Method for evaluating inflammation after parturition

Abstract

The present invention addresses the problem of providing, for example, an evaluation method with which highly reliable information about the condition of inflammation after parturition can be provided before parturition. In the present embodiment, the condition of inflammation in a ruminant after parturition is evaluated using at least one value from among: concentration values of 25 kinds of amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) in the blood of the ruminant before parturition; test values of 37 biochemical components (ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, kynurenine) in the blood; and measurement values (d-ROMs value, BAP value, BAP value / d-ROMs value, and d-ROMs value / BAP value) of four measurement items obtained from the blood.

Description

Methods for assessing postpartum inflammation

[0001] The present invention relates to a method for evaluating postpartum inflammation, a calculation method, an evaluation device, a calculation device, an evaluation program, a recording medium, an evaluation system, and a terminal device.

[0002] The periparturient period, approximately three weeks before and after parturition, is a critical period for cows. During this period, many metabolic diseases occur, such as ketosis, milk fever, retained placenta, abomasal displacement, metritis, hoof disease, and mastitis. In recent years, it has been reported that many of these diseases may be caused by inflammatory conditions (Non-Patent Document 1).

[0003] Patent Document 1 describes a method for evaluating postpartum ketosis, etc.

[0004] Mastitis is diagnosed after parturition by the appearance of the udder during lactation, changes in milk properties, bacteriological tests and somatic cell counts (SCC).

[0005] It has been reported that blood and urine samples were collected before parturition, and that differences in concentration were observed between cows that were normal after parturition and cows that developed mastitis (Non-patent documents 2-5).

[0006] Metritis and endometritis are diagnosed after delivery through mucosal and cytological examinations and clinical findings.

[0007] It has been reported that blood samples taken before parturition show differences in concentration between cows that were normal after parturition and cows that developed metritis (Non-patent documents 6-11).

[0008] It has been reported that d-ROMs, an indicator of oxidative stress, and BAP, an indicator of antioxidant capacity, fluctuate 52 weeks before the onset of inflammatory conditions in humans (Non-Patent Documents 12-13, 19). On the other hand, it has been reported that in cows undergoing ketosis, these values ​​fluctuate 14 days before and 15 days after calving (Non-Patent Document 20). Based on these findings, it is possible that d-ROMs, BAP, and the ratio of d-ROMs to BAP may be effective in predicting disease before calving and understanding inflammatory conditions after calving.

[0009] Blood haptoglobin is known as an indicator of inflammatory conditions. In dairy cows with high blood haptoglobin concentrations and considered to be in an inflammatory state, reduced milk yield and reduced conception rates have been confirmed (Non-Patent Documents 1, 14-17). There are several different standards for haptoglobin levels depending on the disease, but no unified consensus has yet been established.

[0010] IL-1β and serum albumin A are known as indicators of inflammatory conditions (Non-Patent Document 5). Although standard values ​​have been established for each disease, no unified findings have been established yet.

[0011] LBP is known as an indicator of inflammatory conditions (Non-Patent Document 18). Although standard values ​​have been established for each disease, no consensus has yet been reached.

[0012] International Publication No. 2018 / 003638

[0013] Horst, E. A., et al. (2021). "Invited review: The influence of immune activation on transition cow health and performance-A critical evaluation of traditional dogmas." J Dairy Sci 104(8): 8380-8410.Hu, H., et al. (2021). "Application of Metabolomics in Diagnosis of Cow Mastitis: A Review." Front Vet Sci 8: 747519.Zhang, G., et al. (2022). "Identification of Serum-Predictive Biomarkers for Subclinical Mastitis in Dairy Cows and New Insights into the Pathobiology of the Disease." J Agric Food Chem 70(5): 1724-1746.Sakemi, Y., et al. (2011). "Interleukin-6 in quarter milk as a further prediction marker for bovine subclinical mastitis." J Dairy Res 78(1): 118-121.Dervishi, E., et al. (2015). "Innate immunity and carbohydrate metabolism alterations precede occurrence of subclinical mastitis in transition dairy cows." J Anim Sci Technol 57: 46.Dervishi, E., et al. (2016)."Alterations in innate immunity reactants and carbohydrate and lipid metabolism precede occurrence of metritis in transition dairy cows." Res Vet Sci 104: 30-39.Dervishi, E., et al. (2018). "Urine metabolic fingerprinting can be used to predict the risk of metritis and highlight the pathobiology of the disease in dairy cows." Metabolomics 14(6): 83.Wisnieski, L., et al. (2019). "Predictive models for early lactation diseases in transition dairy cattle at dry-off." Prev Vet Med 163: 68-78.Paiano, R. B., et al. (2021). "Metritis in dairy cows is preceded by alterations in biochemical profile prepartum and at parturition." Res Vet Sci 135: 167-174.Chebel, R. C. (2021). "Predicting the risk of retained fetal membranes and metritis in dairy cows according to prepartum hemogram and immune and metabolic status." Prev Vet Med 187: 105204.Casaro, S., et al. (2023). "Blood metabolomics and impacted cellular mechanisms during transition into lactation in dairy cows that develop metritis." J Dairy Sci 106(11): 8098-8109.Nakajima, A., et al. (2019). "Serum levels of reactive oxygen metabolites at 12 weeks during tocilizumab therapy are predictive of 52 weeks-disease activity score-remission in patients with rheumatoid arthritis." BMC Rheumatol 3: 48.Masaki, N., et al. (2016). "Usefulness of the d-ROMs test for prediction of cardiovascular events." Int J Cardiol 222: 226-232.Dubuc, J., et al. (2010). "Risk factors for postpartum uterine diseases in dairy cows." J Dairy Sci 93(12): 5764-5771.Huzzey, J. M., et al. (2009). "Short communication: Haptoglobin as an early indicator of metritis." J Dairy Sci 92(2): 621-625.Kerwin, A. L., et al. (2022). "Transition cow nutrition and management strategies of dairy herds in the northeastern United States: Part II-Associations of metabolic- and inflammation-related analytes with health, milk yield, and reproduction." J Dairy Sci 105(6): 5349-5369.Kerwin, A. L., et al. (2022)."Transition cow nutrition and management strategies of dairy herds in the northeastern United States: Part I - Herd description and performance characteristics." J Dairy Sci 105(6): 5327-5348. Abuajamieh, M., et al. (2016). "Inflammatory biomarkers are associated with ketosis in periparturient Holstein cows." Res Vet Sci 109: 81-85. Nakajima, A., et al. (2019). "Serum levels of reactive oxygen metabolites at 12 weeks during tocilizumab therapy are predictive of 52-week disease activity score-remission in patients with rheumatoid arthritis." BMC Rheumatol 3: 48. Shibano, K., et al. (2021). "Oxidative stress markers and blood biochemistry in dairy cows with ketosis." Journal of the Japan Veterinary Society 74, 59-63.

[0014] If it were possible to diagnose the risk of postpartum inflammation before parturition, it would be possible to reduce the incidence of inflammation by, for example, providing preventive nutritional intervention before parturition, which would ultimately contribute to efficient production by dairy farmers.

[0015] As mentioned above, it has been reported that in cows that develop inflammation after parturition, fluctuations in several components are observed before parturition. However, there was a problem in that there was no risk diagnostic technology that was actually used on farms to diagnose the risk of postpartum inflammation before parturition.

[0016] The present invention has been made in consideration of the above, 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 provide highly reliable information regarding the state of inflammation after delivery before delivery.

[0017] In order to solve the above-mentioned problems and achieve the object, the evaluation method of the present invention involves measuring the concentration values ​​of 25 types of amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) in the blood of ruminants before parturition and measuring 37 types of biochemical parameters in the blood (specifically, ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, The method is characterized by including an evaluation step of evaluating the postpartum inflammatory state of the ruminant using at least one value of test values ​​of the following inflammatory proteins: CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine, and measured values ​​of four types of measurement items obtained from the blood (d-ROMs value (oxidative stress value), BAP value (antioxidant power value), BAP value / d-ROMs value, and d-ROMs value / BAP value), or using an equation containing a variable into which the at least one value is substituted and a value of the equation calculated using the at least one value.

[0018] In this specification, various amino acids, blood biochemistry and measurement items are mainly represented by abbreviations, but their official names are as follows:

[0019] (Amino acid abbreviation) (Full name) Ala Alanine Arg Arginine Asn Asparagine Asp Aspartic acid BCAA Branched chain amino acids Cit Citrulline Cys Cysteine Gln Glutamine Glu Glutamic acid Gly Glycine His Histidine Ile Isoleucine Leu Leucine Lys Lysine Met Methionine 3MeHis 3-Methyl histidine Orn Ornithine Phe Phenylalanine Pro Proline Ser Serine Tau Taurine Thr Threonine Trp Tryptophan Tyr Tyrosine Val Valine

[0020] (Blood biochemistry abbreviation) (Official name) ALB Albumin ALT Alanine transaminase AST Apartate Aminotransferase BHBA β-Hydroxybutyric acid BUN Blood urea nitrogen Ca Calcium gGTP γ-glutamyltransferase Glc Glucose Glob Globulin NEFA Non esterified fat acid T-Bil Total bilirubin TCHO Total cholesterol TG Triglyceride TP Total protein P Phosphorus Na Sodium K Potassium Cl Chlor GGT γ-glutartransferase ALP Alkaline phosphatase LDH Lactate dehydrogenase CPK Creatine phosphokinase LDL-C LDL cholesterol HDL-C HDL cholesterol Mg Magnesium AMY Amylase PA Pyruvate LA Lactate CRE Creatine LBP Lipopolysaccharide Binding Protein IL-1β Interleukin-1β IL-6 Interleukin-6 TNF-α Tumor Necrosis Factor-α

[0021] (Measurement item abbreviation) (Official name) d-ROMs Diacron-Reactive oxygen metabolites BAP Biological antioxidant potential

[0022] The inflammation may be metritis, mastitis, hypocalcemia, milk fever, abomasal derangement, or lameness.

[0023] The ruminant may also be a dairy or beef cattle.

[0024] The evaluation step may be executed by a control unit provided in the information processing device.

[0025] The calculation method of the present invention is also characterized by including a calculation step of calculating the value of the formula for evaluating the inflammatory state of the ruminant after parturition, using the formula including the concentration values ​​of the 25 types of amino acids in the blood of the ruminant before parturition, the test values ​​of the 37 types of biochemical tests in the blood, and at least one value of the measurement values ​​of the four types of measurement items obtained from the blood, and a variable into which the at least one value is substituted.

[0026] The calculation step may be executed by a control unit provided in the information processing device.

[0027] The evaluation device of the present invention is also characterized by comprising an evaluation unit that evaluates the inflammatory state of the ruminant after parturition using at least one value selected from the concentration values ​​of the 25 types of amino acids in the blood of the ruminant before parturition, the test values ​​of the 37 types of biochemical tests in the blood, and the measured values ​​of the four types of measurement items obtained from the blood, or using an equation containing a variable into which the at least one value is substituted and the value of the equation calculated using the at least one value.

[0028] In addition, the evaluation device of the present invention may be communicatively connected via a network to a terminal device that provides the at least one value or the value of the formula, and may further include a data receiving unit that receives the at least one value or the value of the formula transmitted from the terminal device, and a result transmitting unit that transmits the evaluation result obtained by the evaluation unit to the terminal device, and the evaluation unit may use the at least one value or the value of the formula received by the data receiving unit.

[0029] The calculation device according to the present invention is also characterized by comprising a calculation unit that calculates the value of an equation for evaluating the inflammatory state of the ruminant after parturition, using the equation including at least one value selected from the concentration values ​​of the 25 amino acids in the blood of the ruminant before parturition, the 37 biochemical test values ​​in the blood, and the measurement values ​​of the four measurement items obtained from the blood, and a variable into which the at least one value is substituted.

[0030] In addition, the evaluation program of the present invention is intended to cause an information processing device to execute an evaluation step of evaluating the postpartum inflammatory state of the dairy cow using at least one value selected from the concentration values ​​of the 25 types of amino acids in the blood of a ruminant before parturition, the 37 types of biochemical test values ​​in the blood, and the measurement values ​​of the four types of measurement items obtained from the blood, or using an equation containing a variable into which the at least one value is substituted and the value of the equation calculated using the at least one value.

[0031] In addition, the calculation program of the present invention is intended to cause an information processing device to execute a calculation step of calculating the value of an equation for evaluating the inflammatory state of the ruminant after parturition, using an equation that includes at least one value out of the concentration values ​​of the 25 types of amino acids in the blood of the ruminant before parturition, the 37 types of biochemical test values ​​in the blood, and the measurement values ​​of the four types of measurement items obtained from the blood, and a variable into which the at least one value is substituted.

[0032] Furthermore, the recording medium according to the present invention is a computer-readable recording medium having the evaluation program or the calculation program recorded thereon. In other words, the recording medium according to the present invention is a non-transitory computer-readable recording medium that includes programmed instructions for causing an information processing device to execute the evaluation method or the calculation method.

[0033] Furthermore, an evaluation system according to the present invention is an evaluation system configured by connecting an evaluation device and a terminal device via a network so that they can communicate with each other, wherein the terminal device comprises a data transmitting unit that transmits to the evaluation device at least one value out of the concentration values ​​of the 25 types of amino acids in the blood of a ruminant before parturition, the test values ​​of the 37 types of biochemical tests in the blood, and the measured values ​​of the four measurement items obtained from the blood, or an equation including a variable into which the at least one value is substituted and the value of the equation calculated using the at least one value, and a result receiving unit that receives the evaluation result relating to the inflammatory state after parturition transmitted from the evaluation device, and the evaluation device comprises a data receiving unit that receives the at least one value or the value of the equation transmitted from the terminal device, an evaluation unit that evaluates the inflammatory state of the ruminant after parturition using the at least one value or the value of the equation received by the data receiving unit, and a result transmitting unit that transmits the evaluation result obtained by the evaluation unit to the terminal device.

[0034] The terminal device of the present invention is also characterized in that it comprises a result acquisition unit that acquires evaluation results regarding the state of inflammation after parturition, and the evaluation results are a result of evaluating the state of inflammation after parturition of the ruminant using at least one value selected from the concentration values ​​of the 25 types of amino acids in the blood of the ruminant before parturition, the 37 types of biochemical test values ​​in the blood, and the measurement values ​​of the four types of measurement items obtained from the blood, or using an equation including a variable into which the at least one value is substituted and the value of the equation calculated using the at least one value.

[0035] The terminal device according to the present invention may be communicatively connected via a network to an evaluation device that evaluates the state of postpartum inflammation, and may further include a data transmission unit that transmits the at least one value or the value of the formula to the evaluation device, and the result acquisition unit may receive the evaluation result transmitted from the evaluation device.

[0036] In addition, the evaluation method of the present invention may further include a suggestion step of proposing preventive treatment to cows that are evaluated in the evaluation step as having a high possibility of contracting the metabolic disease after delivery.

[0037] The preventative treatment may also be at least one selected from the group consisting of administration of RumenProtect amino acids, administration of a feed additive, administration of a drug, and veterinary diagnosis.

[0038] The feed additive may also be at least one selected from the group consisting of pH adjusters, ion balance adjusters, mycotoxin adsorbents, propionic acid analogues such as calcium propionate, vitamins, minerals, amino acids, fatty acids, urea, probiotics, yeast, enzymes, antibiotics, antioxidants, antibacterial agents, and organic acids.

[0039] In addition, in the proposing step, statistical causal inference may be performed on cattle that are assessed as having the potential to contract the metabolic disease to infer the cause of the disease and propose preventive treatment corresponding to that cause.

[0040] According to the present invention, the postpartum inflammatory state of a ruminant is evaluated using the concentration values ​​of the 25 amino acids in the blood of the ruminant before parturition, the test values ​​of the 37 biochemical parameters in the blood, and at least one of the measured values ​​of the four measurement items obtained from the blood, thereby achieving the effect of providing reliable information regarding the postpartum inflammatory state before parturition. Furthermore, dairy farmers can reduce the incidence of postpartum inflammation by implementing preventive nutritional interventions based on the information provided by the present invention before parturition. In other words, the present invention contributes to efficient production at dairy farms.

[0041] FIG. 1 is a principle configuration diagram illustrating the basic principle of the first embodiment. FIG. 2 is a principle configuration diagram illustrating the basic principle of the second embodiment. FIG. 3 is a diagram illustrating an example of the overall configuration of the present system. FIG. 4 is a diagram illustrating another example of the overall configuration of the present system. FIG. 5 is a block diagram illustrating an example of the configuration of the evaluation device 100 of the present system. FIG. 6 is a diagram illustrating an example of information stored in the blood data file 106a. FIG. 7 is a diagram illustrating an example of information stored in the index status information file 106b. FIG. 8 is a diagram illustrating an example of information stored in the specified index status information file 106c. FIG. 9 is a diagram illustrating an example of information stored in the formula file 106d1. FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106e. FIG. 11 is a block diagram illustrating the configuration of the evaluation unit 102d. FIG. 12 is a block diagram illustrating an example of the configuration of the client device 200 of the present system. FIG. 13 is a block diagram illustrating an example of the configuration of the database device 400 of the present system. FIG. 14 is a diagram illustrating a linear regression model extracted in Example 4. FIG. 15 is a diagram illustrating a linear regression model extracted in Example 4. FIG. 16 is a diagram showing a linear regression model extracted in Example 4. FIG. 17 is a diagram showing a linear regression model extracted in Example 4. FIG. 18 is a diagram showing a logistic regression model extracted in Example 4. FIG. 19 is a diagram showing a logistic regression model extracted in Example 4. FIG. 20 is a diagram showing a logistic regression model extracted in Example 4. FIG. 21 is a diagram showing a logistic regression model extracted in Example 4. FIG. 22 is a diagram showing a logistic regression model extracted in Example 4. FIG. 23 is a diagram showing a logistic regression model extracted in Example 4. FIG. 24 is a diagram showing a logistic regression model extracted in Example 4. FIG. 25 is a diagram showing a logistic regression model extracted in Example 4. FIG. 26 is a diagram showing a logistic regression model extracted in Example 4. FIG. 27 is a diagram showing a logistic regression model extracted in Example 4. FIG. 28 is a diagram showing a logistic regression model extracted in Example 5. FIG. 29 is a diagram showing a logistic regression model extracted in Example 5.FIG. 30 is a diagram showing a logistic regression model extracted in Example 5. FIG. 31 is a diagram showing a logistic regression model extracted in Example 5. FIG. 32 is a diagram showing a logistic regression model extracted in Example 5. FIG. 33 is a diagram showing a logistic regression model extracted in Example 5. FIG. 34 is a diagram showing a logistic regression model extracted in Example 5. FIG. 35 is a diagram showing a logistic regression model for milk fever discrimination extracted in Example 6. FIG. 36 is a diagram showing a logistic regression model for milk fever discrimination and a logistic regression model for abomasal mutation discrimination extracted in Example 6. FIG. 37 is a diagram showing a logistic regression model for abomasal mutation discrimination extracted in Example 6. FIG. 38 is a diagram showing a logistic regression model for abomasal mutation discrimination extracted in Example 6. FIG. 39 is a diagram showing a logistic regression model for metritis discrimination extracted in Example 6. FIG. 40 is a diagram showing a logistic regression model for metritis discrimination extracted in Example 6. Fig. 41 is a diagram showing a logistic regression model for metritis discrimination extracted in Example 6. Fig. 42 is a diagram showing a logistic regression model for metritis discrimination extracted in Example 6. Fig. 43 is a diagram showing a logistic regression model for metritis discrimination extracted in Example 6. Fig. 44 is a diagram showing a logistic regression model for mastitis discrimination extracted in Example 6. Fig. 45 is a diagram showing a logistic regression model for mastitis discrimination extracted in Example 6. Fig. 46 is a diagram showing a logistic regression model for mastitis discrimination extracted in Example 6. Fig. 47 is a diagram showing a logistic regression model for mastitis discrimination extracted in Example 6. Fig. 48 is a diagram showing a logistic regression model for mastitis discrimination extracted in Example 6. Fig. 49 is a diagram showing a logistic regression model for lameness discrimination extracted in Example 6. Fig. 50 is a diagram showing a logistic regression model for lameness discrimination and a logistic regression model for inflammatory disease discrimination extracted in Example 6. Fig. 51 is a diagram showing a logistic regression model for inflammatory disease discrimination extracted in Example 6. FIG. 52 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.Fig. 53 is a diagram showing a logistic regression model for inflammatory disease discrimination extracted in Example 6. Fig. 54 is a diagram showing a logistic regression model for inflammatory disease discrimination extracted in Example 6. Fig. 55 is a diagram showing a logistic regression model for inflammatory disease discrimination extracted in Example 6. Fig. 56 is a diagram showing a logistic regression model for inflammatory disease discrimination extracted in Example 6. Fig. 57 is a diagram showing a logistic regression model for inflammatory disease discrimination extracted in Example 6. Fig. 58 is a diagram showing a logistic regression model for inflammatory disease discrimination extracted in Example 6. Fig. 59 is a diagram showing a logistic regression model for inflammatory disease discrimination extracted in Example 6 and a linear regression model for predicting serum amyloid A concentration 7 days after delivery extracted in Example 7. Fig. 60 is a diagram showing a linear regression model for predicting serum amyloid A concentration 7 days after delivery extracted in Example 7. Fig. 61 is a diagram showing a linear regression model for predicting serum amyloid A concentration 7 days after delivery extracted in Example 7. FIG. 62 is a diagram showing a linear regression model for predicting serum amyloid A concentrations 7 days after the date of delivery extracted in Example 7. FIG. 63 is a diagram showing a linear regression model for predicting serum amyloid A concentrations 7 days after the date of delivery extracted in Example 7. FIG. 64 is a diagram showing a linear regression model for predicting IL-1β concentrations 7 days after the date of delivery extracted in Example 7. FIG. 65 is a diagram showing a linear regression model for predicting IL-1β concentrations 7 days after the date of delivery extracted in Example 7. FIG. 66 is a diagram showing a linear regression model for predicting IL-1β concentrations 7 days after the date of delivery extracted in Example 7. FIG. 67 is a diagram showing a linear regression model for predicting IL-1β concentrations 7 days after the date of delivery extracted in Example 7 and a linear regression model for predicting d-ROMs values ​​7 days after the date of delivery extracted in Example 7. FIG. 68 is a diagram showing a linear regression model for predicting d-ROMs values ​​7 days after the date of delivery extracted in Example 7. FIG. 69 is a diagram showing a linear regression model for predicting d-ROMs values ​​7 days after the date of delivery extracted in Example 7. Fig. 70 is a diagram showing a linear regression model for predicting the d-ROMs value 7 days after the delivery date extracted in Example 7. Fig. 71 is a diagram showing a linear regression model for predicting the BAP value 7 days after the delivery date extracted in Example 7.Fig. 72 is a diagram showing a linear regression model for predicting the BAP value 7 days after the date of parturition extracted in Example 7. Fig. 73 is a diagram showing a linear regression model for predicting the BAP value 7 days after the date of parturition extracted in Example 7. Fig. 74 is a diagram showing a linear regression model for predicting the BAP value 7 days after the date of parturition extracted in Example 7 and a linear regression model for predicting the d-ROMs value / BAP value 7 days after the date of parturition extracted in Example 7. Fig. 75 is a diagram showing a linear regression model for predicting the d-ROMs value / BAP value 7 days after the date of parturition extracted in Example 7.

[0042] Hereinafter, an embodiment (first embodiment) of an evaluation method and a calculation method according to the present invention, as well as an embodiment (second embodiment) of an evaluation device, a calculation device, an evaluation method, a calculation program, a recording medium, an evaluation system, and a terminal device according to the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.

[0043] [First embodiment] [1-1. Outline of the first embodiment] Here, an outline of the first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the basic principle of the first embodiment.

[0044] First, blood data is obtained (step S11), which includes the concentration values ​​of the 25 amino acids in blood (including, for example, plasma, serum, etc.) collected from the ruminant animal (e.g., dairy cow) to be evaluated before parturition (e.g., a certain period before the expected parturition date), the test values ​​of the 37 biochemical tests in the blood, and at least one value selected from the measured values ​​of the four measurement items (any one or more values ​​selected from the concentration values ​​of the 25 amino acids, the test values ​​of the 37 biochemical tests, and the measured values ​​of the four measurement items).

[0045] In step S11, blood data measured by a company or the like that measures concentration values ​​or test values ​​may be obtained. Alternatively, blood data may be obtained by measuring concentration values ​​or test values ​​from pre-partum blood collected from the subject using, for example, the following measurement method (A), (B), or (C). Here, the unit of concentration value may be, for example, molar concentration, weight concentration, or enzyme activity, or may be obtained by adding, subtracting, multiplying, or dividing any constant by these concentrations. (A) The collected blood sample is centrifuged to separate plasma from the blood. All plasma samples are frozen and stored at -80°C until the concentration values ​​are measured. For concentration measurement, 0.02 N hydrochloric acid is added and ultrafiltration is used to deproteinize the blood, followed by pre-column derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and the concentration is analyzed using a liquid chromatography mass spectrometer (LC / MS) (see International Publication Nos. WO 2003 / 069328 and WO 2005 / 116629). (B) The collected blood sample is centrifuged to separate plasma from the blood. All plasma samples are frozen and stored at -80°C until the concentration is measured. For concentration measurement, 0.02 N hydrochloric acid is added and ultrafiltration is used to deproteinize the blood, followed by analysis using an amino acid analyzer based on post-column derivatization using a ninhydrin reagent. (C) The collected blood sample is subjected to blood cell separation using membranes, MEMS technology, or centrifugation to separate plasma or serum from the blood. Plasma or serum samples whose concentration is not measured immediately after collection are stored frozen at −80° C. until the time of measurement. When measuring the concentration, the concentration is analyzed by quantifying substances or spectroscopic values ​​that increase or decrease upon substrate recognition using molecules such as enzymes or aptamers that react with or bind to the target amino acid or biochemical.

[0046] Next, at least one value of the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items contained in the blood data acquired in step S11 is used to evaluate (predict / estimate) the state of inflammation (e.g., metritis, mastitis, hypocalcemia, milk fever, abomasal abnormality, lameness, etc.) after parturition of the subject (e.g., a certain period after parturition) (step S12). Note that before executing step S12, data such as missing values ​​and outliers may be removed from the blood data acquired in step S11.

[0047] This could provide reliable information about the postpartum inflammatory state before delivery.

[0048] Here, in step S12, the postpartum inflammatory state of the subject may be evaluated by calculating a value of an equation using an equation including at least one value of the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items, and a variable into which this at least one value is substituted. Furthermore, the variable into which the concentration value, test value, or measured value is substituted may be substituted with a value obtained by converting the concentration value, test value, or measured value, for example, using a method described below.

[0049] Furthermore, when evaluating the state of inflammation after parturition, in addition to at least one value of the concentration values ​​of the above 25 amino acids, the above 37 biochemical test values, and the measured values ​​of the above four measurement items, values ​​relating to the factors listed below that affect the onset of inflammation may also be used. Furthermore, in addition to the variables into which at least one value of the concentration values ​​of the above 25 amino acids, the above 37 biochemical test values, and the measured values ​​of the above four measurement items is substituted, the formula may further include variables into which values ​​relating to the factors listed below that affect the onset of inflammation are substituted: - parity term (binary variable) indicating whether the cow is multiparous or nulliparity - body weight, food intake, body condition score (BCS), temperature, humidity, stocking density, and season

[0050] Alternatively, the concentration value or formula value may be converted using, for example, the methods listed below, and the converted value may be used to evaluate the postpartum inflammatory state of the subject. To ensure that the possible range of concentration values ​​or formula values ​​falls within a predetermined range (e.g., 0.0 to 1.0, 0.0 to 10.0, 0.0 to 100.0, or -10.0 to 10.0, etc.), the concentration value or formula value may be converted by, for example, adding, subtracting, multiplying, or dividing any value, or by converting the concentration value or formula value using a predetermined transformation method (e.g., exponential transformation, logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by performing a combination of these calculations on the concentration value or formula value. For example, the value of an exponential function with the concentration value or the value of the formula 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 or the value of the formula when p is defined as the probability that the state of postpartum inflammation is a predetermined state (e.g., a state in which the blood haptoglobin concentration exceeds a reference value)) may be further calculated, or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, the value of the probability p) may be further calculated. Furthermore, the concentration value or the value of the formula may be converted so that the converted value under specific conditions becomes a specific value. For example, the concentration value or the value of the formula may be converted so that the converted value when the sensitivity is 95% becomes 5.0 and the converted value when the sensitivity is 80% becomes 8.0. Furthermore, with regard to the concentration values, the concentration distribution may be normalized for each amino acid, and then the concentration values ​​may be standardized to have an average of 50 and a standard deviation of 10. Furthermore, with regard to the value of the formula, the value of the formula may be standardized to have an average of 50 and a standard deviation of 10. Note that these transformations described above may be applied to test values ​​and measurements.

[0051] Alternatively, position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper may be generated using at least one value (if the value is converted, the converted value) or formula value (if the formula value is converted, the converted value) of the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items, and the generated position information may be used as an evaluation result regarding the postpartum inflammatory state of the subject. The predetermined ruler is used to evaluate the postpartum inflammatory state, and may be, for example, a ruler with a scale that indicates at least the upper and lower limits of the "range of possible concentration values, test values, measured values, or formula values, or converted values" or "part of the range." The predetermined mark corresponds to the concentration value, test value, measured value, or formula value, or converted value, and may be, for example, a circle or a star.

[0052] Furthermore, if at least one of the values ​​or formula values ​​among the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items is lower than a predetermined value (such as the mean ± 1 SD, 2 SD, 3 SD, N quantile, N percentile, or a cutoff value recognized for clinical significance), or is equal to or higher than the predetermined value, the subject may be evaluated as having developed inflammation after delivery. In this case, a standard deviation may be used instead of the concentration value, test value, or formula value itself. For example, if the standard deviation is less than the mean value - 2 SD (if the standard deviation is < 30) or if the standard deviation is higher than the mean value + 2 SD (if the standard deviation is > 70), the subject may be evaluated as having developed inflammation after delivery.

[0053] The risk (possibility) of the subject developing postpartum inflammation may also be qualitatively evaluated. For example, the subject may be classified into one of a plurality of categories defined by at least considering the degree of risk of developing postpartum inflammation using at least one value selected from the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measurement values ​​of the four measurement items, and one or more preset thresholds, or using an equation containing the at least one value and a variable into which the at least one value is substituted, and one or more preset thresholds. The plurality of categories may include a category for subjects with a high risk of developing postpartum inflammation (e.g., subjects whose postpartum blood haptoglobin concentration is equal to or greater than a reference value (e.g., 800 μg / mL)) and a category for subjects with a low risk of developing postpartum inflammation (e.g., subjects whose postpartum blood haptoglobin concentration is less than a reference value (e.g., 800 μg / mL)). The multiple categories may include a category for subjects at high risk of developing postpartum inflammation, a category for subjects at low risk of developing postpartum inflammation, and a category for subjects at a moderate risk of developing postpartum inflammation. For example, the haptoglobin concentration in the subject's postpartum blood may be estimated using at least one of the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items, and one or more preset thresholds, or using the at least one value, a formula containing a variable into which the at least one value is substituted, and one or more preset thresholds. The concentration value, test value, measured value, or formula value may be converted using a predetermined method, and the converted value may be used to classify the subject into one of the multiple categories.

[0054] Here, as an indicator showing that inflammation is in a predetermined state, for example, a state in which the blood haptoglobin concentration exceeds a reference value, or d-ROMs, which is an indicator of oxidative stress, BAP, IL-1β, serum albumin A, LBP, or somatic cell count (SCC) in milk, which are indicators of antioxidant capacity, may be used.

[0055] The formula used for evaluation may be in any format, but may be, for example, in the format shown below.・Linear models such as multiple regression equations based on the least squares method, linear discriminant equations, principal component analysis, and canonical discriminant analysis ・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 inter-individual differences and inter-facility differences in addition to generalized linear models ・Equations created by cluster analysis such as K-means method, k-nearest neighbor method, and hierarchical cluster analysis ・Equations created based on Bayesian statistics such as MCMC (Markov chain Monte Carlo method), Bayesian network, hierarchical Bayesian method, and Gaussian process model ・Equations created by class classification such as support vector machines and decision trees ・Equations created by ensemble learning models that combine multiple models such as RandomForest, GBDT, lightGBM, and ExtraTrees ・Equations created based on neural network structures such as deep learning models, self-attention, and transformers ・Equations created by methods that do not belong to the above categories, such as fractional equations - An expression that can be expressed as a sum of expressions of different forms

[0056] Furthermore, the equation used in the evaluation may be prepared, for example, by the method described in International Publication No. WO 2004 / 052191 or International Publication No. WO 2006 / 098192, both of which are international applications filed by the present applicant. Note that, if the equation is obtained by these methods, it can be suitably used to evaluate the state of postpartum inflammation, regardless of the units of amino acid concentration values, biochemical test values, and measurement items in the blood data used as input data.

[0057] Here, in multiple regression equations, multiple logistic regression equations, canonical discriminant functions, etc., coefficients and constant terms are added to each variable, and these coefficients and constant terms are preferably real numbers, more preferably values ​​within the 99% confidence interval range of the coefficients and constant terms obtained for performing the various classifications from the data, and even more preferably values ​​within the 95% confidence interval range of the coefficients and constant terms obtained for performing the various classifications from the data. Furthermore, the value of each coefficient and its confidence interval may be multiplied by a real number, and the value of the constant term and its confidence interval may be obtained by adding, subtracting, multiplying, or dividing it by any real constant. When using logistic regression equations, linear discriminants, multiple regression equations, etc. for evaluation, linear transformations (addition of a constant, multiplication of a constant) and monotonically increasing (decreasing) transformations (e.g., logit transformation, etc.) do not change the evaluation performance, and the evaluation performance of the transformed equations is equivalent to that before transformation, so the transformed equations may be used for evaluation.

[0058] A fractional expression is one in which the numerator is the sum of variables A, B, C, ... and / or the denominator is the sum of variables a, b, c, .... Fractional expressions also include sums of fractional expressions α, β, γ, ... (such as α + β) with this structure. Fractional expressions also include divided fractional expressions. The variables used in the numerator and denominator may each have an appropriate coefficient. The variables used in the numerator and denominator may also be duplicated. Each fractional expression may also have an appropriate coefficient. The coefficient values ​​of each variable and the value of the constant term may be real numbers. Furthermore, although the positive or negative sign of the correlation with the objective variable is generally reversed between a fractional formula and a fractional formula after the numerator variable and the denominator variable have been swapped (the "fractional formula after swapping"), the correlation with the objective variable is maintained and the evaluation performance of the formula can be considered to be equivalent, so the fractional formula also includes the fractional formula after swapping.

[0059] [1-2. Proposal of Preventive Treatment] Next, a preventive treatment is proposed for cows that are evaluated in the evaluation step as having a high possibility of suffering from the metabolic disease after calving. The proposal of the preventive treatment may be made after only the evaluation. Alternatively, the proposal may be made after the evaluation shown in Japanese Patent Application No. 2023-074785 is performed before the evaluation and then the evaluation of the present application is performed.

[0060] The preventive treatment may be at least one selected from the group consisting of administration of RumenProtect amino acids such as AjiPro®-L, administration of feed additives, administration of medication, and veterinary diagnosis. AjiPro®-L may be, for example, that described in International Publication No. WO 2008 / 041371, an international application filed by the present applicant. The feed additive may be at least one selected from the group consisting of pH adjusters, ion balance adjusters, mycotoxin adsorbents, propionic acid analogs such as calcium propionate, vitamins, minerals, amino acids, fatty acids, urea, probiotics, yeast, enzymes, antibiotics, antioxidants, antibacterial agents, and organic acids.

[0061] Here, for the preventive treatment, for cattle assessed as having a possibility of contracting the metabolic disease, statistical causal inference may be performed to infer the cause of the disease and preventive treatment corresponding to the cause may be proposed. Note that for animals assessed as being at high risk, preventive treatment may be proposed through individual treatment by a veterinarian.

[0062] [Second Embodiment] [2-1. Overview of the Second Embodiment] Here, an overview of the second embodiment will be described with reference to FIG. 2. FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. Note that in the description of this second embodiment, explanations that overlap with those of the first embodiment described above may be omitted. In particular, here, a case is described as an example in which a formula value or a value after conversion of the formula value is used when evaluating the state of postpartum inflammation. However, for example, at least one value of the concentration values ​​of the above 25 types of amino acids, the above 37 types of biochemical test values, and the measured values ​​of the above four types of measurement items, or a value after conversion of the value, may also be used.

[0063] The control unit evaluates the postpartum inflammatory state of the ruminant being evaluated by calculating the value of the equation using an equation previously stored in the memory unit, the equation containing at least one value contained in previously acquired blood data including the concentration values ​​of the 25 types of amino acids in the blood of the ruminant being evaluated before parturition, the test values ​​of the 37 types of biochemical tests in the blood, and the measured values ​​of the four types of measurement items obtained from the blood, and a variable into which the at least one value is substituted (step S21).

[0064] This could provide reliable information about the postpartum inflammatory state before delivery.

[0065] The formula used in step S21 may be one created based on the formula creation process (steps 1 to 4) described below. An outline of the formula creation process will now be described.

[0066] First, the control unit creates a candidate formula (e.g., y = a1x1 + a2x2 + ... + anxn, where y is the index data, xi is the blood data, ai is a constant, i = 1, 2, ..., n) based on a predetermined formula creation method from index state information previously stored in the storage unit (which may have data with missing values ​​and outliers removed in advance) (Step 1). The index state information includes blood data related to at least one of the concentrations of the 25 amino acids in the blood before delivery, the test values ​​of the 37 biochemical tests in the blood, and the measured values ​​of the four measurement items obtained from the blood, as well as index data related to the state of inflammation after delivery (e.g., the concentration of haptoglobin in the blood after delivery).

[0067] In step 1, multiple candidate formulas may be created from the index status information by combining multiple different formula creation methods (including those related to multivariate analysis such as principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, decision tree, k-nearest neighbor method, Gaussian process model, Random Forest, GBDT, ExtraTrees, and neural network). Specifically, multiple groups of candidate formulas may be created simultaneously in parallel using multiple different algorithms for index status information, which is multivariate data composed of pre-partum blood data and post-partum index data obtained from a large number of ruminants. For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate formulas. Alternatively, candidate formulas may be created by converting index status information using candidate formulas created by performing principal component analysis, and then performing discriminant analysis on the converted index status information. This ultimately allows the creation of a formula optimal for evaluation.

[0068] Here, the candidate equation created using principal component analysis is a linear equation including each variable that maximizes the variance of all blood data. Furthermore, the candidate equation created using discriminant analysis is a higher-order equation (including exponentials and logarithms) including each variable that minimizes the ratio of the sum of variances within each group to the variance of all blood data. Furthermore, the candidate equation created using support vector machines is a higher-order equation (including kernel functions) including each variable that maximizes the boundary between groups. Furthermore, the candidate equation created using multiple regression analysis is a higher-order equation including each variable that minimizes the sum of distances from all blood data. Furthermore, the candidate equation created using Cox regression analysis is a linear model including a logarithmic hazard ratio, and is a linear equation including each variable and its coefficient that maximizes the likelihood of the model. Furthermore, the candidate equation created using logistic regression analysis is a linear model representing the logarithmic odds of probability, and is a linear equation including each variable that maximizes the likelihood of the probability. The k-means method is a method of searching k neighbors of each blood data, defining the group to which the most neighboring points belong as the group to which the data belongs, and selecting a variable that best matches the defined group to which the input blood data belongs. Cluster analysis is a method of clustering (grouping) points that are closest to each other among all blood data. A decision tree is a method of ranking variables and predicting a group of blood data from the possible patterns of variables with higher rankings.

[0069] Returning to the explanation of the formula creation process, the control unit verifies (cross-validates) 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. Note that in step 2, the candidate formula may be verified for at least one of a plurality of indices, such as the discrimination rate, sensitivity, specificity, information criterion (Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC)), ROC_AUC (area under the receiver characteristic curve), etc., based on at least one of a plurality of methods, such as the bootstrap method, the holdout method, the N-fold method, and the leave-one-out method. This makes it possible to create candidate formulas with high predictability or robustness, taking into account index status information and evaluation conditions.

[0070] Here, the discrimination rate refers to the proportion of subjects whose true state is negative (e.g., subjects who did not develop inflammation after delivery) correctly evaluated as negative, and subjects whose true state is positive (e.g., subjects who developed inflammation after delivery) correctly evaluated as positive, in the evaluation method according to this embodiment. Sensitivity refers to the proportion of subjects whose true state is positive correctly evaluated as positive, in the evaluation method according to this embodiment. Specificity refers to the proportion of subjects whose true state is negative correctly evaluated as negative, in the evaluation method according to this embodiment. Akaike Information Criterion (AIC) is a measure of how well observed data matches a statistical model in cases such as regression analysis, and the model with the smallest value defined as "-2 × (maximum logarithmic likelihood of the statistical model) + 2 × (number of free parameters of the statistical model)" is determined to be the best. The Bayesian Information Criterion (BIC) is a model selection criterion derived based on the concept of Bayesian statistics, and determines that the model (model with few parameters) with the smallest value defined by "-2 x (maximum logarithmic likelihood of the statistical model) + (number of free parameters of the statistical model) x ln (sample size)" is the best. 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 ROC_AUC value is 1 for perfect discrimination, and the closer this value is to 1, the higher the discrimination ability. Predictability is the average of the discrimination rate, sensitivity, and specificity obtained by repeatedly verifying candidate formulas. Robustness is the variance of the discrimination rate, sensitivity, and specificity obtained by repeatedly verifying candidate formulas.

[0071] Returning to the explanation of the formula creation process, the control unit selects variables for the candidate formula based on a predetermined variable selection method, thereby selecting a combination of blood data included in the index status information used to create the candidate formula (step 3). In step 3, variable selection may be performed for each candidate formula created in step 1. This allows for appropriate selection of variables for the candidate formula. Step 1 is then executed again using the index status information including the blood data selected in step 3. In step 3, variables for the candidate formula may be selected based on the verification results of step 2 using at least one of the following methods: stepwise search, best-path search, local search, genetic algorithm, and multi-objective optimization. The best-path search is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index provided 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 calculation time).

[0072] Returning to the explanation of the formula creation process, the control unit repeatedly executes the above-mentioned steps 1, 2, and 3, and selects a candidate formula to be used in evaluation from among a plurality of candidate formulas based on the verification results accumulated thereby, thereby creating a formula to be used in evaluation (step 4). Note that the selection of candidate formulas may involve, for example, selecting the optimal or multi-objective optimal one from candidate formulas created using the same formula creation method, or selecting the optimal or multi-objective optimal one from all candidate formulas.

[0073] As explained above, the formula creation process systematizes (executes) the creation of candidate formulas, the validation of candidate formulas, and the selection of variables for the candidate formulas in a single flow, thereby creating a formula that is optimal for evaluating the state of postpartum inflammation. In other words, the formula creation process uses the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items in multivariate statistical analysis, and combines variable selection and cross-validation to select an optimal and robust set of variables, thereby extracting a formula with high evaluation performance.

[0074] [2-2. Configuration of the Second Embodiment] Here, the configuration of an evaluation system according to the second embodiment (hereinafter sometimes referred to as the present system) will be described with reference to FIGS. 3 to 13. Note that the present system is merely an example, and the present invention is not limited thereto. In particular, here, a case where the value of the formula or a converted value thereof is used when evaluating the state of postpartum inflammation is described as an example, but for example, at least one value of the concentration values ​​of the above 25 types of amino acids, the above 37 types of biochemical test values, and the measured values ​​of the above four types of measurement items, or a converted value of that value, may also be used.

[0075] First, the overall configuration of this system will be described with reference to Figures 3 and 4. Figure 3 is a diagram showing an 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 an evaluation device 100 that evaluates the state of postpartum inflammation and a client device 200 (corresponding to the terminal device of the present invention) that provides blood data, which are communicably connected via a network 300.

[0076] In this system, the client device 200 that provides the data used in the evaluation and the client device 200 that receives the evaluation results may be separate devices. As shown in Fig. 4, this system may be configured by connecting, in addition to the evaluation device 100 and the client device 200, a database device 400 that stores index state information used when creating a formula in the evaluation device 100, formulas used in the evaluation, and the like, so that they can communicate with each other via a network 300.

[0077] 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, and conceptually shows only the parts of the configuration that are related to the present invention.

[0078] The evaluation device 100 is composed of a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in an integrated manner, a communication interface unit 104 that communicatively connects the evaluation device to a network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line, a memory 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 these units are communicatively connected via any communication path. Here, the evaluation device 100 may be configured in the same housing as various analyzers (e.g., amino acid / biochemical analyzers, etc.). For example, a small analytical device having a configuration (hardware and software) for calculating (measuring) at least one value among the concentration values ​​of the above 25 types of amino acids in blood, the above 37 types of biochemical test values ​​in blood, and the measurement values ​​of the above four types of measurement items based on blood, and outputting the calculated value (by printing, displaying on a monitor, etc.) may further be equipped with an evaluation unit 102d described below, and the results obtained by the evaluation unit 102d may be output using the configuration.

[0079] The communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.

[0080] The input / output interface unit 108 is connected to an input device 112 and an output device 114. Here, the output device 114 may be a monitor (including a home television), a speaker, or a printer (hereinafter, the output device 114 may be referred to as the monitor 114). The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that cooperates with a mouse to realize a pointing device function.

[0081] The memory unit 106 is a storage means, and may be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, etc. The memory unit 106 stores computer programs that work in cooperation with an OS (Operating System) to give commands to a CPU to perform various processes. As shown in the figure, the memory unit 106 stores a blood 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.

[0082] The blood data file 106a stores at least one of the concentration values ​​of the 25 amino acids in blood, the 37 biochemical test values ​​in blood, and the measured values ​​of the four measurement items obtained from blood. FIG. 6 is a diagram showing an example of information stored in the blood data file 106a. As shown in FIG. 6, the information stored in the blood data file 106a is configured by correlating an individual number for uniquely identifying the individual (sample) being evaluated with blood data. Here, FIG. 6 treats the blood data as a numerical value, i.e., a continuous scale, but the blood data may also be a nominal or ordinal scale. In the case of a nominal or ordinal scale, analysis may be performed by assigning an arbitrary numerical value to each condition. Furthermore, values ​​related to the aforementioned factors that affect the onset of inflammation may be combined with the blood data.

[0083] Returning to FIG. 5 , the index status information file 106b stores index status information used when creating equations. FIG. 7 is a diagram showing an example of information stored in the index status information file 106b. As shown in FIG. 7 , the information stored in the index status information file 106b is configured by interrelating an individual number, index data (T) related to postpartum inflammation, such as the concentration value of haptoglobin in the blood after delivery, and blood data. Here, in FIG. 7 , the index data and blood data are treated as numerical values ​​(i.e., continuous scales), but the index data and blood data may also be nominal or ordinal scales. Note that if the index data and blood data are nominal or ordinal scales, any numerical value assigned to each state may be used for various processes, such as evaluation and calculation of the value of an equation.

[0084] Returning to Fig. 5, the designated index status information file 106c stores index status information designated by the designation unit 102b, which will be described later. Fig. 8 is a diagram showing an example of information stored in the designated index status information file 106c. As shown in Fig. 8, the information stored in the designated index status information file 106c is configured by associating an individual number, designated index data, and designated blood data with each other.

[0085] Returning to FIG. 5 , the formula-related information database 106d is configured with a formula file 106d1 that stores formulas created by the formula creation unit 102c (described later). The formula file 106d1 stores formulas used during evaluation. FIG. 9 is a diagram showing an example of information stored in the formula file 106d1. As shown in FIG. 9 , the information stored in the formula file 106d1 is configured by interrelating ranks, formulas (in FIG. 9 , Fp(Gly, . . .), Fp(Gly, Leu, Phe), Fk(Gly, Leu, Phe, . . .), etc.), thresholds corresponding to each formula creation method, and verification results of each formula (e.g., the value of each formula).

[0086] Returning to Fig. 5, the evaluation result file 106e stores the evaluation results obtained by the evaluation unit 102d, which will be described later. Fig. 10 is a diagram showing an example of information stored in the evaluation result file 106e. The information stored in the evaluation result file 106e is configured by interrelating an individual number for uniquely identifying the individual (sample) to be evaluated, blood data of the individual obtained in advance, and evaluation results regarding the state of postpartum inflammation (e.g., the value of a formula calculated by a calculation unit 102d1, which will be described later, the converted value obtained by a conversion unit 102d2, which will be described later, the position information generated by a generation unit 102d3, which will be described later, or the classification result obtained by a classification unit 102d4, which will be described later).

[0087] 5, the control unit 102 has an internal memory for storing control programs such as an OS, programs defining various processing procedures, required data, etc., and executes various information processing based on these programs. As shown in the figure, the control unit 102 is roughly divided into 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 the index status information transmitted from the database device 400 and the blood data transmitted from the client device 200, such as removing data with missing values, removing data with many outliers, and removing variables with many missing values.

[0088] The acquiring unit 102a acquires information (specifically, blood data, index status information, formulas, etc.). For example, the acquiring unit 102a may acquire information by receiving information (specifically, blood data, index status information, formulas, etc.) transmitted from the client device 200 or the database device 400 via the network 300. The acquiring unit 102a may also receive data used for evaluation transmitted from a client device 200 other than the client device 200 to which the evaluation results are transmitted. Furthermore, for example, if the evaluation device 100 includes a mechanism (including hardware and software) for reading information recorded on a recording medium, the acquiring unit 102a may acquire information by reading the information (specifically, concentration data, index status information, formulas, etc.) recorded on the recording medium via the mechanism. The designating unit 102b designates the index data and blood data to be used for creating a formula.

[0089] The formula creation unit 102c creates a formula based on the index state information acquired by the acquisition unit 102a and the index state information designated by the designation unit 102b. Note that, if the formula is stored in advance in a predetermined storage area of ​​the storage unit 106, the formula creation unit 102c may create the formula by selecting a desired formula from the storage unit 106. Alternatively, the formula creation unit 102c may create the formula by selecting and downloading a desired formula from another computer device (e.g., database device 400) that has formulas stored in advance.

[0090] The evaluation unit 102d evaluates the individual's postpartum inflammatory state by calculating the value of the formula using a formula obtained in advance (e.g., a formula created by the formula creation unit 102c or a formula acquired by the acquisition unit 102a) and at least one value selected from the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items contained in the blood data acquired by the acquisition unit 102a. Note that the evaluation unit 102d may evaluate the individual's postpartum inflammatory state using at least one value selected from the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items, or a value obtained by converting that value.

[0091] The configuration of the evaluation unit 102d will now be described with reference to Fig. 11. Fig. 11 is a block diagram showing the configuration of the evaluation unit 102d, conceptually illustrating only the parts of the configuration that are relevant to the present invention. The evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.

[0092] The calculation unit 102d1 calculates the value of the formula using a formula including at least one value selected from the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items, and a variable into which the at least one value is substituted. Note that the evaluation unit 102d may store the value of the formula calculated by the calculation unit 102d1 as the evaluation result in a predetermined storage area of ​​the evaluation result file 106e.

[0093] The conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1, for example, using the conversion method described above. Note that the conversion unit 102d2 may convert at least one of the concentration values ​​of the 25 amino acids, the 37 biochemical test values, and the measured values ​​of the four measurement items contained in the blood data, for example, using the conversion method described above. Furthermore, the evaluation unit 102d may store the converted value obtained by the conversion unit 102d2 as the evaluation result in a predetermined storage area of ​​the evaluation result file 106e.

[0094] The generating unit 102d3 generates position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper, using the value of the formula calculated by the calculating unit 102d1 or the converted value obtained by the converting unit 102d2 (which may be a concentration value or an inspection value, or a converted value of the concentration value or inspection value). Note that the evaluating unit 102d may store the position information generated by the generating unit 102d3 in a predetermined storage area of ​​the evaluation result file 106e as the evaluation result.

[0095] The classification unit 102d4 uses the value of the formula calculated by the calculation unit 102d1 or the converted value obtained by the conversion unit 102d2 (which may be a concentration value, test value, or measurement value, or a converted value of the concentration value, test value, or measurement value) to classify the individual into one of multiple categories defined taking into account at least the degree of risk of developing inflammation after delivery.

[0096] The result output unit 102e outputs the processing results of each processing unit in the control unit 102 (including the evaluation results obtained by the evaluation unit 102d) to the output device 114.

[0097] The sending unit 102f sends the evaluation results to the client device 200 that sent the individual's blood data, and sends the formula created by the evaluation device 100 and the evaluation results to the database device 400. Note that the sending unit 102f may send the evaluation results to a client device 200 different from the client device 200 that sent the data used for the evaluation.

[0098] Next, the configuration of the client device 200 of this system will be described with reference to Fig. 12. Fig. 12 is a block diagram showing an example of the configuration of the client device 200 of this system, and conceptually shows only the parts of the configuration that are relevant to the present invention.

[0099] The client device 200 is composed of a control unit 210, a ROM 220, a HD 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280, and these units are communicably connected via any communication path. The client device 200 may be based on an information processing device (e.g., an information processing terminal such as a personal computer, a workstation, a home game device, an Internet TV, a PHS (Personal Handyphone System) terminal, a mobile terminal, a mobile communication terminal, or a PDA (Personal Digital Assistant)) to which peripheral devices such as a printer, a monitor, and an image scanner are connected as needed.

[0100] The input device 250 includes a keyboard, a mouse, a microphone, etc. A monitor 261, which will be described later, also functions as a pointing device in cooperation with the mouse. The output device 260 is an output means for outputting information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker, etc. The input / output IF 270 is connected to the input device 250 and the output device 260.

[0101] The communication IF 280 communicatively connects the client device 200 to the network 300 (or a communication device such as a router). In other words, the client device 200 is connected to the network 300 via a communication device (e.g., a modem, a TA (Terminal Adapter), a router, etc.) and a telephone line or via a dedicated line. This allows the client device 200 to access the evaluation device 100 in accordance with a predetermined communication protocol.

[0102] The control unit 210 includes a receiving unit 211 and a transmitting unit 212. The receiving unit 211 receives various information such as the evaluation results transmitted from the evaluation device 100 via the communication IF 280. The transmitting unit 212 transmits various information such as the blood data of the individual to the evaluation device 100 via the communication IF 280.

[0103] 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. Computer programs that work in cooperation with the OS to issue instructions to the CPU and perform various processes are recorded in the ROM 220 or the HD 230. The computer programs are executed by being loaded into the RAM 240 and cooperate with the CPU to form the control unit 210. The computer programs may also be recorded on an application program server connected to the client device 200 via a network, and the client device 200 may download all or any part of the computer programs as needed. All or any part of the processing performed by the control unit 210 may also be implemented using hardware such as wired logic.

[0104] Here, the control unit 210 may include an evaluation unit 210a (including a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having functions similar to those of the evaluation unit 102d included in the evaluation device 100. When the control unit 210 includes the evaluation unit 210a, the evaluation unit 210a may convert the value of the formula (which may be a concentration value or a test value) using the conversion unit 210a2, generate position information corresponding to the value of the formula or the converted value (which may be a concentration value or a test value, or a value after conversion of the concentration value or test value) using the generation unit 210a3, and classify the individual into one of a plurality of categories using the value of the formula or the converted value (which may be a concentration value or a test value, or a value after conversion of the concentration value or test value) using the classification unit 210a4.

[0105] Next, the network 300 of this system will be described with reference to Figures 3 and 4. The network 300 has a 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 networks). The network 300 may be a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog and digital), a leased line network (including both analog and digital), a CATV (Community Antenna TeleVision) network, a mobile circuit switching network or a mobile packet switching network (IMT (International Mobile Telecommunication) 2000 system, GSM (Registered Trademark) (Global System for Mobile Communications) system, or PDC (Personal Digital The communication network may be a wireless communication network (including a cellular / PDC-P system, etc.), a radio paging network, a local wireless network such as Bluetooth (registered trademark), a PHS network, a satellite communication network (including a CS (Communication Satellite), a BS (Broadcasting Satellite), or an ISDB (Integrated Services Digital Broadcasting), etc.), etc.

[0106] Next, the configuration of the database device 400 of this system will be described with reference to Fig. 13. Fig. 13 is a block diagram showing an example of the configuration of the database device 400 of this system, and conceptually shows only the parts of the configuration that are relevant to the present invention.

[0107] The database device 400 has a function of storing index state information used when creating an equation in the evaluation device 100 or the database device itself, the equation created in the evaluation device 100, the evaluation results in the evaluation device 100, etc. As shown in Fig. 13, the database device 400 is composed of a control unit 402 such as a CPU that controls the database device in an overall manner, a communication interface unit 404 that communicatively connects the database device to the network 300 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, files (for example, files for Web pages), etc., and an input / output interface unit 408 that connects to an input device 412 and an output device 414, and these units are communicatively connected via any communication path.

[0108] The memory unit 406 is a storage means, and may be, for example, a memory device such as RAM or ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like. 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 an input device 412 and an output device 414. Here, the output device 414 may be a monitor (including a home television), a speaker, or a printer. The input device 412 may be a keyboard, a mouse, a microphone, or a monitor that functions as a pointing device in cooperation with a mouse.

[0109] The control unit 402 has an internal memory for storing control programs such as an OS, programs defining various processing procedures, required data, etc., and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into a transmitting unit 402a and a receiving unit 402b. The transmitting unit 402a transmits various information such as index state information and equations to the evaluation device 100. The receiving unit 402b receives various information such as equations and evaluation results transmitted from the evaluation device 100.

[0110] In the present description, an example has been given in which the evaluation device 100 receives blood data, calculates the value of the formula, classifies the individual into categories, and transmits the evaluation results, and the client device 200 receives the evaluation results. However, if the client device 200 is provided with the evaluation unit 210a, it is sufficient for the evaluation device 100 to calculate the value of the formula. For example, the conversion of the value of the formula, the generation of position information, and the classification of the individual into categories may be appropriately shared between 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 position information corresponding to the value of the formula or the converted value in the generation unit 210a3, and classify the individual into one of a plurality of categories using the value of the formula or the converted value in the classification unit 210a4. Furthermore, when the client device 200 receives a converted value from the evaluation device 100, the evaluation unit 210a may generate position information corresponding to the converted value in the generation unit 210a3, or may classify the individual into one of a plurality of categories using the converted value in the classification unit 210a4. Furthermore, when the client device 200 receives the value of the expression or the converted value and the position information from the evaluation device 100, the evaluation unit 210a may classify the individual into one of a plurality of categories using the value of the expression or the converted value in the classification unit 210a4.

[0111] [2-3. Other Embodiments] The evaluation device, calculation device, 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 other than the second embodiment described above within the scope of the technical idea set forth in the claims.

[0112] 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 using known methods.

[0113] In addition, the processing procedures, control procedures, specific names, registered data for each process, information including parameters such as search conditions, screen examples, and database configurations shown in the above documents and drawings can be changed as desired unless otherwise specified.

[0114] Furthermore, with regard to each device constituting the evaluation system, the components shown in the figures are functional concepts, and do not necessarily have to be physically configured as shown in the figures.

[0115] For example, all or any part of the processing functions of the evaluation device 100, particularly the processing functions performed by the control unit 102, may be implemented by a CPU and a program interpreted and executed by the CPU, or may be implemented as hardware using wired logic. The program is recorded on a non-transitory computer-readable recording medium containing programmed instructions for causing an information processing device to execute the evaluation method or calculation method of the present invention, and is mechanically read by the evaluation device 100 as needed. That is, a computer program for providing instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106, such as a ROM or HDD (hard disk drive). This computer program is executed by being loaded into RAM and cooperates with the CPU to form the control unit.

[0116] This computer program may also be stored in an application program server connected to the evaluation device 100 via any network, and all or part of it may be downloaded as needed.

[0117] Furthermore, the evaluation program or calculation program according to the present invention may be stored in a non-transitory computer-readable recording medium, or may be configured as a program product. Here, this "recording medium" includes memory cards, USB (Universal Serial Bus) memories, SD (Secure Digital) cards, flexible disks, magneto-optical disks, ROMs, EPROMs (Erasable Programmable Read Only Memory), EEPROMs (Electrically Erasable and Programmable Read Only Memory) (registered trademark), CD-ROMs (Compact Disc Read Only Memory), MOs (Magneto-Optical disks), DVDs (Digital Versatile Disks), and more. This includes any "portable physical medium" such as a Blu-ray Disc, a DVD player, a DVD player, a Blu-ray Disc, etc.

[0118] Furthermore, a "program" is a data processing method written in any language or description method, regardless of the format, such as source code or binary code. Note that a "program" is not necessarily limited to a single program, but also includes programs that are distributed as multiple modules or libraries, or programs that achieve their functions by working together with other programs, such as an OS. Note that the specific configurations and reading procedures for reading a recording medium in each device shown in the embodiments, as well as the installation procedures after reading, can use well-known configurations and procedures.

[0119] The various databases stored in the memory unit 106 are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and providing websites.

[0120] The evaluation device 100 may be configured as an information processing device such as a known personal computer or workstation, or as an information processing device connected to any peripheral device. The evaluation device 100 may also be realized by installing software (including a program or data) that causes the information processing device to implement the evaluation method or calculation method of the present invention.

[0121] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of the devices can be functionally or physically distributed and integrated in any unit depending on various additions or functional loads. In other words, the above-mentioned embodiments can be implemented in any combination, or the above-mentioned embodiments can be implemented selectively.

[0122] A total of 1,038 blood samples were collected from 519 female Holstein dairy cows at the University of Minnesota farm in 2019. The samples were collected 21 days before and 7 days after calving. The 519 cows included 210 primiparous heifers, which were experiencing their first calving, and 309 multiparous cows, which had previously given birth.

[0123] Blood samples were collected 21 days before the expected delivery date to measure the blood concentrations of 25 amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and 29 biochemical parameters (ALB (g / dl), ALT (IU / l), AST (IU / l), B The following laboratory values ​​were measured: HBA (μmol / L), BUN (mg / dL), Ca (mg / dL), gGTP (IU / L), Glc (mg / dL), Glob (mg / dL), NEFA (μEq / L), T-Bil (mg / dL), TCHO (mg / dL), TG (mg / dL), TP (g / dL), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, and CRE. Blood concentrations of BHBA were measured from blood samples collected 7 days after delivery. The blood concentrations of amino acids were measured using the above-mentioned measurement method (A) or (B).

[0124] The onset of metritis was monitored up to DIM 15 (days in milk), the onset of mastitis was monitored up to DIM 380, and the onset of clinical ketosis was monitored up to DIM 27. Regarding mastitis, individuals diagnosed with mastitis based on clinical findings were classified as individuals with clinical mastitis, and individuals determined to have mastitis based on the somatic cell count (SCC) in milk were classified as individuals with SCC mastitis. Here, SCC is a collective term for milk leukocytes and sloughed epithelial cells. It is said that SCC increases when a cow is affected by mastitis. In this example, mastitis is determined when the SCC is ≥ 200,000 / mL.

[0125] First, 519 individuals were randomly divided into 8:2 groups. Data associated with 80% of these individuals (measured concentration values ​​and observation results regarding the presence or absence of disease onset) were used as training data, and data associated with the remaining 20% ​​of individuals (measured concentration values ​​and observation results regarding the presence or absence of each disease onset) were used as validation data.

[0126] In the training data, the incidence of metritis was 15%, clinical mastitis was 45%, SCC mastitis was 22%, and clinical ketosis was 19%. In the validation data, the incidence of metritis was 20%, clinical mastitis was 44%, SCC mastitis was 28%, and clinical ketosis was 18%.

[0127] In the training data, blood concentration values ​​of the aforementioned 25 amino acids 21 days before the expected delivery date and test values ​​of the aforementioned 29 biochemical items 21 days before the expected delivery date were used to search for an index (formula) for predicting (discriminating) the occurrence of each disease using the stepwise method (minimum AICc, incremental variables, and component combination) provided by the statistical analysis software "JMP (registered trademark) software." The searched formula was evaluated by the AUC of the ROC curve.

[0128] Furthermore, the prediction performance of the formula searched for based on the training data was evaluated by applying the validation data to the formula and using the AUC of the ROC curve.

[0129] Some of the formulas that showed good predictive ability for the occurrence of each disease are shown below. These formulas are useful for assessing the occurrence of each disease. ● Prediction formula for metritis = -4.4 + 0.31 * BUN + 0.13 * Gly - 1.91 * ASP - 0.25 * Val + 0.44 * Ile - 0.02 * ALP - 0.01 * TCHO ROC_AUC of this prediction formula based on training data = 0.70 ROC_AUC of this prediction formula based on validation data = 0.60 ● Prediction formula for clinical mastitis = -1 + 0.26 * Tau - 0.03 * BUN + 1.83 * 3 MeHis - 0.06 * Gly + 1.64 * Asp - 0.13 * Glu - 0.17 * Pro - 0.14 * Orn + 0.23 * Lys - 0.33 * Phe + 0.005 * TCHO + 0.18 * TP ROC_AUC of this prediction formula based on training data = 0.65 ROC_AUC of this prediction formula based on validation data = 0.58 Prediction formula for clinical mastitis found by narrowing down the training data with DIM≦21 = 1.49 + 0.29 * Tau + 0.35 * Arg - 0.36 * Ala - 1.44 * Orn + 0.42 * Lys + 1.30 * Met ROC_AUC of this prediction formula based on the training data = 0.81 ROC_AUC of this prediction formula based on the validation data = 0.66 Prediction formula for clinical ketosis = 3.87 - 0.59 * Arg + 0.5 * Thr + 0.38 * Lys - 1.04 * Met - 0.57 * Phe - 0.02 * ALP - 0.03 * Glc ROC_AUC of this prediction formula based on the training data = 0.75 ROC_AUC of this prediction formula based on the validation data = 0.64

[0130] A total of 210 blood samples were collected from 105 female Holstein dairy cows (all multiparous) at three experimental sites: University of Florida, University of Illinois, and a farm in Japan. The 105 multiparous cows included 48 from the University of Florida, 17 from the University of Illinois, and 40 from the farm in Japan. The samples were collected 21 days before and 7 days after calving.

[0131] Blood samples were collected 21 days before the expected delivery date to measure the blood concentrations of the 25 amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and 28 biochemical parameters (ALB (g / dl), ALT (IU / l), AST ( The following laboratory values ​​were measured: Calcium (IU / L), BHBA (μmol / L), BUN (mg / dL), Ca (mg / dL), gGTP (IU / L), Glc (mg / dL), NEFA (μEq / L), T-Bil (mg / dL), TCHO (mg / dL), TG (mg / dL), TP (g / dL), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, and CRE. Blood concentrations of haptoglobin were measured from blood samples collected 7 days after delivery. The blood concentrations of amino acids were measured using the measurement method (A) or (B) described above. The blood concentration of haptoglobin was measured by a method known as ELISA (Enzyme-Linked Immunosorbent Assay).

[0132] Postpartum haptoglobin levels below 800 μg / ml were classified as healthy, while those above 800 μg / ml were classified as inflammatory. The 800 μg / ml threshold was determined based on the publication "Dubuc et al. 2010. Risk factors for postpartum uterine diseases in dairy cows." Of the 48 cows at the University of Florida, 17% were classified as inflammatory, of the 17 cows at the University of Illinois, 35% were classified as inflammatory, and of the 40 cows at the Japanese farm, 18% were classified as inflammatory.

[0133] Using the blood concentration values ​​of the 25 amino acids mentioned above 21 days before the expected delivery date and the test values ​​of the 28 biochemical items mentioned above 21 days before the expected delivery date, an index (formula) for predicting (discriminating) the inflammatory state was searched for using the stepwise method (minimum AICc, incremental variables, and component combination) provided by the statistical analysis software "JMP (registered trademark) software." The searched formula was evaluated by the AUC of the ROC curve.

[0134] Some of the formulas that showed good predictive performance for inflammatory status are shown below. This formula is useful for evaluating inflammatory status (high hydroxylase globin). Prediction formula for inflammatory status (high hydroxylase globin) = 15.21 + 0.3 * Ile + 0.06 * His - 1.09 * Phe - 0.37 * Glu - 0.03 * Gly - 0.96 * T-Bil - 0.03 * TCHO ROC_AUC of this prediction formula = 0.84

[0135] A total of 80 blood samples were obtained from 40 female Holstein dairy cows (all multiparous) at a farm in Japan, collected 21 days before and 7 days after expected calving.

[0136] Blood samples were collected 21 days before the expected delivery date, and blood concentrations of the 25 amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and the 28 biochemical parameters (ALB (g / dl), ALT (IU / l), AS (IU / l)) were measured. Laboratory values ​​for T (IU / L), BHBA (μmol / L), BUN (mg / dL), Ca (mg / dL), gGTP (IU / L), Glc (mg / dL), NEFA (μEq / L), T-Bil (mg / dL), TCHO (mg / dL), TG (mg / dL), TP (g / dL), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, and CRE were measured. Blood concentrations of haptoglobin, IL-1β, and serum albumin A, as well as d-ROMs and BAP, were measured from blood samples collected 7 days after delivery. The blood amino acid concentrations were measured using the measurement method (A) or (B) described above. The blood concentrations of haptoglobin, IL-1β, and serum albumin A were measured by ELISA (Enzyme-Linked Immunosorbent Assay). The d-ROMs and BAP levels were measured by colorimetry.

[0137] Using the blood concentration values ​​of the 25 amino acids and the test values ​​of the 28 biochemical parameters 21 days before the expected delivery date, an index (formula) for predicting (discriminating) the inflammatory state was searched for using the stepwise method (minimum AICc, incremental variables, and component combination) provided by the statistical analysis software "JMP (registered trademark) software." The multiple regression equations created were evaluated using the coefficient of determination R2. The results are shown in Table 1.

[0138] IL-1β, d-ROMs levels, and serum amyloid A had good prediction accuracy, with R2 of 0.7 or higher. On the other hand, the prediction accuracy of BAP levels was not very good, but when the ratio to d-ROMs levels was used as the objective variable, the prediction accuracy became good.

[0139]

[0140] A total of 1,524 blood samples were collected from 762 female Holstein dairy cows at 20 dairy farms, including 99 primiparous heifers (calves that had not given birth for the first time) and 663 multiparous cows (calves that had given birth before this one).

[0141] Blood samples were collected 21 days before the expected delivery date to measure the blood concentrations of the 25 amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and 29 biochemical parameters (ALB (g / dl), ALT (IU / l), AST (IU / l), The following laboratory values ​​were measured: BHBA (μmol / L), BUN (mg / dL), Ca (mg / dL), gGTP (IU / L), Glc (mg / dL), Glob (mg / dL), NEFA (μEq / L), T-Bil (mg / dL), TCHO (mg / dL), TG (mg / dL), TP (g / dL), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, and CRE. The blood concentration of haptoglobin was measured from a blood sample collected 7 days after delivery. The blood concentration values ​​of amino acids were measured using the above-mentioned measurement method (A) or (B). The blood concentration of haptoglobin was measured by a method known as ELISA (Enzyme-Linked Immunosorbent Assay).

[0142] Of the data set of 762 animals in total, those with missing values, component concentrations outside the quantitative range, deaths during the test, etc. were excluded. Two types of prediction models were created: a linear regression model and a logistic regression model.

[0143] First, a linear regression model was created. Using measurements taken 21 days before the expected delivery date (blood concentration values ​​of the 25 amino acids and test values ​​of the 29 biochemical parameters), a prediction formula with one to six variables was created to predict haptoglobin concentration 7 days after delivery. The created prediction formula was evaluated using the R2 value and the R2 value adjusted for degrees of freedom. In the case of one variable, one variable was selected with a p-value of <0.05. In the case of two to six variables, the variables were narrowed down to those with a p-value below a certain level, and variable combinations were extracted. The top 100 linear regression models with adjusted R2 values ​​were then extracted. Linear regression models are shown in Figures 14 to 17.

[0144] Next, a logistic regression model was created. Three criteria (330, 450, and 800 μg / ml) were established for haptoglobin concentrations 7 days after delivery. Individuals with haptoglobin concentrations below the respective criteria were classified into a healthy group, and individuals with haptoglobin concentrations equal to or greater than the criteria were classified into a highly inflammatory group. Using the measurements taken 21 days before the expected delivery date, an index consisting of one to six variables that maximized the discriminant performance between the two groups was searched for using logistic analysis (variable exhaustive method based on the minimum AIC criterion). The searched equations were evaluated using the AUC of the ROC curve. For one variable, one variable was selected with a p-value of <0.05. For two to six variables, the search was narrowed down to variables with a p-value below a certain level, and variable combinations were extracted. The top 100 logistic regression models with AIC were then extracted. Figures 18 to 27 show the logistic regression models.

[0145] A total of 1,428 blood samples were collected from 714 female Holstein dairy cows at 19 dairy farms, 21 days before and 7 days after calving. The 714 cows included 99 primiparous heifers, which were experiencing their first calving, and 615 multiparous cows, which had previously given birth.

[0146] Blood samples were collected 21 days before the expected delivery date, and blood concentrations of the 25 amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and the 29 biochemical parameters (ALB (g / dl), ALT (IU / l), AST (IU / l)) were measured. The following laboratory values ​​were measured: BHBA (μmol / L), BUN (mg / dL), Ca (mg / dL), gGTP (IU / L), Glc (mg / dL), Glob (mg / dL), NEFA (μEq / L), T-Bil (mg / dL), TCHO (mg / dL), TG (mg / dL), TP (g / dL), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE). The blood concentration values ​​of amino acids were measured using the measurement method (B) described above.

[0147] Of the total 714 datasets, those with missing values, component concentrations outside the quantitative range, or deaths during the test were excluded.

[0148] A logistic regression model was created. A standard of 7.4 mg / dL was set for the calcium concentration 7 days after delivery. Individuals with calcium concentrations higher than the standard value were classified into a healthy group, and those with calcium concentrations below the standard value were classified into a low calcium group. Using the measurements taken 21 days before the expected delivery date, an index consisting of one to six variables that maximized the discriminatory performance between the two groups was searched for using logistic analysis (variable exhaustive method based on the minimum AIC criterion). The searched equations were evaluated using the AUC of the ROC curve. For one variable, one variable was selected with a p-value of <0.05. For two to six variables, the search was narrowed down to variables with a p-value below a certain level, and variable combinations were extracted. The top 100 logistic regression models with AIC were then extracted. Figures 28 to 34 show the logistic regression models.

[0149] A total of 1,038 blood samples were collected from 519 female Holstein dairy cows at one dairy farm, 21 days before and 7 days after expected calving. The 519 cows included 210 primiparous heifers, which were experiencing their first calving, and 309 multiparous cows, which had previously given birth.

[0150] Blood samples were collected 21 days before the expected delivery date, and blood concentrations of the 25 amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and the 29 biochemical parameters (ALB (g / dl), ALT (IU / l), AST (IU / l)) were measured. The following laboratory values ​​were measured: BHBA (μmol / L), BUN (mg / dL), Ca (mg / dL), gGTP (IU / L), Glc (mg / dL), Glob (mg / dL), NEFA (μEq / L), T-Bil (mg / dL), TCHO (mg / dL), TG (mg / dL), TP (g / dL), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE. The blood concentrations of amino acids were measured by the above-mentioned measurement method (A) or (B).

[0151] Of the 519 datasets in total, those with missing values, component concentrations outside the quantitative range, or deaths during the test were excluded.

[0152] A logistic regression model formula was created. Cows were classified into a healthy group and a diseased group based on the presence or absence of postpartum diseases: milk fever, abomasal mutation, metritis, mastitis, and lameness. Using the measurements taken 21 days before the expected delivery date, an index consisting of one to six variables that maximized the discriminatory performance between the two groups was searched for using logistic analysis (variable exhaustive method based on the minimum AIC criterion). The searched formula was evaluated using the AUC of the ROC curve. In the case of one variable, one variable was selected with a p-value of <0.05. In the case of two to six variables, the search was narrowed down to variables with a p-value below a certain level, and variable combinations were extracted. The top 100 logistic regression models with AIC were extracted. Figures 35 to 36 show logistic regression models for milk fever discrimination. Figures 36 to 38 show logistic regression models for abomasal mutation discrimination. Figures 39 to 43 show logistic regression models for metritis discrimination. Figures 44 to 48 show logistic regression models for discriminating mastitis. Figures 49 to 50 show logistic regression models for discriminating lameness. Figures 50 to 59 show logistic regression models for discriminating whether or not a cow will develop any of the inflammatory diseases of milk fever, abomasum mutation, metritis, mastitis, lameness, or ketosis after parturition.

[0153] A total of 80 blood samples were obtained from 40 female Holstein dairy cows at one dairy farm, collected 21 days before and 7 days after the expected calving date. All 40 dairy cows had previously given birth in addition to this one.

[0154] Blood samples were collected 21 days before the expected delivery date to measure the blood concentrations of the 25 amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and the 29 biochemical parameters (ALB (g / dl), ALT (IU / l), AST (IU / l), BHBA (μmol / l)). The following laboratory values ​​were measured: BUN (mg / dl), Ca (mg / dl), gGTP (IU / l), Glc (mg / dl), Glob (mg / dl), NEFA (μEq / l), T-Bil (mg / dl), TCHO (mg / dl), TG (mg / dl), TP (g / dl), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, and two other parameters (d-ROMs and BAP). Blood samples collected 7 days after delivery were used to measure serum amyloid A (SAA) and IL-1β levels, as well as d-ROMs and BAP. The blood concentrations of amino acids were measured by the above-mentioned measurement method (A) or (B). The blood concentrations of serum amyloid A and IL-1β were measured by an enzyme-linked immunosorbent assay (ELISA). The d-ROMs and BAP levels were measured by a colorimetric method.

[0155] Of the 40 datasets in total, those with missing values, component concentrations outside the quantitative range, or deaths during the test were excluded.

[0156] Linear regression models were created. Using measurements taken 21 days before the expected delivery date (blood concentration values ​​of the 25 amino acids, test values ​​for the 29 biochemical parameters, and measurements of the two parameters), a prediction formula with one to six variables was created to predict serum amyloid A concentration 7 days after delivery, a prediction formula with one to six variables to predict IL-1β concentration 7 days after delivery, a prediction formula with one to six variables to predict d-ROMs levels 7 days after delivery, a prediction formula with one to six variables to predict BAP levels 7 days after delivery, and a prediction formula with one to six variables to predict d-ROMs / BAP levels 7 days after delivery. The created prediction formulas were evaluated using the adjusted R2 value. In the case of a single variable, one variable was selected with a p-value <0.05. In the case of two to six variables, the combinations were narrowed down to those with a p-value below a certain level for one variable, and the linear regression models with the highest adjusted R2 values ​​were extracted. Figures 59 to 63 show linear regression models predicting serum amyloid A concentrations 7 days after calving. Figures 64 to 67 show linear regression models predicting IL-1β concentrations 7 days after calving. Figures 67 to 70 show linear regression models predicting d-ROMs values ​​7 days after calving. Figures 71 to 74 show linear regression models predicting BAP values ​​7 days after calving. Figures 74 to 75 show linear regression models predicting d-ROMs values / BAP values ​​7 days after calving.

[0157] As described above, the present invention can be widely implemented in many industrial fields, particularly in fields such as dairy farming, the development of cattle medicines or cattle feed, and veterinary medicine for cattle, and is extremely useful.

[0158] 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 Blood data file 106b Index status information file 106c Designated index 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 communication terminal device)) 300 Network 400 Database device

Claims

1. An evaluation step of evaluating the state of inflammation after parturition of the ruminant animal, using at least one value among the concentration values of Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, and Val, the test values of ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine in the blood of the ruminant animal before parturition, and the d-ROMs value, BAP value, BAP value / d-ROMs value, and d-ROMs value / BAP value obtained from the blood, or using a formula including a variable into which the at least one value is substituted and the value of the formula calculated using the at least one value. This is an evaluation method characterized by the above.

2. The inflammation is characterized by being metritis, mastitis, hypocalcemia, milk fever, abomasal displacement, or lameness. This is the evaluation method according to claim 1.

3. The ruminant animal is a dairy cow or beef cattle. This is the evaluation method according to claim 1 or 2.

4. The evaluation step is executed in a control unit provided in an information processing device. This is the evaluation method according to claim 3.

5. Using the concentration values of Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, and Val in the blood of a ruminant before parturition, the test values of ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine in the blood, and at least one value among the d-ROMs value, BAP value, BAP value / d-ROMs value, and d-ROMs value / BAP value obtained from the blood, and a variable into which the at least one value is substituted, including a calculation step of calculating the value of the formula for evaluating the inflammatory state of the ruminant after parturition. A calculation method characterized by the above.

6. The calculation step is executed in a control unit provided in an information processing apparatus. The calculation method according to claim 5, characterized by the above.

7. An evaluation device, comprising an evaluation unit that evaluates the state of inflammation after parturition of a ruminant animal, using at least one value among the concentration values of Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, and Val, the test values of ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine in the blood of the ruminant animal before parturition, and the d-ROMs value, BAP value, BAP value / d-ROMs value, and d-ROMs value / BAP value obtained from the blood, or using an expression including a variable into which the at least one value is substituted and the value of the expression calculated using the at least one value.

8. The evaluation device according to claim 7, further comprising a data receiving unit that is communicably connected via a network to a terminal device that provides the at least one value or the value of the expression, and receives the at least one value or the value of the expression transmitted from the terminal device, and a result transmitting unit that transmits the evaluation result obtained by the evaluation unit to the terminal device, wherein the evaluation unit uses the at least one value or the value of the expression received by the data receiving unit.

9. A calculation device comprising a calculation unit that calculates a value of an expression for evaluating the state of inflammation after parturition of a ruminant animal, using the concentration values of Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, and Val in the blood of the ruminant animal before parturition, the test values of ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine in the blood, and at least one value among the d-ROMs value, BAP value, BAP value / d-ROMs value, and d-ROMs value / BAP value obtained from the blood, and a variable into which the at least one value is substituted.

10. An evaluation program for causing an information processing device to execute an evaluation step of evaluating the state of inflammation after parturition of a ruminant animal, using the concentration values of Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, and Val in the blood of the ruminant animal before parturition, the test values of ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine in the blood, and at least one value among the d-ROMs value, BAP value, BAP value / d-ROMs value, and d-ROMs value / BAP value obtained from the blood, or using an expression containing a variable into which the at least one value is substituted and the value of the expression calculated using the at least one value.

11. A calculation program for causing an information processing apparatus to execute a calculation step of calculating a value of an expression for evaluating a state of inflammation after parturition of a ruminant animal, the expression including concentration values of Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, and Val in blood of the ruminant animal before parturition, test values of ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine in the blood, and at least one value among a d-ROMs value, a BAP value, a BAP value / d-ROMs value, and a d-ROMs value / BAP value obtained from the blood, and a variable into which the at least one value is substituted.

12. A computer-readable recording medium recording the program according to claim 10 or 11.

13. An evaluation system configured by communicably connecting an evaluation device and a terminal device via a network, wherein the terminal device includes: a data transmission unit that transmits to the evaluation device concentration values of Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, and Val in the blood of a pregnant animal before parturition; test values of ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine in the blood; and at least one value among the d-ROMs value, BAP value, BAP value / d-ROMs value, and d-ROMs value / BAP value obtained from the blood, or an expression including a variable into which the at least one value is substituted and a value of the expression calculated using the at least one value; a result receiving unit that receives an evaluation result regarding the state of inflammation after parturition transmitted from the evaluation device; the evaluation device includes: a data receiving unit that receives the at least one value or the value of the expression transmitted from the terminal device; an evaluation unit that evaluates the state of inflammation after parturition of the pregnant animal using the at least one value or the value of the expression received by the blood data receiving unit; and a result transmitting unit that transmits the evaluation result obtained by the evaluation unit to the terminal device. An evaluation system characterized by the above.

14. A terminal device comprising a result acquisition unit that acquires an evaluation result regarding the inflammatory state after childbirth, wherein the evaluation result is based on the concentration values of Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, and Val in the blood of a dairy cow before childbirth, the test values of ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1β, haptoglobin, serum amyloid A, IL-6, TNF-α, cortisol, and kynurenine in the blood, and at least one value among the d-ROMs value, BAP value, BAP value / d-ROMs value, and d-ROMs value / BAP value obtained from the blood, or a value of an expression containing a variable into which the at least one value is substituted and calculated using the at least one value, and is a result of evaluating the inflammatory state after childbirth of the dairy cow.

15. The terminal device according to claim 14, further comprising a data transmission unit that is communicably connected to an evaluation device for evaluating the inflammatory state after childbirth and transmits the at least one value or the value of the expression to the evaluation device, wherein the result acquisition unit receives the evaluation result transmitted from the evaluation device.

16. The evaluation method according to claim 1, further comprising a proposal step of proposing a preventive measure for a cow evaluated to have a high likelihood of suffering from the metabolic disease after childbirth in the evaluation step.

17. The evaluation method according to claim 16, wherein the preventive measure is at least one selected from the group consisting of administration of RumenProtect amino acids, administration of feed additives, administration of drugs, and veterinary diagnosis.

18. The evaluation method according to claim 17, characterized in that the feed additive is at least one selected from the group consisting of pH adjusters, ion balance adjusters, mycotoxin adsorbents, propionic acid analogues such as calcium propionate, vitamins, minerals, amino acids, fatty acids, urea, probiotics, yeast, enzymes, antibiotics, antioxidants, antibacterial agents and organic acids.

19. An evaluation method as described in any one of claims 16 to 18, characterized in that in the suggestion step, for cows assessed as having the possibility of suffering from the metabolic disease, statistical causal inference is performed to infer the cause of the disease and preventive treatment corresponding to the cause is proposed.

Images