Method for combined analysis of high sensitivity cardiac troponin and copeptin for non-diagnostic purposes

Abstract

The application discloses a method for combined analysis of high-sensitivity cardiac troponin and copeptin for non-diagnostic purposes, and belongs to the technical field of protein detection. The method comprises the following steps: separating a plasma sample through centrifugation, preparing a phosphate buffer for dilution, and preparing a sample for detection; constructing an immune analysis system on a multi-well plate by adopting a double-antibody sandwich method, sequentially performing coating, cleaning, blocking, and preparing a standard sample and a labeled antibody; adding the sample and the standard sample into a detection plate, sequentially performing incubation, adding a detection antibody, and adding enzyme-labeled avidin, and finally developing color and measuring absorbance; collecting detection data to establish a standard curve, calculating a sample concentration by using a double-index collaborative correction model, and performing cross-validation and abnormal value processing. According to the scheme, more stable quantitative results can be obtained, and the anti-interference capability of data detection is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of protein detection, and particularly relates to a combined analysis method of high-sensitivity cardiac troponin and copeptin for non-diagnostic purposes. BACKGROUND

[0002] In recent years, in the field of biomarker detection technology for myocardial injury, the current detection technology has significant technical limitations in practical application. Specifically, the detection system used in the prior art shows obvious technical defects in processing the time sequence characteristics of different biomarkers. Taking commonly used detection indicators as an example, the first type of marker detection data needs to go through a time period of 10-24 hours to reach the peak concentration level, and in about 1% of the detection samples, there will be an abnormal phenomenon of delayed increase. This hysteresis results in a serious lack of data reliability obtained by the detection system in the early stage. Especially in the area where the concentration of the detected substance is slightly increased, the detection data shows great volatility and instability, which makes it difficult for the simple difference comparison method widely used in the 0h / 3h or 0h / 1h detection process in the prior art to obtain quantifiable results with repeatability. The data processing method based on the concentration difference at a single time point and the upper limit of the normal value commonly used in the current technical solution cannot effectively solve the problem of significant differences in time dynamics characteristics of different types of indicators.

[0003] In view of the above technical problems, the prior art discloses an improved scheme using the second type of marker as a supplementary detection indicator. This type of marker has the technical characteristics of short response time and reaching the peak concentration within 0-3 hours after sampling, which to some extent makes up for the data blank of the first type of marker in the early detection stage. However, the improved scheme still faces multiple technical difficulties in practical application: first, the concentration change of the second type of marker is disturbed by multiple physiological and biochemical factors, and the existence of these multiple influencing factors makes it difficult for the detection system to establish a stable data correction mechanism; second, because different types of markers have their own specific limitations, the detection data of any single indicator cannot establish a universal quantitative analysis standard; third, the existing data analysis method is mainly limited to linear models, and cannot effectively process the non-linear correlation characteristics between multi-dimensional data. In addition, the detection system also has technical problems such as mismatch between data acquisition frequency and dynamic change rate of biomarkers, too low signal-to-noise ratio affecting detection sensitivity, and too high computational complexity of data processing algorithm. These technical defects seriously restrict the practical performance of the detection system, and new technical solutions need to be developed for optimization and improvement.

[0004] Therefore, a technical solution is needed to obtain more stable quantitative results and enhance the anti-interference ability of data detection. SUMMARY

[0005] To solve the problems in the prior art, the application discloses a combined analysis method of high-sensitivity cardiac troponin and copeptin for non-diagnostic purposes.

[0006] The application discloses a combined analysis method of high-sensitivity cardiac troponin and copeptin for non-diagnostic purposes, which comprises the following steps: separating a plasma sample by centrifugation, preparing a phosphate buffer solution for dilution, and preparing a sample for detection; constructing an immune analysis system on a multi-well plate by using a double-antibody sandwich method, sequentially performing coating, cleaning, blocking, and preparing a standard and a labeled antibody; adding the sample and the standard to a detection plate, sequentially performing incubation, adding a detection antibody, and adding enzyme-labeled avidin, and finally developing color and measuring absorbance; collecting detection data to establish a standard curve, calculating a sample concentration by using a double-index collaborative correction model, and performing cross-validation and abnormal value processing.

[0007] In one possible implementation, the sample for detection is prepared by separating a plasma sample by centrifugation, and preparing a phosphate buffer solution for dilution, which comprises the following steps: collecting a venous blood sample and adding a dipotassium ethylenediaminetetraacetate anticoagulant, mixing the anticoagulant with the blood in a certain proportion; placing the mixed blood sample in a polypropylene blood collection tube and inverting to mix uniformly; centrifuging the mixed blood sample in a low-temperature environment, and sucking the upper plasma; transferring the separated plasma to a sterile frozen storage tube for storage; preparing a phosphate buffer solution, and adding a surfactant thereto; diluting the thawed plasma sample by using the prepared phosphate buffer solution.

[0008] In one possible implementation, the immune analysis system is constructed on a multi-well plate by using a double-antibody sandwich method, and sequentially performing coating, cleaning, blocking, and preparing a standard and a labeled antibody, which comprises the following steps: selecting a multi-well plate made of polystyrene, and constructing an immune analysis system by using a double-antibody sandwich method; preparing a coating buffer solution, and adding a capture antibody solution diluted to an appropriate concentration to the multi-well plate; using an automatic cleaning device to clean the multi-well plate added with the capture antibody for multiple times; preparing a blocking solution, and adding the blocking solution to the multi-well plate for incubation; preparing high-sensitivity cardiac troponin standards and copeptin standards with multiple concentration gradients; preparing a biotin-labeled detection antibody and a horseradish peroxidase-labeled avidin.

[0009] In one possible implementation, the method further includes: preparing two independent detection plates for detecting high-sensitivity cardiac troponin and copeptin, respectively; adding standard samples and samples to be detected into corresponding detection holes, and setting parallel holes and control holes; placing the micro-holes plate after adding samples in a constant-temperature oscillation incubator for incubation; adding biotin-labeled detection antibodies and incubating; adding horseradish peroxidase-labeled avidin and incubating; sequentially performing color developing reaction and termination reaction, and measuring absorbance values.

[0010] In one possible implementation, the method further includes: collecting absorbance data of each detection hole and subtracting blank control values; establishing a curve fitting equation of standard sample concentration values and absorbance values; and substituting absorbance values of samples to be detected into the fitting equation to calculate initial concentration values.

[0011] In one possible implementation, the method further includes: establishing a double-index collaborative correction model to correct detection results; establishing a compensation model by using dynamic change relations of two indexes; and verifying the model by using a cross-validation method, and identifying and processing abnormal values.

[0012] In one possible implementation, the method further includes: setting standard sample concentration values as horizontal axis parameters, and corresponding absorbance values as vertical axis parameters; using a four-parameter logistic regression equation to perform curve fitting, wherein the four-parameter logistic regression equation includes maximum absorbance value parameters, minimum absorbance value parameters, inflection point concentration value parameters, and slope factor parameters; and calculating a fitting goodness parameter value.

[0013] In one possible implementation, the method further includes: converting high-sensitivity cardiac troponin initial concentration values into natural logarithm forms; converting copeptin initial concentration values into natural logarithm forms; using an improved five-parameter logistic equation to perform fitting, wherein the improved five-parameter logistic equation includes maximum response value parameters, minimum response value parameters, inflection point concentration parameters, slope factor parameters, and asymmetric correction factor parameters; introducing a time dimension correction term, and performing product operation on the time dimension correction term and the original equation; and determining parameter values by using a least square method.

[0014] In an implementation manner, the dynamic change relationship of the two indexes is used to establish the compensation model, including: calculating the concentration growth rate of high-sensitivity cardiac troponin at adjacent time points; calculating the concentration growth rate of copeptin at adjacent time points; introducing a nonlinear dynamic coupling function, obtaining the coupling coefficient through historical data; judging whether the initial measurement value is located at the edge area of the detection range; calculating the compensation value and superimposing the initial value to obtain the correction result; setting the upper limit of the correction amplitude and performing statistical test.

[0015] In an implementation manner, the model is verified by using a cross-validation method, and abnormal value identification and processing are performed, including: determining the model parameter value by using the training set data; evaluating the model performance parameter by using the test set; repeating the verification process and taking the average result as the final model parameter; setting a data processing sliding window and calculating the standard deviation of the data in the window; marking the data points exceeding the standard deviation of a certain multiple of the mean value; and performing smoothing processing on the marked data points by using a local weighted regression method.

[0016] In the combined analysis method of high-sensitivity cardiac troponin and copeptin disclosed above, the embodiments of the present application can obtain more stable quantitative results and enhance the anti-interference ability of data detection by jointly detecting high-sensitivity cardiac troponin and copeptin and combining a double-index dynamic coupling model and a complementary correction algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A flowchart of a non-diagnostic high-sensitivity cardiac troponin and copeptin combined analysis method disclosed by the embodiments of the present application;

[0019] Figure 2 A time-concentration dynamic change curve graph of a non-diagnostic high-sensitivity cardiac troponin and copeptin combined analysis method disclosed by the embodiments of the present application;

[0020] Figure 3 A scatter plot of a correction model disclosed by the embodiments of the present application. DETAILED DESCRIPTION

[0021] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0022] Those skilled in the art can understand that the terms "first", "second" and the like in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, and do not represent any specific technical meaning, nor indicate their logical order. It should also be understood that in the embodiments of the present disclosure, "multiple" can mean two or more, and "at least one" can mean one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present disclosure, unless specifically limited or the context gives the opposite indication, it can be understood as one or more in general. In addition, the term "and / or" in the present disclosure is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B together, and the existence of B alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the front and rear associated objects. It should also be understood that the description of various embodiments of the present disclosure emphasizes the differences between various embodiments, and the same or similar parts can be referred to each other, and for the sake of brevity, they will not be repeated.

[0023] It should be understood that the sizes of the various portions shown in the drawings are not necessarily shown to scale for the sake of convenience. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way limiting. Techniques, methods, and devices known to those of skill in the relevant art can not be discussed in detail, but should be understood as part of the description. It should be noted that like reference numerals and letters refer to like items in the various drawings, and thus, once an item is defined in one drawing, it need not be discussed further in subsequent drawings.

[0024] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0025] Figure 1 A flowchart of a non-diagnostic high-sensitivity cardiac troponin and copeptin combined analysis method for non-diagnostic purposes disclosed in the embodiments of the present application.

[0026] It is important to understand that existing detection technologies exhibit significant temporal discrepancies in the data characteristics of troponin I and troponin. Troponin I requires 10-24 hours to reach its peak concentration, and approximately 1% of samples show a delayed increase, resulting in low data reliability in the early stages. Particularly in areas of slight concentration elevation, the data exhibits significant fluctuations, making it difficult to obtain stable and reliable quantitative results using simple difference comparison methods in existing 0h / 3h or 0h / 1h detection procedures. This data processing method, based on the ratio of the concentration difference at a single time point to the upper limit of normal (ULN), cannot effectively address the mismatch in the temporal dynamics of the two indicators. While troponin reaches its peak within 0-3 hours, filling the data gap in early detection, its concentration changes are simultaneously influenced by multiple physiological and biochemical factors, making it difficult to establish reliable quantitative standards based solely on data from either indicator.

[0027] The dual-indicator collaborative correction model disclosed in this scheme establishes a nonlinear dynamic coupling relationship through an improved multi-parameter logistic regression equation, unlike the simple linear superposition or threshold comparison methods in existing technologies. This model introduces a time-dimensional correction term K(t), fully considering the temporal characteristics of rapid changes in troponin I and slow changes in troponin I. Especially when processing data in the early stages, the nonlinear dynamic coupling function f(v1, v2) based on the growth rate discloses an adaptive data correction mechanism, effectively overcoming data instability caused by individual differences. The dual-indicator complementary correction algorithm, by constructing a time-series compensation model, achieves accurate correction of data in the edge regions of the detection range. This algorithm not only considers the concentration change characteristics of the two indicators but also establishes a mathematical correlation based on the dynamic change rate, disclosing a more rigorous computational foundation for improving the reliability of early detection data. Outlier identification through a sliding window and local weighted regression processing further enhance the anti-interference capability of the data processing.

[0028] like Figure 1 As shown, in step S101, plasma samples are separated by centrifugation, diluted with phosphate buffer, and prepared for testing. This includes: collecting venous blood samples and adding dipotassium ethylenediaminetetraacetate (EDTA) anticoagulant to mix the anticoagulant with the blood in a specific ratio; placing the mixed blood sample in a polypropylene blood collection tube and inverting it to mix thoroughly; centrifuging the mixed blood sample at low temperature and aspirating the supernatant plasma; transferring the separated plasma to sterile cryopreservation tubes for aliquoting and storage; preparing phosphate buffer and adding a surfactant; and diluting the thawed plasma sample with the prepared phosphate buffer.

[0029] Specifically, after collecting the venous blood sample, add ethylenediamine tetraacetic acid dipotassium anticoagulant, and the ratio of anticoagulant to blood is 1:9. The collected blood sample is placed in a polypropylene blood collection tube, and gently inverted and mixed for 8-10 times. Centrifuge at 1600 rpm for 15 minutes in a 4°C environment, and carefully aspirate the upper plasma with a pipette. Transfer the separated plasma to a sterile frozen storage tube, and each tube is divided into not less than 250 microliters. Avoid generating bubbles during the division process, and maintain the stability of the sample. The plasma sample can be stored in a -80°C environment, and the storage period is not more than 6 months. When taking the sample, thaw it slowly in a 4°C water bath, and gently mix it during the period to ensure sample uniformity. Prepare phosphate buffered saline (PBS), dissolve in pure water, and adjust the pH to 7.4±0.1. Add 0.05% Tween-20 to the PBS as a surfactant. Dilute the thawed plasma sample with the prepared PBS at a ratio of 1:4. Place the diluted sample in a 4°C environment, and the subsequent operation should be completed within 2 hours. and Dissolve in pure water, and adjust the pH to 7.4±0.1. Add 0.05% Tween-20 to the PBS as a surfactant. Dilute the thawed plasma sample with the prepared PBS at a ratio of 1:4. Place the diluted sample in a 4°C environment, and the subsequent operation should be completed within 2 hours.

[0030] In one embodiment, when preparing the phosphate buffered saline, weigh 8.76g and 1.56g are dissolved in 950mL of ultrapure water, and the pH is monitored and adjusted to 7.4±0.1 using a pH meter, and finally the volume is adjusted to 1000mL. Add 0.05% Tween-20 to the prepared phosphate buffered saline, and mix well. Dilute the thawed plasma sample with the prepared buffer at a ratio of 1:4, and dilute gently and uniformly to avoid generating bubbles.

[0031] At step S102, an immunological analysis system is constructed on a multi-well plate using a double antibody sandwich method, and coating, washing, blocking, and preparation of standard and labeled antibodies are sequentially performed. Among them, the multi-well plate of polystyrene material is selected, and the immunological analysis system is constructed by the double antibody sandwich method; the coating buffer is prepared, and the capture antibody solution diluted to an appropriate concentration is added to the multi-well plate; the multi-well plate with the added capture antibody is washed multiple times using an automatic washing device; the blocking solution is prepared and added to the multi-well plate for incubation; a plurality of concentration gradients of high-sensitivity cardiac troponin standard and peptide standard are prepared; biotin-labeled detection antibody and horseradish peroxidase-labeled avidin are prepared.

[0032] In one embodiment, a 96-well polystyrene plate can be selected, and a double antibody sandwich method can be used to establish an immunoassay system. A coating buffer is prepared by adding 0.02% sodium azide to 0.1 mol / L carbonate buffer (pH 9.6). 100 microliters of capture antibody solution diluted to the optimal concentration is added to each well of the microplate and incubated at 4°C overnight. The automatic plate washer is used for washing, 350 microliters of PBS containing 0.05% Tween-20 is added to each well, and the washing is repeated 4 times. After each washing, the residual liquid is removed by gently tapping on the absorbent paper. A blocking solution is prepared by adding 2% bovine serum albumin to PBS, and 200 microliters is added to each well, and incubated at 37°C for 60 minutes. A standard sample series is prepared by diluting the high-sensitivity cardiac troponin standard with sample diluent to 6 concentration gradients of 0.1-10 ng / mL, and diluting the peptide standard with sample diluent to 6 concentration gradients of 5-500 pmol / L. A biotin-labeled detection antibody and a horseradish peroxidase-labeled avidin are prepared, and the concentrations are determined according to the optimization results of the pre-experiment.

[0033] Preferably, a carbonate coating buffer is prepared by dissolving 1.59g and 2.93g in 950 mL of ultrapure water, adjusting the pH to 9.6, and diluting to 1000 mL. According to the pre-experiment results, the capture antibody is diluted to a working concentration of 2.5 μg / mL with the coating buffer.

[0034] At step S103, the sample and standard are added to the detection plate, and the incubation, addition of detection antibody, enzyme-labeled avidin, and finally color development and determination of absorbance are performed in sequence. This includes: preparing two independent detection plates for detecting high-sensitivity cardiac troponin and peptide, respectively; adding standard samples and samples to be tested to the corresponding detection wells, and setting up parallel wells and control wells; placing the sample-added microplate in a constant-temperature shaking incubator for incubation; adding biotin-labeled detection antibody and incubating; adding horseradish peroxidase-labeled avidin and incubating; sequentially performing color development and termination reactions, and determining the absorbance value.

[0035] Specifically, two separate detection plates are prepared for the detection of high-sensitivity cardiac troponin and copeptin, respectively. The standard and the sample to be tested are added to the corresponding detection holes, 100 microliters per hole, and two parallel holes are set for each sample. The control hole is added with the same amount of sample diluent. The microwell plate is placed in a constant temperature shaking incubator at 37°C, with a shaking frequency of 150 revolutions per minute, and incubated for 75 minutes. Wash 5 times, each time using 350 microliters / hole of washing solution. Add biotin-labeled detection antibody, 100 microliters per hole, and continue to incubate at 37°C for 60 minutes. After washing, add horseradish peroxidase-labeled avidin, 100 microliters per hole, and incubate at 37°C for 30 minutes. After the last washing, add tetramethylbenzidine / hydrogen peroxide developing solution, 100 microliters per hole, and develop at room temperature for 15 minutes in the dark. Add 2 mol / L sulfuric acid stop solution, 50 microliters. Use the enzyme label instrument to measure the absorbance values at 450 nm and 630 nm.

[0036] In one embodiment, when performing joint detection, two microwell plates that have undergone complete coating and blocking treatment are prepared. Use an 8-channel pipettor to add the standard and the sample to be tested to the corresponding detection holes, 100 μL per hole. The standard is added from low concentration to high concentration, and the sample to be tested is added according to the preset position map. Two parallel holes are set for each sample, and at least two blank control holes are set. Place the sample-added microwell plate into a constant temperature shaking incubator, set the temperature to 37°C, the shaking frequency to 150 r / min, and the incubation time to 75 minutes. After incubation, use a fully automatic plate washer to wash, add 350 μL of washing solution per hole, soak for 10 seconds, and then dry, repeat 5 times. Use an 8-channel pipettor to add biotin-labeled detection antibody, 100 μL per hole, and continue to incubate under the same conditions for 60 minutes. After washing again, add horseradish peroxidase-labeled avidin, 100 μL per hole, and incubate at 37°C for 30 minutes. After the last washing, add TMB substrate developing solution, 100 μL per hole, under light-proof conditions, and develop at room temperature for 15 minutes. After the timer ends, quickly add 2 mol / L sulfuric acid stop solution, 50 μL per hole, and mix gently. The solution can be observed to change from blue to yellow.

[0037] At step S104, detection data is collected to establish a standard curve, the sample concentration is calculated using a two-index collaborative correction model, and cross-validation and abnormal value processing are performed. This includes: collecting the absorbance data of each detection hole and subtracting the blank control value; establishing a curve fitting equation of the standard concentration value and the absorbance value; and substituting the absorbance value of the sample to be tested into the fitting equation to calculate the initial concentration value.

[0038] The curve fitting equation of the standard concentration value and the absorbance value is established, including: setting the standard concentration value as the horizontal axis parameter, and setting the corresponding absorbance value as the vertical axis parameter; adopting a four-parameter logistic regression equation for curve fitting, which includes a maximum absorbance value parameter, a minimum absorbance value parameter, an inflection point concentration value parameter, and a slope factor parameter; and obtaining a goodness-of-fit parameter value through calculation.

[0039] In one embodiment, the absorbance data of each detection hole is collected, and the blank control value is subtracted. A data processing electronic table is prepared, and the standard concentration value (x-axis) and the corresponding absorbance value (y-axis) are input. A four-parameter logistic regression function is used for curve fitting. The formula is where A is the maximum absorbance value, D is the minimum absorbance value, C is the inflection point concentration value, and B is the slope factor. The goodness-of-fit is calculated. The absorbance value of the sample to be tested is substituted into the fitting equation to obtain the initial concentration value. For samples outside the linear range, re-detection is performed according to the preset dilution scheme. A joint data processing model is established to calculate the correlation coefficient between the two indicators. Automatic data screening conditions are set to eliminate outliers.

[0040] In addition, based on the detection data of the two indicators, the cooperative optimization also includes: establishing a double-index cooperative correction model to correct the detection results; using the dynamic change relationship of the two indicators to establish a compensation model; using a cross-validation method to verify the model, and identifying and processing outliers.

[0041] Further, the double-index cooperative correction model is established to correct the detection results, including: converting the initial concentration value of high-sensitivity cardiac troponin into a natural logarithm form; converting the initial concentration value of copeptin into a natural logarithm form; using an improved five-parameter logistic equation for fitting, including a maximum response value parameter, a minimum response value parameter, an inflection point concentration parameter, a slope factor parameter, and an asymmetric correction factor parameter; introducing a time dimension correction term, which is multiplied by the original equation; and determining the parameter values by the least square method.

[0042] Further, the dynamic change relationship of the two indicators is used to establish a compensation model, including: calculating the concentration growth rate of copeptin at adjacent time points; calculating the concentration growth rate of high-sensitivity cardiac troponin at adjacent time points; introducing a nonlinear dynamic coupling function to obtain a coupling coefficient through historical data; determining whether the initial determination value is located in the edge region of the detection range; calculating a compensation value and superimposing it with the initial value to obtain a correction result; setting an upper limit of the correction amplitude and performing statistical testing.

[0043] It should be noted that the existing 0h / 1h and 0h / 3h detection processes mainly use static threshold comparison method and simple linear interpolation method to process detection data. Among them, the static threshold comparison method only compares the detection difference of two time points with the upper limit of normal value (ULN), and when the difference is greater than 1ULN, it is determined as significant change. This method ignores the dynamic change process of the detection data, especially when dealing with the data of slight increase of troponin I, due to its slow change characteristics of 10-24 hours, the difference of 1 hour interval is often small, which is easily disturbed by measurement noise. While the simple linear interpolation method considers the continuity of time series, but it assumes that the detection value changes linearly between adjacent time points, which cannot accurately describe the nonlinear accumulation process of troponin I and the rapid peak characteristics of copeptin. These two conventional methods have obvious technical limitations in processing double-index joint detection data, especially when the concentration change rates of the two indicators are quite different, the error generated by linear interpolation will rapidly increase with time accumulation.

[0044] The double-index dynamic coupling model and complementary correction algorithm disclosed by the embodiments of the present application overcome the simplification problem of existing methods in mathematical model by introducing improved multi-parameter logistic regression function and nonlinear dynamic coupling function. Unlike the static threshold comparison method, the model realizes accurate description of the dynamic characteristics of the detection data through the time dimension correction term K(t). Compared with the simple linear interpolation method, the present scheme establishes a more realistic data correlation model based on the nonlinear dynamic coupling function f(v1, v2) of the growth rate. Especially in dealing with the asynchrony problem of the rapid change of copeptin 0-3 hours and the slow accumulation of troponin I, the time series compensation model discloses an adaptive data correction mechanism, which effectively avoids the cumulative error of linear interpolation when dealing with nonlinear change data. In addition, the present scheme also processes abnormal values through sliding window and local weighted regression method, which further improves the stability of data processing, which is a technical feature that the existing static threshold comparison and linear interpolation methods do not have.

[0045] Specifically, the double-index collaborative correction model can be established to correct the detection results of high-sensitivity cardiac troponin I using the copeptin level at an interval of 1h. A multi-parameter logistic regression equation is used to fit the standard curve: where E is the asymmetry factor. The double-index complementary correction algorithm can be designed to construct a time series compensation model based on the characteristics that copeptin reaches peak value at 0-3h and troponin reaches peak at a later time. When one index is at the edge of the detection range, the value of the other index is used for correction to improve the detection accuracy of the boundary region. The specific operation is as follows: the relative change rates of the two indicators are calculated, a mathematical model is established to describe the dynamic change relationship between the two, and the preliminary measurement results are corrected accordingly.

[0046] In one embodiment, the collaborative correction model is constructed by first standardizing the detection data of high-sensitivity cardiac troponin I and copeptin. For the initial concentration value x of high-sensitivity cardiac troponin I, it is converted to natural logarithm form ln(x), and for the initial concentration value y of copeptin, it is also converted to ln(y). The distribution of the converted data shows good linear characteristics, which helps subsequent modeling analysis. A nonlinear regression method is used to introduce an improved multi-parameter logistic regression equation for fitting. The equation contains two core parts: a concentration response curve and a time compensation term. The concentration response curve uses the basic form where parameter A represents the maximum response value, D represents the minimum response value, C represents the inflection point concentration, B is the slope factor, and E is the asymmetry correction factor. To improve the fitting accuracy, the equation is improved by introducing a correction term in the time dimension. The correction term expression is where t is the detection time point, and a and β are undetermined coefficients. Multiply K(t) by the original equation to obtain the improved expression: The parameter values are determined by the least squares method to minimize the deviation sum of squares between the fitting curve and the experimental data.

[0047] During the correction process, the dynamic correlation between the two indicators needs to be considered. Since copeptin reaches its peak within 0-3 hours after onset, while high-sensitivity cardiac troponin I takes a relatively longer time to reach its peak, this time difference can be used to construct a complementary correction model. First, calculate the growth rate of copeptin where , are the concentration values at the adjacent two time points, is the time interval. Similarly, calculate the growth rate of high-sensitivity cardiac troponin I Introduce a nonlinear dynamic coupling function where and are coupling coefficients obtained by training historical data. Based on this function, a dynamic compensation model is constructed: when one indicator is at the edge of the detection range (i.e., less than 1.2 times the lower limit of detection or greater than 0.8 times the upper limit of detection), the dynamic trend of the other indicator is used for correction.

[0048] The specific correction steps are as follows: for the initial determination value , first determine whether it is at the edge of the detection range (i.e., whether it is less than 1.2 times the lower limit of detection or greater than 0.8 times the upper limit of detection). If it is at the edge region, calculate the compensation value where t is the current time point and t0 is the first detection time. Add the compensation value to the initial value to obtain the corrected result To avoid over-correction, set an upper limit for the correction amplitude to ensure Finally, the reliability of the corrected result is tested by a non-parametric statistical method.

[0049] In another embodiment, the model can also be validated by cross-validation method, and abnormal value identification and processing, including: determining the model parameter value using the training set data; evaluating the model performance parameter using the test set; repeating the validation process and taking the average result as the final model parameter; setting the data processing sliding window and calculating the standard deviation of the data in the window; marking the data points that exceed the standard deviation of a certain multiple of the mean value; using the local weighted regression method to smooth the marked data points.

[0050] Preferably, model validation can use cross-validation method, randomly divide the data set into training set and test set. Using training set data to determine model parameters, and using test set to evaluate model performance. Repeat the process several times, take the average result as the final model parameter. In practical application, new validation data is collected regularly, and model parameters are updated to ensure model adaptability. For abnormal values beyond the normal range, signal processing-based methods are used for identification and rejection to avoid interference with the model. The specific operation includes: setting the sliding window w=5, calculating the standard deviation s of the data in the window, marking the data points deviating from the mean value more than 2.8s as abnormal values, and using local weighted regression method to smooth them.

[0051] In addition, for quality control, three concentration levels of quality control samples can also be prepared. Intra-batch and inter-batch precision verification is performed, and the coefficient of variation is calculated. When the quality control sample result exceeds the allowed range, the experiment needs to be repeated.

[0052] Figure 2 A time-concentration dynamic change curve graph of high-sensitivity cardiac troponin and copeptin disclosed in an embodiment of the present application is provided. As shown in the graph, the graph consists of three subgraphs, which show the dynamic change rule of high-sensitivity cardiac troponin and copeptin and the correlation analysis result. Figure 2

[0053] Figure 2 The upper subgraph shows the change trend of the two indexes over time. The blue curve represents the change of high-sensitivity cardiac troponin level, which shows a trend of first increasing and then decreasing within 0-6 hours, and the peak value appears at about 2-3 hours, and then gradually decreases and tends to be stable. The red curve represents the change of copeptin level, which rapidly rises to a peak within 0-3 hours, and then shows a downward trend. The shaded area around the two curves represents the 95% confidence interval range of the measured data. The change trend of the two indexes has time difference, and copeptin reaches the peak earlier than troponin, and this time difference is the basis for the joint application.

[0054] Figure 2 ​The lower left subgraph shows the mutual compensation correction curve between high-sensitivity cardiac troponin and copeptin (p = 0.46). The green curve indicates that there is a certain degree of correlation between the two indicators, but not a simple linear relationship. The curve shows a rising and then falling trend in the 0-4 hour range, reflecting the change in the strength of the complementary effect of the two indicators at different time points. This non-linear relationship reveals the basis for establishing a dual-index synergistic correction model.

[0055] Figure 2 The lower right subgraph gives the statistical correlation scatter plot (γ = 0.72). The distribution trend of the purple scatter points indicates that there is a strong correlation between the levels of high-sensitivity cardiac troponin and copeptin. The scatter plot has the troponin level (ng / L) on the horizontal axis and the copeptin level (pmol / L) on the vertical axis, and the distribution of data points shows a certain degree of aggregation and regularity. Through this correlation analysis, the expression pattern of the two indicators and their corresponding relationship in different concentration intervals can be determined, providing data support for the development of a joint detection strategy.

[0056] By analyzing the dynamic change rule, complementary effect and correlation of the two indicators, the selection of detection time points can be optimized, and a more reasonable joint detection strategy can be established.

[0057] Figure 3 A correction model scatter plot correlation diagram disclosed in an embodiment of the present application.

[0058] As Figure 3 shown, Figure 3 A graph in the figure shows a three-dimensional curve graph of biomarker concentration changes. The X-axis represents dilution, the Y-axis represents time (minutes), and the Z-axis represents concentration value [ng / mL]. The blue line in A graph represents the distribution of multiple experimental data, and the red line is the average trajectory. By observing the trajectory distribution in the three-dimensional space, the dynamic change rule of biomarker concentration at different dilutions and time points can be observed. This space distribution graph reveals the nonlinear change characteristics of the biomarker with time and dilution.

[0059] Figure 3 B graph in the figure presents a dual-index correlation analysis scatter plot. The horizontal axis is dilution (mmol / L), and the vertical axis is concentration value [ng / mL]. The purple scatter points represent experimental data points, the red dashed line is a quadratic fitting curve, and the gray area represents the 95% confidence interval. From the scatter distribution, it can be seen that as the dilution increases, the concentration value shows a certain correlation trend, but there is obvious discreteness. The correlation coefficient r = 0.100, p = 0.160, indicating that there is a weak correlation between the two indicators.

[0060] Figure 3The C chart in FIG. 6 shows the calibration curve and correlation analysis. The horizontal axis is the predicted value, and the vertical axis is the measured value. The data points are marked with different colors according to the concentration gradient. The area under the curve (AUC) of the fitted curve is 0.811 (95% CI: 0.666-0.956), indicating that the model has good prediction ability.

[0061] Figure 3 The D chart in FIG. 6 shows the two-index relative change distribution chart, where the variability of hs-cTnI level is 69.66±11.1%, the relative baseline change rate is -20.35±62.2%, and the p value is 0.624. These data reflect the dynamic relationship and stability between the indexes.

[0062] The above Figure 3 The four sub-charts in FIG. 6 illustrate the dynamic change characteristics of the biomarkers, the correlation analysis results, and the performance evaluation of the prediction model from different angles. The data verify the accuracy indicators of the detection system. Through the comprehensive analysis of the above four sub-charts, the performance characteristics of the detection method based on the two-index collaborative correction model of the embodiments of the present application are fully illustrated.

[0063] Further, the embodiments of the present application also disclose a device for two-index collaborative processing of detection data, comprising a processor, a memory, and a system bus; the processor and the memory are connected through the system bus; the memory is used for storing one or more programs, the one or more programs comprising instructions, which, when executed by the processor, cause the processor to perform any of the above-described methods.

[0064] Further, the embodiments of the present application also disclose a computer program product, which, when running on a terminal device, causes the terminal device to perform any of the above-described methods.

[0065] From the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment methods can be implemented by means of software and the necessary general hardware platforms. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) execute the methods described in the various embodiments or some parts of the embodiments of the present application.

[0066] It should be noted that the various embodiments described herein are described in progressive order of complexity where appropriate. Therefore, features and concepts described in one or more embodiments should be understood to be applicable to other embodiments as well, unless explicitly described otherwise.

[0067] It should also be noted that, as used in the specification and in the claims, the article "a", "an", or "the" are intended to not be limiting, but are intended to mean "one or more". It will be further noted that the various links described herein can be coupled together in different ways, sizing, and / or order, depending in part on specific implementation needs and preferences, and alternatives having the same or similar function would be understood to be within the scope of the application. Furthermore, expressions such as "at least one of X, Y and Z" when preceding a list of two or more members, are comprehended to refer to options such as X alone, Y alone, Z alone, or any combination of X and Y and Z, wherein X, Y, and Z represent members of the list.

[0068] The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for the combined analysis of non-diagnostic high-sensitivity cardiac troponin and troponin, characterized in that, include: Plasma samples were separated by centrifugation, diluted with phosphate buffer, and used to prepare samples for testing. An immunoassay system was constructed on a multi-well plate using a double-antibody sandwich method. The plate was coated, washed, and blocked sequentially, and standards and labeled antibodies were prepared. Add the sample and standard to the detection plate, incubate in sequence, add the detection antibody and enzyme-labeled avidin, and finally develop the color and measure the absorbance. Collect test data to establish a standard curve, calculate sample concentration using a dual-index co-calibration model, and perform cross-validation and outlier handling, including: Collect absorbance data for each detection well and subtract the blank control value; Establish a curve fitting equation between the concentration value and absorbance value of the standard, including: setting the concentration value of the standard as the horizontal axis parameter and the corresponding absorbance value as the vertical axis parameter; using a four-parameter logistic regression equation for curve fitting, including the maximum absorbance value parameter, the minimum absorbance value parameter, the inflection point concentration value parameter, and the slope factor parameter; and calculating the goodness-of-fit parameter value. Substitute the absorbance value of the sample to be tested into the fitting equation to calculate the initial concentration value; A dual-index collaborative correction model was established to correct the detection results. This correction included: converting the initial concentration values ​​of high-sensitivity cardiac troponin to natural logarithmic form; converting the initial concentration values ​​of troponin to natural logarithmic form; fitting the results using an improved five-parameter logistic equation, including parameters for maximum response value, minimum response value, inflection point concentration, slope factor, and asymmetric correction factor; and introducing a time dimension correction term, which was multiplied by the original equation to obtain an improved multi-parameter logistic regression equation containing a concentration response curve and a time compensation term for fitting. , Where y is the corrected peptide concentration, A represents the maximum response value, D represents the minimum response value, C represents the inflection point concentration, B is the slope factor, E is the asymmetric correction factor, and K(t) is the time compensation term. , where t is the detection time point, and α and β are undetermined coefficients; the values ​​of each parameter are determined by the least squares method; A compensation model is established by utilizing the dynamic relationship between the two indicators; Cross-validation was used to validate the model, and outlier identification and handling were performed. Calculation and growth rate of peptides ,in , These are the concentration values ​​at two adjacent time points. For time intervals; similarly, the rate of increase of high-sensitivity cardiac troponin I was calculated. Introducing a nonlinear dynamic coupling function ,in and The coupling coefficient is obtained through training with historical data.

2. The joint analysis method according to claim 1, characterized in that, in, Plasma samples were separated by centrifugation, diluted with phosphate buffer, and used to prepare samples for testing, including: Collect venous blood samples and add dipotassium ethylenediaminetetraacetate (EDTA) anticoagulant to mix the anticoagulant with the blood in a specific ratio; The mixed blood sample was placed in a polypropylene blood collection tube and inverted to mix thoroughly. The mixed blood sample was centrifuged in a low-temperature environment, and the supernatant plasma was collected. The separated plasma was transferred to sterile cryopreservation tubes for aliquoting and storage; Prepare a phosphate buffer solution and add a surfactant to it; The thawed plasma samples were diluted using a prepared phosphate buffer solution.

3. The joint analysis method according to claim 1, characterized in that, in, An immunoassay system was constructed on a multi-well plate using a double-antibody sandwich method, and the following steps were performed sequentially: coating, washing, and blocking. Standards and labeled antibodies were then prepared, including: A multi-well plate made of polystyrene was selected, and an immunoassay system was constructed using a double-antibody sandwich method. Prepare the coating buffer and add the capture antibody solution diluted to an appropriate concentration to the multi-well plate; The multi-well plate containing the capture antibody was washed multiple times using an automatic cleaning device. Prepare the blocking solution and add it to a multi-well plate for incubation; Prepare high-sensitivity cardiac troponin and peptide standards with multiple concentration gradients; Prepare biotin-labeled detection antibodies and horseradish peroxidase-labeled avidin.

4. The joint analysis method according to claim 1, characterized in that, in, Add samples and standards to the detection plate, incubate sequentially, add detection antibodies and enzyme-labeled avidin, and finally develop color and measure absorbance, including: Two independent detection plates were prepared for the detection of high-sensitivity cardiac troponin and peptides, respectively. Add the standard and the sample to be tested into the corresponding test wells, and set up parallel wells and control wells; The microplate with the added samples was placed in a constant temperature shaking incubator for incubation. Add biotin-labeled detection antibodies and incubate; Add horseradish peroxidase-labeled avidin and incubate; The colorimetric reaction and the termination reaction were carried out sequentially, and the absorbance value was measured.

5. The joint analysis method according to claim 1, characterized in that, in, A compensation model is established using the dynamic relationship between two indicators, including: Calculate the concentration growth rate of peptides at adjacent time points; Calculate the rate of increase in the concentration of high-sensitivity cardiac troponin at adjacent time points; A nonlinear dynamic coupling function is introduced, and the coupling coefficient is obtained through historical data; Determine whether the initial measured value is located at the edge of the detection range; The compensation value is calculated and superimposed on the initial value to obtain the correction result; Set an upper limit for the correction range and perform statistical testing.

6. The joint analysis method according to claim 1, characterized in that, in, Cross-validation was used to validate the model, and outlier identification and handling were performed, including: Use training set data to determine model parameter values; The model performance parameters were evaluated using a test set. Repeat the validation process and take the average result as the final model parameters; Set up a data processing sliding window and calculate the standard deviation of the data within the window; Mark data points that exceed 2.8 times the standard deviation of the mean; The labeled data points are smoothed using a locally weighted regression method.

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