Method for detecting multicellular factors in blood sample

Abstract

The invention belongs to the field of medical examination, and particularly discloses a method for detecting multicellular factors in a blood sample, which comprises the steps of collection, pretreatment, specific capture particle combination, separation detection, algorithm optimization treatment, result analysis, output and the like. According to the method, original detection data is obtained by specifically capturing a binding reaction of particles and cell factors and combining advanced separation and detection technologies, and optimization processing is performed by applying an algorithm, so that the data quality and the analysis efficiency are improved. The invention further relates to computer equipment or a memory, a computer program for storing and executing the detection method and an integrated detection device, the functions of sample treatment, detection, data processing and analysis, result output and the like are integrated, and rapid, accurate and high-throughput multi-cytokine detection is realized. The method is suitable for clinical laboratories, medical institutions and the like, provides powerful support for disease diagnosis, prognosis evaluation and treatment effect monitoring, and has a wide application prospect.

Description

Technical Field

[0001] The invention belongs to the field of medical testing, and specifically discloses a method for detecting multiple cytokines in a blood sample. Background Art

[0002] In clinical medicine and biological research, cytokines, as important mediators of intercellular signaling, play a vital role in regulating physiological and pathological processes such as immune response, inflammatory response, cell proliferation and differentiation. However, due to the wide variety of cytokines in the blood and the significant differences in their concentration levels between different individuals and different disease states, accurate and efficient detection of multiple cytokines in the blood is of great significance for early diagnosis of diseases, disease monitoring, evaluation of treatment effects, and prognosis.

[0003] Traditional cytokine detection methods, such as enzyme-linked immunosorbent assay (ELISA) and radioimmunoassay (RIA), have high specificity, but they can usually only detect a single or a few cytokines, and the operation is cumbersome and time-consuming, making it difficult to meet the needs of simultaneous detection of multiple cytokines in clinical and scientific research. With the continuous development of biotechnology, especially the emergence of high-throughput detection technologies such as flow cytometry, fluorescence resonance energy transfer, and mass spectrometry, it has become possible to detect multiple cytokines simultaneously.

[0004] However, these high-throughput detection technologies still face many challenges in practical applications, such as unstable quality of original detection data, large system errors, and correlation interference between data, which seriously affect the accuracy and reliability of the detection results. In addition, how to extract the cytokines most relevant to the disease state from these massive data and conduct effective analysis and interpretation is also a problem that needs to be solved urgently. Summary of the invention

[0005] To solve the above problems, we disclose a method for detecting multiple cytokines in a blood sample.

[0006] The present invention includes the following specific technical solutions:

[0007] A method for detecting multiple cytokines in a blood sample comprises the following steps:

[0008] a) collecting blood samples and obtaining samples to be tested after pretreatment;

[0009] b) using specific capture particles to react with multiple cytokines in the sample to be tested to form a capture particle-cytokine complex;

[0010] c) separating and detecting the captured particle-cytokine complex to obtain raw detection data;

[0011] d) Applying an algorithm to optimize the original detection data, the algorithm includes but is not limited to the following formula:

[0012]

[0013] Among them, xi is the original detection value of the i-th cytokine, μi and σi are the mean and standard deviation of the i-th cytokine, wi is the weight coefficient of the i-th cytokine, and k is the adjustment factor used to adjust the skewness of data distribution;

[0014] e) Calculating the standardized score or comprehensive evaluation index of each cytokine based on the optimized data to determine the abnormal level of the cytokine and its correlation with the disease state;

[0015] f) Output the test results, including the optimized value, standardized score or comprehensive evaluation index for each cytokine, and clinical interpretation or recommendations based on these data.

[0016] Preferably, in the above method for detecting multiple cytokines in a blood sample, the pretreatment in step a) includes at least one of centrifugation, removal of red blood cells, plasma separation, sample dilution, pH adjustment, and addition of a protective agent or a stabilizer to obtain a test sample suitable for a subsequent binding reaction.

[0017] Preferably, in the above-mentioned method for detecting multiple cytokines in a blood sample, the specific capture particles in step b) have a clear particle size distribution, are surface-modified with antibodies, aptamers, receptors or other specific binding molecules targeting specific cytokines, and the material and shape of the capture particles are optimized to improve binding efficiency and reaction kinetics.

[0018] Preferably, in the above-mentioned method for detecting multiple cytokines in a blood sample, the separation method in step c) includes at least one of magnetic separation, centrifugal separation, filtration separation, electrophoretic separation or fluid dynamic separation, and the detection method adopts at least one of flow cytometry, fluorescence resonance energy transfer, surface plasmon resonance, mass spectrometry, enzyme-linked immunosorbent assay or chemiluminescence immunoassay.

[0019] Preferably, in the above-mentioned method for detecting multiple cytokines in a blood sample, the algorithm in step d) also includes a data preprocessing step for removing noise, correcting systematic errors, and performing baseline adjustment or normalization processing to improve the quality of the original detection data; the algorithm in step d) also includes a feature selection step for screening out the cytokines that are most relevant to a specific disease state, disease progression, treatment effect or patient prognosis from a plurality of cytokines to reduce data dimensions and improve analysis efficiency.

[0020] Preferably, in the above-mentioned method for detecting multiple cytokines in a blood sample, the standardized score or comprehensive evaluation index in step e) is also used to construct a classification model, a regression model or a prediction model to achieve the classification of disease status, the prediction of disease progression or the evaluation of treatment effect.

[0021] Preferably, in the above-mentioned method for detecting multiple cytokines in a blood sample, the output result in step f) also includes a visual report, and the visual report includes a display in the form of a chart, a curve, a scatter plot, a heat map or a three-dimensional map, so that the user can intuitively understand the test results and clinical interpretation.

[0022] The present invention also discloses a computer device or memory, wherein the computer device or memory is configured to store and execute a computer program for implementing the above detection method, wherein the computer program comprises:

[0023] Instruction 1, for controlling the sample processing device to perform sample collection, pretreatment and binding reaction in steps a) and b);

[0024] Instruction 2 is used to receive and process the original detection data output by the detection device, and execute the algorithm optimization processing in step d), including data preprocessing, feature selection, iterative optimization of weight coefficients and adjustment factors, outlier detection, etc.;

[0025] Instruction 3, for calculating the standardized score or comprehensive evaluation index of each cytokine based on the optimized data, and performing the analysis and judgment in step e);

[0026] Instruction 4 is used to generate and output a test result report, including an optimized value, a standardized score or a comprehensive evaluation index for each cytokine, and a clinical interpretation or suggestion based on these data, and execute the result output in step f);

[0027] Optionally, instruction 5 is further included for executing a quality control step to monitor and evaluate the operation quality and result accuracy during the detection process;

[0028] The computer device includes a processor, a memory, a storage and an input-output interface. The processor is configured to execute instructions in the computer program. The memory is used to store the computer program and data generated during the detection process. The input-output interface is used to communicate with a sample processing device, a detection device, a user terminal or other external devices.

[0029] The present invention also discloses an integrated detection device, which performs the detection method according to any one of claims 1 to 7, wherein the detection device integrates a sample processing module, a detection module, a data processing and analysis module, and a result output module, wherein:

[0030] A sample processing module, used to perform the blood sample collection, pretreatment and binding reaction with specific capture particles in steps a) and b), the module comprising components such as an automatically controlled blood collection needle, a centrifuge, a mixer, and a temperature control device;

[0031] A detection module, used to perform separation and detection of the captured microparticle-cytokine complex in step c), the module using flow cytometry, fluorescence resonance energy transfer, surface plasmon resonance, mass spectrometry and other technologies, and equipped with high-precision detection instruments and optical systems;

[0032] A data processing and analysis module, including the computer device or memory, is used to perform the data reception, algorithm optimization processing, calculation of standardized scores or comprehensive evaluation indexes, and execution of quality control steps in steps d) and e). The module has built-in efficient algorithms and data processing software, which can process and analyze detection data in real time;

[0033] The result output module is used to execute the generation and output of the test result report in step f), including the generation, printing or electronic transmission of a visual report, and interactive communication with a user terminal. The module is equipped with output devices such as a display screen, a printer, and a network interface.

[0034] Compared with the prior art, the present invention has the following outstanding beneficial effects:

[0035] Improve detection efficiency and accuracy: The present invention can detect multiple cytokines simultaneously by specifically capturing microparticles and efficiently combining them with various cytokines in the blood, combining them with advanced separation and detection technologies, thus greatly improving detection efficiency. At the same time, by applying optimized algorithms to process the original detection data, noise and system errors are effectively removed, thus improving the accuracy of the detection results.

[0036] Enhanced data interpretability: The present invention not only provides the raw detection value of each cytokine, but also calculates a standardized score or comprehensive evaluation index through an algorithm, making the data easier to interpret and compare. This helps clinicians identify abnormal cytokine levels more quickly, thereby making more accurate diagnosis and treatment decisions.

[0037] Realize high-throughput detection: The integrated detection device of the present invention can process multiple samples at the same time, and can detect multiple cytokines in each sample, realizing true high-throughput detection. This greatly improves the detection capability and meets the needs of large-scale sample detection in clinical and scientific research.

[0038] Improve disease diagnosis and monitoring capabilities: The multi-cytokine detection method provided by the present invention can provide a more comprehensive understanding of the patient's cytokine levels, which helps to discover potential disease risks, monitor disease progression, and evaluate treatment effects. This is of great significance for improving the early diagnosis rate of diseases, optimizing treatment plans, and improving patient prognosis.

[0039] Promoting scientific research progress: The present invention provides a powerful tool for biological and medical research, which can help researchers to explore the physiological and pathological mechanisms of cytokines in more depth and promote scientific research progress in related fields. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] Test example: Detection of IL-6 (interleukin-6) cytokine levels in the blood

[0043] a) Sample collection and pretreatment

[0044] Collection: Venous blood samples were collected from healthy volunteers (n=20) and patients with suspected inflammatory diseases (n=30).

[0045] Preprocessing:

[0046] The sample was centrifuged at 3000 rpm for 10 minutes to separate plasma from blood cells.

[0047] The upper plasma fraction was taken and diluted 1:10 to ensure the detection sensitivity.

[0048] The pH of plasma was adjusted to 7.4 using a pH adjuster to maintain the stability of cytokines.

[0049] 0.1% sodium azide was added as a protective agent to prevent sample degradation during storage.

[0050] b) Binding reaction of specific capture particles

[0051] Capture microparticles: Polystyrene microparticles with anti-IL-6 antibodies modified on the surface were used, with a particle size of 10±0.5 μm and a spherical shape.

[0052] Binding reaction:

[0053] The pretreated plasma samples were mixed with the capture particles at a volume ratio of 1:5.

[0054] Incubate in a 37°C water bath for 30 min with gentle shaking to promote binding reaction.

[0055] c) Separation and detection

[0056] Separation method: Magnetic separation technology is used to combine antibodies labeled with magnetic beads with capture particles, and the complex is separated by an external magnetic field.

[0057] Detection method:

[0058] Use a flow cytometer and set the excitation wavelength to 488 nm and the detection wavelength to 525 nm.

[0059] The fluorescence intensity of the separated captured microparticle-IL-6 complex was detected, and the fluorescence intensity value of each sample was recorded as the original detection data.

[0060] d) Data optimization processing

[0061] Algorithm application:

[0062] The Z-score standardization formula was used to preprocess the data to eliminate the systematic errors caused by differences in experimental operations among different samples.

[0063] Apply the weighted Z-score formula to calculate the optimized value:

[0064]

[0065] In the formula, x IL-6 Represents the original detection value of IL-6 cytokine. IL-6 and σ IL-6 w represents the mean (i.e., average value) and standard deviation (i.e., the degree of dispersion of the value) of IL-6 in all samples. IL-6 is the weight coefficient of IL-6, which is specially set to 1.2 here to emphasize or adjust the importance of IL-6 in the overall analysis. And k is an adjustment factor, set to 1.5, which is used to further adjust or scale the results of the entire formula.

[0066] Data preprocessing: remove outliers (such as data points exceeding the mean ± 3 times the standard deviation) and perform baseline adjustments to ensure data accuracy.

[0067] Feature selection: In this example, the feature selection step is simplified because we are only interested in one cytokine, IL-6.

[0068] e) Standardized scores and model building

[0069] Normalized score: The IL-6 standardized score was calculated for each sample and compared with the data of the healthy volunteer group to determine the abnormal threshold.

[0070] Model construction: Based on the standardized score of IL-6, a logistic regression model was used to construct a classifier for inflammatory diseases to assess whether the patient had an inflammatory disease.

[0071] f) Output results

[0072] Optimized Value: Lists the optimized IL-6 detection value for each sample.

[0073] Standardized score: Provides the IL-6 standardized score for each sample and indicates whether it exceeds the abnormal threshold.

[0074] Clinical interpretation: Based on the standardized score, a preliminary judgment is given as to whether the patient has an inflammatory disease.

[0075] Visual report: Generate a scatter plot to show the distribution of IL-6 levels in the patient group and the healthy group; at the same time, provide the ROC curve of the logistic regression model to evaluate the classification performance of the model.

[0076] Example 2

[0077] Test example: Simultaneously detect the levels of TNF-α (tumor necrosis factor-α) and IL-10 (interleukin-10) cytokines in the blood.

[0078] a) Sample collection and pretreatment

[0079] Collection: Venous blood samples were collected from patients with autoimmune diseases (n=40).

[0080] Preprocessing:

[0081] The sample was placed in a centrifuge and centrifuged at 4000 rpm for 15 minutes to completely separate the plasma from the blood cells.

[0082] Take the upper plasma portion and dilute it 1:20 to improve the sensitivity of the test.

[0083] The pH of plasma was adjusted to 7.2 using a pH adjuster to ensure the stability of cytokines.

[0084] 0.05% EDTA was added as a stabilizer to prevent the sample from agglutination during storage.

[0085] b) Binding reaction of specific capture particles

[0086] Capture particles: Two polystyrene particles with different particle sizes, 5±0.3 μm and 15±0.5 μm, were used to modify anti-TNF-α antibody and anti-IL-10 antibody, respectively.

[0087] Binding reaction:

[0088] The pretreated plasma samples were mixed with the two capture particles at a volume ratio of 1:3.

[0089] Incubate at room temperature for 45 minutes with gentle shaking to allow the capture particles to fully bind to the cytokines.

[0090] c) Separation and detection

[0091] Separation method: Fluid mechanics separation technology is used to separate the complex using the microchannel structure in the microfluidic chip.

[0092] Detection method:

[0093] Fluorescence resonance energy transfer technology was used, with excitation wavelengths set at 550 nm and detection wavelengths at 575 nm (for TNF-α) and excitation wavelengths set at 495 nm and detection wavelengths at 520 nm (for IL-10).

[0094] The fluorescence intensity of the separated captured microparticle-cytokine complex was detected, and the fluorescence intensity value of each sample at two wavelengths was recorded as the original detection data.

[0095] d) Data optimization processing

[0096] Algorithm application:

[0097] The original detection values ​​of TNF-α and IL-10 were preprocessed by Z-score standardization.

[0098] The weighted Z-score formula is used to calculate the optimized value. The formula is the same as in Example 1, but the weight coefficient and adjustment factor are different. TNF-α is set to 1.0, k is set to 1.2; w of IL-10 IL-10 Set it to 0.8 and k to 1.3.

[0099] Data preprocessing: remove outliers and perform baseline adjustments to ensure data accuracy.

[0100] Feature selection: Correlation analysis is used to select the cytokines that are most correlated with the activity of autoimmune diseases. Assume that the analysis results show that TNF-α is more correlated, so TNF-α is more important in subsequent analysis.

[0101] e) Standardized scores and model building

[0102] Normalized scores: Normalized scores for TNF-α and IL-10 were calculated for each sample.

[0103] Model construction: Based on the standardized score of TNF-α, a prediction model for the activity of autoimmune diseases was constructed using a support vector machine (SVM) model.

[0104] f) Output results

[0105] Optimized Values: Lists the optimized detection values ​​of TNF-α and IL-10 for each sample.

[0106] Normalized scores: The normalized scores of TNF-α and IL-10 for each sample are provided.

[0107] Clinical interpretation: Based on standardized scores, this provides a preliminary assessment of the patient's autoimmune disease activity.

[0108] Visual report: Generates a heat map to show the distribution of TNF-α and IL-10 levels in the patient group; at the same time, provides the prediction curve of the SVM model to evaluate the model's prediction performance for disease activity. In addition, a scatter plot of the relationship between TNF-α and disease activity can be provided to further intuitively show the relationship between the two.

[0109] Example 3

[0110] A computer device specifically designed to perform multi-cytokine assays.

[0111] The device integrates advanced computing power and storage resources to efficiently process and analyze multiple cytokine detection data from blood samples. The following is a detailed description of the computer device:

[0112] 1. Equipment composition

[0113] The computer device is mainly composed of a processor, memory, storage, and input and output interfaces.

[0114] Processor: It uses a high-performance central processing unit (CPU), which is responsible for executing instructions in computer programs and performing complex data calculations and analysis.

[0115] Memory: Equipped with a large capacity random access memory (RAM), it is used to temporarily store the data and instructions required by the processor when executing programs, thereby increasing data access speed.

[0116] Memory: Use a reliable hard disk or solid-state drive (SSD) as a permanent storage device to store computer programs, data generated during the test process, and historical test results.

[0117] Input and output interface: including USB interface, network interface, HDMI interface, etc., used for communication and data transmission with sample processing devices, detection devices, user terminals (such as monitors, printers) or other external devices.

[0118] 2. Software Configuration

[0119] The computer device is configured to store and execute a computer program for implementing a multi-cytokine detection method. The program includes the following instructions:

[0120] Instruction 1: Control the sample processing device to perform the collection, pretreatment and binding reaction of blood samples with specific capture particles. This includes steps such as centrifugation, removal of red blood cells, plasma separation, sample dilution, pH adjustment, and addition of protective agents or stabilizers.

[0121] Instruction 2: Receive and process the raw test data output by the detection device. Perform data preprocessing steps, such as removing noise, correcting system errors, and performing baseline adjustment or normalization to improve data quality. At the same time, perform feature selection to screen out the cytokines most relevant to a specific disease state.

[0122] Instruction 3: Apply the algorithm to optimize the raw test data. This includes calculating the mean, standard deviation, weight coefficient and adjustment factor for each cytokine, and calculating the standardized score or comprehensive evaluation index according to the formula. Through iterative optimization algorithms, the weight coefficient and adjustment factor are continuously adjusted to obtain more accurate results.

[0123] Instruction 4: Calculate the standardized score or comprehensive evaluation index for each cytokine based on the optimized data, and classify the disease state, predict disease progression, or evaluate the treatment effect. Generate and output the test result report, including the optimized value, standardized score or comprehensive evaluation index for each cytokine, and clinical interpretation or recommendations based on these data.

[0124] Optional Instruction 5: Perform quality control steps to monitor and evaluate the quality of the assay and the accuracy of the results. This includes regular calibration of the assay, verification of specific capture of particles, and evaluation of algorithm stability and accuracy.

[0125] 3. Equipment features

[0126] Efficiency: With high-performance processors and large-capacity memory, the device can quickly process and analyze large amounts of test data, improving test efficiency.

[0127] Accuracy: Advanced algorithms and optimized data processing steps are used to ensure the accuracy and reliability of test results.

[0128] Flexibility: Supports a variety of sample processing, detection and data analysis methods, and can be flexibly configured and adjusted according to actual needs.

[0129] Ease of use: Provides an intuitive user interface and visual reports to facilitate users to understand and use test results.

[0130] 4. Application scenarios

[0131] This computer device can be widely used in medical institutions, scientific research institutions, biopharmaceutical companies and other fields for the detection and analysis of multiple cytokines. By accurately measuring the levels of multiple cytokines in the blood, it can help doctors diagnose diseases, evaluate treatment effects and predict disease progression, providing strong support for clinical decision-making. At the same time, this device can also be used for cytokine research in the field of scientific research to promote the development of biomedicine.

[0132] Example 4

[0133] An integrated multi-cytokine detection device

[0134] This embodiment provides a highly integrated multi-cytokine detection device, which can automatically, efficiently and accurately complete the detection of multiple cytokines in a blood sample and output a detailed test result report. The following is a detailed description of the detection device:

[0135] 1. Device composition

[0136] The integrated detection device mainly consists of the following four modules:

[0137] Sample processing module

[0138] Components: Automatically controlled blood collection needle, centrifuge, mixer, temperature control device.

[0139] Function: Responsible for the collection, pretreatment and binding reaction of blood samples with specific capture particles. The blood collection needle is used to automatically collect blood samples; the centrifuge is used to centrifuge the samples to remove red blood cells and separate plasma; the mixer is used to dilute and mix the samples; the temperature control device ensures that the reaction is carried out at an appropriate temperature.

[0140] Detection module

[0141] Techniques: Flow cytometry, fluorescence resonance energy transfer, surface plasmon resonance, mass spectrometry.

[0142] Equipment: High-precision testing instruments and optical systems.

[0143] Function: Responsible for the separation and detection of the capture particle-cytokine complex. By adopting advanced technical means, it can achieve accurate identification and quantitative analysis of the complex.

[0144] Data processing and analysis module

[0145] Equipment: Built-in efficient computer equipment or storage.

[0146] Software: Algorithms and data processing software.

[0147] Function: Responsible for receiving the raw test data output by the detection module, performing data preprocessing, feature selection, algorithm optimization processing (including iterative optimization of weight coefficients and adjustment factors), outlier detection and other steps, and calculating the standardized score or comprehensive evaluation index of each cytokine. At the same time, this module also performs quality control steps to monitor and evaluate the operation quality and result accuracy during the detection process.

[0148] Result output module

[0149] Equipment: display screen, printer, network interface.

[0150] Function: Responsible for generating and outputting test result reports, including optimized values, standardized scores or comprehensive evaluation indexes for each cytokine, and clinical interpretations or recommendations based on these data. The report can be presented in a visual form (such as charts, curves, scatter plots, heat maps or three-dimensional graphs) for intuitive understanding by users. At the same time, the module also supports interactive communication with user terminals to achieve electronic transmission and remote access of test results.

[0151] 2. Workflow

[0152] Sample collection and pretreatment: Blood samples are collected through an automated blood collection needle, and then pretreated using a centrifuge, mixer, and temperature control device to obtain test samples suitable for subsequent binding reactions.

[0153] Binding reaction and detection: The pre-treated sample to be tested is subjected to a binding reaction with the specific capture particles to form a capture particle-cytokine complex. Then, the complex is separated and detected using the high-precision detection instrument and optical system in the detection module to obtain the original detection data.

[0154] Data processing and analysis: After receiving the original test data, the data processing and analysis module performs a series of data processing and analysis steps, including data preprocessing, feature selection, algorithm optimization, etc., to calculate the standardized score or comprehensive evaluation index of each cytokine. At the same time, quality control is performed to ensure the accuracy of the results.

[0155] Result output and interpretation: The result output module generates and outputs the test result report based on the calculation results of the data processing and analysis module. The report includes detailed test data, standardized scores or comprehensive evaluation indexes, and clinical interpretations or recommendations based on these data. Users can view the report on the display screen, print it on a printer, or electronically transmit the report to other devices through a network interface.

[0156] 3. Device Features

[0157] Highly integrated: The device integrates sample processing, detection, data processing and analysis, and result output, achieving automation and efficiency of the detection process.

[0158] Advanced technology: Flow cytometry, fluorescence resonance energy transfer and other advanced technologies are used for detection, which improves the accuracy and sensitivity of detection.

[0159] Intelligent processing: Built-in efficient algorithms and data processing software can process and analyze test data in real time, providing accurate test results and clinical interpretations.

[0160] User-friendly: Equipped with output devices such as display screen, printer and network interface, it is convenient for users to view and transmit test results. At the same time, it provides visual reports so that users can intuitively understand the test results and clinical significance.

[0161] IV. Application Scenarios

[0162] This integrated multi-cytokine detection device can be widely used in medical institutions, scientific research institutions, biopharmaceutical companies and other fields for the detection and analysis of multiple cytokines. By accurately measuring the levels of multiple cytokines in the blood, it can help doctors diagnose diseases, evaluate treatment effects and predict disease progression; at the same time, it can also provide strong technical support for cytokine research in the scientific research field.

[0163] As can be seen from the above embodiments, the present invention discloses a method for detecting multiple cytokines in a blood sample, including the steps of collection, pretreatment, specific capture of microparticle binding, separation and detection, algorithm optimization processing, result analysis and output. By specifically capturing the binding reaction of microparticles and cytokines, combined with advanced separation and detection technology, the original detection data is obtained, and the algorithm is applied for optimization processing to improve data quality and analysis efficiency. The present invention also relates to a computer device or memory for storing and executing a computer program for implementing the detection method, and an integrated detection device, which integrates the functions of sample processing, detection, data processing and analysis, and result output, to achieve rapid, accurate, and high-throughput detection of multiple cytokines. The present invention is suitable for clinical laboratories, medical institutions, etc., and provides strong support for disease diagnosis, prognosis evaluation, and treatment effect monitoring, and has broad application prospects.

[0164] The above are only a few preferred embodiments of the present invention, which are described in a more specific and detailed manner, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for detecting multiple cytokines in a blood sample, characterized in that: The following steps are involved: a) collecting blood samples and obtaining samples to be tested after pretreatment; b) using specific capture particles to react with multiple cytokines in the sample to be tested to form a capture particle-cytokine complex; c) separating and detecting the captured particle-cytokine complex to obtain raw detection data; d) Applying an algorithm to optimize the original detection data, the algorithm includes but is not limited to the following formula: Among them, xi is the original detection value of the i-th cytokine, μi and σi are the mean and standard deviation of the i-th cytokine, wi is the weight coefficient of the i-th cytokine, and k is the adjustment factor used to adjust the skewness of data distribution; e) Calculating the standardized score or comprehensive evaluation index of each cytokine based on the optimized data to determine the abnormal level of the cytokine and its correlation with the disease state; f) Output the test results, including the optimized value, standardized score or comprehensive evaluation index for each cytokine, and clinical interpretation or recommendations based on these data.

2. The method for detecting multiple cytokines in a blood sample according to claim 1, characterized in that: The pretreatment in step a) includes at least one of centrifugation, red blood cell removal, plasma separation, sample dilution, pH adjustment, and the addition of a protective agent or a stabilizer to obtain a sample suitable for a subsequent binding reaction.

3. The method for detecting multiple cytokines in a blood sample according to claim 1, characterized in that: The specific capture particles in step b) have a clear particle size distribution, are surface-modified with antibodies, aptamers, receptors or other specific binding molecules targeting specific cytokines, and the material and shape of the capture particles are optimized to improve binding efficiency and reaction kinetics.

4. The method for detecting multiple cytokines in a blood sample according to claim 1, characterized in that: The separation method in step c) includes at least one of magnetic separation, centrifugal separation, filtration separation, electrophoretic separation or fluid dynamic separation, and the detection method adopts at least one of flow cytometry, fluorescence resonance energy transfer, surface plasmon resonance, mass spectrometry, enzyme-linked immunosorbent assay or chemiluminescent immunoassay.

5. The method for detecting multiple cytokines in a blood sample according to claim 1, characterized in that: The algorithm in step d) also includes a data preprocessing step for removing noise, correcting systematic errors, and performing baseline adjustment or normalization to improve the quality of the original detection data; the algorithm in step d) also includes a feature selection step for screening out the cytokines that are most relevant to a specific disease state, disease progression, treatment effect or patient prognosis from a variety of cytokines to reduce data dimensions and improve analysis efficiency.

6. The method for detecting multiple cytokines in a blood sample according to claim 1, characterized in that: The standardized score or comprehensive evaluation index in step e) is also used to construct a classification model, a regression model or a prediction model to achieve the classification of disease status, the prediction of disease progression or the evaluation of treatment effect.

7. The method for detecting multiple cytokines in a blood sample according to claim 1, characterized in that: The output results in step f) also include a visual report, which includes a display in the form of a chart, a curve, a scatter plot, a heat map or a three-dimensional map, so that the user can intuitively understand the test results and clinical interpretation.

8. A computer device or memory, the computer device or memory being configured to store and execute a computer program for implementing the detection method according to any one of claims 1 to 7, wherein the computer program comprises: Instruction 1, for controlling the sample processing device to perform sample collection, pretreatment and binding reaction in steps a) and b); Instruction 2 is used to receive and process the original detection data output by the detection device, and execute the algorithm optimization processing in step d), including data preprocessing, feature selection, iterative optimization of weight coefficients and adjustment factors, outlier detection, etc.; Instruction 3, for calculating the standardized score or comprehensive evaluation index of each cytokine based on the optimized data, and performing the analysis and judgment in step e); Instruction 4 is used to generate and output a test result report, including an optimized value, a standardized score or a comprehensive evaluation index for each cytokine, and a clinical interpretation or suggestion based on these data, and execute the result output in step f); Optionally, instruction 5 is further included for executing a quality control step to monitor and evaluate the operation quality and result accuracy during the detection process; The computer device includes a processor, a memory, a storage and an input-output interface. The processor is configured to execute instructions in the computer program. The memory is used to store the computer program and data generated during the detection process. The input-output interface is used to communicate with a sample processing device, a detection device, a user terminal or other external devices.

9. An integrated detection device, which performs the detection method according to any one of claims 1 to 7, wherein the detection device integrates a sample processing module, a detection module, a data processing and analysis module, and a result output module, wherein: A sample processing module, used to perform the blood sample collection, pretreatment and binding reaction with specific capture particles in steps a) and b), the module comprising components such as an automatically controlled blood collection needle, a centrifuge, a mixer, and a temperature control device; A detection module, used to perform separation and detection of the captured microparticle-cytokine complex in step c), the module using flow cytometry, fluorescence resonance energy transfer, surface plasmon resonance, mass spectrometry and other technologies, and equipped with high-precision detection instruments and optical systems; A data processing and analysis module, including the computer device or memory, is used to perform the data reception, algorithm optimization processing, calculation of standardized scores or comprehensive evaluation indexes, and execution of quality control steps in steps d) and e). The module has built-in efficient algorithms and data processing software, which can process and analyze detection data in real time; The result output module is used to execute the generation and output of the test result report in step f), including the generation, printing or electronic transmission of a visual report, and interactive communication with a user terminal. The module is equipped with output devices such as a display screen, a printer, and a network interface.

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