Conjoint analysis method of high-sensitivity cardiac troponin and peptin

Abstract

The invention discloses a combined analysis method for high-sensitivity cardiac troponin and peptin, and belongs to the technical field of protein detection.The method comprises the steps that a sample for detection is prepared through centrifugal separation of a plasma sample and preparation of a phosphate buffer solution for dilution; constructing an immunoassay system on a porous plate by adopting a double-antibody sandwich method, sequentially coating, cleaning and sealing, and preparing a standard substance and a labeled antibody; the method comprises the following steps: adding a sample and a standard substance into a detection plate, sequentially incubating, adding a detection antibody and enzyme-labeled avidin, and finally developing and determining absorbance; and collecting detection data to establish a standard curve, calculating the sample concentration by using a double-index collaborative correction model, and carrying out cross validation and abnormal value processing. Through the scheme of the invention, a more stable quantification result can be obtained, and the anti-interference capability of data detection is enhanced.

Description

Technical Field

[0001] This application relates to the field of protein detection, and particularly to a combined analysis method for high-sensitivity cardiac troponin and copeptin. Background Art

[0002] In recent years, in the field of biomarker detection technology for myocardial injury, the current detection technology has significant technical limitations in practical applications. Specifically, the detection systems adopted in the prior art exhibit obvious technical defects when dealing with the temporal characteristics of different biomarkers. Taking common detection indicators as an example, the detection data of the first type of biomarker requires a time period of 10 - 24 hours to reach the peak concentration level, and abnormal phenomena of delayed increase occur in approximately 1% of the detection samples. This lag results in severely insufficient reliability of the data obtained by the detection system in the early stage. Especially in the range where the concentration of the substance to be detected increases slightly, the detection data shows large fluctuations and instabilities, making it difficult for the simple difference comparison method in the widely used 0h / 3h or 0h / 1h detection processes in the prior art to obtain reproducible quantitative results. The data processing method commonly adopted in the current technical solutions, which is based on the ratio of the concentration difference at a single time point to the upper limit of the normal value, cannot effectively solve the significant differences in the time kinetic characteristics of different types of indicators.

[0003] In response to the above technical problems, an improved solution using the second type of biomarker as a supplementary detection indicator is disclosed in the prior art. This type of biomarker has the technical characteristics of a short response time and can reach the peak concentration within 0 - 3 hours after sampling, which makes up for the data gap of the first type of biomarker in the early detection stage to a certain extent. However, the improved solution still faces multiple technical difficulties in the actual application process: First, the concentration change of the second type of biomarker is simultaneously interfered 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, due to the specific limitations of different types of biomarkers, it is impossible to establish a universal quantitative analysis standard relying solely on the detection data of any one indicator; Third, the existing data analysis methods are mainly limited to linear models and cannot effectively handle the non-linear correlation characteristics between multi-dimensional data. In addition, there are also technical problems in the actual operation process of the detection system, such as the mismatch between the data acquisition frequency and the dynamic change rate of the biomarker, the too low signal-to-noise ratio affecting the detection sensitivity, and the high computational complexity of the data processing algorithm. These technical defects severely restrict the practical performance of the detection system, and there is an urgent need to develop new technical solutions for optimization and improvement.

[0004] Therefore, there is an urgent need for a technical solution that can obtain more stable quantitative results and enhance the anti-interference ability of data detection. Summary of the Invention

[0005] To address the deficiencies of the prior art, an embodiment of the present application discloses a combined analysis method for high-sensitivity cardiac troponin and copeptin. The present application solves the technical problems such as large fluctuations and instabilities existing in the prior art.

[0006] An embodiment of the present application discloses a combined analysis method for high-sensitivity cardiac troponin and copeptin, including: centrifugally separating plasma samples, preparing phosphate buffer solution for dilution, and preparing samples for detection; constructing an immunoassay system on a multi-well plate using the double-antibody sandwich method, and sequentially performing coating, washing, blocking, and preparing standards and labeled antibodies; adding samples and standards to the detection plate, sequentially performing incubation, adding detection antibodies, enzyme-labeled avidin, and finally developing color and measuring absorbance; collecting detection data to establish a standard curve, calculating the sample concentration using a dual-index collaborative correction model, and performing cross-validation and outlier processing.

[0007] In one possible implementation, centrifugally separating plasma samples, preparing phosphate buffer solution for dilution, and preparing samples for detection includes: collecting venous blood samples and adding potassium ethylenediaminetetraacetate anticoagulant, mixing the anticoagulant with the blood in proportion; placing the mixed blood samples in polypropylene material blood collection tubes and inverting and mixing; centrifuging the mixed blood samples in a low-temperature environment and aspirating the upper plasma; transferring the separated plasma to a sterile cryogenic storage tube for sub-packaging and preservation; preparing phosphate buffer solution and adding a surfactant thereto; diluting the thawed plasma samples using the prepared phosphate buffer solution.

[0008] In one possible implementation, constructing an immunoassay system on a multi-well plate using the double-antibody sandwich method, and sequentially performing coating, washing, blocking, and preparing standards and labeled antibodies includes: selecting a multi-well plate made of polystyrene material and constructing an immunoassay system using the double-antibody sandwich method; preparing coating buffer solution and adding a capture antibody solution diluted to an appropriate concentration to the multi-well plate; using an automatic washing device to wash the multi-well plate with the added capture antibody multiple times; preparing a blocking solution and adding it to the multi-well plate for incubation; preparing high-sensitivity cardiac troponin standards and copeptin standards with multiple concentration gradients; preparing biotin-labeled detection antibodies and horseradish peroxidase-labeled avidin.

[0009] In one possible implementation, a sample and a standard are added to a detection plate, and incubation, addition of a detection antibody, and enzyme-labeled avidin are performed in sequence, and finally color development and absorbance measurement are carried out, including: preparing two independent detection plates for detecting high-sensitivity cardiac troponin and copeptin respectively; adding the standard and the sample to be tested into the corresponding detection wells, and setting parallel wells and control wells; placing the microplate after sample addition in a constant-temperature shaking incubator for incubation; adding a biotin-labeled detection antibody and incubating; adding a horseradish peroxidase-labeled avidin and incubating; performing color development reaction and termination reaction in sequence, and measuring the absorbance value.

[0010] In one possible implementation, collecting detection data to establish a standard curve, calculating the sample concentration using a dual-index collaborative correction model, and performing cross-validation and outlier processing, including: collecting the absorbance data of each detection well and subtracting the blank control value; establishing a curve fitting equation between the standard concentration value and the absorbance value; substituting the absorbance value of the sample to be tested into the fitting equation to calculate the initial concentration value.

[0011] In one possible implementation, it further includes: establishing a dual-index collaborative correction model to correct the detection result; establishing a compensation model using the dynamic change relationship between the two indexes; validating the model using a cross-validation method, and performing outlier identification and processing.

[0012] In one possible implementation, establishing a curve fitting equation between the standard concentration value and the absorbance value, including: setting the standard concentration value as the horizontal axis parameter and the corresponding absorbance value as the vertical axis parameter; performing curve fitting using a four-parameter logistic regression equation, including the maximum absorbance value parameter, the minimum absorbance value parameter, the inflection point concentration value parameter, and the slope factor parameter; obtaining the goodness-of-fit parameter value through calculation.

[0013] In one possible implementation, establishing a dual-index collaborative correction model to correct the detection result, including: converting the initial concentration value of high-sensitivity cardiac troponin into the natural logarithm form; converting the initial concentration value of copeptin into the natural logarithm form; performing fitting using an improved five-parameter logistic equation, including the maximum response value parameter, the minimum response value parameter, the inflection point concentration parameter, the slope factor parameter, and the asymmetry correction factor parameter; introducing a time dimension correction term and performing a product operation with the original equation; determining the values of each parameter by the least squares method.

[0014] In one possible implementation, a compensation model is established by using the dynamic change relationship between two indicators, 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 non-linear dynamic coupling function to obtain a coupling coefficient through historical data; determining whether the initial measurement value is located in the edge region of the detection range; calculating a compensation value and adding it to the initial value to obtain a corrected result; setting an upper limit for the correction amplitude and performing a statistical test.

[0015] In one possible implementation, a cross-validation method is used to verify the model and identify and process outliers, including: determining model parameter values using training set data; evaluating model performance parameters using a 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 within the window; marking data points that exceed a specific multiple of the standard deviation from the mean; using a locally weighted regression method to smooth the marked data points.

[0016] In the joint 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 dual-index dynamic coupling model and a complementary correction algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of a joint analysis method of high-sensitivity cardiac troponin and copeptin disclosed in the embodiments of the present application;

[0019] Figure 2 It is a time-concentration dynamic change curve graph of high-sensitivity cardiac troponin and copeptin disclosed in the embodiments of the present application;

[0020] Figure 3 It is a scatter correlation schematic diagram of a correction model disclosed in the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0022] Those skilled in the art can understand that terms such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate an inevitable logical order between them. It should also be understood that in the embodiments of the present disclosure, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present disclosure, without clear limitation or contrary indication in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present disclosure is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the associated objects before and after. It should also be understood that the present disclosure emphasizes the differences between various embodiments, and their similarities can be referred to each other. For the sake of brevity, they will not be elaborated one by one.

[0023] At the same time, it should be understood that for the convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and in no way restricts the present disclosure and its application or use. Technologies, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods and devices should be regarded as part of the specification. It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0024] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0025] Figure 1 It is a schematic flow diagram of a method for the combined analysis of high-sensitivity cardiac troponin and copeptin disclosed in the embodiments of the present application.

[0026] It should be understood that in existing detection technologies, there are obvious temporal difference problems in the data characteristics of cardiac troponin I and copeptin. It takes 10 - 24 hours for cardiac troponin I to reach the peak concentration, and about 1% of the samples will show a delayed increase phenomenon, resulting in relatively low data reliability in the early stage. Especially in the area where the concentration increases slightly, the volatility of the detection data is relatively large, making it difficult for the simple difference comparison method in the existing 0h / 3h or 0h / 1h detection process to obtain stable and reliable quantitative results. This data processing method based on the ratio of the concentration difference at a single time point to the upper limit of normal value (ULN) cannot effectively cope with the mismatch problem in the time kinetic characteristics of the two indicators. Although copeptin can reach the peak within 0 - 3 hours, filling the data gap in early detection, its concentration change is affected by various physiological and biochemical factors at the same time, making it difficult to establish a reliable quantitative standard relying solely on the data of any one indicator.

[0027] The dual - indicator collaborative correction model disclosed in this solution establishes a non - linear dynamic coupling relationship through an improved multi - parameter logistic regression equation, which is different from the simple linear superposition or threshold comparison methods in the prior art. This model introduces a correction term K(t) in the time dimension, fully considering the temporal characteristics of the rapid change of copeptin and the slow change of cardiac troponin I. Especially when processing the data in the early stage, the non - linear dynamic coupling function f(v1, v2) based on the growth rate discloses an adaptive data correction mechanism, effectively overcoming the data instability caused by individual differences. The dual - indicator complementary correction algorithm realizes the precise correction of the data in the marginal area of the detection range by constructing a time - series compensation model. This algorithm not only considers the concentration change characteristics of the two indicators respectively, but also establishes a mathematical correlation based on the dynamic change rate, providing a more rigorous calculation basis for improving the reliability of early - stage detection data. Through the outlier identification of the sliding window and the locally weighted regression processing, the anti - interference ability of data processing is further enhanced.

[0028] As Figure 1 shown, at step S101, the plasma sample is prepared for detection by centrifugally separating the plasma sample and diluting it with a phosphate buffer solution. This includes: collecting a venous blood sample and adding dipotassium ethylenediaminetetraacetate anticoagulant, mixing the anticoagulant with the blood in proportion; placing the mixed blood sample in a polypropylene blood collection tube and inverting it for mixing; centrifuging the mixed blood sample in a low - temperature environment and sucking the upper - layer plasma; transferring the separated plasma to a sterile cryogenic storage tube for sub - packaging and storage; preparing a phosphate buffer solution and adding a surfactant to it; using the prepared phosphate buffer solution to dilute the thawed plasma sample.

[0029] Specifically, after collecting venous blood samples, add dipotassium ethylenediaminetetraacetate anticoagulant, and the ratio of anticoagulant to blood is 1:9. Place the collected blood samples in polypropylene centrifuge tubes and gently invert and mix 8 - 10 times. Centrifuge at a speed of 1600 revolutions per minute for 15 minutes in a 4°C environment, and carefully aspirate the upper plasma using a pipette. Transfer the separated plasma to a sterile cryogenic storage tube, and aliquot no less than 250 μL per tube. Avoid generating air bubbles during the aliquoting process to maintain sample stability. The plasma samples can be stored at -80°C for a period not exceeding 6 months. When taking samples, thaw the samples slowly in a 4°C water bath and gently mix during the period to ensure sample homogeneity. Prepare phosphate buffer (PBS), dissolve Na 2 HPO 4 and KH 2 PO 4 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 samples with the prepared PBS at a ratio of 1:4. Place the diluted samples in a 4°C environment, and subsequent operations should be completed within 2 hours.

[0030] In one embodiment, when preparing the phosphate buffer, weigh 8.76 g of Na 2 HPO 4 and 1.56 g of KH 2 PO 4 dissolve in 950 mL of ultrapure water, monitor and adjust the pH to 7.4 ± 0.1 using a pH meter, and finally make up the volume to 1000 mL. Add 0.05% Tween-20 to the prepared phosphate buffer and mix well. Dilute the thawed plasma samples with the prepared buffer at a ratio of 1:4, and the dilution process should be gentle and uniform to avoid generating air bubbles.

[0031] At step S102, construct an immunoassay system on a microplate using the double antibody sandwich method, and perform coating, washing, blocking in sequence, and prepare standards and labeled antibodies. This includes: selecting a microplate made of polystyrene and constructing an immunoassay system using the double antibody sandwich method; preparing coating buffer and adding the capture antibody solution diluted to an appropriate concentration to the microplate; using an automatic washing device to wash the microplate with the added capture antibody multiple times; preparing blocking solution and adding it to the microplate for incubation; preparing high-sensitivity cardiac troponin standards and copeptin standards with multiple concentration gradients; preparing biotin-labeled detection antibodies and horseradish peroxidase-labeled avidin.

[0032] In one embodiment, a 96-well plate made of polystyrene can be selected, and an immunoassay system can be established using the double antibody sandwich method. Prepare the coating buffer by adding 0.02% sodium azide to 0.1 mol / L carbonate buffer (pH 9.6). Add 100 μL of the capture antibody solution diluted to the optimal concentration to each well of the microplate, and let it stand overnight at 4°C. Use an automatic plate washer for washing. Add 350 μL of PBS washing solution containing 0.05% Tween-20 to each well and repeat 4 times. Gently tap on the absorbent paper to remove the residual liquid after each washing. Prepare the blocking solution by adding 2% bovine serum albumin to PBS, and add 200 μL to each well. Incubate at 37°C for 60 minutes. Prepare a series of standard products. Dilute the high-sensitivity cardiac troponin standard product with the sample diluent to 6 concentration gradients of 0.1 - 10 ng / mL, and dilute the copeptin standard product to 6 concentration gradients of 5 - 500 pmol / L. Prepare the biotin-labeled detection antibody and horseradish peroxidase-labeled avidin, and the concentrations are determined according to the optimization results of the preliminary experiment.

[0033] Preferably, prepare the carbonate coating buffer by dissolving 1.59 g of Na 2 CO 3 and 2.93 g of NaHCO 3 in 950 mL of ultrapure water, adjust the pH to 9.6, and make up the volume to 1000 mL. According to the results of the preliminary experiment, dilute the capture antibody with the coating buffer to a working concentration of 2.5 μg / mL.

[0034] In step S103, add the sample and the standard product to the detection plate, and successively carry out incubation, addition of the detection antibody, enzyme-labeled avidin, and finally color development and absorbance measurement. It includes: preparing two independent detection plates for detecting high-sensitivity cardiac troponin and copeptin respectively; adding the standard product and the sample to be tested to the corresponding detection wells, and setting parallel wells and control wells; placing the microplate after adding the samples in a thermostatic shaking incubator for incubation; adding the biotin-labeled detection antibody and incubating; adding the horseradish peroxidase-labeled avidin and incubating; successively carrying out the color reaction and the termination reaction, and measuring the absorbance value.

[0035] Specifically, prepare two independent test plates for the detection of high-sensitivity cardiac troponin and copeptin respectively. Add the standards and the samples to be tested into the corresponding test wells, 100 μL per well, and set up two parallel wells for each sample. Add an equal amount of sample diluent to the control wells. Place the microplate in a constant temperature shaking incubator at 37°C with a shaking frequency of 150 revolutions per minute and incubate for 75 minutes. Wash 5 times, with 350 μL of washing solution per well each time. Add the biotin-labeled detection antibody, 100 μL per well, and continue to incubate at 37°C for 60 minutes. After washing, add the horseradish peroxidase-labeled avidin, 100 μL per well, and incubate at 37°C for 30 minutes. After the last wash, add the tetramethylbenzidine / hydrogen peroxide chromogenic solution, 100 μL per well, and develop color in the dark at room temperature for 15 minutes. Add 50 μL of 2 mol / L sulfuric acid termination solution. Use an enzyme-linked immunosorbent assay (ELISA) reader to measure the absorbance values at the main wavelength of 450 nm and the reference wavelength of 630 nm.

[0036] In one embodiment, when performing the combined detection, prepare two microplates that have been completely coated and blocked. Use an 8-channel pipette to add the standards and the samples to be tested into the corresponding test wells, accurately adding 100 μL per well. The standards are added in order from low concentration to high concentration, and the samples to be tested are added according to a preset position map. Set up two parallel wells for each sample and at least two blank control wells at the same time. Place the loaded microplate 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 an automatic plate washer for washing. Add 350 μL of washing solution per well, soak for 10 seconds and then drain, repeating 5 times. Use an 8-channel pipette to add 100 μL / well of the biotin-labeled detection antibody and continue to incubate under the same conditions for 60 minutes. After washing again, add 100 μL / well of the horseradish peroxidase-labeled avidin and incubate at 37°C for 30 minutes. After the last wash, add 100 μL / well of the TMB substrate chromogenic solution under light-proof conditions and develop color at room temperature for 15 minutes. After the timing ends, quickly add 50 μL / well of 2 mol / L sulfuric acid termination solution, gently shake and mix well, and it can be observed that the solution changes from blue to yellow.

[0037] At step S104, collect the detection data to establish a standard curve, calculate the sample concentration using a dual-index collaborative correction model, and perform cross-validation and outlier processing. This includes: collecting the absorbance data of each test well and subtracting the blank control value; establishing a curve fitting equation between the standard concentration value and the absorbance value; substituting the absorbance value of the sample to be tested into the fitting equation to calculate the initial concentration value.

[0038] Among them, establishing a curve fitting equation for the standard product concentration value and absorbance value includes: setting the standard product concentration value 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; obtaining the goodness-of-fit parameter value through calculation.

[0039] In one embodiment, collect the absorbance data of each detection well and subtract the blank control value. Prepare a data processing spreadsheet and input the standard product concentration value (x-axis) and the corresponding absorbance value (y-axis). Use the four-parameter logistic regression function 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. Calculate the goodness of fit. Substitute the absorbance value of the sample to be tested into the fitting equation to obtain the initial concentration value. For samples outside the linear range, re-detect according to the preset dilution scheme. Establish a joint data processing model and calculate the correlation coefficient between the two indicators. Set the automated data screening conditions to eliminate outliers.

[0040] In addition, for the collaborative optimization of detection data based on dual indicators, it further includes: establishing a dual-indicator collaborative correction model to correct the detection results; establishing a compensation model using the dynamic change relationship between the two indicators; using the cross-validation method to verify the model and perform outlier identification and processing.

[0041] Furthermore, establishing a dual-indicator collaborative correction model to correct the detection results includes: converting the initial concentration value of high-sensitivity cardiac troponin into the natural logarithm form; converting the initial concentration value of copeptin into the natural logarithm form; using an improved five-parameter logistic equation for fitting, including the maximum response value parameter, the minimum response value parameter, the inflection point concentration parameter, the slope factor parameter, and the asymmetry correction factor parameter; introducing a time dimension correction term and performing a multiplication operation with the original equation; determining the values of each parameter through the least squares method.

[0042] Furthermore, establishing a compensation model using the dynamic change relationship between the two indicators includes: 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 non-linear dynamic coupling function and obtaining the coupling coefficient through historical data; determining whether the initial measured value is located in the edge area of the detection range; calculating the compensation value and superimposing it with the initial value to obtain the corrected result; setting the upper limit of the correction amplitude and performing a statistical test.

[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 between two time points with the upper limit of normal value (ULN), and determines it as a significant change when the difference is greater than 1ULN. This method ignores the dynamic change process of detection data. Especially when dealing with data of slightly elevated cardiac troponin I, due to its slow change characteristic of 10 - 24 hours, the difference within 1 hour interval is often small and is easily interfered by measurement noise. Although the simple linear interpolation method takes into account the continuity of time series, its assumption that the detection value changes linearly between adjacent time points cannot accurately describe the non-linear cumulative process of cardiac troponin I and the rapid peak characteristics of copeptin. These two conventional methods have obvious technical limitations when dealing with dual-index joint detection data. Especially when the concentration change rates of the two indicators are quite different, the error generated by linear interpolation will increase rapidly with the accumulation of time.

[0044] The dual-index dynamic coupling model and complementary correction algorithm disclosed in the embodiments of the present application overcome the problem of simplified processing in the mathematical model of the existing methods by introducing an improved multi-parameter logistic regression function and a non-linear dynamic coupling function. Different from the static threshold comparison method, this model realizes the accurate characterization of the dynamic characteristics of detection data through the time dimension correction term K(t). Compared with the simple linear interpolation method, this solution establishes a data association model that is more in line with the actual situation based on the non-linear dynamic coupling function f(v1, v2) of the growth rate. Especially when dealing with the asynchrony problem of the rapid change of copeptin within 0 - 3 hours and the slow accumulation of cardiac troponin I, the time series compensation model discloses an adaptive data correction mechanism, effectively avoiding the cumulative error of linear interpolation when dealing with non-linearly changing data. In addition, this solution also processes outliers through a sliding window and local weighted regression method, further improving the stability of data processing, which is a technical feature not possessed by the existing static threshold comparison and linear interpolation methods.

[0045] Specifically, to establish a dual-index collaborative correction model, the detection result of high-sensitivity cardiac troponin I can be corrected by 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. To design a dual-index complementary correction algorithm, based on the characteristics that copeptin reaches its peak within 0 - 3h while cardiac troponin reaches its peak at a later time, a time series compensation model can be constructed. When one of the indicators is at the edge of the detection range, the value of the other indicator is used for correction to improve the detection accuracy in the boundary region. The specific operation is as follows: calculate the relative change rates of the two indicators, establish a mathematical model to describe their dynamic change relationship, and accordingly correct the preliminary measurement result.

[0046] In one embodiment, when the synergistic correction model is constructed, the detection data of high-sensitivity cardiac troponin I and copeptin are first processed in a standardized manner. For the initial concentration value x of high-sensitivity cardiac troponin I, it is converted into the natural logarithm form ln(x), and for the initial concentration value y of copeptin, it is also converted into ln(y). The distribution of the converted data shows good linear characteristics, which is helpful for subsequent modeling and analysis. The nonlinear regression method is used to introduce an improved multi-parameter logistic regression equation for fitting. The equation contains two core parts: the concentration response curve and the time compensation term. The concentration response curve adopts the basic form The 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. In order to improve the fitting accuracy, the equation is improved and a correction term in the time dimension is introduced. The correction term expression is K(t) = α·exp(-βt), where t is the detection time point, and α and β are unknown coefficients. Multiply K(t) by the original equation to obtain the improved expression: The value of each parameter is determined by the least square method so that the sum of squares of deviations between the fitting curve and the experimental data is minimized.

[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 reaches its peak relatively later, this time difference can be used to construct a complementary correction model. First, calculate the growth rate of copeptin Among them C 2 , C 1 are the concentration values ​​at two adjacent time points, and Δt is the time interval. Similarly, the growth rate v of high-sensitivity cardiac troponin I is calculated. 2 . Introducing the nonlinear dynamic coupling function f(v 1 , v 2 )=λ·v 1 ·exp(-μv 2 ), where λ and μ are coupling coefficients obtained through historical data training. A dynamic compensation model is constructed based on this function: when a certain indicator is at the edge of the detection range, the dynamic change trend of another indicator is used for correction.

[0048] The specific calibration steps are as follows: For the initial measured value x 0 First, determine whether it is at the edge of the detection range (i.e., whether it is less than 1.2 times the lower detection limit or greater than 0.8 times the upper detection limit). If it is at the edge, calculate the compensation value δ = f(v 1 , v 2 )·(tt 0 ), where t is the current time point, t 0 is the first detection time. Add the compensation value to the initial value to get the corrected result x′=X0 +δ. To avoid overcorrection, an upper limit of the correction amplitude is set to ensure that Finally, the reliability of the correction result is tested by non-parametric statistical methods.

[0049] In another embodiment, a cross-validation method can also be used to validate the model, and outlier identification and processing are performed, including: determining model parameter values using training set data; evaluating model performance parameters using a test set; repeating the validation process and taking the average result as the final model parameters; setting a data processing sliding window and calculating the standard deviation of the data within the window; marking data points that exceed a specific multiple of the standard deviation from the mean; and smoothing the marked data points using a locally weighted regression method.

[0050] Preferably, the model validation can adopt a cross-validation method, randomly dividing the data set into a training set and a test set. Determine model parameters using the training set data, and then evaluate the model performance using the test set. Repeat this process multiple times and take the average result as the final model parameters. In practical applications, new validation data is collected regularly to update the model parameters to ensure the adaptability of the model. For outliers outside the normal range, a signal processing-based method is used for identification and elimination to avoid interference with the model. The specific operations include: setting a sliding window w = 5, calculating the standard deviation s of the data within the window, marking data points that deviate more than 2.8s from the mean as outliers, and smoothing them using a locally weighted regression method.

[0051] In addition, for quality control, quality control samples with three concentration levels can also be prepared. Intra-batch and inter-batch precision validations are performed, and the coefficient of variation is calculated. When the results of the quality control samples exceed the allowable range, the experiment needs to be repeated.

[0052] Figure 2 This is a time-concentration dynamic change curve graph of high-sensitivity cardiac troponin and copeptin disclosed in the embodiments of the present application. As Figure 2 shown, this graph consists of 3 subgraphs, showing the dynamic change rules and correlation analysis results of high-sensitivity cardiac troponin and copeptin.

[0053] Figure 2 The upper subgraph shows the change trends of the two indicators over time. The blue curve represents the change in the level of high-sensitivity cardiac troponin, showing a trend of first increasing and then decreasing within 0 - 6 hours, with the peak appearing around 2 - 3 hours and then gradually decreasing and tending to be stable. The red curve represents the change in the copeptin level, rapidly rising to the peak within 0 - 3 hours and then showing a downward trend. The shaded areas around the two curves represent the 95% confidence interval range of the measured data. There is a time difference in the change trends of the two indicators, with copeptin reaching the peak earlier and troponin lagging relatively, and this time difference discloses the basis for combined application.

[0054] Figure 2 The lower left sub - figure shows the mutual compensation correction curve between high - sensitivity cardiac troponin and copeptin (ρ = 0.46). The green curve indicates a certain degree of correlation between the two indicators, but it is not a simple linear relationship. The curve shows a trend of first increasing and then decreasing in the range of 0 - 4 hours, reflecting the change in the intensity of the complementary effect of the two indicators at different time points. This non - linear relationship provides a basis for establishing a dual - indicator collaborative correction model.

[0055] Figure 2 The lower right sub - figure gives a statistical correlation scatter plot (γ = 0.72). The distribution trend of the purple scatter points indicates a strong correlation between high - sensitivity cardiac troponin and copeptin levels. In the scatter plot, the abscissa is the troponin level (ng / L), and the ordinate is the copeptin level (pmol / L). The distribution of the data points shows a certain degree of aggregation and regularity. Through this correlation analysis, the expression patterns of the two indicators and their corresponding relationships in different concentration ranges can be determined, providing data support for the formulation of a joint detection strategy.

[0056] By analyzing the dynamic change rules, complementary effects, and correlations of the two indicators, the selection of the detection time point can be optimized, and a more reasonable joint detection strategy can be established.

[0057] Figure 3 This is a scatter - related schematic diagram of a correction model disclosed in an embodiment of the present application.

[0058] As Figure 3 shown, Figure 3 Figure A in

[0059] Figure 3 shows a three - dimensional curve graph of the change in biomarker concentration. The X - axis represents the dilution factor, the Y - axis represents time (minutes), and the Z - axis represents the concentration value [ng / mL]. The blue line in Figure A represents the distribution of multiple groups of experimental data, and the red line is the average trajectory. Through the trajectory distribution in three - dimensional space, the dynamic change rules of biomarker concentration at different dilution factors and time points can be observed. This spatial distribution graph reveals the non - linear change characteristics of the biomarker with time and dilution factor.

[0060] Figure 3Figure C in [reference] shows the calibration curve and correlation analysis. The horizontal axis represents the predicted values, and the vertical axis represents the measured values. 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 predictive ability.

[0061] Figure 3 Figure D in [reference] shows the distribution map of relative changes in dual indicators. Among them, the coefficient of variation of hs-cTnI level is 69.66 ± 11.1%, the relative change rate compared to the baseline is -20.35 + 62.2%, and the p-value is 0.624. These data reflect the dynamic relationship and stability between the indicators.

[0062] The above Figure 3 The four sub-figures in [reference] elaborate on the dynamic change characteristics of biomarkers, the results of correlation analysis, and the performance evaluation of the prediction model from different perspectives. This set of data verifies the accuracy index of the detection system. Through the comprehensive analysis of the above four sub-figures, the performance characteristics of the detection method based on the dual-indicator collaborative calibration model in the embodiments of the present application are fully elaborated.

[0063] Furthermore, the embodiments of the present application also disclose a device for processing detection data through dual-indicator collaboration, including: a processor, a memory, and a system bus; the processor and the memory are connected through the system bus; the memory is used to store one or more programs, and the one or more programs include instructions that, when executed by the processor, cause the processor to execute any of the above methods.

[0064] Furthermore, the embodiments of the present application also disclose a computer program product that, when running on a terminal device, causes the terminal device to execute any of the above methods.

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

[0066] It should be noted that the various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0067] It should also be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0068] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A combined analysis method of high-sensitivity cardiac troponin and copeptin, characterized in that: include: The plasma samples were separated by centrifugation and diluted with phosphate buffer to prepare the samples for testing; The double antibody sandwich method is used to construct an immunoassay system on a multi-well plate, and coating, washing, blocking, and preparation of standard products and labeled antibodies are performed in sequence; Add samples and standards to the test plate, incubate, add detection antibodies, enzyme-labeled avidin, and finally develop color and measure absorbance; The test data were collected to establish a standard curve, and the sample concentration was calculated using a dual-index collaborative correction model, and cross-validation and outlier processing were performed.

2. The joint analysis method according to claim 1, characterized in that: in, Prepare the sample for testing by centrifuging the plasma sample and diluting it in phosphate buffered saline, including: Collect venous blood samples and add dipotassium EDTA anticoagulant to mix the anticoagulant and blood in appropriate proportions; Place the mixed blood sample in a polypropylene blood collection tube and mix by inversion; The mixed blood sample is centrifuged in a low temperature environment, and the upper plasma is aspirated; The separated plasma was transferred into sterile frozen storage tubes for aliquoting and storage; preparing a phosphate buffer and adding a surfactant thereto; Thawed plasma samples were diluted with phosphate-buffered saline.

3. The joint analysis method according to claim 1, characterized in that: in, The double antibody sandwich method is used to construct an immunoassay system on a multi-well plate, and coating, washing, blocking, and preparation of standard products and labeled antibodies are performed in sequence, including: A polystyrene multiwell plate was used to construct an immunoassay system using the double antibody sandwich method; Prepare coating buffer and add capture antibody solution diluted to an appropriate concentration to the multi-well plate; The multi-well plate to which the capture antibody has been added is washed multiple times using an automatic washing device; Prepare blocking solution and add to multi-well plate for incubation; Preparation of high-sensitivity cardiac troponin standards and copeptin standards at multiple concentration gradients; Prepare biotin-labeled detection antibody and horseradish peroxidase-labeled avidin.

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

5. The joint analysis method according to claim 1, characterized in that: in, Collect test data to establish a standard curve, use a dual-index collaborative correction model to calculate sample concentration, and perform cross-validation and outlier processing, including: Collect the absorbance data of each detection well and subtract the blank control value; Establish a curve fitting equation between the standard concentration value and the absorbance value; Substitute the absorbance value of the sample to be tested into the fitting equation to calculate the initial concentration value.

6. The joint analysis method according to claim 5, characterized in that: Also includes: Establish a dual-index collaborative correction model to correct the test results; The compensation model is established by using the dynamic relationship between the two indicators; The cross-validation method was used to validate the model and identify and process outliers.

7. The joint analysis method according to claim 5, characterized in that: in, Establish a curve fitting equation between the standard concentration value and the absorbance value, including: Set the standard concentration value as the horizontal axis parameter and the corresponding absorbance value as the vertical axis parameter; The curve fitting was performed using a four-parameter logistic regression equation, which included the maximum absorbance value parameter, the minimum absorbance value parameter, the inflection point concentration value parameter, and the slope factor parameter; The goodness of fit parameter value is obtained by calculation.

8. The joint analysis method according to claim 6, characterized in that: in, A dual-index collaborative correction model was established to correct the test results, including: The initial concentration values ​​of high-sensitivity cardiac troponin were converted into natural logarithmic form; The initial concentration value of copeptin was converted into natural logarithm form; An improved five-parameter logistic equation was used for fitting, including the maximum response value parameter, the minimum response value parameter, the inflection point concentration parameter, the slope factor parameter and the asymmetry correction factor parameter; Introduce the time dimension correction term and multiply it with the original equation; The value of each parameter is determined by the least squares method.

9. The joint analysis method according to claim 6, characterized in that: in, The compensation model is established by using the dynamic relationship between the two indicators, including: Calculate the concentration growth rate of copeptin at adjacent time points; Calculate the concentration growth rate of high-sensitivity cardiac troponin at adjacent time points; Introduce nonlinear dynamic coupling function and obtain coupling coefficient through historical data; Determine whether the initial measurement value is located at the edge of the detection range; Calculate the compensation value and superimpose it with the initial value to obtain the correction result; Set an upper limit on the correction range and perform statistical tests.

10. The joint analysis method according to claim 6, characterized in that: in, The cross-validation method is used to validate the model and identify and process outliers, including: Determine the model parameter values ​​using the training set data; The test set is used to evaluate the model performance parameters; Repeat the validation process and take the average results as the final model parameters; Set up a data processing sliding window and calculate the standard deviation of the data within the window; Flag data points that are a certain number of standard deviations above the mean; The labeled data points are smoothed using a local weighted regression method.

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