 0.834); AdjCre_Arg, AdjCre_Glu, AdjCre_Leu, 0.712 (0.59, 0.834); AdjCre_His, AdjCre_Pro, AdjCre_Gly, 0.712 (0.591, 0.834); AdjCre_Gln, AdjCre_Ile, AdjCre_Val, 0.712 (0.593, 0.83); AdjCre_His, AdjCre_Trp, AdjCre_Tyr, 0.712 (0.589, 0.835); AdjCre_Glu, AdjCre_Leu, AdjCre_Ser, 0.712 (0.586, 0.837); AdjCre_Met, AdjCre_Ser, AdjCre_Tyr, 0.711 (0.592, 0.83); AdjCre_Ala, AdjCre_Glu, AdjCre_Trp, 0.711 (0.589, 0.833); AdjCre_Arg, AdjCre_Thr, AdjCre_Tyr, 0.711 (0.591, 0.831); AdjCre_Gln, AdjCre_Glu, AdjCre_Phe, 0.711 (0.587, 0.835); AdjCre_His, AdjCre_Pro, AdjCre_Trp, 0.711 (0.589, 0.832); AdjCre_Leu, AdjCre_Met, AdjCre_Pro, 0.711 (0.589, 0.832); AdjCre_Arg, AdjCre_His, AdjCre_Tyr, 0.71 (0.591, 0.83); AdjCre_Ala, AdjCre_Glu, AdjCre_Phe, 0.71 (0.588, 0.832); AdjCre_Glu, AdjCre_His, AdjCre_Gly, 0.71 (0.589, 0.831); AdjCre_Pro, AdjCre_Ser, AdjCre_Trp, 0.71 (0.589, 0.831); AdjCre_Glu, AdjCre_His, AdjCre_Lys, 0.709 (0.586, 0.833); AdjCre_Glu, AdjCre_Leu, AdjCre_Lys, 0.709 (0.583, 0.836); AdjCre_Arg, AdjCre_Tyr, AdjCre_Val, 0.709 (0.591, 0.828); AdjCre_Ser, AdjCre_Trp, AdjCre_Tyr, 0.709 (0.587, 0.832); AdjCre_Glu, AdjCre_His, AdjCre_Trp, 0.709 (0.586, 0.832); AdjCre_Glu, AdjCre_Phe, AdjCre_Ser, 0.709 (0.585, 0.833); AdjCre_Leu, AdjCre_Pro, AdjCre_Thr, 0.709 (0.587, 0.83); AdjCre_His, AdjCre_Ile, AdjCre_Met, 0.709 (0.587, 0.83); AdjCre_Glu, AdjCre_Ile, AdjCre_Gly, 0.708 (0.583, 0.834); AdjCre_Met, AdjCre_Tyr, AdjCre_Gly, 0.708 (0.588, 0.828); AdjCre_Asn, AdjCre_Pro, AdjCre_Gly, 0.708 (0.585, 0.83); AdjCre_Ile, AdjCre_Phe, AdjCre_Val, 0.707 (0.587, 0.827); AdjCre_Tyr, AdjCre_Val, AdjCre_Gly, 0.707 (0.588, 0.826); AdjCre_Arg, AdjCre_Lys, AdjCre_Pro, 0.707 (0.585, 0.829); AdjCre_Lys, AdjCre_Pro, AdjCre_Thr, 0.707 (0.584, 0.83); AdjCre_Arg, AdjCre_His, AdjCre_Pro, 0.707 (0.584, 0.83); AdjCre_Asn, AdjCre_Glu, AdjCre_Trp, 0.707 (0.583, 0.831); AdjCre_Asn, AdjCre_Pro, AdjCre_Trp, 0.707 (0.584, 0.83); AdjCre_Glu, AdjCre_His, AdjCre_Leu, 0.707 (0.585, 0.829); AdjCre_Phe, AdjCre_Pro, AdjCre_Trp, 0.707 (0.584, 0.83); AdjCre_Ala, AdjCre_Pro, AdjCre_Trp, 0.706 (0.584, 0.828); AdjCre_Gln, AdjCre_His, AdjCre_Pro, 0.706 (0.585, 0.827); AdjCre_Glu, AdjCre_Ile, AdjCre_Leu, 0.706 (0.583, 0.829); AdjCre_Pro, AdjCre_Trp, AdjCre_Gly, 0.706 (0.584, 0.828); AdjCre_Gln, AdjCre_Pro, AdjCre_Thr, 0.706 (0.583, 0.828); AdjCre_Glu, AdjCre_Thr, AdjCre_Gly, 0.706 (0.583, 0.828); AdjCre_Gln, AdjCre_Glu, AdjCre_Lys, 0.705 (0.58, 0.831); AdjCre_Glu, AdjCre_Lys, AdjCre_Ser, 0.705 (0.58, 0.831); AdjCre_Ala, AdjCre_Asn, AdjCre_Glu, 0.705 (0.582, 0.829); AdjCre_Ala, AdjCre_Gln, AdjCre_Glu, 0.705 (0.583, 0.828); AdjCre_Arg, AdjCre_Glu, AdjCre_Met, 0.705 (0.582, 0.828); AdjCre_Asn, AdjCre_Glu, AdjCre_Phe, 0.705 (0.582, 0.828); AdjCre_Ala, AdjCre_Arg, AdjCre_Lys, 0.705 (0.583, 0.827); AdjCre_Asn, AdjCre_Glu, AdjCre_Pro, 0.704 (0.58, 0.829); AdjCre_Asn, AdjCre_His, AdjCre_Pro, 0.704 (0.582, 0.827); AdjCre_Ser, AdjCre_Tyr, AdjCre_Val, 0.704 (0.584, 0.824); AdjCre_Glu, AdjCre_Lys, AdjCre_Pro, 0.704 (0.578, 0.829); AdjCre_Glu, AdjCre_Phe, AdjCre_Trp, 0.704 (0.578, 0.829); AdjCre_His, AdjCre_Phe, AdjCre_Pro, 0.704 (0.581, 0.826); AdjCre_Glu, AdjCre_Thr, AdjCre_Val, 0.703 (0.581, 0.826); AdjCre_His, AdjCre_Tyr, AdjCre_Val, 0.703 (0.584, 0.823); AdjCre_Ala, AdjCre_Thr, AdjCre_Tyr, 0.703 (0.581, 0.824); AdjCre_Glu, AdjCre_His, AdjCre_Phe, 0.703 (0.58, 0.826); AdjCre_Glu, AdjCre_Leu, AdjCre_Val, 0.703 (0.58, 0.826); AdjCre_His, AdjCre_Lys, AdjCre_Pro, 0.703 (0.579, 0.827); AdjCre_Arg, AdjCre_Ser, AdjCre_Tyr, 0.702 (0.582, 0.823); AdjCre_Arg, AdjCre_Glu, AdjCre_His, 0.702 (0.581, 0.823); AdjCre_Ala, AdjCre_Glu, AdjCre_Leu, 0.701 (0.577, 0.825); AdjCre_Leu, AdjCre_Pro, AdjCre_Ser, 0.701 (0.579, 0.823); AdjCre_Leu, AdjCre_Thr, AdjCre_Val, 0.701 (0.581, 0.822); AdjCre_Asn, AdjCre_Glu, AdjCre_Lys, 0.701 (0.576, 0.826); AdjCre_Arg, AdjCre_Leu, AdjCre_Pro, 0.7 (0.576, 0.825); AdjCre_Glu, AdjCre_His, AdjCre_Val, 0.7 (0.579, 0.822); AdjCre_Glu, AdjCre_Met, AdjCre_Pro, 0.7 (0.575, 0.826); AdjCre_Arg, AdjCre_Asn, AdjCre_Ser, 0.7 (0.578, 0.822); AdjCre_Ile, AdjCre_Val, AdjCre_Gly, 0.7 (0.579, 0.821); AdjCre_Phe, AdjCre_Tyr, AdjCre_Gly, 0.7 (0.576, 0.824); AdjCre_Glu, AdjCre_Pro, AdjCre_Thr, 0.7 (0.577, 0.823); AdjCre_Arg, AdjCre_Glu, AdjCre_Phe, 0.7 (0.577, 0.822); AdjCre_Gln, AdjCre_Pro, AdjCre_Trp, 0.7 (0.577, 0.822).

[0209] [5: Formulas selected in Example 7] Pain_Calf, AdjCre_Ile, AdjCre_Pro, 0.815 (0.712, 0.918); Pain_Achilles Tendon, AdjCre_Ile, AdjCre_Pro, 0.814 (0.711, 0.917); Pain_Calf, AdjCre_Asn, AdjCre_Pro, 0.813 (0.708, 0.917); Pain_Achilles Tendon, AdjCre_Pro, AdjCre_Trp, 0.813 (0.71, 0.916); Pain_Achilles Tendon, AdjCre_Asn, AdjCre_Pro, 0.811 (0.708, 0.914); Pain_Achilles Tendon, AdjCre_Pro, AdjCre_Tyr, 0.811 (0.707, 0.915); Pain_Calf, AdjCre_Pro, AdjCre_Tyr, 0.81 (0.706, 0.913); Pain_Achilles Tendon, AdjCre_Met, AdjCre_Pro, 0.808 (0.704, 0.912); Pain_Achilles Tendon, AdjCre_Pro, AdjCre_Val, 0.808 (0.702, 0.913); Pain_Calf, AdjCre_Leu, AdjCre_Pro, 0.807 (0.702, 0.912); Pain_Calf, AdjCre_Pro, AdjCre_Val, 0.807 (0.701, 0.913); Pain_Knee Joint, AdjCre_Ile, AdjCre_Pro, 0.806 (0.699, 0.912); Pain_Achilles Tendon, AdjCre_Glu, AdjCre_Pro, 0.805 (0.698, 0.911); Pain_Achilles Tendon, Pain_Knee Joint, AdjCre_Pro, 0.804 (0.696, 0.912); Pain_Achilles Tendon, AdjCre_Leu, AdjCre_Pro, 0.803 (0.697, 0.909); Pain_Achilles Tendon, AdjCre_Gln, AdjCre_Pro, 0.802 (0.696, 0.908); Pain_Achilles Tendon, AdjCre_Pro, AdjCre_Ser, 0.8 (0.693, 0.907); Pain_Achilles tendon, VAS, AdjCre_Pro, 0.798 (0.69, 0.906); Pain_Achilles tendon, AdjCre_Phe, AdjCre_Pro, 0.798 (0.691, 0.905); Pain_Achilles tendon, AdjCre_Ala, AdjCre_Pro, 0.797 (0.688, 0.906); Pain_front of thigh, Pain_Achilles tendon, AdjCre_Pro, 0.795 (0.687, 0.903); Pain_Achilles tendon, AdjCre_Pro, AdjCre_Gly, 0.795 (0.686, 0.904); Pain_calf, AdjCre_Met, AdjCre_Pro, 0.794 (0.685, 0.902); Pain_Achilles tendon, AdjCre_Arg, AdjCre_Pro, 0.794 (0.684, 0.904); Pain_Knee Joint, AdjCre_Asn, AdjCre_Pro, 0.794 (0.685, 0.903); Pain_Knee Joint, AdjCre_Met, AdjCre_Pro, 0.794 (0.686, 0.902); Pain_Calf, VAS, AdjCre_Pro, 0.793 (0.684, 0.901); Pain_Knee Joint, AdjCre_Pro, AdjCre_Val, 0.793 (0.682, 0.903); Pain_Achilles Tendon, AdjCre_His, AdjCre_Pro, 0.791 (0.682, 0.9); Pain_Front of Thigh, AdjCre_Asn, AdjCre_Pro, 0.79 (0.681, 0.898); Pain_Calf, Pain_Achilles Tendon, AdjCre_Pro, 0.789 (0.68, 0.897); AdjCre_Arg, AdjCre_Pro, AdjCre_Val, 0.788 (0.673, 0.903); Pain_Achilles Tendon, AdjCre_Pro, AdjCre_Thr, 0.787 (0.674, 0.9); Pain_Front of Thigh, AdjCre_Ile, AdjCre_Pro, 0.787 (0.675, 0.899); Pain_Knee Joint, AdjCre_Leu, AdjCre_Pro, 0.786 (0.675, 0.896); Pain_Achilles tendon, AdjCre_Lys, AdjCre_Pro, 0.785 (0.671, 0.899); AdjCre_Asn, AdjCre_Pro, AdjCre_Thr, 0.784 (0.671, 0.897); Pain_Achilles Tendon, RPE, AdjCre_Pro, 0.783 (0.67, 0.896); Pain_Calf, AdjCre_Pro, AdjCre_Trp, 0.783 (0.672, 0.893); Pain_Front of Thigh, AdjCre_Pro, AdjCre_Tyr, 0.782 (0.671, 0.893); Pain_Calf, AdjCre_Pro, AdjCre_Ser, 0.782 (0.671, 0.892); Pain_Knee Joint, AdjCre_Pro, AdjCre_Tyr, 0.782 (0.669, 0.895); AdjCre_Asn, AdjCre_Ile, AdjCre_Pro, 0.782 (0.666, 0.897); AdjCre_Asn, AdjCre_Pro, AdjCre_Gly, 0.782 (0.67, 0.894); AdjCre_Ile, AdjCre_Pro, AdjCre_Tyr, 0.782 (0.666, 0.898); AdjCre_Pro, AdjCre_Tyr, AdjCre_Gly, 0.782 (0.663, 0.901); AdjCre_Arg, AdjCre_Leu, AdjCre_Pro, 0.781 (0.666, 0.896); Pain_Front of Thigh, AdjCre_Leu, AdjCre_Pro, 0.78 (0.668, 0.892); Pain_Knee_Joint, AdjCre_Pro, AdjCre_Trp, 0.78 (0.667, 0.892); AdjCre_His, AdjCre_Pro, AdjCre_Tyr, 0.78 (0.663, 0.897); AdjCre_Ile, AdjCre_Pro, AdjCre_Trp, 0.78 (0.663, 0.897); AdjCre_Lys, AdjCre_Pro, AdjCre_Val, 0.779 (0.659, 0.899); AdjCre_Glu, AdjCre_Ile, AdjCre_Pro, 0.779 (0.664, 0.894); AdjCre_Ile, AdjCre_Leu, AdjCre_Pro, 0.779 (0.662, 0.896); AdjCre_Pro, AdjCre_Val, AdjCre_Gly, 0.779 (0.657, 0.9); Pain_Calf, AdjCre_Gln, AdjCre_Pro, 0.778 (0.667, 0.889); AdjCre_Ala, AdjCre_Ile, AdjCre_Pro, 0.778 (0.661, 0.894); AdjCre_Arg, AdjCre_Ile, AdjCre_Pro, 0.778 (0.662, 0.894); Pain_Front of Thigh, AdjCre_Pro, AdjCre_Val, 0.777 (0.663, 0.89); AdjCre_Gln, AdjCre_Ile, AdjCre_Pro, 0.777 (0.66, 0.894); AdjCre_Ile, AdjCre_Pro, AdjCre_Ser, 0.777 (0.66, 0.894); AdjCre_Ile, AdjCre_Pro, AdjCre_Gly, 0.777 (0.659, 0.894); AdjCre_Ile, AdjCre_Pro, AdjCre_Thr, 0.777 (0.661, 0.893); Pain_Knee_Joint, AdjCre_Pro, AdjCre_Ser, 0.776 (0.663, 0.888); AdjCre_His, AdjCre_Ile, AdjCre_Pro, 0.776 (0.659, 0.893); AdjCre_Ile, AdjCre_Phe, AdjCre_Pro, 0.776 (0.659, 0.892); AdjCre_Pro, AdjCre_Thr, AdjCre_Val, 0.776 (0.659, 0.892); AdjCre_Ile, AdjCre_Lys, AdjCre_Pro, 0.776 (0.658, 0.893); Pain_Front of Thigh, AdjCre_Met, AdjCre_Pro, 0.775 (0.663, 0.887); AdjCre_His, AdjCre_Pro, AdjCre_Val, 0.775 (0.656, 0.894); AdjCre_Arg, AdjCre_Asn, AdjCre_Gly, 0.774 (0.661, 0.886); VAS, AdjCre_Ile, AdjCre_Pro, 0.774 (0.657, 0.891); AdjCre_Ile, AdjCre_Met, AdjCre_Pro, 0.774 (0.656, 0.892); AdjCre_Pro, AdjCre_Thr, AdjCre_Tyr, 0.774 (0.657, 0.89); Pain_Front of Thigh, AdjCre_Pro, AdjCre_Trp, 0.773 (0.659, 0.887); Pain_Calf, AdjCre_Glu, AdjCre_Pro, 0.773 (0.66, 0.886); Pain_Knee Joint, AdjCre_Glu, AdjCre_Pro, 0.773 (0.659, 0.887); AdjCre_Ile, AdjCre_Pro, AdjCre_Val, 0.773 (0.655, 0.891); AdjCre_Asn, AdjCre_Lys, AdjCre_Pro, 0.772 (0.656, 0.889); Pain_Calf, RPE, AdjCre_Pro, 0.772 (0.656, 0.889); Pain_Achilles Tendon, AdjCre_Asn, AdjCre_Gly, 0.772 (0.656, 0.888); AdjCre_Asn, AdjCre_Pro, AdjCre_Val, 0.772 (0.656, 0.888); AdjCre_Pro, AdjCre_Trp, AdjCre_Gly, 0.772 (0.647, 0.896); Pain_Calf, AdjCre_His, AdjCre_Pro, 0.771 (0.658, 0.884); AdjCre_Arg, AdjCre_Asn, AdjCre_Pro, 0.77 (0.655, 0.884); AdjCre_Leu, AdjCre_Pro, AdjCre_Val, 0.77 (0.651, 0.889); AdjCre_Met, AdjCre_Pro, AdjCre_Gly, 0.77 (0.651, 0.889); AdjCre_Phe, AdjCre_Pro, AdjCre_Val, 0.77 (0.651, 0.888); AdjCre_Pro, AdjCre_Ser, AdjCre_Val, 0.77 (0.65, 0.889); AdjCre_Arg, AdjCre_Pro, AdjCre_Tyr, 0.769 (0.652, 0.886); AdjCre_Asn, AdjCre_Pro, AdjCre_Ser, 0.769 (0.654, 0.884); AdjCre_His, AdjCre_Pro, AdjCre_Trp, 0.769 (0.647, 0.891); AdjCre_Lys, AdjCre_Pro, AdjCre_Tyr, 0.768 (0.65, 0.887); Pain_Front of Thigh, VAS, AdjCre_Pro, 0.768 (0.653, 0.883); Pain_Knee Joint, VAS, AdjCre_Pro, 0.768 (0.651, 0.885); VAS, AdjCre_Pro, AdjCre_Val, 0.768 (0.649, 0.887); AdjCre_Gln, AdjCre_Pro, AdjCre_Val, 0.768 (0.648, 0.888); AdjCre_Leu, AdjCre_Lys, AdjCre_Pro, 0.767 (0.647, 0.887); Pain_Calf, AdjCre_Phe, AdjCre_Pro, 0.767 (0.653, 0.881); AdjCre_Leu, AdjCre_Pro, AdjCre_Gly, 0.767 (0.648, 0.886); AdjCre_Met, AdjCre_Pro, AdjCre_Val, 0.767 (0.647, 0.886); AdjCre_Ala, AdjCre_Pro, AdjCre_Val, 0.766 (0.646, 0.886); Pain_Front of Thigh, AdjCre_Asn, AdjCre_Gly, 0.765 (0.646, 0.884); AdjCre_Asn, AdjCre_His, AdjCre_Pro, 0.765 (0.649, 0.88); AdjCre_Gln, AdjCre_Pro, AdjCre_Tyr, 0.765 (0.646, 0.884); AdjCre_Glu, AdjCre_Pro, AdjCre_Val, 0.765 (0.644, 0.885); AdjCre_Asn, AdjCre_Lys, AdjCre_Gly, 0.764 (0.644, 0.885); Pain_Calf, AdjCre_Ala, AdjCre_Pro, 0.764 (0.649, 0.879); Pain_Knee Joint, AdjCre_Gln, AdjCre_Pro, 0.764 (0.648, 0.879); AdjCre_Arg, AdjCre_Met, AdjCre_Pro, 0.764 (0.646, 0.882); AdjCre_Pro, AdjCre_Trp, AdjCre_Val, 0.764 (0.643, 0.884); AdjCre_Pro, AdjCre_Tyr, AdjCre_Val, 0.764 (0.644, 0.883); Pain_Front of Thigh, AdjCre_Pro, AdjCre_Ser, 0.763 (0.648, 0.878); AdjCre_Asn, AdjCre_Glu, AdjCre_Pro, 0.763 (0.647, 0.879); Pain_Front of Thigh, AdjCre_Phe, AdjCre_Pro, 0.762 (0.647, 0.877); RPE, AdjCre_Ile, AdjCre_Pro, 0.762 (0.639, 0.884); Pain_Calf, AdjCre_Asn, AdjCre_Gly, 0.761 (0.643, 0.88); AdjCre_Gln, AdjCre_Pro, AdjCre_Gly, 0.761 (0.642, 0.88); AdjCre_Leu, AdjCre_Pro, AdjCre_Thr, 0.76 (0.642, 0.879); Pain_Achilles Tendon, AdjCre_Ile, AdjCre_Gly, 0.76 (0.642, 0.878); AdjCre_Asn, AdjCre_Leu, AdjCre_Pro, 0.76 (0.642, 0.877); AdjCre_Asn, AdjCre_Pro, AdjCre_Tyr, 0.76 (0.642, 0.878); Pain_Calf, AdjCre_Ile, AdjCre_Gly, 0.759 (0.639, 0.879); Pain_Achilles Tendon, AdjCre_Tyr, AdjCre_Gly, 0.759 (0.641, 0.878); AdjCre_Met, AdjCre_Pro, AdjCre_Thr, 0.759 (0.642, 0.877); AdjCre_Phe, AdjCre_Pro, AdjCre_Tyr, 0.759 (0.64, 0.877); AdjCre_Pro, AdjCre_Ser, AdjCre_Tyr, 0.759 (0.639, 0.878); Pain_Calf, AdjCre_Val, AdjCre_Gly, 0.758 (0.638, 0.879); Pain_Front of Thigh, Pain_Knee Joint, AdjCre_Pro, 0.758 (0.641, 0.874); Pain_Front of Thigh, AdjCre_Gln, AdjCre_Pro, 0.758 (0.642, 0.874); Pain_Calf, AdjCre_Arg, AdjCre_Pro, 0.758 (0.642, 0.874); Pain_Knee Joint, AdjCre_Phe, AdjCre_Pro, 0.758 (0.64, 0.876); AdjCre_Ala, AdjCre_Pro, AdjCre_Tyr, 0.758 (0.637, 0.879); AdjCre_Asn, AdjCre_Gln, AdjCre_Pro, 0.758 (0.64, 0.875); AdjCre_Leu, AdjCre_Pro, AdjCre_Trp, 0.758 (0.638, 0.878); AdjCre_Leu, AdjCre_Pro, AdjCre_Tyr, 0.758 (0.639, 0.877); Pain_Front of Thigh, AdjCre_Val, AdjCre_Gly, 0.757 (0.636, 0.879); Pain_Calf, AdjCre_Tyr, AdjCre_Gly, 0.757 (0.637, 0.878); AdjCre_Asn, AdjCre_Ile, AdjCre_Gly, 0.757 (0.638, 0.877); Pain_Front of Thigh, Pain_Calf, AdjCre_Pro, 0.757 (0.641, 0.873); AdjCre_Asn, AdjCre_Met, AdjCre_Pro, 0.757 (0.638, 0.875); AdjCre_Asn, AdjCre_Phe, AdjCre_Pro, 0.757 (0.64, 0.874); Pain_Calf, Pain_Knee Joint, AdjCre_Pro, 0.756 (0.639, 0.873); Pain_Knee Joint, AdjCre_His, AdjCre_Pro, 0.756 (0.638, 0.873); Pain_Knee Joint, AdjCre_Pro, AdjCre_Gly, 0.756 (0.638, 0.874); AdjCre_Asn, AdjCre_Thr, AdjCre_Gly, 0.756 (0.638, 0.874); AdjCre_Glu, AdjCre_Pro, AdjCre_Tyr, 0.756 (0.636, 0.875); Pain_Front of Thigh, AdjCre_Glu, AdjCre_Pro, 0.755 (0.638, 0.872); Pain_Knee Joint, AdjCre_Arg, AdjCre_Pro, 0.755 (0.636, 0.873); AdjCre_Asn, AdjCre_Pro, AdjCre_Trp, 0.755 (0.636, 0.874); AdjCre_Pro, AdjCre_Trp, AdjCre_Tyr, 0.755 (0.634, 0.875); AdjCre_Pro, AdjCre_Thr, AdjCre_Trp, 0.754 (0.633, 0.875); Pain_Calf, AdjCre_Pro, AdjCre_Gly, 0.754 (0.637, 0.871); AdjCre_Ala, AdjCre_Asn, AdjCre_Pro, 0.754 (0.636, 0.872); RPE, AdjCre_Pro, AdjCre_Val, 0.753 (0.629, 0.878); Pain_Calf, AdjCre_Pro, AdjCre_Thr, 0.753 (0.635, 0.871); AdjCre_Pro, AdjCre_Ser, AdjCre_Thr, 0.753 (0.633, 0.873); Pain_Front of Thigh, AdjCre_His, AdjCre_Pro, 0.753 (0.635, 0.87); AdjCre_Asn, AdjCre_Glu, AdjCre_Gly, 0.753 (0.633, 0.873); AdjCre_His, AdjCre_Leu, AdjCre_Pro, 0.753 (0.632, 0.873); AdjCre_His, AdjCre_Met, AdjCre_Pro, 0.753 (0.633, 0.872); AdjCre_Phe, AdjCre_Pro, AdjCre_Trp, 0.753 (0.63, 0.875); Pain_Calf, AdjCre_Lys, AdjCre_Pro, 0.752 (0.632, 0.872); Pain_Knee_Joint, AdjCre_Ala, AdjCre_Pro, 0.752 (0.633, 0.871); VAS, AdjCre_Asn, AdjCre_Pro, 0.752 (0.633, 0.871); AdjCre_Arg, AdjCre_Pro, AdjCre_Ser, 0.752 (0.633, 0.871); AdjCre_Arg, AdjCre_Pro, AdjCre_Trp, 0.752 (0.629, 0.874); AdjCre_Met, AdjCre_Pro, AdjCre_Tyr, 0.752 (0.631, 0.873); VAS, AdjCre_Pro, AdjCre_Tyr, 0.751 (0.629, 0.873); AdjCre_Gln, AdjCre_Leu, AdjCre_Pro, 0.751 (0.631, 0.871); AdjCre_Glu, AdjCre_Leu, AdjCre_Pro, 0.751 (0.631, 0.87); Pain_Front of Thigh, AdjCre_Arg, AdjCre_Pro, 0.75 (0.632, 0.868); AdjCre_Glu, AdjCre_Pro, AdjCre_Ser, 0.75 (0.631, 0.868); AdjCre_Glu, AdjCre_Pro, AdjCre_Trp, 0.75 (0.629, 0.87); AdjCre_Met, AdjCre_Phe, AdjCre_Pro, 0.75 (0.63, 0.87); Pain_Achilles tendon, AdjCre_Val, AdjCre_Gly, 0.75 (0.628, 0.871); AdjCre_Lys, AdjCre_Pro, AdjCre_Ser, 0.749 (0.628, 0.871); AdjCre_Gln, AdjCre_Pro, AdjCre_Thr, 0.749 (0.627, 0.871); AdjCre_Leu, AdjCre_Pro, AdjCre_Ser, 0.749 (0.628, 0.869); AdjCre_Met, AdjCre_Pro, AdjCre_Ser, 0.749 (0.628, 0.869); AdjCre_Met, AdjCre_Pro, AdjCre_Trp, 0.749 (0.627, 0.871); Pain_Calf, AdjCre_Ser, AdjCre_Gly, 0.749 (0.629, 0.869); AdjCre_Lys, AdjCre_Met, AdjCre_Pro, 0.748 (0.626, 0.871); Pain_Knee Joint, AdjCre_Pro, AdjCre_Thr, 0.748 (0.628, 0.868); Pain_Knee Joint, RPE, AdjCre_Pro, 0.748 (0.625, 0.871); Pain_Front of Thigh, AdjCre_Ala, AdjCre_Pro, 0.748 (0.629, 0.866); Pain_Achilles_Tendon, AdjCre_Glu, AdjCre_Gly, 0.748 (0.627, 0.868); VAS, AdjCre_Pro, AdjCre_Trp, 0.748 (0.625, 0.871); AdjCre_Ala, AdjCre_Leu, AdjCre_Pro, 0.748 (0.627, 0.869); AdjCre_Leu, AdjCre_Phe, AdjCre_Pro, 0.748 (0.627, 0.869); AdjCre_Asn, AdjCre_His, AdjCre_Gly, 0.748 (0.628, 0.867); Pain_Front of thigh, AdjCre_Ile, AdjCre_Gly, 0.747 (0.623, 0.87); VAS, AdjCre_Asn, AdjCre_Gly, 0.747 (0.625, 0.868); Pain_Front of Thigh, AdjCre_Pro, AdjCre_Gly, 0.746 (0.627, 0.865); AdjCre_Ala, AdjCre_Pro, AdjCre_Trp, 0.746 (0.62, 0.871); AdjCre_Gln, AdjCre_Pro, AdjCre_Trp, 0.746 (0.623, 0.868); AdjCre_Glu, AdjCre_Met, AdjCre_Pro, 0.746 (0.626, 0.866); AdjCre_Pro, AdjCre_Ser, AdjCre_Trp, 0.746 (0.623, 0.868); Pain_Front of Thigh, RPE, AdjCre_Pro, 0.746 (0.625, 0.867); Pain_Front of Thigh, AdjCre_Tyr, AdjCre_Gly, 0.746 (0.621, 0.871); Pain_Achilles Tendon, AdjCre_Gln, AdjCre_Gly, 0.746 (0.624, 0.867); AdjCre_Asn, AdjCre_Phe, AdjCre_Gly, 0.746 (0.625, 0.867); AdjCre_Phe, AdjCre_Tyr, AdjCre_Gly, 0.746 (0.621, 0.87); AdjCre_Lys, AdjCre_Pro, AdjCre_Trp, 0.745 (0.621, 0.869); VAS, AdjCre_Leu, AdjCre_Pro, 0.745 (0.623, 0.866); AdjCre_Ala, AdjCre_Met, AdjCre_Pro, 0.745 (0.624, 0.866); Pain_Achilles Tendon, AdjCre_Trp, AdjCre_Gly, 0.745 (0.62, 0.869); Pain_Knee Joint, AdjCre_Asn, AdjCre_Gly, 0.745 (0.624, 0.865); AdjCre_Gln, AdjCre_His, AdjCre_Pro, 0.744 (0.623, 0.865); AdjCre_Gln, AdjCre_Met, AdjCre_Pro, 0.744 (0.622, 0.865); AdjCre_His, AdjCre_Pro, AdjCre_Gly, 0.744 (0.623, 0.865); AdjCre_Leu, AdjCre_Met, AdjCre_Pro, 0.744 (0.622, 0.866); AdjCre_Arg, AdjCre_Tyr, AdjCre_Gly, 0.744 (0.623, 0.864); AdjCre_Arg, AdjCre_Val, AdjCre_Gly, 0.744 (0.62, 0.867); RPE, AdjCre_Asn, AdjCre_Pro, 0.744 (0.622, 0.866); AdjCre_Lys, AdjCre_Val, AdjCre_Gly, 0.743 (0.618, 0.868); VAS, AdjCre_Met, AdjCre_Pro, 0.743 (0.621, 0.865); AdjCre_Gln, AdjCre_Glu, AdjCre_Pro, 0.743 (0.623, 0.862); AdjCre_Pro, AdjCre_Ser, AdjCre_Gly, 0.743 (0.621, 0.865); Pain_Achilles Tendon, AdjCre_His, AdjCre_Gly, 0.743 (0.621, 0.864); AdjCre_Ala, AdjCre_Asn, AdjCre_Gly, 0.743 (0.621, 0.864); AdjCre_Glu, AdjCre_Lys, AdjCre_Pro, 0.742 (0.62, 0.864); AdjCre_Asn, AdjCre_Met, AdjCre_Gly, 0.742 (0.62, 0.863); AdjCre_Asn, AdjCre_Val, AdjCre_Gly, 0.742 (0.62, 0.864); Pain_Front of Thigh, AdjCre_Lys, AdjCre_Pro, 0.741 (0.619, 0.863); Pain_Knee Joint, AdjCre_Lys, AdjCre_Pro, 0.741 (0.617, 0.864); VAS, AdjCre_Glu, AdjCre_Pro, 0.741 (0.62, 0.861); VAS, AdjCre_Pro, AdjCre_Ser, 0.741 (0.619, 0.863); AdjCre_Ala, AdjCre_Gln, AdjCre_Pro, 0.741 (0.619, 0.862); AdjCre_Arg, AdjCre_Gln, AdjCre_Pro, 0.741 (0.619, 0.862); AdjCre_Glu, AdjCre_Tyr, AdjCre_Gly, 0.741 (0.617, 0.864); Pain_Calf, AdjCre_Gln, AdjCre_Gly, 0.74 (0.617, 0.862); AdjCre_Asn, AdjCre_Trp, AdjCre_Gly, 0.74 (0.618, 0.861); VAS, AdjCre_Gln, AdjCre_Pro, 0.74 (0.618, 0.861); AdjCre_Arg, AdjCre_Glu, AdjCre_Pro, 0.74 (0.619, 0.86); AdjCre_Gln, AdjCre_Phe, AdjCre_Pro, 0.74 (0.618, 0.862); AdjCre_His, AdjCre_Pro, AdjCre_Ser, 0.74 (0.618, 0.862); Pain_Front of Thigh, AdjCre_Pro, AdjCre_Thr, 0.74 (0.618, 0.861); VAS, AdjCre_Lys, AdjCre_Pro, 0.74 (0.617, 0.862); Pain_Front of Thigh, AdjCre_Ser, AdjCre_Gly, 0.739 (0.614, 0.863); Pain_Calf, AdjCre_Trp, AdjCre_Gly, 0.739 (0.614, 0.864); Pain_Achilles Tendon, AdjCre_Met, AdjCre_Gly, 0.739 (0.617, 0.861); AdjCre_Asn, AdjCre_Gln, AdjCre_Gly, 0.739 (0.617, 0.861); AdjCre_Ala, AdjCre_Glu, AdjCre_Pro, 0.739 (0.618, 0.86); AdjCre_Gln, AdjCre_Pro, AdjCre_Ser, 0.739 (0.617, 0.861); AdjCre_Glu, AdjCre_Pro, AdjCre_Thr, 0.739 (0.617, 0.86); RPE, AdjCre_Pro, AdjCre_Tyr, 0.738 (0.612, 0.864); RPE, AdjCre_Asn, AdjCre_Gly, 0.738 (0.613, 0.864); Pain_Front of Thigh, AdjCre_Gln, AdjCre_Gly, 0.738 (0.613, 0.862); Pain_Calf, VAS, AdjCre_Gly, 0.738 (0.613, 0.862); Pain_Calf, AdjCre_His, AdjCre_Gly, 0.738 (0.616, 0.86); Pain_Front of Thigh, AdjCre_Trp, AdjCre_Gly, 0.737 (0.61, 0.863); Pain_Calf, AdjCre_Leu, AdjCre_Gly, 0.737 (0.613, 0.861); Pain_Achilles Tendon, AdjCre_Leu, AdjCre_Gly, 0.737 (0.614, 0.86); AdjCre_Glu, AdjCre_His, AdjCre_Pro, 0.737 (0.616, 0.857); AdjCre_Gln, AdjCre_Lys, AdjCre_Pro, 0.737 (0.613, 0.86); Pain_Front of Thigh, AdjCre_Leu, AdjCre_Gly, 0.736 (0.612, 0.86); Pain_Calf, AdjCre_Met, AdjCre_Gly, 0.736 (0.613, 0.859); AdjCre_Asn, AdjCre_Leu, AdjCre_Gly, 0.736 (0.613, 0.858); AdjCre_Asn, AdjCre_Ser, AdjCre_Gly, 0.736 (0.614, 0.857); AdjCre_Phe, AdjCre_Pro, AdjCre_Ser, 0.736 (0.613, 0.859); Pain_Front of thigh, Pain_Achilles tendon, AdjCre_Gly, 0.735 (0.611, 0.859); Pain_Achilles Tendon, AdjCre_Ser, AdjCre_Gly, 0.735 (0.612, 0.858); AdjCre_Arg, AdjCre_Asn, AdjCre_His, 0.735 (0.607, 0.863); AdjCre_Gln, AdjCre_Ile, AdjCre_Gly, 0.735 (0.609, 0.861); AdjCre_Asn, AdjCre_Tyr, AdjCre_Gly, 0.734 (0.61, 0.858); AdjCre_Glu, AdjCre_Phe, AdjCre_Pro, 0.734 (0.612, 0.856); AdjCre_Glu, AdjCre_Pro, AdjCre_Gly, 0.734 (0.612, 0.856); AdjCre_Phe, AdjCre_Pro, AdjCre_Thr, 0.734 (0.61, 0.857); AdjCre_Lys, AdjCre_Phe, AdjCre_Pro, 0.733 (0.609, 0.858); AdjCre_Lys, AdjCre_Pro, AdjCre_Thr, 0.733 (0.61, 0.857); Pain_Front of Thigh, VAS, AdjCre_Gly, 0.733 (0.608, 0.858); AdjCre_Ile, AdjCre_Ser, AdjCre_Gly, 0.733 (0.608, 0.857); RPE, AdjCre_Leu, AdjCre_Pro, 0.733 (0.607, 0.859); VAS, AdjCre_Pro, AdjCre_Thr, 0.733 (0.609, 0.856); AdjCre_Ala, AdjCre_Pro, AdjCre_Thr, 0.733 (0.61, 0.855); AdjCre_Ile, AdjCre_Lys, AdjCre_Gly, 0.733 (0.607, 0.858); AdjCre_Arg, AdjCre_Lys, AdjCre_Pro, 0.732 (0.608, 0.856); Pain_Front of Thigh, AdjCre_His, AdjCre_Gly, 0.732 (0.607, 0.857); Pain_Achilles tendon, Pain_Knee joint, AdjCre_Gly, 0.732 (0.608, 0.856); Pain_Knee Joint, AdjCre_Gln, AdjCre_Gly, 0.732 (0.608, 0.856); Pain_Knee Joint, AdjCre_Tyr, AdjCre_Gly, 0.732 (0.61, 0.854); AdjCre_Gln, AdjCre_Tyr, AdjCre_Gly, 0.732 (0.608, 0.856); AdjCre_His, AdjCre_Phe, AdjCre_Pro, 0.732 (0.609, 0.855); AdjCre_Phe, AdjCre_Pro, AdjCre_Gly, 0.732 (0.608, 0.855); Pain_Calf, Pain_Achilles Tendon, AdjCre_Gly, 0.731 (0.607, 0.855); Pain_Achilles Tendon, VAS, AdjCre_Gly, 0.731 (0.608, 0.854); VAS, AdjCre_Ala, AdjCre_Pro, 0.731 (0.608, 0.854); AdjCre_Ala, AdjCre_Pro, AdjCre_Gly, 0.731 (0.607, 0.854); AdjCre_Arg, AdjCre_Pro, AdjCre_Thr, 0.73 (0.607, 0.854); Pain_Achilles Tendon, AdjCre_Arg, AdjCre_Gly, 0.73 (0.607, 0.853); AdjCre_Ala, AdjCre_Tyr, AdjCre_Gly, 0.73 (0.605, 0.855); AdjCre_Pro, AdjCre_Thr, AdjCre_Gly, 0.729 (0.606, 0.853); Pain_Knee_Joint, AdjCre_Ile, AdjCre_Gly, 0.729 (0.605, 0.853); Pain_Knee_Joint, AdjCre_Met, AdjCre_Gly, 0.729 (0.605, 0.853); AdjCre_Ser, AdjCre_Tyr, AdjCre_Gly, 0.729 (0.605, 0.853); AdjCre_Arg, AdjCre_Phe, AdjCre_Pro, 0.729 (0.605, 0.852); AdjCre_Gln, AdjCre_Thr, AdjCre_Gly, 0.729 (0.604, 0.853); RPE, AdjCre_Pro, AdjCre_Trp, 0.729 (0.6, 0.857); VAS, AdjCre_Tyr, AdjCre_Gly, 0.728 (0.602, 0.854); AdjCre_His, AdjCre_Lys, AdjCre_Pro, 0.728 (0.603, 0.853); AdjCre_Lys, AdjCre_Pro, AdjCre_Gly, 0.728 (0.603, 0.853); VAS, AdjCre_Phe, AdjCre_Pro, 0.728 (0.603, 0.852); VAS, AdjCre_Pro, AdjCre_Gly, 0.728 (0.605, 0.851); AdjCre_Ala, AdjCre_Phe, AdjCre_Pro, 0.728 (0.604, 0.851); AdjCre_Thr, AdjCre_Tyr, AdjCre_Gly, 0.728 (0.603, 0.853); AdjCre_Ala, AdjCre_Lys, AdjCre_Pro, 0.727 (0.602, 0.852); AdjCre_Ala, AdjCre_His, AdjCre_Pro, 0.727 (0.603, 0.85); AdjCre_Arg, AdjCre_Pro, AdjCre_Gly, 0.727 (0.603, 0.85); RPE, AdjCre_Met, AdjCre_Pro, 0.726 (0.599, 0.854); Pain_Front of Thigh, AdjCre_Met, AdjCre_Gly, 0.726 (0.6, 0.852); Pain_Achilles Tendon, AdjCre_Ala, AdjCre_Gly, 0.726 (0.602, 0.85); Pain_Knee Joint, AdjCre_Ser, AdjCre_Gly, 0.726 (0.603, 0.85); Pain_Knee Joint, AdjCre_Val, AdjCre_Gly, 0.726 (0.602, 0.85); AdjCre_Glu, AdjCre_Val, AdjCre_Gly, 0 .726 (0.599, 0.852); AdjCre_Ala, AdjCre_Arg, AdjCre_Asn, 0.725 (0.602, 0.849); AdjCre_Tyr, AdjCre_Val, AdjCre_Gly, 0.725 (0.6, 0.85); VAS, AdjCre_Arg, AdjCre_Pro, 0.725 (0.601, 0.849); AdjCre_Ala, AdjCre_Pro, AdjCre_Ser, 0.725 (0.6, 0.849); RPE, AdjCre_Pro, AdjCre_Ser, 0.724 (0.597, 0.852); Pain_Achilles tendon, AdjCre_Lys, AdjCre_Gly, 0.724 (0.597, 0.852); VAS, AdjCre_Val, AdjCre_Gly, 0.724 (0.597, 0.851); AdjCre_Arg, AdjCre_Leu, AdjCre_Gly, 0.724 (0.6, 0.849); AdjCre_Gln, AdjCre_Phe, AdjCre_Gly, 0.724 (0.597, 0.851); VAS, AdjCre_His, AdjCre_Pro, 0.724 (0.599, 0.848); AdjCre_His, AdjCre_Pro, AdjCre_Thr, 0.723 (0.597, 0.849); AdjCre_Lys, AdjCre_Tyr, AdjCre_Gly, 0.723 (0.596, 0.851); AdjCre_Arg, AdjCre_Ile, AdjCre_Gly, 0.723 (0.598, 0.849); AdjCre_Met, AdjCre_Tyr, AdjCre_Gly, 0.723 (0.597, 0.849); AdjCre_Ala, AdjCre_Arg, AdjCre_Pro, 0.723 (0.598, 0.848); AdjCre_Arg, AdjCre_His, AdjCre_Pro, 0.723 (0.598, 0.847); Pain_knee joint, AdjCre_His, AdjCre_Gly, 0.722 (0.597, 0.847); AdjCre_Gln, AdjCre_Glu, AdjCre_Gly, 0.722 (0.593, 0.85); AdjCre_Thr, AdjCre_Val, AdjCre_Gly, 0.722 (0.595, 0.848); Pain_Achilles Tendon, RPE, AdjCre_Gly, 0.721 (0.595, 0.848); AdjCre_Gln, AdjCre_Trp, AdjCre_Gly, 0.721 (0.594, 0.849); AdjCre_Phe, AdjCre_Val, AdjCre_Gly, 0.721 (0.594, 0.849); Pain_Achilles Tendon, AdjCre_Thr, AdjCre_Gly, 0.721 (0.595, 0.846); AdjCre_Ser, AdjCre_Thr, AdjCre_Gly, 0.721 (0.596, 0.845); Pain_Achilles Tendon, AdjCre_Phe, AdjCre_Gly, 0.72 (0.594, 0.846); AdjCre_Ala, AdjCre_Arg, AdjCre_Tyr, 0.72 (0.595, 0.846); AdjCre_Gln, AdjCre_Met, AdjCre_Gly, 0.72 (0.593, 0.847); AdjCre_Gln, AdjCre_Val, AdjCre_Gly, 0.72 (0.593, 0.848); AdjCre_Leu, AdjCre_Tyr, AdjCre_Gly, 0.72 (0.594, 0.847); RPE, AdjCre_Ala, AdjCre_Pro, 0.72 (0.592, 0.848); RPE, AdjCre_Gln, AdjCre_Pro, 0.72 (0.593, 0.848); RPE, AdjCre_Glu, AdjCre_Pro, 0.72 (0.593, 0.847); Pain_Calf, AdjCre_Glu, AdjCre_Gly, 0.72 (0.594, 0.845); AdjCre_Ile, AdjCre_Thr, AdjCre_Gly, 0.72 (0.594, 0.846); AdjCre_Ala, AdjCre_Gln, AdjCre_Gly, 0.719 (0.591, 0.848); AdjCre_Gln, AdjCre_Leu, AdjCre_Gly, 0.719 (0.592, 0.847); AdjCre_His, AdjCre_Tyr, AdjCre_Gly, 0.719 (0.593, 0.846); AdjCre_Leu, AdjCre_Ser, AdjCre_Gly, 0.719 (0.593, 0.845); AdjCre_Met, AdjCre_Ser, AdjCre_Gly, 0.719 (0.593, 0.845); AdjCre_Ser, AdjCre_Val, AdjCre_Gly, 0.719 (0.593, 0.846); AdjCre_Asn, AdjCre_His, AdjCre_Lys, 0.719 (0.586, 0.853); AdjCre_Lys, AdjCre_Ser, AdjCre_Gly, 0.719 (0.592, 0.847); Pain_Knee_Joint, AdjCre_Trp, AdjCre_Gly, 0.718 (0.592, 0.845); AdjCre_Ile, AdjCre_Tyr, AdjCre_Gly, 0.718 (0.592, 0.845); AdjCre_Met, AdjCre_Val, AdjCre_Gly, 0.718 (0.592, 0.845); AdjCre_Glu, AdjCre_Ile, AdjCre_Gly, 0.718 (0.59, 0.845); AdjCre_His, AdjCre_Ile, AdjCre_Gly, 0.717 (0.59, 0.845); AdjCre_Leu, AdjCre_Val, AdjCre_Gly, 0.717 (0.59, 0.844); AdjCre_Trp, AdjCre_Tyr, AdjCre_Gly, 0.717 (0.593, 0.842); Pain_Calf, RPE, AdjCre_Gly, 0.717 (0.588, 0.846); Pain_Front of Thigh, AdjCre_Glu, AdjCre_Gly, 0.717 (0.589, 0.844); Pain_Knee Joint, AdjCre_Leu, AdjCre_Gly, 0.716 (0.591, 0.842); VAS, AdjCre_Ile, AdjCre_Gly, 0.716 (0.589, 0.844); Pain_Achilles tendon, AdjCre_Asn, AdjCre_His, 0.715 (0.589, 0.842); AdjCre_His, AdjCre_Val, AdjCre_Gly, 0.715 (0.587, 0.843); AdjCre_Asn, AdjCre_His, AdjCre_Thr, 0.715 (0.586, 0.844); VAS, AdjCre_Gln, AdjCre_Gly, 0.714 (0.586, 0.843); Pain_Knee joint, RPE, AdjCre_Gly, 0.714 (0.585, 0.843); RPE, AdjCre_Lys, AdjCre_Pro, 0.714 (0.583, 0.844); VAS, RPE, AdjCre_Pro, 0.714 (0.585, 0.843); RPE, AdjCre_Tyr, AdjCre_Gly, 0.713 (0.583, 0.843); Pain_Calf, VAS, AdjCre_Thr, 0.713 (0.585, 0.84); Pain_Achilles Tendon, AdjCre_Ala, AdjCre_Glu, 0.713 (0.587, 0.838); Pain_Achilles Tendon, AdjCre_Glu, AdjCre_Met, 0.713 (0.583, 0.842); Pain_Calf, AdjCre_Phe, AdjCre_Gly, 0.712 (0.585, 0.84); VAS, AdjCre_Ser, AdjCre_Gly, 0.712 (0.586, 0.839); AdjCre_Ser, AdjCre_Trp, AdjCre_Gly, 0.712 (0.586, 0.839); RPE, AdjCre_Arg, AdjCre_Pro, 0.712 (0.582, 0.841); RPE, AdjCre_Phe, AdjCre_Pro, 0.712 (0.582, 0.841); Pain_Front of Thigh, Pain_Achilles Tendon, AdjCre_Met, 0.712 (0.58, 0.843); AdjCre_Ile, AdjCre_Leu, AdjCre_Gly, 0.712 (0.584, 0.839); RPE, AdjCre_Pro, AdjCre_Thr, 0.711 (0.AdjCre_Ala, AdjCre_Asn, AdjCre_Thr, 0.711 (0.585, 0.836); AdjCre_Phe, AdjCre_Trp, AdjCre_Gly, 0.711 (0.583, 0.838); RPE, AdjCre_His, AdjCre_Pro, 0.71 (0.581, 0.84); AdjCre_Gln, AdjCre_Lys, AdjCre_Gly, 0.71 (0.581, 0.839); AdjCre_Leu, AdjCre_Lys, AdjCre_Gly, 0.71 (0.58, 0.84); AdjCre_Lys, AdjCre_Met, AdjCre_Gly, 0.71 (0.58, 0.839); Pain_Calf, Pain_Achilles Tendon, AdjCre_Ala, 0.71 (0.583, 0.836); Pain_Calf, Pain_Knee Joint, AdjCre_Gly, 0.71 (0.584, 0.835); AdjCre_Ile, AdjCre_Met, AdjCre_Gly, 0.71 (0.581, 0.838); RPE, AdjCre_Pro, AdjCre_Gly, 0.709 (0.579, 0.839); AdjCre_Glu, AdjCre_Trp, AdjCre_Gly, 0.709 (0.58, 0.837); AdjCre_Met, AdjCre_Thr, AdjCre_Gly, 0.709 (0.58, 0.837); Pain_Achilles Tendon, AdjCre_Ile, AdjCre_Met, 0.709 (0.582, 0.835); AdjCre_Ala, AdjCre_Val, AdjCre_Gly, 0.709 (0.58, 0.837); AdjCre_Gln, AdjCre_His, AdjCre_Gly, 0.709 (0.58, 0.837); AdjCre_Ile, AdjCre_Phe, AdjCre_Gly, 0.709 (0.58, 0.837); AdjCre_Ile, AdjCre_Val, AdjCre_Gly, 0.709 (0.58, 0.837); AdjCre_Trp, AdjCre_Val, AdjCre_Gly, 0.709 (0.58, 0.837); Pain_Calf, AdjCre_Lys, AdjCre_Gly, 0.708 (0.58, 0.836); Pain_Achilles Tendon, AdjCre_Ala, AdjCre_Arg, 0.708 (0.582, 0.833); Pain_Achilles Tendon, AdjCre_Ala, AdjCre_Ile, 0.708 (0.582, 0.833); AdjCre_Ala, AdjCre_His, AdjCre_Gly, 0.708 (0.579, 0.836); AdjCre_Arg, AdjCre_Gln, AdjCre_Gly, 0.708 (0.58, 0.835); AdjCre_Arg, AdjCre_Met, AdjCre_Gly, 0.708 (0.579, 0.836); AdjCre_Ile, AdjCre_Trp, AdjCre_Gly, 0.708 (0.579, 0.836); Pain_Calf, Pain_Achilles Tendon, AdjCre_Glu, 0.707 (0.576, 0.837); Pain_Achilles Tendon, AdjCre_Glu, AdjCre_Phe, 0.707 (0.578, 0.836); Pain_Front of Thigh, Pain_Calf, AdjCre_Gly, 0.707 (0.579, 0.834); Pain_Front of Thigh, AdjCre_Phe, AdjCre_Gly, 0.707 (0.578, 0.835); Pain_Calf, AdjCre_Arg, AdjCre_Gly, 0.707 (0.58, 0.833); AdjCre_Ala, AdjCre_Ile, AdjCre_Gly, 0.707 (0.578, 0.835); AdjCre_Gln, AdjCre_Ser, AdjCre_Gly, 0.707 (0.579, 0.834); AdjCre_His, AdjCre_Met, AdjCre_Gly, 0.707 (0.578, 0.835); Pain_Front of Thigh, Pain_Achilles Tendon, AdjCre_Glu, 0.706 (0.573, 0.838); Pain_Calf, AdjCre_Ala, AdjCre_Gly, 0.706 (0.578, 0.833); Pain_Calf, AdjCre_Ile, AdjCre_Met, 0.706 (0.576, 0.835); AdjCre_Glu, AdjCre_His, AdjCre_Gly, 0.705 (0.576, 0.833); AdjCre_Glu, AdjCre_Ser, AdjCre_Gly, 0.705 (0.576, 0.833); Pain_Calf, VAS, AdjCre_Ala, 0.705 (0.578, 0.831); AdjCre_Met, AdjCre_Trp, AdjCre_Gly, 0.705 (0.575, 0.835); Pain_Calf, AdjCre_Thr, AdjCre_Gly, 0.704 (0.575, 0.832); Pain_Achilles_tendon, AdjCre_Ala, AdjCre_Asn, 0.704 (0.577, 0.83); AdjCre_Ala, AdjCre_Asn, AdjCre_Lys, 0.704 (0.574, 0.834); AdjCre_Ala, AdjCre_Trp, AdjCre_Gly, 0.704 (0.575, 0.833); AdjCre_Arg, AdjCre_Ser, AdjCre_Gly, 0.704 (0.576, 0.832); AdjCre_His, AdjCre_Ser, AdjCre_Gly, 0.704 (0.576, 0.832); AdjCre_Lys, AdjCre_Trp, AdjCre_Gly, 0.704 (0.574, 0.833); Pain_Achilles tendon, Pain_Knee joint, AdjCre_Ala, 0.703 (0.576, 0.83); Pain_Achilles tendon, AdjCre_Ala, AdjCre_Tyr, 0.703 (0.575, 0.83); Pain_Knee joint, AdjCre_Phe, AdjCre_Gly, 0.703 (0.575, 0.831); AdjCre_His, AdjCre_Trp, AdjCre_Gly, 0.703 (0.574, 0.831); Pain_Front of thigh, AdjCre_Thr, AdjCre_Gly, 0.703 (0.574, 0.832); Pain_Achilles_Tendon, AdjCre_Arg, AdjCre_Glu, 0.703 (0.575, 0.831); AdjCre_Glu, AdjCre_Met, AdjCre_Gly, 0.703 (0.572, 0.833); AdjCre_Leu, AdjCre_Thr, AdjCre_Gly, 0.703 (0.574, 0.831); AdjCre_Glu, AdjCre_Lys, AdjCre_Gly, 0.703 (0.573, 0.832); Pain_Front of Thigh, Pain_Achilles Tendon, AdjCre_Ala, 0.702 (0.573, 0.831); Pain_Achilles Tendon, AdjCre_Glu, AdjCre_His, 0.702 (0.571, 0.832); Pain_Front of Thigh, AdjCre_Lys, AdjCre_Gly, 0.702 (0.571, 0.832); Pain_Front of Thigh, Pain_Knee Joint, AdjCre_Gly, 0.701 (0.573, 0.829); Pain_Front of Thigh, VAS, AdjCre_Ala, 0.701 (0.573, 0.829); AdjCre_Asn, AdjCre_His, AdjCre_Met, 0.701 (0.565, 0.836); Pain_Achilles Tendon, AdjCre_Ala, AdjCre_Phe, 0.7 (0.572, 0.828); AdjCre_His, AdjCre_Leu, AdjCre_Gly, 0.7 (0.569, 0.83); AdjCre_Asn, AdjCre_Glu, AdjCre_His, 0.7 (0.563, 0.836).

[0210] [6: Formulae selected in Example 8] AdjCre_Asn, AdjCre_P, AdjCre_Cr, 0.786 (0.679, 0.893); AdjCre_Lys, AdjCre_P, AdjCre_Cr, 0.781 (0.673, 0.889); AdjCre_Ala, AdjCre_Asn, AdjCre_Cr, 0.777 (0.675, 0.878); AdjCre_Leu, AdjCre_P, AdjCre_Cr, 0.777 (0.67, 0.884); AdjCre_Trp, AdjCre_P, AdjCre_Cr, 0.775 (0.671, 0.879); AdjCre_Lys, AdjCre_P, AdjCre_Mn, 0.775 (0.667, 0.883); AdjCre_Thr, AdjCre_P, AdjCre_Cr, 0.774 (0.667, 0.881); VAS, AdjCre_Lys, AdjCre_P, 0.774 (0.663, 0.884); AdjCre_Gln, AdjCre_P, AdjCre_Cr, 0.773 (0.665, 0.881); RPE, AdjCre_Lys, AdjCre_P, 0.773 (0.66, 0.886); AdjCre_Met, AdjCre_P, AdjCre_Cr, 0.772 (0.663, 0.881); AdjCre_Val, AdjCre_P, AdjCre_Cr, 0.771 (0.664, 0.879); AdjCre_Leu, AdjCre_Lys, AdjCre_P, 0.77 (0.659, 0.882); AdjCre_His, AdjCre_P, AdjCre_Cr, 0.77 (0.663, 0.876); AdjCre_Leu, AdjCre_P, AdjCre_Mn, 0.77 (0.658, 0.881); AdjCre_Trp, AdjCre_P, AdjCre_Mn, 0.77 (0.664, 0.876); AdjCre_Lys, AdjCre_P, AdjCre_Fe, 0.769 (0.659, 0.88); AdjCre_Ser, AdjCre_P, AdjCre_Cr, 0.769 (0.661, 0.877); AdjCre_Lys, AdjCre_Trp, AdjCre_P, 0.769 (0.656, 0.881); AdjCre_Phe, AdjCre_P, AdjCre_Cr, 0.768 (0.664, 0.873); AdjCre_Lys, AdjCre_Val, AdjCre_P, 0.768 (0.656, 0.88); VAS, AdjCre_Leu, AdjCre_P, 0.767 (0.656, 0.879); AdjCre_Tyr, AdjCre_P, AdjCre_Cr, 0.766 (0.661, 0.871); AdjCre_Arg, AdjCre_P, AdjCre_Cr, 0.764 (0.657, 0.87); AdjCre_Ile, AdjCre_Lys, AdjCre_P, 0.763 (0.651, 0.876); AdjCre_Val, AdjCre_P, AdjCre_Mn, 0.763 (0.652, 0.874); VAS, AdjCre_Asn, AdjCre_P, 0.761 (0.652, 0.87); AdjCre_Lys, AdjCre_Gly, AdjCre_P, 0.761 (0.65, 0.872); AdjCre_Lys, AdjCre_P, AdjCre_Zn, 0.76 (0.648, 0.872); VAS, AdjCre_Val, AdjCre_P, 0.759 (0.65, 0.869); RPE, AdjCre_Trp, AdjCre_P, 0.759 (0.645, 0.873); AdjCre_Ala, AdjCre_Lys, AdjCre_P, 0.758 (0.647, 0.869); AdjCre_Asn, AdjCre_Lys, AdjCre_P, 0.758 (0.643, 0.872); AdjCre_Lys, AdjCre_P, AdjCre_Se, 0.758 (0.647, 0.869); VAS, AdjCre_Arg, AdjCre_P, 0.757 (0.65, 0.865); Anterior thigh pain, AdjCre_Lys, AdjCre_P, 0.757 (0.645, 0.869); AdjCre_Gln, AdjCre_Lys, AdjCre_P, 0.757 (0.643, 0.871); AdjCre_Lys, AdjCre_P, AdjCre_Ca, 0.757 (0.644, 0.87); AdjCre_Lys, AdjCre_Tyr, AdjCre_P, 0.756 (0.643, 0.869); AdjCre_Asn, AdjCre_P, AdjCre_Mn, 0.756 (0.642, 0.869); VAS, AdjCre_P, AdjCre_Cr, 0.755 (0.647, 0.863); AdjCre_Gln, AdjCre_P, AdjCre_Mn, 0.755 (0.644, 0.866); AdjCre_Trp, AdjCre_P, AdjCre_Ca, 0.755 (0.647, 0.863); AdjCre_Ala, AdjCre_Gln, AdjCre_P, 0.754 (0.651, 0.858); Pain_Knee_Joint, AdjCre_Trp, AdjCre_P, 0.754 (0.639, 0.868); AdjCre_Ala, AdjCre_Asn, AdjCre_P, 0.752 (0.644, 0.86); AdjCre_Asn, AdjCre_Gly, AdjCre_Cr, 0.752 (0.643, 0.861); AdjCre_Lys, AdjCre_Mg, AdjCre_P, 0.752 (0.638, 0.866); AdjCre_Lys, AdjCre_Na, AdjCre_P, 0.751 (0.637, 0.864); AdjCre_Thr, AdjCre_Trp, AdjCre_P, 0.751 (0.636, 0.865); AdjCre_Leu, AdjCre_P, AdjCre_Fe, 0.75 (0.636, 0.865); Pain_Front of Thigh, AdjCre_Trp, AdjCre_P, 0.75 (0.636, 0.865); AdjCre_Gln, AdjCre_Leu, AdjCre_P, 0.75 (0.636, 0.864); AdjCre_Lys, AdjCre_Ser, AdjCre_P, 0.75 (0.638, 0.862); AdjCre_Trp, AdjCre_P, AdjCre_Fe, 0.75 (0.636, 0.863); VAS, AdjCre_Ala, AdjCre_Asn, 0.75 (0.643, 0.856); AdjCre_Gln, AdjCre_Gly, AdjCre_P, 0.75 (0.641, 0.858); AdjCre_Tyr, AdjCre_P, AdjCre_Mn, 0.75 (0.64, 0.859); AdjCre_Lys, AdjCre_Phe, AdjCre_P, 0.749 (0.637, 0.862); AdjCre_Lys, AdjCre_P, AdjCre_K, 0.749 (0.637, 0.862); RPE, AdjCre_Leu, AdjCre_P, 0.749 (0.633, 0.866); RPE, AdjCre_Tyr, AdjCre_P, 0.749 (0.634, 0.865); Pain_anterior thigh, AdjCre_Leu, AdjCre_P, 0.749 (0.633, 0.864); AdjCre_Leu, AdjCre_Ser, AdjCre_P, 0.748 (0.634, 0.863); AdjCre_Asn, AdjCre_Val, AdjCre_P, 0.748 (0.634, 0.862); AdjCre_Gln, AdjCre_Trp, AdjCre_P, 0.748 (0.635, 0.861); AdjCre_Met, AdjCre_Trp, AdjCre_P, 0.748 (0.634, 0.861); AdjCre_Ser, AdjCre_Trp, AdjCre_P, 0.748 (0.634, 0.861); AdjCre_P, AdjCre_Cr, AdjCre_Mn, 0.748 (0.639, 0.857); VAS, AdjCre_Ser, AdjCre_P, 0.747 (0.636, 0.858); VAS, AdjCre_Trp, AdjCre_P, 0.747 (0.632, 0.863); AdjCre_Leu, AdjCre_Trp, AdjCre_P, 0.747 (0.634, 0.86); AdjCre_Leu, AdjCre_Na, AdjCre_P, 0.747 (0.63, 0.864); AdjCre_P, AdjCre_K, AdjCre_Cr, 0.747 (0.639, 0.855); Pain_knee joint, AdjCre_Lys, AdjCre_P, 0.747 (0.634, 0.859); AdjCre_Lys, AdjCre_Met, AdjCre_P, 0.747 (0.634, 0.86); AdjCre_Asn, AdjCre_Leu, AdjCre_P, 0.747 (0.632, 0.861); AdjCre_Ile, AdjCre_P, AdjCre_Cr, 0.747 (0.637, 0.856); AdjCre_Trp, AdjCre_P, AdjCre_Zn, 0.747 (0.631, 0.862); AdjCre_Tyr, AdjCre_P, AdjCre_Ca, 0.747 (0.639, 0.854); AdjCre_His, AdjCre_Leu, AdjCre_P, 0.746 (0.632, 0.86); AdjCre_Trp, AdjCre_P, AdjCre_Se, 0.746 (0.632, 0.86); AdjCre_Arg, AdjCre_P, AdjCre_Mn, 0.745 (0.637, 0.854); AdjCre_His, AdjCre_P, AdjCre_Mn, 0.745 (0.633, 0.857); AdjCre_Leu, AdjCre_Thr, AdjCre_P, 0.745 (0.629, 0.86); AdjCre_Asn, AdjCre_Trp, AdjCre_P, 0.745 (0.631, 0.859); AdjCre_Leu, AdjCre_P, AdjCre_Ca, 0.745 (0.632, 0.857); AdjCre_Lys, AdjCre_Pro, AdjCre_Cr, 0.745 (0.63, 0.859); Pain_Achilles tendon, AdjCre_Lys, AdjCre_P, 0.744 (0.632, 0.857); AdjCre_Lys, AdjCre_P, AdjCre_Cu, 0.744 (0.631, 0.858); AdjCre_Thr, AdjCre_Val, AdjCre_P, 0.744 (0.627, 0.862); RPE, AdjCre_Asn, AdjCre_P, 0.744 (0.63, 0.858); AdjCre_Trp, AdjCre_Na, AdjCre_P, 0.744 (0.633, 0.855); Pain_Front of Thigh, AdjCre_P, AdjCre_Cr, 0.744 (0.635, 0.853); AdjCre_His, AdjCre_Lys, AdjCre_P, 0.744 (0.63, 0.858); AdjCre_Lys, AdjCre_P, AdjCre_Mo, 0.744 (0.632, 0.855); RPE, AdjCre_P, AdjCre_Cr, 0.743 (0.63, 0.857); AdjCre_Leu, AdjCre_P, AdjCre_Zn, 0.743 (0.631, 0.855); AdjCre_Asn, AdjCre_Pro, AdjCre_Cr, 0.743 (0.632, 0.855); AdjCre_Glu, AdjCre_Lys, AdjCre_P, 0.743 (0.627, 0.859); AdjCre_His, AdjCre_Trp, AdjCre_P, 0.743 (0.631, 0.855); AdjCre_Trp, AdjCre_P, AdjCre_K, 0.743 (0.629, 0.857); Pain_Calf, AdjCre_Trp, AdjCre_P, 0.742 (0.628, 0.856); AdjCre_Trp, AdjCre_Tyr, AdjCre_P, 0.742 (0.628, 0.856); AdjCre_Trp, AdjCre_P, AdjCre_Mo, 0.742 (0.629, 0.856); AdjCre_Gly, AdjCre_P, AdjCre_Cr, 0.742 (0.632, 0.852); VAS, AdjCre_Gln, AdjCre_P, 0.742 (0.63, 0.853); AdjCre_Leu, AdjCre_Tyr, AdjCre_P, 0.742 (0.628, 0.855); AdjCre_Phe, AdjCre_Trp, AdjCre_P, 0.742 (0.628, 0.855); Pain_Calf, AdjCre_Lys, AdjCre_P, 0.741 (0.628, 0.854); AdjCre_Arg, AdjCre_P, AdjCre_Ca, 0.741 (0.63, 0.852); AdjCre_Tyr, AdjCre_P, AdjCre_Fe, 0.741 (0.627, 0.854); VAS, AdjCre_Tyr, AdjCre_P, 0.74 (0.623, 0.858); AdjCre_Asn, AdjCre_Phe, AdjCre_P, 0.74 (0.627, 0.854); AdjCre_Asn, AdjCre_Gly, AdjCre_P, 0.74 (0.628, 0.853); AdjCre_His, AdjCre_Val, AdjCre_P, 0.74 (0.627, 0.854); AdjCre_Ser, AdjCre_Val, AdjCre_P, 0.74 (0.625, 0.856); AdjCre_Trp, AdjCre_Gly, AdjCre_P, 0.74 (0.627, 0.854); AdjCre_Leu, AdjCre_Pro, AdjCre_P, 0.74 (0.623, 0.857); Pain_Achilles Tendon, AdjCre_Trp, AdjCre_P, 0.74 (0.626, 0.854); Pain_Achilles Tendon, AdjCre_P, AdjCre_Cr, 0.74 (0.631, 0.849); AdjCre_Asn, AdjCre_P, AdjCre_Zn, 0.74 (0.627, 0.852); AdjCre_Ile, AdjCre_Leu, AdjCre_P, 0.74 (0.628, 0.851); AdjCre_Ile, AdjCre_Trp, AdjCre_P, 0.74 (0.626, 0.854); AdjCre_Leu, AdjCre_P, AdjCre_Se, 0.74 (0.625, 0.854); AdjCre_Na, AdjCre_P, AdjCre_Cr, 0.74 (0.631, 0.849); Pain_Calf, AdjCre_Leu, AdjCre_P, 0.739 (0.624, 0.855); Pain_Achilles Tendon, AdjCre_Leu, AdjCre_P, 0.739 (0.623, 0.855); Pain_Knee_Joint, AdjCre_Tyr, AdjCre_P, 0.739 (0.624, 0.855); AdjCre_Asn, AdjCre_Na, AdjCre_P, 0.739 (0.624, 0.855); AdjCre_Arg, AdjCre_Lys, AdjCre_P, 0.739 (0.623, 0.855); AdjCre_Asn, AdjCre_P, AdjCre_Fe, 0.739 (0.623, 0.855); Pain_Knee_Joint, AdjCre_Leu, AdjCre_P, 0.739 (0.623, 0.854); Pain_Knee_Joint, AdjCre_P, AdjCre_Cr, 0.739 (0.629, 0.848); AdjCre_Ala, AdjCre_Asn, AdjCre_Met, 0.739 (0.63, 0.847); AdjCre_Leu, AdjCre_Met, AdjCre_P, 0.739 (0.624, 0.853); AdjCre_Lys, AdjCre_Thr, AdjCre_P, 0.739 (0.626, 0.851); AdjCre_Ala, AdjCre_Trp, AdjCre_P, 0.738 (0.626, 0.85); AdjCre_Gln, AdjCre_Val, AdjCre_P, 0.738 (0.622, 0.854); AdjCre_Phe, AdjCre_P, AdjCre_Mn, 0.738 (0.627, 0.849); AdjCre_P, AdjCre_Cr, AdjCre_Mo, 0.738 (0.629, 0.847); AdjCre_Ala, AdjCre_Asn, AdjCre_Lys, 0.738 (0.629, 0.847); AdjCre_Ala, AdjCre_Asn, AdjCre_Thr, 0.738 (0.625, 0.851); AdjCre_Leu, AdjCre_Phe, AdjCre_P, 0.737 (0.622, 0.852); AdjCre_Val, AdjCre_P, AdjCre_Zn, 0.737 (0.623, 0.852); AdjCre_Mg, AdjCre_P, AdjCre_Cr, 0.737 (0.628, 0.847); AdjCre_P, AdjCre_Ca, AdjCre_Cr, 0.737 (0.628, 0.847); AdjCre_Pro, AdjCre_Trp, AdjCre_P, 0.737 (0.62, 0.854); AdjCre_Pro, AdjCre_Val, AdjCre_P, 0.737 (0.624, 0.85); Pain_Front of Thigh, AdjCre_Tyr, AdjCre_P, 0.737 (0.621, 0.852); Pain_Calf, AdjCre_P, AdjCre_Cr, 0.737 (0.627, 0.846); Pain_Achilles Tendon, AdjCre_Asn, AdjCre_P, 0.737 (0.622, 0.851); AdjCre_Asn, AdjCre_Tyr, AdjCre_P, 0.737 (0.622, 0.851); AdjCre_Asn, AdjCre_P, AdjCre_Ca, 0.737 (0.623, 0.85); AdjCre_Leu, AdjCre_P, AdjCre_Mo, 0.737 (0.622, 0.852); AdjCre_Ser, AdjCre_P, AdjCre_Mn, 0.737 (0.623, 0.85); AdjCre_Trp, AdjCre_Val, AdjCre_P, 0.737 (0.622, 0.851); AdjCre_Val, AdjCre_P, AdjCre_Ca, 0.737 (0.623, 0.851); AdjCre_Trp, AdjCre_P, AdjCre_Cu, 0.736 (0.62, 0.852); AdjCre_P, AdjCre_Cr, AdjCre_Se, 0.736 (0.627, 0.846); AdjCre_Glu, AdjCre_Trp, AdjCre_P, 0.736 (0.615, 0.856); VAS, AdjCre_His, AdjCre_P, 0.736 (0.623, 0.848); AdjCre_Ala, AdjCre_Asn, AdjCre_Mn, 0.736 (0.624, 0.847); AdjCre_Asn, AdjCre_Ser, AdjCre_P, 0.736 (0.619, 0.852); AdjCre_P, AdjCre_Cr, AdjCre_Cu, 0.736 (0.626, 0.845); AdjCre_P, AdjCre_Cr, AdjCre_Zn, 0.736 (0.626, 0.846); AdjCre_Thr, AdjCre_Na, AdjCre_P, 0.735 (0.62, 0.851); AdjCre_Arg, AdjCre_Leu, AdjCre_P, 0.735 (0.622 , 0.848); AdjCre_Leu, AdjCre_Val, AdjCre_P, 0.735 (0.619, 0.851); AdjCre_Leu, AdjCre_Gly, AdjCre_P, 0.735 (0.621, 0.849); AdjCre_Leu, AdjCre_P, AdjCre_K, 0.735 (0.619, 0.851); AdjCre_Leu, AdjCre_P, AdjCre_Cu, 0.735 (0.618, 0.852); AdjCre_Trp, AdjCre_Mg, AdjCre_P, 0.735 (0.62, 0.85); AdjCre_Asn, AdjCre_Pro, AdjCre_P, 0.734 (0.618, 0.851); AdjCre_Asn, AdjCre_Met, AdjCre_P, 0.734 (0.62, 0.849); AdjCre_Val, AdjCre_P, AdjCre_Fe, 0.734 (0.616, 0.852); RPE, AdjCre_Ala, AdjCre_Asn, 0.734 (0.623, 0.845); Pain_anterior thigh, AdjCre_Ala, AdjCre_Asn, 0.734 (0.622, 0.846); AdjCre_Ala, AdjCre_Asn, AdjCre_Ile, 0.734 (0.624, 0.844); AdjCre_Ala, AdjCre_P, AdjCre_Cr, 0.734 (0.624, 0.844); AdjCre_Arg, AdjCre_Na, AdjCre_P, 0.734 (0.621, 0.846); AdjCre_Asn, AdjCre_P, AdjCre_Mo, 0.734 (0.618, 0.849); AdjCre_Gln, AdjCre_Tyr, AdjCre_P, 0.734 (0.619, 0.849); AdjCre_Glu, AdjCre_P, AdjCre_Cr, 0.733 (0.62, 0.846); Pain_Front of Thigh, AdjCre_Val, AdjCre_P, 0.733 (0.612, 0.854); Pain_Calf, AdjCre_Asn, AdjCre_P, 0.733 (0.618, 0.849); Pain_Knee Joint, AdjCre_Asn, AdjCre_P, 0.733 (0.617, 0.849); AdjCre_Arg, AdjCre_P, AdjCre_Zn, 0.733 (0.622, 0.844); AdjCre_Asn, AdjCre_Gln, AdjCre_P, 0.733 (0.618, 0.848); AdjCre_Asn, AdjCre_His, AdjCre_P, 0.733 (0.617, 0.849); AdjCre_Asn, AdjCre_P, AdjCre_Se, 0.733 (0.618, 0.848); AdjCre_Leu, AdjCre_Mg, AdjCre_P, 0.733 (0.616, 0.849); AdjCre_Tyr, AdjCre_Na, AdjCre_P, 0.733 (0.619, 0.846); AdjCre_Val, AdjCre_Na, AdjCre_P, 0.733 (0.615, 0.85); AdjCre_Ala, AdjCre_Asn, AdjCre_Ser, 0.732 (0.62, 0.844); AdjCre_Ala, AdjCre_Asn, AdjCre_Ca, 0.732 (0.622, 0.842); AdjCre_Arg, AdjCre_Trp, AdjCre_P, 0.732 (0.619, 0.845); AdjCre_Asn, AdjCre_P, AdjCre_K, 0.732 (0.616, 0.848); Pain_Front of Thigh, AdjCre_Asn, AdjCre_P, 0.731 (0.614, 0.849); Pain_Front of thigh, AdjCre_Gln, AdjCre_P, 0.731 (0.617, 0.846); AdjCre_Gln, AdjCre_P, AdjCre_Ca, 0.731 (0.618, 0.844); AdjCre_Ile, AdjCre_Ser, AdjCre_P, 0.731 (0.616, 0.847); AdjCre_Tyr, AdjCre_P, AdjCre_Zn, 0.731 (0.616, 0.847); AdjCre_Ala, AdjCre_Asn, AdjCre_Se, 0.731 (0.62, 0.841); AdjCre_Asn, AdjCre_Ile, AdjCre_P, 0.731 (0.615, 0.846); AdjCre_P, AdjCre_Cr, AdjCre_Fe, 0.73 (0.619, 0.842); RPE, AdjCre_Arg, AdjCre_P, 0.73 (0.617, 0.843); RPE, AdjCre_Ser, AdjCre_P, 0.73 (0.616, 0.845); AdjCre_Gln, AdjCre_Phe, AdjCre_P, 0.73 (0.616, 0.844); AdjCre_Asn, AdjCre_Thr, AdjCre_P, 0.73 (0.612, 0.847); VAS, AdjCre_Phe, AdjCre_P, 0.729 (0.616, 0.843); AdjCre_Arg, AdjCre_Tyr, AdjCre_P, 0.729 (0.617, 0.842); AdjCre_Gln, AdjCre_P, AdjCre_Zn, 0.729 (0.617, 0.842); AdjCre_His, AdjCre_P, AdjCre_Ca, 0.729 (0.617, 0.842); AdjCre_Phe, AdjCre_Ser, AdjCre_P, 0.729 (0.614, 0.845); AdjCre_Tyr, AdjCre_Val, AdjCre_P, 0.729 (0.613, 0.846); AdjCre_Tyr, AdjCre_P, AdjCre_Mo, 0.729 (0.615, 0.844); AdjCre_Ala, AdjCre_Asn, AdjCre_Fe, 0.729 (0.615, 0.843); AdjCre_Thr, AdjCre_Tyr, AdjCre_P, 0.729 (0.611, 0.847); AdjCre_Ala, AdjCre_Ser, AdjCre_Cr, 0.729 (0.619, 0.839); AdjCre_Arg, AdjCre_His, AdjCre_P, 0.729 (0.615, 0.843); AdjCre_Asn, AdjCre_Mg, AdjCre_P, 0.729 (0.613, 0.845); AdjCre_Ser, AdjCre_Tyr, AdjCre_P, 0.729 (0.613, 0.845); Calf pain, AdjCre_Tyr, AdjCre_P, 0.728 (0.613, 0.843); VAS, AdjCre_Met, AdjCre_P, 0.728 (0.614, 0.842); AdjCre_Ala, AdjCre_His, AdjCre_P, 0.728 (0.62, 0.837); AdjCre_Val, AdjCre_P, AdjCre_Se, 0.728 (0.611, 0.845); RPE, AdjCre_His, AdjCre_P, 0.728 (0.612, 0.844); AdjCre_Pro, AdjCre_Tyr, AdjCre_P, 0.728 (0.607, 0.849); AdjCre_Ala, AdjCre_Tyr, AdjCre_P, 0.728 (0.613, 0.842); AdjCre_Arg, AdjCre_Asn, AdjCre_P, 0.728 (0.613, 0.842); AdjCre_Asn, AdjCre_P, AdjCre_Cu, 0.728 (0.612, 0.844); AdjCre_Met, AdjCre_Tyr, AdjCre_P, 0.728 (0.611, 0.844); AdjCre_Tyr, AdjCre_Gly, AdjCre_P, 0.728 (0.613, 0.842); AdjCre_Lys, AdjCre_Pro, AdjCre_P, 0.728 (0.611, 0.844); Calf pain, AdjCre_Val, AdjCre_P, 0.727 (0.609, 0.845); Pain_Achilles tendon, AdjCre_Val, AdjCre_P, 0.727 (0.609, 0.845); AdjCre_Ala, AdjCre_Asn, AdjCre_Mg, 0.727 (0.616, 0.838); AdjCre_Arg, AdjCre_Val, AdjCre_P, 0.727 (0.614, 0.84); AdjCre_Asn, AdjCre_Gln, AdjCre_Cr, 0.727 (0.616, 0.838); AdjCre_Ile, AdjCre_Tyr, AdjCre_P, 0.727 (0.613, 0.842); AdjCre_Met, AdjCre_Val, AdjCre_P, 0.727 (0.61, 0.844); AdjCre_Ser, AdjCre_P, AdjCre_Cu, 0.727 (0.611, 0.843); AdjCre_Tyr, AdjCre_P, AdjCre_K, 0.727 (0.612, 0.842); AdjCre_Asn, AdjCre_Pro, AdjCre_Thr, 0.727 (0.611, 0.843); Pain_Achilles tendon, AdjCre_Tyr, AdjCre_P, 0.726 (0.612, 0.841); Pain_Knee joint, AdjCre_Val, AdjCre_P, 0.726 (0.608, 0.845); AdjCre_Ala, AdjCre_Arg, AdjCre_Asn, 0.726 (0.614, 0.838); AdjCre_Ala, AdjCre_Asn, AdjCre_Cu, 0.726 (0.615, 0.838); AdjCre_Ile, AdjCre_Val, AdjCre_P, 0.726 (0.609, 0.844); AdjCre_Met, AdjCre_P, AdjCre_Mn, 0.726 (0.61, 0.843); AdjCre_Phe, AdjCre_Val, AdjCre_P, 0.726 (0.61, 0.843); RPE, AdjCre_Val, AdjCre_P, 0.726 (0.608, 0.844); AdjCre_Ala, AdjCre_Lys, AdjCre_Ser, 0.726 (0.616, 0.836); Pain_Front of Thigh, AdjCre_Arg, AdjCre_P, 0.726 (0.614, 0.837); Pain_Achilles Tendon, AdjCre_Ser, AdjCre_P, 0.726 (0.611, 0.841); AdjCre_Pro, AdjCre_Trp, AdjCre_Mn, 0.725 (0.61, 0.84); AdjCre_Glu, AdjCre_Leu, AdjCre_P, 0.725 (0.605, 0.845); AdjCre_Glu, AdjCre_Val, AdjCre_P, 0.725 (0.602, 0.848); AdjCre_Ala, AdjCre_Asn, AdjCre_Leu, 0.725 (0.613, 0.837); AdjCre_Asn, AdjCre_Gly, AdjCre_Mn, 0.725 (0.612, 0.839); AdjCre_Tyr, AdjCre_P, AdjCre_Se, 0.725 (0.61, 0.84); AdjCre_Arg, AdjCre_Pro, AdjCre_P, 0.725 (0.613, 0.837); AdjCre_Gln, AdjCre_Pro, AdjCre_Cr, 0.725 (0.609, 0.841); VAS, AdjCre_Ala, AdjCre_Ser, 0.725 (0.614, 0.835); AdjCre_Ala, AdjCre_Asn, AdjCre_Trp, 0.725 (0.614, 0.836); AdjCre_Arg, AdjCre_Gln, AdjCre_P, 0.725 (0.61, 0.84); RPE, AdjCre_Phe, AdjCre_P, 0.724 (0.608, 0.841); AdjCre_Ala, AdjCre_Asn, AdjCre_His, 0.724 (0.612, 0.836); AdjCre_Ala, AdjCre_Asn, AdjCre_Glu, 0.724 (0.607, 0.841); AdjCre_Arg, AdjCre_Thr, AdjCre_P, 0.724 (0.611, 0.837); Pain_Knee Joint, AdjCre_Phe, AdjCre_P, 0.723 (0.608, 0.839); AdjCre_Ser, AdjCre_P, AdjCre_Zn, 0.723 (0.609, 0.838); AdjCre_Val, AdjCre_P, AdjCre_Mo, 0.723 (0.607, 0.84); AdjCre_Ala, AdjCre_Lys, AdjCre_Cr, 0.723 (0.608, 0.838); Calf_pain, AdjCre_Ala, AdjCre_Asn, 0.723 (0.61, 0.835); AdjCre_Ala, AdjCre_Asn, AdjCre_Tyr, 0.723 (0.611, 0.835); AdjCre_Ala, AdjCre_Asn, AdjCre_Gly, 0.723 (0.609, 0.836); AdjCre_Arg, AdjCre_Ser, AdjCre_P, 0.723 (0.608, 0.838); AdjCre_Gln, AdjCre_Na, AdjCre_P, 0.723 (0.608, 0.838); AdjCre_His, AdjCre_Tyr, AdjCre_P, 0.723 (0.606, 0.839); AdjCre_Ser, AdjCre_Na, AdjCre_P, 0.723 (0.607, 0.839); AdjCre_Ser, AdjCre_P, AdjCre_Ca, 0.723 (0.609, 0.836); AdjCre_Val, AdjCre_Mg, AdjCre_P, 0.723 (0.604, 0.841); AdjCre_Val, AdjCre_P, AdjCre_K, 0.723 (0.603, 0.843); AdjCre_Glu, AdjCre_Cr, AdjCre_Cu, 0.723 (0.609, 0.837); RPE, AdjCre_Gln, AdjCre_P, 0.723 (0.608, 0.837); AdjCre_Arg, AdjCre_P, AdjCre_Se, 0.722 (0.611, 0.833); AdjCre_Asn, AdjCre_Gly, AdjCre_Na, 0.722 (0.609, 0.835); AdjCre_Asn, AdjCre_Cr, AdjCre_Se, 0.722 (0.612, 0.833); AdjCre_Val, AdjCre_P, AdjCre_Cu, 0.722 (0.603, 0.841); VAS, AdjCre_Pro, AdjCre_Trp, 0.722 (0.602, 0.842); AdjCre_His, AdjCre_Pro, AdjCre_Cr, 0.722 (0.606, 0.839); AdjCre_Phe, AdjCre_Na, AdjCre_P, 0.722 (0.607, 0.836); AdjCre_Phe, AdjCre_P, AdjCre_Ca, 0.722 (0.61, 0.833); AdjCre_Ser, AdjCre_P, AdjCre_K, 0.722 (0.606, 0.837); AdjCre_Tyr, AdjCre_P, AdjCre_Cu, 0.722 (0.606, 0.837); AdjCre_Pro, AdjCre_Thr, AdjCre_Trp, 0.721 (0.605, 0.838); Anterior thigh pain, AdjCre_Phe, AdjCre_P, 0.721 (0.606, 0.836); AdjCre_Ala, AdjCre_Asn, AdjCre_Phe, 0.721 (0.609, 0.833); AdjCre_Ala, AdjCre_Leu, AdjCre_P, 0.721 (0.607, 0.835); AdjCre_Phe, AdjCre_P, AdjCre_Fe, 0.721 (0.605, 0.837); AdjCre_Ala, AdjCre_Asn, AdjCre_Zn, 0.72 (0.608, 0.833); AdjCre_Arg, AdjCre_P, AdjCre_Cu, 0.72 (0.608, 0.833); AdjCre_Ile, AdjCre_P, AdjCre_Mn, 0.72 (0.603, 0.837); AdjCre_Thr, AdjCre_P, AdjCre_Mn, 0.72 (0.603, 0.836); AdjCre_Ala, AdjCre_Asn, AdjCre_K, 0.72 (0.607, 0.832); AdjCre_Asn, AdjCre_Ile, AdjCre_Gly, 0.72 (0.604, 0.836); AdjCre_His, AdjCre_Na, AdjCre_P, 0.72 (0.605, 0.835); AdjCre_Met, AdjCre_Ser, AdjCre_P, 0.72 (0.603, 0.837); AdjCre_Ser, AdjCre_P, AdjCre_Mo, 0.72 (0.604, 0.836); AdjCre_Gln, AdjCre_P, AdjCre_Fe, 0.72 (0.604, 0.835); AdjCre_Thr, AdjCre_P, AdjCre_Zn, 0.719 (0.605, 0.833); AdjCre_Ala, AdjCre_Asn, AdjCre_Val, 0.719 (0.606, 0.832); AdjCre_Ala, AdjCre_Phe, AdjCre_P, 0.719 (0.607, 0.832); AdjCre_Val, AdjCre_Gly, AdjCre_P, 0.719 (0.602, 0.836); AdjCre_Asn, AdjCre_Glu, AdjCre_P, 0.719 (0.597, 0.84); AdjCre_Ile, AdjCre_Thr, AdjCre_P, 0.719 (0.6, 0.837); Pain_Achilles Tendon, AdjCre_Ala, AdjCre_Asn, 0.719 (0.606, 0.831); Pain_Achilles Tendon, AdjCre_Arg, AdjCre_P, 0.719 (0.607, 0.831); Pain_Achilles Tendon, AdjCre_Asn, AdjCre_Gln, 0.719 (0.603, 0.834); AdjCre_Ser, AdjCre_Mg, AdjCre_P, 0.719 (0.601, 0.836); AdjCre_Pro, AdjCre_P, AdjCre_Cr, 0.718 (0.604, 0.832); AdjCre_Asn, AdjCre_Glu, AdjCre_Cr, 0.718 (0.602, 0.834); AdjCre_Glu, AdjCre_Tyr, AdjCre_P, 0.718 (0.597, 0.839); Pain_Calf, AdjCre_Ser, AdjCre_P, 0.718 (0.602, 0.834); AdjCre_Ala, AdjCre_Asn, AdjCre_Na, 0.718 (0.607, 0.829); AdjCre_Arg, AdjCre_Ile, AdjCre_P, 0.718 (0.606, 0.83); AdjCre_Arg, AdjCre_P, AdjCre_K, 0.718 (0.606, 0.83); AdjCre_Asn, AdjCre_Met, AdjCre_Gly, 0.718 (0.604, 0.832); AdjCre_Ile, AdjCre_Phe, AdjCre_P, 0.718 (0.602, 0.834); AdjCre_Phe, AdjCre_Tyr, AdjCre_P, 0.718 (0.602, 0.834); AdjCre_Phe, AdjCre_Gly, AdjCre_P, 0.718 (0.603, 0.833); AdjCre_ Arg, AdjCre_P, AdjCre_Fe, 0.718 (0.604, 0.831); AdjCre_Lys, AdjCre_Cr, AdjCre_Se, 0.717 (0.605, 0.829); AdjCre_Arg, AdjCre_Phe, AdjCre_P, 0.717 (0.604, 0.831); AdjCre_Arg, AdjCre_P, AdjCre_Mo, 0.717 (0.605, 0.83); AdjCre_Phe, AdjCre_P, AdjCre_Se, 0.717 (0.602, 0.833); AdjCre_Tyr, AdjCre_Mg, AdjCre_P, 0.717 (0.601, 0.834); AdjCre_Pro, AdjCre_Trp, AdjCre_Cr, 0.717 (0.603, 0.831); Achilles tendon pain, AdjCre_Phe, AdjCre_P, 0.717 (0.602, 0.832); Knee joint pain, AdjCre_Ser, AdjCre_P, 0.717 (0.6, 0.833); AdjCre_Gln, AdjCre_Ile, AdjCre_P, 0.717 (0.601, 0.832); AdjCre_Phe, AdjCre_P, AdjCre_Mo, 0.717 (0.601, 0.832); AdjCre_Asn, AdjCre_Gln, AdjCre_Fe, 0.717 (0.599, 0.835); AdjCre_His, AdjCre_Thr, AdjCre_P, 0.716 (0.599, 0.834); Calf pain, AdjCre_Arg, AdjCre_P, 0.716 (0.604, 0.828); AdjCre_Arg, AdjCre_Met, AdjCre_P, 0.716 (0.604, 0.828); AdjCre_Gln, AdjCre_P, AdjCre_Mo, 0.716 (0.601, 0.831); AdjCre_His, AdjCre_Ser, AdjCre_P, 0.716 (0.6, 0.832); AdjCre_Ser, AdjCre_P, AdjCre_Fe, 0.716 (0.598, 0.834); AdjCre_Pro, AdjCre_Ser, AdjCre_Cr, 0.716 (0.6, 0.831); AdjCre_Pro, AdjCre_Tyr, AdjCre_Cr, 0.716 (0.601, 0.83); AdjCre_Arg, AdjCre_Gly, AdjCre_P, 0.716 (0.603, 0.828); AdjCre_Phe, AdjCre_Thr, AdjCre_P, 0.715 (0.599, 0.832); AdjCre_Ser, AdjCre_Thr, AdjCre_P, 0.715 (0.597, 0.834); AdjCre_Pro, AdjCre_Trp, AdjCre_Fe, 0.715 (0.598, 0.833); AdjCre_Pro, AdjCre_Cr, AdjCre_Cu, 0.715 (0.601, 0.829); VAS, AdjCre_P, AdjCre_Mn, 0.715 (0.594, 0.836); AdjCre_Ala, AdjCre_Arg, AdjCre_P, 0.715 (0.603, 0.827); AdjCre_Ala, AdjCre_Asn, AdjCre_Gln, 0.715 (0.603, 0.827); AdjCre_Arg, AdjCre_Mg, AdjCre_P, 0.715 (0.602, 0.828); AdjCre_Gln, AdjCre_Met, AdjCre_P, 0.715 (0.598, 0.832); AdjCre_Met, AdjCre_Na, AdjCre_P, 0.715 (0.593, 0.837); VAS, AdjCre_Thr, AdjCre_P, 0.715 (0.598, 0.831); AdjCre_Arg, AdjCre_Glu, AdjCre_P, 0.715 (0.6, 0.83); AdjCre_Ala, AdjCre_Asn, AdjCre_Pro, 0.714 (0.598, 0.83); Pain_Front of Thigh, AdjCre_Ser, AdjCre_P, 0.714 (0.597, 0.832); Pain_Knee_Joint, AdjCre_Arg, AdjCre_P, 0.714 (0.602, 0.827); VAS, AdjCre_Na, AdjCre_P, 0.714 (0.599, 0.829); AdjCre_Asn, AdjCre_Gln, AdjCre_Mn, 0.714 (0.596, 0.833); AdjCre_Asn, AdjCre_Gln, AdjCre_Cu, 0.714 (0.597, 0.831); AdjCre_Gln, AdjCre_Ser, AdjCre_P, 0.714 (0.599, 0.83); AdjCre_Ser, AdjCre_P, AdjCre_Se, 0.714 (0.598, 0.831); AdjCre_Ala, AdjCre_Lys, AdjCre_Pro, 0.714 (0.599, 0.829); AdjCre_Pro, AdjCre_Val, AdjCre_Mn, 0.714 (0.597, 0.83); Pain_Knee_Joint, AdjCre_Ala, AdjCre_Asn, 0.714 (0.6, 0.827); AdjCre_His, AdjCre_Gly, AdjCre_P, 0.714 (0.599, 0.828); AdjCre_Ser, AdjCre_Gly, AdjCre_P, 0.714 (0.599, 0.828); AdjCre_Lys, AdjCre_Mg, AdjCre_Cr, 0.714 (0.594, 0.833); AdjCre_Glu, AdjCre_Phe, AdjCre_P, 0.713 (0.596, 0.831); Calf pain, AdjCre_Phe, AdjCre_P, 0.713 (0.598, 0.828); AdjCre_Asn, AdjCre_His, AdjCre_Gly, 0.713 (0.596, 0.83); AdjCre_Gln, AdjCre_His, AdjCre_P, 0.713 (0.598, 0.828); AdjCre_His, AdjCre_Phe, AdjCre_P, 0.713 (0.596, 0.83); AdjCre_Met, AdjCre_P, AdjCre_Ca, 0.713 (0.595, 0.831); AdjCre_Cr, AdjCre_Mn, AdjCre_Cu, 0.713 (0.6, 0.826); AdjCre_Ala, AdjCre_Lys, AdjCre_Se, 0.713 (0.6, 0.825); AdjCre_Gln, AdjCre_P, AdjCre_K, 0.712 (0.597, 0.828); AdjCre_Ala, AdjCre_Pro, AdjCre_Trp, 0.712 (0.597, 0.828); AdjCre_Phe, AdjCre_P, AdjCre_K, 0.712 (0.596, 0.828); AdjCre_Gln, AdjCre_P, AdjCre_Se, 0.711 (0.596, 0.827); AdjCre_Pro, AdjCre_Trp, AdjCre_Gly, 0.711 (0.594, 0.829); AdjCre_Glu, AdjCre_Lys, AdjCre_Cr, 0.711 (0.592, 0.83); AdjCre_Asn, AdjCre_Gln, AdjCre_Thr, 0.711 (0.592, 0.83); AdjCre_Asn, AdjCre_Glu, AdjCre_Gly, 0.711 (0.594, 0.828); AdjCre_Ala, AdjCre_Ser, AdjCre_P, 0.711 (0.597, 0.824); AdjCre_Gln, AdjCre_Mg, AdjCre_P, 0.711 (0.594, 0.827); AdjCre_Ala, AdjCre_Pro, AdjCre_Tyr, 0.711 (0.593, 0.828); AdjCre_Pro, AdjCre_Trp, AdjCre_Val, 0.711 (0.59, 0.831); RPE, AdjCre_P, AdjCre_Mn, 0.71 (0.591, 0.83); Pain_Calf, AdjCre_Asn, AdjCre_Gln, 0.71 (0.594, 0.827); Pain_Achilles Tendon, AdjCre_Gln, AdjCre_P, 0.71 (0.594, 0.826); AdjCre_Gln, AdjCre_P, AdjCre_Cu, 0.71 (0.594, 0.827); AdjCre_Phe, AdjCre_Pro, AdjCre_P, 0.71 (0.596, 0.824); AdjCre_Pro, AdjCre_Val, AdjCre_Cu, 0.71 (0.593, 0.827); AdjCre_Gln, AdjCre_Thr, AdjCre_P, 0.71 (0.592, 0.827); AdjCre_Thr, AdjCre_P, AdjCre_Ca, 0.71 (0.595, 0.825); AdjCre_Asn, AdjCre_Gln, AdjCre_Ca, 0.709 (0.593, 0.826); AdjCre_His, AdjCre_Met, AdjCre_P, 0.709 (0.592, 0.827); AdjCre_His, AdjCre_P, AdjCre_Zn, 0.709 (0.595, 0.824); AdjCre_Leu, AdjCre_Pro, AdjCre_Trp, 0.709 (0.587, 0.831); AdjCre_Asn, AdjCre_Lys, AdjCre_Gly, 0.709 (0.592, 0.826); Pain_Knee_Joint, AdjCre_Gln, AdjCre_P, 0.709 (0.593, 0.825); AdjCre_Asn, AdjCre_Gln, AdjCre_Ser, 0.709 (0.592, 0.826); AdjCre_Asn, AdjCre_Gln, AdjCre_Na, 0.709 (0.592, 0.825); AdjCre_Met, AdjCre_Phe, AdjCre_P, 0.709 (0.591, 0.827); VAS, AdjCre_Pro, AdjCre_Tyr, 0.709 (0.587, 0.83); AdjCre_Lys, AdjCre_Pro, AdjCre_Trp, 0.709 (0.589, 0.828); AdjCre_Lys, AdjCre_Pro, AdjCre_Val, 0.709 (0.591, 0.826); RPE, AdjCre_Pro, AdjCre_Trp, 0.708 (0.588, 0.829); VAS, AdjCre_Asn, AdjCre_Gly, 0.708 (0.59, 0.827); AdjCre_Ala, AdjCre_Val, AdjCre_P, 0.708 (0.591, 0.825); AdjCre_Phe, AdjCre_P, AdjCre_Zn, 0.708 (0.59, 0.826); AdjCre_Na, AdjCre_P, AdjCre_Mn, 0.708 (0.593, 0.824); AdjCre_Asn, AdjCre_Pro, AdjCre_Gly, 0.708 (0.586, 0.83); AdjCre_His, AdjCre_Ile, AdjCre_P, 0.708 (0.592, 0.824); AdjCre_Mg, AdjCre_Cr, AdjCre_Se, 0.708 (0.593, 0.822); AdjCre_P, AdjCre_K, AdjCre_Mn, 0.708 (0.59, 0.826); AdjCre_Pro, AdjCre_Thr, AdjCre_Tyr, 0.707 (0.586, 0.828); AdjCre_Lys, AdjCre_K, AdjCre_Cr, 0.707 (0.588, 0.827); VAS, AdjCre_Cr, AdjCre_Se, 0.707 (0.594, 0.82); AdjCre_Asn, AdjCre_Gln, AdjCre_Tyr, 0.707 (0.589, 0.825); AdjCre_Gln, AdjCre_Glu, AdjCre_P, 0.707 (0.588, 0.826); VAS, AdjCre_Asn, AdjCre_Pro, 0.707 (0.585, 0.828); AdjCre_Lys, AdjCre_Met, AdjCre_Gly, 0.707 (0.59, 0.823); AdjCre_Lys, AdjCre_Gly, AdjCre_Cr, 0.707 (0.587, 0.826); Pain_Calf, AdjCre_Gln, AdjCre_P, 0.706 (0.591, 0.822); AdjCre_Ala, AdjCre_Asn, AdjCre_Mo, 0.706 (0.593, 0.82); AdjCre_Pro, AdjCre_Val, AdjCre_Fe, 0.706 (0.588, 0.824); Pain_Front of Thigh, AdjCre_Pro, AdjCre_Trp, 0.706 (0.588, 0.824); Pain_Knee Joint, AdjCre_Pro, AdjCre_Trp, 0.706 (0.588, 0.824); AdjCre_Asn, AdjCre_Pro, AdjCre_Mn, 0.706 (0.589, 0.823); AdjCre_Leu, AdjCre_Pro, AdjCre_Mn, 0.706 (0.59, 0.822); AdjCre_Met, AdjCre_Pro, AdjCre_Trp, 0.706 (0.588, 0.824); AdjCre_Pro, AdjCre_Trp, AdjCre_Zn, 0.706 (0.588, 0.824); VAS, AdjCre_Ala, AdjCre_Lys, 0.706 (0.591, 0.821); AdjCre_Thr, AdjCre_P, AdjCre_Cu, 0.706 (0.588, 0.823); AdjCre_Lys, AdjCre_Met, AdjCre_Mn, 0.706 (0.586, 0.826); VAS, AdjCre_Ala, AdjCre_Cr, 0.706 (0.591, 0.821); AdjCre_Asn, AdjCre_Gln, AdjCre_His, 0.706 (0.589, 0.822); AdjCre_Asn, AdjCre_Gln, AdjCre_Trp, 0.706 (0.589, 0.823); AdjCre_Gln, AdjCre_Lys, AdjCre_Fe, 0.706 (0.591, 0.82); Pain_Achilles Tendon, AdjCre_Pro, AdjCre_Trp, 0.705 (0.588, 0.823); AdjCre_Lys, AdjCre_Gly, AdjCre_Se, 0.705 (0.588, 0.822); AdjCre_Thr, AdjCre_Cr, AdjCre_Se, 0.705 (0.59, 0.82); AdjCre_Asn, AdjCre_Phe, AdjCre_Gly, 0.705 (0.587, 0.823); AdjCre_Asn, AdjCre_Lys, AdjCre_Pro, 0.705 (0.586, 0.824); AdjCre_Lys, AdjCre_Pro, AdjCre_Mn, 0.705 (0.587, 0.823); AdjCre_His, AdjCre_Pro, AdjCre_Trp, 0.705 (0.587, 0.822); AdjCre_Asn, AdjCre_Gly, AdjCre_Fe, 0.705 (0.587, 0.822); AdjCre_Phe, AdjCre_Mg, AdjCre_P, 0.705 (0.588, 0.821); AdjCre_Ser, AdjCre_Gly, AdjCre_Cr, 0.705 (0.588, 0.821); Pain_Front of Thigh, AdjCre_Gln, AdjCre_Lys, 0.704 (0.589, 0.819); Pain_Front of Thigh, AdjCre_Asn, AdjCre_Gly, 0.704 (0.587, 0.821); AdjCre_Asn, AdjCre_Gly, AdjCre_Se, 0.704 (0.588, 0.82); AdjCre_Leu, AdjCre_Lys, AdjCre_Pro, 0.704 (0.585, 0.823); AdjCre_Gln, AdjCre_Pro, AdjCre_P, 0.703 (0.585, 0.822); AdjCre_Pro, AdjCre_Trp, AdjCre_K, 0.703 (0.587, 0.819); Pain_Achilles tendon, AdjCre_His, AdjCre_P, 0.703 (0.587, 0.819); Pain_Knee joint, AdjCre_Asn, AdjCre_Gln, 0.703 (0.586, 0.821); AdjCre_Asn, AdjCre_Tyr, AdjCre_Gly, 0.703 (0.584, 0.823); AdjCre_Asn, AdjCre_Gly, AdjCre_K, 0.703 (0.588, 0.819); Pain_Front of thigh, AdjCre_Ala, AdjCre_Lys, 0.703 (0.59, 0.817); Pain_Front of thigh, AdjCre_Thr, AdjCre_P, 0.703 (0.583, 0.824); AdjCre_Asn, AdjCre_Met, AdjCre_Pro, 0.703 (0.585, 0.82); AdjCre_Leu, AdjCre_Pro, AdjCre_Se, 0.703 (0.582, 0.823); AdjCre_Arg, AdjCre_Asn, AdjCre_Gln, 0.703 (0.585, 0.82); AdjCre_Asn, AdjCre_Leu, AdjCre_Gly, 0.703 (0.584, 0.822); AdjCre_Ala, AdjCre_Gln, AdjCre_Lys, 0.703 (0.589, 0.816); AdjCre_Ala, AdjCre_Ile, AdjCre_Lys, 0.703 (0.589, 0.816); AdjCre_Ala, AdjCre_Lys, AdjCre_Met, 0.703 (0.589, 0.817); AdjCre_Ala, AdjCre_Lys, AdjCre_K, 0.703 (0.589, 0.816); VAS, AdjCre_Mg, AdjCre_Cr, 0.702 (0.585, 0.82); AdjCre_Arg, AdjCre_Pro, AdjCre_Trp, 0.702 (0.58, 0.824); AdjCre_Asn, AdjCre_Gln, AdjCre_Leu, 0.702 (0.584, 0.82); AdjCre_Asn, AdjCre_Pro, AdjCre_Ser, 0.702 (0.584, 0.821); AdjCre_Asn, AdjCre_Pro, AdjCre_Fe, 0.702 (0.584, 0.82); AdjCre_Asn, AdjCre_Mg, AdjCre_Cr, 0.702 (0.583, 0.821); AdjCre_Leu, AdjCre_Pro, AdjCre_Cr, 0.702 (0.584, 0.821); AdjCre_Ala, AdjCre_Lys, AdjCre_Fe, 0.702 (0.586, 0.817); AdjCre_Glu, AdjCre_Ser, AdjCre_P, 0.702 (0.581, 0.822); AdjCre_Asn, AdjCre_Gln, AdjCre_Phe, 0.702 (0.583, 0.82); AdjCre_His, AdjCre_P, AdjCre_K, 0.702 (0.585, 0.818); AdjCre_Phe, AdjCre_P, AdjCre_Cu, 0.702 (0.585, 0.818); Pain_Calf, AdjCre_Pro, AdjCre_Trp, 0.701 (0.582, 0.821); AdjCre_Ile, AdjCre_Pro, AdjCre_Trp, 0.701 (0.583, 0.82); AdjCre_Lys, AdjCre_Mn, AdjCre_Se, 0.701 (0.584, 0.819); AdjCre_Thr, AdjCre_Mg, AdjCre_P, 0.701 (0.582, 0.82); RPE, AdjCre_Asn, AdjCre_Gln, 0.701 (0.583, 0.819); VAS, AdjCre_Glu, AdjCre_Cr, 0.701 (0.581, 0.821); AdjCre_Glu, AdjCre_Pro, AdjCre_Trp, 0.701 (0.582, 0.82); AdjCre_Lys, AdjCre_Met, AdjCre_Fe, 0.701 (0.58, 0.822); AdjCre_Thr, AdjCre_P, AdjCre_Fe, 0.701 (0.582, 0.82); Pain_Front of thigh, AdjCre_His, AdjCre_P, 0.701 (0.585, 0.817); VAS, AdjCre_Asn, AdjCre_Se, 0.701 (0.582, 0.82); AdjCre_Ala, AdjCre_Gln, AdjCre_Mn, 0.701 (0.587, 0.815); AdjCre_His, AdjCre_P, AdjCre_Fe, 0.701 (0.584, 0.818); AdjCre_Asn, AdjCre_Pro, AdjCre_Trp, 0.701 (0.584, 0.817); AdjCre_Pro, AdjCre_Trp, AdjCre_Ca, 0.701 (0.581, 0.821); AdjCre_Pro, AdjCre_Trp, AdjCre_Mo, 0.701 (0.582, 0.82); AdjCre_Ala, AdjCre_Lys, AdjCre_Mg, 0.701 (0.587, 0.814); AdjCre_Ala, AdjCre_Lys, AdjCre_Mn, 0.701 (0.586, 0.815); AdjCre_Lys, AdjCre_Pro, AdjCre_Na, 0.7 (0.581, 0.82); Calf_pain, AdjCre_Asn, AdjCre_Pro, 0.7 (0.583, 0.818); AdjCre_Asn, AdjCre_Gln, AdjCre_Pro, 0.7 (0.583, 0.817); AdjCre_Gln, AdjCre_Pro, AdjCre_Trp, 0.7 (0.58, 0.821); AdjCre_Pro, AdjCre_Trp, AdjCre_Cu, 0.7 (0.579, 0.821); AdjCre_Pro, AdjCre_Tyr, AdjCre_Mn, 0.7 (0.582, 0.819); AdjCre_Pro, AdjCre_Val, AdjCre_Se, 0.7 (0.583, 0.817); AdjCre_Pro, AdjCre_Thr, AdjCre_Cr, 0.7 (0.58, 0.819); Knee joint_pain, AdjCre_His, AdjCre_P, 0.7 (0.583, 0.816); VAS, AdjCre_Asn, AdjCre_K, 0.7 (0.58, 0.819); RPE, AdjCre_Met, AdjCre_P, 0.7 (0.579, 0.821).

[0211] [7-1: Formulas selected in Example 9 (Part 1)] Glu, Asn, Trp, 0.736 (0.639, 0.833), Ser, Leu, Trp, 0.722 (0.625, 0.819), Ser, Glu, Trp, 0.719 (0.615, 0.824), Trp, Asn, Gly, 0.719 (0.619, 0.819), Gln, Ala, Gly, 0.717 (0.623, 0.81), Leu, Ala, Trp, 0.716 (0.619, 0.814), Asn, Arg, Trp, 0.716 (0.619, 0.812), Arg, Ala, Trp, 0.712 (0.611, 0.813), Ser, Gln, Trp, 0.709 (0.614, 0.804), Trp, Arg, Gly, 0.709 (0.609, 0.809), Trp, Ala, Gly, 0.709 (0.606, 0.812), Ser, Arg, Trp, 0.708 (0.607, 0.81), Arg, Ala, Gln, 0.708 (0.614, 0.802), Leu, Asn, Trp, 0.708 (0.609, 0.807), Ser, Met, Trp, 0.708 (0.608, 0.808), Trp, Ser, Gly, 0.707 (0.605, 0.809), Trp, Leu, Gly, 0.707 (0.61, 0.804), Ser, Ile, Trp, 0.706 (0.606, 0.806), Met, Ala, Trp, 0.706 (0.606, 0.806), Glu, Ala, Trp, 0.706 (0.606, 0.806), Met, Arg, Trp, 0.705 (0.612, 0.799), Glu, Gln, Ser, 0.705 (0.6, 0.809), Gln, Arg, Ser, 0.704 (0.606, 0.802), Asn, Arg, His, 0.703 (0.602, 0.805), Ser, Ala, Trp, 0.703 (0.597, 0.809), Trp, Met, Gly, 0.702 (0.603, 0.802), His, Asn, Gly, 0.701 (0.603, 0.799), Ile, Asn, Trp, 0.701 (0.6, 0.802),Asn, Ala, Trp, 0.701(0.599, 0.802),Tyr, Trp, Gly, 0.7(0.598, 0.802),Lys, Ala, Trp, 0.7(0.593, 0.807),Ile, Ala, Trp, 0.7(0.597, 0.802),Ser, Asn, Trp, 0.7(0.597, 0.803),Thr, Ala, Trp, 0.7(0.595, 0.804),Trp, Ala, Val, 0.698(0.593, 0.802),Ser, Gln, Gly, 0.697(0.598, 0.796),Trp, Gln, Gly, 0.697(0.603, 0.791),Pro, Ala, Trp, 0.697(0.594, 0.799),Trp, Glu, Gly, 0.696(0.597, 0.796),Trp, Ile, Gly, 0.696(0.597, 0.795),Trp, Ser, Val, 0.696(0.593, 0.799),Leu, Arg, Trp, 0.696(0.601, 0.79),Met, Asn, Trp, 0.695(0.597, 0.794),Pro, Gln, Ser, 0.695(0.596, 0.795),Trp, Leu, Tyr, 0.695(0.593, 0.796),His, Ala, Trp, 0.695(0.588, 0.801),Gln, Ala, Ser, 0.692(0.598, 0.787),Glu, Asn, Tyr, 0.692(0.589, 0.796),Trp, Asn, Val, 0.692(0.588, 0.797),Ser, Pro, Trp, 0.692(0.591, 0.794),Gln, Asn, Gly, 0.692(0.593, 0.791),Trp, Lys, Gly, 0.691(0.59, 0.793),Ser, Lys, Trp, 0.691(0.588, 0.795),Phe, Ala, Trp, 0.691(0.586, 0.797),Lys, Arg, Trp, 0.691(0.598, 0.784),Thr, Ser, Trp, 0.691(0.588, 0.793),Gln, Arg, Trp, 0.69(0.594, 0.786),Val, Trp, Gly, 0.69(0.59, 0.791),Ser, His, Trp, 0.69(0.586, 0.794),Trp, Arg, Tyr, 0.689(0.595, 0.784),Lys, Asn, Trp, 0.689(0.587, 0.792),Leu, His, Trp, 0.689(0.588, 0.789),Thr, Asn, Trp, 0.688(0.587, 0.79),His, Arg, Gly, 0.688(0.584, 0.793),Trp, Ser, Tyr, 0.688(0.585, 0.791),His, Asn, Trp, 0.688(0.583, 0.792),Pro, Arg, Trp, 0.687(0.591, 0.783),His, Arg, Ser, 0.687(0.585, 0.789),His, Arg, Trp, 0.687(0.59, 0.784),Glu, Arg, Trp, 0.687(0.588, 0.786),Met, Gln, Trp, 0.687(0.591, 0.782),Lys, Leu, Trp, 0.687(0.587, 0.786),Trp, His, Gly, 0.686(0.585, 0.787),Trp, Thr, Gly, 0.686(0.584, 0.788),Phe, Arg, Trp, 0.685(0.588, 0.782),Phe, Arg, Ser, 0.685(0.584, 0.787),Trp, Phe, Gly, 0.685(0.583, 0.787),Pro, Asn, Trp, 0.685(0.583, 0.788),Met, Lys, Trp, 0.685(0.586, 0.784),Ser, His, Gly, 0.684(0.579, 0.79),Met, Leu, Trp, 0.684(0.585, 0.782),Met, Glu, Trp, 0.684(0.587, 0.78),Asn, Arg, Tyr, 0.684(0.581, 0.786),Ser, Phe, Trp, 0.683(0.577, 0.789),Phe, Met, Trp, 0.683(0.582, 0.784),Phe, Asn, Trp, 0.683(0.578, 0.788),Glu, Asn, Phe, 0.682(0.576, 0.789),Phe, Gln, Gly, 0.682(0.585, 0.78),Ser, Arg, Tyr, 0.682(0.576, 0.788),Thr, Arg, Trp, 0.682(0.583, 0.78),Trp, Met, Tyr, 0.681(0.584, 0.779),Gln, Ala, Trp, 0.681(0.582, 0.78),Phe, Arg, Gly, 0.681(0.578, 0.784),Ile, Arg, Trp, 0.68(0.583, 0.778),Trp, Arg, Val, 0.68(0.582, 0.777),Thr, Leu, Trp, 0.679(0.58, 0.778),Gln, Asn, Trp, 0.679(0.574, 0.783),Trp, Ala, Tyr, 0.678(0.57, 0.787),Trp, Met, Val, 0.678(0.578, 0.778),Trp, Asn, Tyr, 0.678(0.573, 0.783),Trp, Pro, Gly, 0.678(0.579, 0.776),Val, His, Gly, 0.678(0.571, 0.784),Thr, Met, Trp, 0.677(0.576, 0.778),Asn, Arg, Phe, 0.676(0.576, 0.777),Gln, Arg, Gly, 0.676(0.575, 0.776),Leu, Glu, Trp, 0.676(0.579, 0.772),Arg, Ala, Phe, 0.675(0.572, 0.778),Met, Ile, Trp, 0.675(0.575, 0.775),Met, His, Trp, 0.675(0.575, 0.775),Met, Arg, Phe, 0.675(0.574, 0.776),Trp, Leu, Val, 0.674(0.576, 0.772),Gln, Arg, Phe, 0.673(0.575, 0.77),Lys, Gln, Gly, 0.672(0.573, 0.772),Trp, Pro, Tyr, 0.672(0.571, 0.774),Ser, Glu, Tyr, 0.672(0.562, 0.783),Leu, Ile, Trp, 0.672(0.573, 0.771),Leu, Asn, Tyr, 0.672(0.564, 0.78),Met, Gln, Ser, 0.672(0.567, 0.776),Phe, Glu, Ser, 0.672(0.561, 0.782),Arg, Ala, Tyr, 0.671(0.565, 0.777),Pro, Met, Trp, 0.67(0.574, 0.767),Lys, Gln, Ser, 0.67(0.569, 0.772),Glu, Arg, Phe, 0.67(0.568, 0.773),Trp, Ile, Tyr, 0.67(0.566, 0.773),Tyr, Gln, Gly, 0.669(0.569, 0.769),Gln, Ala, Glu, 0.669(0.571, 0.767),Val, Gln, Gly, 0.669(0.57, 0.768),His, Asn, Pro, 0.669(0.575, 0.763),Lys, Ala, Tyr, 0.669(0.559, 0.779),Gln, Ala, Lys, 0.669(0.572, 0.766),Phe, Leu, Trp, 0.668(0.57, 0.767),Trp, Glu, Tyr, 0.668(0.57, 0.767),Val, Glu, Gly, 0.668(0.562, 0.775),Ile, Gln, Ser, 0.668(0.565, 0.772),His, Ala, Gly, 0.668(0.564, 0.773),Gln, Ala, Met, 0.668(0.575, 0.761),Ile, Asn, Tyr, 0.668(0.561, 0.775),Lys, His, Gly, 0.668(0.563, 0.773),Leu, Gln, Trp, 0.668(0.568, 0.767),Tyr, Asn, Gly, 0.668(0.558, 0.777),Lys, Arg, Phe, 0.667(0.571, 0.764),Gln, Ala, Pro, 0.667(0.57, 0.764),Ile, His, Gly, 0.667(0.561, 0.774),Phe, Arg, Val, 0.667(0.567, 0.768),His, Glu, Trp, 0.667(0.567, 0.766),Phe, Gln, Ser, 0.666(0.567, 0.766),Phe, Arg, Pro, 0.666(0.567, 0.766),Asn, Ala, Tyr, 0.666(0.557, 0.774),His, Arg, Phe, 0.666(0.566, 0.766),Tyr, Asn, Val, 0.665(0.556, 0.775),Ile, Gln, Gly, 0.665(0.565, 0.765),Pro, Leu, Trp, 0.665(0.566, 0.763),Ile, His, Trp, 0.665(0.562, 0.767),Leu, Gln, Ser, 0.665(0.561, 0.768),Val, Tyr, Gly, 0.664(0.557, 0.772),Ser, Gln, Tyr, 0.664(0.564, 0.764),Phe, Arg, Thr, 0.664(0.563, 0.765),Lys, Glu, Trp, 0.664(0.557, 0.77),Tyr, Ala, Gly, 0.664(0.553, 0.774),Thr, Asn, Tyr, 0.663(0.558, 0.769),Tyr, His, Gly, 0.663(0.556, 0.77),Tyr, Trp, Val, 0.663(0.558, 0.767),His, Arg, Pro, 0.662(0.564, 0.761),Phe, Arg, Tyr, 0.662(0.562, 0.763),Glu, Ala, Phe, 0.662(0.552, 0.773),Asn, Ala, Gln, 0.662(0.568, 0.757),Met, Arg, Tyr, 0.662(0.565, 0.76),Val, Phe, Gly, 0.662(0.558, 0.766),Tyr, Arg, Gly, 0.662(0.556, 0.768),Lys, Asn, Tyr, 0.662(0.557, 0.767),His, Gln, Gly, 0.661(0.562, 0.761),Ser, Asn, Tyr, 0.66(0.553, 0.768),Glu, Gln, Gly, 0.66(0.558, 0.762),Gln, Arg, Tyr, 0.66(0.558, 0.762),Trp, His, Tyr, 0.66(0.558, 0.762),Pro, Asn, Tyr, 0.66(0.555, 0.765),Val, Thr, Gly, 0.66(0.553, 0.767),Phe, Glu, Gly, 0.66(0.553, 0.768),His, Gln, Ser, 0.66(0.559, 0.761),Glu, Asn, His, 0.66(0.559, 0.76),His, Glu, Gly, 0.66(0.555, 0.764),Met, His, Gly, 0.659(0.557, 0.762),Val, Arg, Gly, 0.659(0.549, 0.77),Ile, Arg, Phe, 0.659(0.558, 0.761),Phe, Ala, Gly, 0.659(0.551, 0.767),Ser, Ala, Tyr, 0.659(0.55, 0.768),Gln, Ala, Thr, 0.659(0.564, 0.754),Trp, Thr, Tyr, 0.659(0.556, 0.761),Tyr, Ser, Gly, 0.659(0.547, 0.77),Leu, Ala, Tyr, 0.658(0.554, 0.763),Ser, Gln, Thr, 0.658(0.558, 0.759),Gln, Asn, Ser, 0.658(0.554, 0.762),Leu, His, Gly, 0.658(0.553, 0.763),Val, Ala, Gly, 0.658(0.551, 0.764),Phe, Leu, Gly, 0.657(0.55, 0.765),Leu, Gln, Gly, 0.657(0.558, 0.757),Tyr, Phe, Gly, 0.657(0.55, 0.764),Phe, His, Gly, 0.657(0.551, 0.763),Val, Ser, Gly, 0.657(0.55, 0.764),Pro, Gln, Gly, 0.657(0.559, 0.755), Ser, Pro, Tyr, 0.657 (0.554, 0.759), Ser, Met, Tyr, 0.656 (0.552, 0.761), Ser, Phe, Gly, 0.656 (0.549, 0.764), Tyr, Arg, Val, 0.656 (0.553, 0.76), Gln, Arg, His, 0.656 (0.55, 0.762), Gln, Ala, Leu, 0.656 (0.561, 0.751), Ser, Leu, Tyr, 0.656 (0.55, 0.762), Leu, Arg, Phe, 0.656 (0.555, 0.757), Lys, Ile, Trp, 0.656 (0.552, 0.759), Gln, Arg, Glu, 0.656 (0.548, 0.763), Val, Asn, Gly, 0.656 (0.548, 0.763), Tyr, Glu, Gly, 0.656 (0.545, 0.766), Ser, Gln, Val, 0.655 (0.55, 0.761), Ile, Ala, Tyr, 0.655 (0.548, 0.763), His, Asn, Met, 0.655 (0.554, 0.756), Phe, Lys, Gly, 0.655 (0.547, 0.764), Ile, Arg, Tyr, 0.655 (0.554, 0.756), Phe, Asn, Gly, 0.655 (0.549, 0.761), Trp, Phe, Tyr, 0.654 (0.551, 0.757), Ile, Asn, Phe, 0.654 (0.551, 0.758), Val, Ile, Gly, 0.654 (0.548, 0.761), Gln, Ala, Tyr, 0.654 (0.559, . Gly, 0.654 (0.544, 0.764), Phe, Met, Gly, 0.654 (0.548, 0.76), Tyr, Lys, Gly, 0.654 (0.543, 0.764),Phe, Ile, Gly, 0.653(0.546, 0.761),Trp, His, Val, 0.653(0.549, 0.757),Phe, Asn, Tyr, 0.653(0.549, 0.757),His, Asn, Tyr, 0.653(0.548, 0.758),Pro, His, Ser, 0.652(0.557, 0.748),His, Arg, Tyr, 0.652(0.55, 0.755),Glu, Gln, Trp, 0.652(0.552, 0.752),Thr, Phe, Gly, 0.652(0.544, 0.76),Asn, Arg, Gln, 0.652(0.549, 0.755),Pro, Lys, Trp, 0.652(0.547, 0.756),Tyr, Met, Gly, 0.652(0.545, 0.758),Met, Asn, Tyr, 0.651(0.548, 0.755),Thr, His, Gly, 0.651(0.546, 0.756),Ser, Lys, Tyr, 0.651(0.545, 0.758),Val, Leu, Gly, 0.651(0.545, 0.757),Tyr, Ile, Gly, 0.651(0.541, 0.761),Gln, Ala, Phe, 0.651( 0.555, 0.746),Met, Gln, Gly, 0.651(0.553, 0.748),Thr, Arg, Tyr, 0.651(0.55, 0.752),Leu, Arg, Tyr, 0.651(0.549, 0.752),Tyr, Ala, Val, 0.65(0.539, 0.761),Leu, Asn, Phe, 0.65(0.548, 0.753),Gln, Arg, Val, 0.65(0.546, 0.754),Trp, Gln, Tyr, 0.65(0.543, 0.757),Tyr, Thr, Gly, 0.65(0.54, 0.761),Gln, Ala, Val, 0.65(0.554, 0.746),His, Asn, Thr, 0.65(0.551, 0.748),Ser, Ile, Tyr, 0.65(0.543, 0.757),Gln, Ala, Ile, 0.649(0.553, 0.746),Arg, Ala, His, 0.649(0.543, 0.755),Trp, Gln, Val, 0.649(0.549, 0.749),Val, Met, Gly, 0.649(0.542, 0.756),Thr, Ile, Gly, 0.649(0.54, 0.758),Gln, Ala, His, 0.649(0.554, 0.744),Pro, Arg, Tyr, 0.649(0.551, 0.747),Met, Glu, Phe, 0.649(0.547, 0.75),Asn, Ala, His, 0.648(0.546, 0.751),Pro, His, Trp, 0.648(0.545, 0.751),Ile, Glu, Trp, 0.648(0.55, 0.746),Trp, Lys, Val, 0.648(0.542, 0.754),Gln, Arg, Ile, 0.648(0.542, 0.754),Phe, Met, Val, 0.648(0.546, 0.749),Ser, His, Tyr, 0.648(0.543, 0.752),Pro, Arg, Val, 0.647(0.539, 0.755),Phe, Ala, Tyr, 0.647(0.538, 0.756),Thr, Gln, Gly, 0.646(0.547, 0.745),Met, Gln, Phe, 0.646(0.543, 0.748),Thr, Ser, Tyr, 0.646(0.54, 0.752),Glu, Arg, Tyr, 0.646(0.543, 0.748),Ile, Gln, Trp, 0.645(0.546, 0.745),His, Glu, Ser, 0.645(0.541, 0.75),Thr, Leu, Gly, 0.645(0.536, 0.755),Gln, Arg, Pro, 0.645(0.541, 0.75),His, Ala, Tyr, 0.645(0.537, 0.753),Met, His, Ser, 0.645(0.542, 0.748),Asn, Ala, Phe, 0.645(0.54, 0.749),Pro, His, Gly, 0.645(0.544, 0.745),Gln, Arg, Thr, 0.645(0.54, 0.749),Glu, Arg, His, 0.644(0.54, 0.748),His, Arg, Leu, 0.644(0.54, 0.749),Ser, Ile, Gly, 0.644(0.535, 0.754),Met, Lys, Phe, 0.644(0.541, 0.747),Met, Ala, Tyr, 0.644(0.541, 0.746),His, Glu, Phe, 0.643(0.533, 0.754),Phe, Asn, Val, 0.643(0.54, 0.746),Leu, Ile, Gly, 0.643(0.534, 0.752),Trp, Ile, Val, 0.643(0.543, 0.742),Thr, Glu, Trp, 0.643(0.542, 0.743),Gln, Arg, Lys, 0.643(0.535, 0.75),Val, Pro, Gly, 0.642(0.536, 0.749),His, Arg, Thr, 0.642(0.539, 0.745),Gln, Arg, Leu, 0.642(0.536, 0.749),Ile, Ala, Gly, 0.642(0.533, 0.752),His, Arg, Met, 0.642(0.541, 0.743),Phe, Asn, Thr, 0.642(0.538, 0.746),Glu, Ala, Tyr, 0.642(0.534, 0.75),Lys, Glu, Phe, 0.642(0.533, 0.75),His, Asn, Lys, 0.641(0.537, 0.746),Phe, Met, Tyr, 0.641(0.538, 0.745),Gln, Asn, Phe, 0.641(0.538, 0.744),Phe, Ala, Ser, 0.641(0.533, 0.749),Phe, Ile, Trp, 0.641(0.54, 0.741),Lys, Asn, Phe, 0.641(0.54, 0.742),Trp, Glu, Val, 0.641(0.539, 0.742),Met, Ile, Gly, 0.641(0.532, 0.749),Ile, Glu, Gly, 0.641(0.532, 0.749),Phe, His, Trp, 0.64(0.537, 0.744),Thr, Ala, Tyr, 0.64(0.534, 0.747),Tyr, Ser, Val, 0.64(0.533, 0.748),Phe, Asn, Ser, 0.64(0.536, 0.744),Ile, Arg, Gly, 0.64(0.53, 0.75),Ser, Phe, Tyr, 0.639(0.533, 0.746),Tyr, Pro, Gly, 0.638(0.535, 0.742),Pro, Gln, Trp, 0.638(0.537, 0.74),Thr, His, Trp, 0.638(0.536, 0.74),Thr, Pro, Trp, 0.638(0.535, 0.741),Phe, Met, Ser, 0.638(0.534, 0.742),Phe, Glu, Thr, 0.638(0.531, 0.745),His, Gln, Trp, 0.638(0.534, 0.741),His, Arg, Ile, 0.638(0.532, 0.744),Phe, Leu, Ser, 0.638(0.531, 0.745),Ile, Ala, Phe, 0.638(0.53, 0.746),Leu, His, Ser, 0.637(0.533, 0.741),Gln, Arg, Met, 0.637(0.533, 0.741),Lys, Ile, Gly, 0.637(0.527, 0.746),Phe, Ile, Ser, 0.636(0.528, 0.744),Pro, Ile, Trp, 0.636(0.534, 0.737),Thr, Ile, Trp, 0.636(0.534, 0.738),His, Asn, Ile, 0.636(0.531, 0.741),Phe, Asn, Pro, 0.636(0.534, 0.737),Met, Ile, Phe, 0.636(0.532, 0.739),Lys, Ala, Phe, 0.635(0.525, 0.745),Ile, Asn, Gly, 0.635(0.527, 0.743),His, Asn, Phe, 0.634(0.531, 0.738),Leu, Ala, Gly, 0.634(0.524, 0.744),His, Asn, Leu, 0.634(0.531, 0.737),His, Arg, Lys, 0.634(0.528, 0.74),His, Arg, Val, 0.634(0.526, 0.742),Phe, Ala, Val, 0.634(0.525, 0.743),Lys, His, Ser, 0.634(0.53, 0.737),Lys, His, Trp, 0.633(0.527, 0.739),Pro, Glu, Trp, 0.633(0.53, 0.735),His, Asn, Val, 0.633(0.529, 0.737),His, Ala, Phe, 0.633(0.524, 0.742),Met, His, Phe, 0.633(0.532, 0.733),Tyr, Met, Val, 0.633(0.53, 0.735),Ser, Leu, Gly, 0.632(0.523, 0.742),Ile, Arg, Pro, 0.632(0.53, 0.734),Pro, Phe, Trp, 0.632(0.529, 0.735),Leu, Arg, Pro, 0.632(0.524, 0.741),Phe, Glu, Trp, 0.632(0.531, 0.732),Trp, Phe, Val, 0.632(0.529, 0.734),Trp, Pro, Val, 0.632(0.528, 0.735),Phe, Gln, Trp, 0.632(0.525, 0.738),Lys, Gln, Pro, 0.632(0.522, 0.741),Phe, Met, Thr, 0.631(0.528, 0.735),Lys, Gln, Trp, 0.631(0.525, 0.737),Lys, His, Pro, 0.631(0.529, 0.733),Pro, Ala, Tyr, 0.631(0.528, 0.734),Met, Ala, Phe, 0.631(0.527, 0.735),Phe, Ala, Thr, 0.631(0.524, 0.737),His, Ala, Ser, 0.63(0.523, 0.737),Glu, Gln, Lys, 0.63(0.522, 0.738), Leu, Arg, Gly, 0.63 (0.519, 0.74), Leu, Glu, Gly, 0.63 (0.52, 0.74), Pro, Lys, Val, 0.629 (0.523, 0.735), Phe, Lys, Ser, 0.629 (0.521, 0.736), Leu, Ala, Phe, 0.628 (0.524, 0.733), Lys, Arg, Val, 0.628 (0.52, 0.736), Lys, Arg, Tyr, 0.628 (0.528, 0.728), Thr, Lys, Trp, 0.628 (0.528, 0.728), Met, Lys, Tyr, 0.628 (0.528, 0.728), Pro, Phe, Gly, 0.628 (0.526, 0.73), Pro, Phe, Ser, 0.628 (0.523, 0.733), Lys, Glu, Tyr, 0.628 (0.52, 0.735), Met, Leu, Gly, 0.628 (0.519, 0.737), Ser, Phe, Thr, 0.627(0.521, 0.733),Met, Ile, Tyr, 0.626(0.524, 0.729),Ser, Glu, Val, 0.626(0.515, 0.738),Gln, Asn, Glu, 0.626(0.523, 0.73),Phe, His, Ser, 0.626(0.522, 0.73), Thr, Phe, Trp, 0.626(0.526, 0.726),Met, Leu, Phe, 0.626(0.523, 0.729),Met, Gln, Tyr, 0.626(0.519, 0.732),Trp, Thr, Val, 0.625(0.523, 0.728),Pro, Lys, Tyr, 0.625 (0.522, 0.729), Ser, Phe, Val, 0.625 (0.518, 0.732), Ser, Arg, Val, 0.624 (0.515, 0.732), Gln, Asn, Tyr, 0.623 (0.516, 0.73), Lys, Leu, Gly, 0.623(0.514, 0.732),Phe, Met, Pro, 0.623(0.522, 0.724),Phe, Ala, Pro, 0.623(0.517, 0.729),Glu, Arg, Val, 0.622(0.511, 0.733),Arg, Ala, Val, 0.622(0.511, 0.733),Phe, Lys, Trp, 0.62(0.512, 0.729),Ser, His, Thr, 0.62(0.521, 0.719),Thr, Asn, Gly, 0.619(0.515, 0.724),Thr, Gln, Trp, 0.619(0.52, 0.717),Met, Arg, Val, 0.619(0.513, 0.725),Pro, Gln, Val, 0.619(0.511, 0.726),Glu, Arg, Leu, 0.618(0.505, 0.73),Ser, Pro, Val, 0.618(0.51, 0.726),Leu, Asn, Gly, 0.618(0.509, 0.727),Thr, Pro, Val, 0.617(0.508, 0.727),Thr, Arg, Gly, 0.617(0.513, 0.72),Thr, Glu, Gly, 0.616(0.512, 0.72),Ser, His, Val, 0.616(0.51, 0.721),Ile, His, Ser, 0.616(0.509, 0.722),Pro, Ile, Gly, 0.615(0.511, 0.719),His, Ala, Met, 0.614(0.519, 0.709),Thr, Met, Gly, 0.614(0.51, 0.718),Met, Leu, Tyr, 0.614(0.511, 0.717),Thr, Lys, Gly, 0.614(0.509, 0.718),Pro, Asn, Val, 0.614(0.509, 0.719),Leu, Glu, Phe, 0.614(0.508, 0.719),Ile, Arg, Lys, 0.614(0.507, 0.721),Phe, Lys, Pro, 0.614(0.507, 0.721),Ile, Glu, Phe, 0.613(0.508, 0.719),Ile, Arg, Val, 0.613(0.502, 0.725),Lys, Ile, Pro, 0.613(0.51, 0.716),Pro, Glu, Val, 0.613(0.506, 0.72),Gln, Asn, His, 0.613(0.507, 0.718),Phe, Glu, Pro, 0.613(0.51, 0.716),His, Glu, Lys, 0.612(0.505, 0.719),Thr, Ser, Gly, 0.612(0.51, 0.715),Leu, Arg, Lys, 0.612(0.503, 0.721),Pro, Ile, Val, 0.612(0.505, 0.718),Phe, Ile, Val, 0.611(0.51, 0.713),Thr, Ala, Gly, 0.611(0.508, 0.714),Phe, Gln, Pro, 0.611(0.504, 0.717),His, Glu, Tyr, 0.611(0.504, 0.718),Pro, His, Val, 0.611(0.504, 0.717),Asn, Arg, Val, 0.611(0.502, 0.719),Lys, Ile, Tyr, 0.61(0.507, 0.714),Thr, Met, Tyr, 0.61(0.507, 0.714).

[0212] [7-2: Formulas selected in Example 9 (Part 2)] Ser, Trp, BCAA, 0.710 (0.611, 0.809), Ala, Trp, BCAA, 0.707 (0.607, 0.806), Asn, Trp, BCAA, 0.703 (0.602, 0.803), Trp, Gly, BCAA, 0.700 (0.603, 0.798), Arg, Trp, BCAA, 0.683 (0.586, 0.780), Trp, Tyr, BCAA, 0.682 (0.578, 0.785), Met, Trp, BCAA, 0.679 (0.579, 0.778), His, Trp, BCAA, 0.678 (0.576, 0.780), Lys, Trp, BCAA, 0.675 (0.572, 0.777), Asn, Tyr, BCAA, 0.669 (0.560, 0.779), His, Gly, BCAA, 0.666 (0.559, 0.772), Gln, Gly, BCAA, 0.663 (0.563, 0.764), Gln, Ser, BCAA, 0.660 (0.555, 0.766), Gln, Trp, BCAA, 0.660 (0.559, 0.760), Phe, Trp, BCAA, 0.659 (0.559, 0.759), Thr, Trp, BCAA, 0.659 (0.558, 0.760), Glu, Trp, BCAA, 0.658 (0.558, 0.757), Arg, Phe, BCAA, 0.658 (0.556, 0.759), Arg, Tyr, BCAA, 0.657 (0.555, 0.759), Ala, Tyr, BCAA, 0.655 (0.548, 0.762), Glu, Gly, BCAA, 0.655 (0.546, 0.763), Tyr, Gly, BCAA, 0.654 (0.545, 0.764), Thr, Gly, BCAA, 0.654 (0.545, 0.762), Phe, Gly, BCAA, 0.653 (0.546, 0.761), Arg, Gly, BCAA, 0.653 (0.543, 0.764), Asn, Phe, BCAA, 0.651 (0.548, 0.754), Arg, Gln, BCAA, 0.651(0.545, 0.757), Ser, Tyr, BCAA, 0.649(0.542, 0.757), Ala, Gln, BCAA, 0.649(0.553, 0.744), Ser, Gly, BCAA, 0.648(0.539, 0.757), Ala, Gly, BCAA, 0.648(0.539, 0.756), Lys, Gly, BCAA, 0.646(0.537, 0.754), Arg, Pro, BCAA, 0.644(0.536, 0.752), Pro, Trp, BCAA, 0.643(0.541, 0.745), Arg, His, BCAA, 0.642(0.535, 0.748), Asn, Gly, BCAA, 0.641(0.534, 0.749), Met, Gly, BCAA, 0.640(0.533, 0.748), Phe, Ser, BCAA, 0.636(0.528, 0.744), Asn, His, BCAA, 0.635(0.530, 0.740), Pro, Gly, BCAA, 0.635(0.527, 0.742), Met, Phe, BCAA, 0.634(0.531, 0.737), Ala, Phe, BCAA, 0.633(0.526, 0.740), Arg, Lys, BCAA, 0.624(0.513, 0.735), Lys, Pro, BCAA, 0.622(0.517, 0.728), Arg, Glu, BCAA, 0.622(0.508, 0.735), Met, Tyr, BCAA, 0.621(0.519, 0.724), His, Ser, BCAA, 0.616(0.510, 0.722), Glu, Phe, BCAA, 0.615(0.508, 0.723).

[0213] [8: Formulae selected in Example 10] Glu_adjCre, Arg_adjCre, Gly_adjCre, 0.773 (0.676, 0.871), Met_adjCre, Glu_adjCre, Phe_adjCre, 0.77 (0.676, 0.865), His_adjCre, Arg_adjCre, Gly_adjCre, 0.766 (0.674, 0.858), Met_adjCre, Glu_adjCre, Gly_adjCre, 0.766 (0.671, 0.86), Glu_adjCre, Arg_adjCre, Pro_adjCre, 0.765 (0.667, 0.863), Pro_adjCre, Glu_adjCre, Gly_adjCre, 0.765 (0.671, 0.859), Val_adjCre, Glu_adjCre, Gly_adjCre, 0.765 (0.67, 0.859), Phe_adjCre, Glu_adjCre, Gly_adjCre, 0.764 (0.67, 0.858), Trp_adjCre, Glu_adjCre, Gly_adjCre, 0.763 (...

Claims

1. An evaluation method characterized by including an evaluation step of evaluating the degree of fatigue in the subject to evaluation using the concentration value of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in the urine collected from the subject to evaluation, or using the value of the formula calculated using the concentration value and a formula containing a variable into which the concentration value is substituted.

2. The evaluation method according to claim 1, characterized in that, in the evaluation step, if the concentration value or the value of the formula is lower than or equal to a predetermined value, the degree of fatigue is evaluated as high, and if the concentration value or the value of the formula is higher than the predetermined value, the degree of fatigue is evaluated as low.

3. The evaluation method according to claim 1 or 2, characterized in that the evaluation step involves evaluating the degree of muscle damage or the state of energy metabolism in the subject to evaluation.

4. The evaluation method according to claim 3, characterized in that the evaluation result regarding the degree of muscle damage obtained in the evaluation step corresponds to the result of evaluating the degree of muscle damage by blood myoglobin concentration or blood creatine kinase concentration.

5. The evaluation method according to claim 3, characterized in that the evaluation result regarding the state of energy metabolism obtained in the evaluation step corresponds to the result of evaluating the state of energy metabolism by the total blood ketone body concentration or the blood NEFA (Non-esterified Fatty Acid) concentration.

6. The evaluation method according to claim 4 or 5, characterized in that the evaluation step uses a formula that further includes a variable into which one of the following values ​​is substituted: the value obtained by dividing the concentration value by the creatinine concentration value obtained from the urine and the value obtained by dividing the concentration value by the urine specific gravity obtained from the urine, and the value of the formula calculated using the concentration value and the one of the above values.

7. The evaluation method according to claim 4 or 5, characterized in that the evaluation step uses a value of the formula calculated using the concentration value and the at least one value, which further includes a variable into which at least one of the following values ​​obtained from the subject to be evaluated, a value of a subjective index representing fatigue or pain, a value of the sleep duration of the subject to be evaluated, and a value of the pH of the urine is substituted.

8. The evaluation method according to claim 4, characterized in that the evaluation step uses the value of the formula calculated using the concentration value of at least one micronutrient, which is at least one of phosphorus and chromium, in the urine, and the formula further includes a variable into which the concentration value of at least one amino acid and the concentration value of at least one micronutrient are substituted.

9. The evaluation method according to claim 1, characterized in that the urine was collected in the morning.

10. The evaluation method according to claim 9, characterized in that the urine was collected on the day following the day on which the exercise subject to evaluation was performed.

11. The evaluation method according to claim 10, characterized in that the exercise is a sport.

12. The evaluation method according to claim 1, characterized in that the gender of the person to be evaluated is male.

13. The evaluation method according to claim 1, characterized in that the evaluation step is performed by a control unit provided in the information processing device.

14. A calculation method characterized by including a calculation step of calculating the value of a formula, using the concentration value of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in the urine collected from the subject to evaluation, and a formula for evaluating the degree of fatigue, which includes a variable into which the concentration value is substituted.

15. The calculation method according to claim 14, characterized in that the calculation step is performed by a control unit provided in the information processing device.

16. An evaluation device comprising a control unit, wherein the control unit comprises an evaluation means for evaluating the degree of fatigue in the subject to be evaluated using the concentration value of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in urine collected from the subject to be evaluated, or using the value of the formula calculated using the formula and the concentration value, wherein the concentration value is substituted into the formula.

17. An evaluation device according to claim 16, characterized in that it is communicably connected via a network to a terminal device that provides the concentration value or the value of the formula, the control unit further comprises: data receiving means for receiving the concentration value or the value of the formula transmitted from the terminal device, and result transmitting means for transmitting evaluation results obtained by the evaluation means to the terminal device, the evaluation means using the concentration value or the value of the formula received by the data receiving means.

18. A calculation device comprising a control unit, wherein the control unit comprises calculation means for calculating the value of a formula, which includes the concentration value of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in urine collected from a subject to evaluation, and a formula for evaluating the degree of fatigue, which includes a variable into which the concentration value is substituted.

19. An evaluation program for causing a control unit of an information processing device to function as an evaluation means for evaluating the degree of fatigue in the subject to be evaluated, using the concentration value of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in urine collected from the subject to be evaluated, or using the value of the formula calculated using the formula containing a variable into which the concentration value is substituted and the concentration value.

20. A calculation program to cause the control unit of an information processing device to function as a calculation means for calculating the value of a formula, using the concentration value of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in urine collected from a subject to evaluation, and a formula for evaluating the degree of fatigue, which includes a variable into which the concentration value is substituted.

21. A computer-readable recording medium having the program described in claim 19 or 20 recorded on it.

22. An evaluation system comprising an evaluation device equipped with a control unit and a terminal device equipped with a control unit, connected via a network to enable communication, wherein the control unit of the terminal device comprises: data transmission means for transmitting to the evaluation device the concentration value of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in urine collected from the object to be evaluated, or the value of a formula calculated using a formula including a variable into which the concentration value is substituted and the concentration value; and result receiving means for receiving evaluation results regarding the degree of fatigue transmitted from the evaluation device, wherein the control unit of the evaluation device comprises: data receiving means for receiving the concentration value or the value of the formula transmitted from the terminal device; and evaluation means for evaluating the degree of fatigue in the object to be evaluated using the concentration value or the value of the formula received by the data receiving means. An evaluation system characterized by comprising: a result transmission means for transmitting evaluation results obtained by the evaluation means to the terminal device; and 23. A terminal device equipped with a control unit, wherein the control unit includes a result acquisition means for acquiring evaluation results regarding the degree of fatigue, and the evaluation results are the result of evaluating the degree of fatigue in the subject to evaluation using the concentration value of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in urine collected from the subject to evaluation, or using the value of the formula calculated using the formula and the concentration value, which includes a variable into which the concentration value is substituted.

24. The terminal device according to claim 23, characterized in that it is communicably connected via a network to an evaluation device for evaluating the degree of fatigue, the control unit further comprises data transmission means for transmitting the concentration value or the value of the formula to the evaluation device, and the result acquisition means for receiving the evaluation result transmitted from the evaluation device.

25. A method for creating an equation for evaluating the degree of fatigue, comprising: a step of obtaining the concentration of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in urine collected from a subject; a step of obtaining the concentration of any one substance from among myoglobin, creatine kinase, total ketone bodies, and NEFA (Non-esterified Fatty Acid) in blood collected from the subject; and a step of creating an equation for determining whether the concentration of any one substance is high or low relative to a predetermined value in the concentration distribution.

26. The method for creating the formula according to claim 25, characterized in that the urine and blood were collected on the morning following the day on which the subject exercise was performed.

27. The method for creating a formula according to claim 25, characterized in that the urine and blood were collected on the morning following the most recent rest day of the subject after the day the subject performed exercise, and the method further includes the step of selecting the combination to be used when creating the formula by excluding combinations of concentrations obtained from the urine and blood that fall outside a predetermined range.

28. The method for creating the formula according to claim 25, characterized in that the urine and blood were collected on the morning following the most recent rest day of the subject after the day the subject performed exercise.

29. The evaluation step includes an evaluation of the degree of fatigue in the subject being evaluated, where if the value of the formula obtained by substituting the concentration of at least one amino acid from among Gly, Lys, BCAA, Met, Ser, Glu, His, Thr, Trp, Arg, Phe, Ala, Val, Leu, Ile, Gln, Asp, Asn, Pro, Cys, and Tyr in the urine collected from the subject being evaluated on the morning following the most recent rest day after the exercise day is lower than a predetermined value, the evaluation step includes evaluating the degree of fatigue in the subject being evaluated using the value of the formula obtained by substituting the concentration of at least one amino acid in the urine collected from the subject being evaluated on the morning following the most recent rest day after the rest day is lower than a predetermined value, wherein the first formula is created using urine and blood collected from the subject on the morning following the most recent rest day after the exercise day by a predetermined formula creation method, The second formula is created using urine and blood collected from the subject on the morning following the most recent exercise day after the rest day, using the predetermined formula creation method, wherein the predetermined formula creation method includes the steps of: obtaining the concentration of at least one amino acid in the urine; obtaining the concentration of any one of myoglobin, creatine kinase, total ketone bodies, and NEFA (Non-esterified Fatty Acid) in the blood; and creating a formula for determining whether the concentration of any one of the substances is high or low relative to a predetermined value in the concentration distribution.

Images