An autoimmune disease antibody detection system based on biosensing technology

Abstract

The application provides an autoimmune disease antibody detection system based on a biosensing technology, and has the characteristics that the system comprises a data acquisition module, a data processing module, a detection analysis module and an interactive management module; the data acquisition module is used for acquiring comprehensive detection information of autoimmune diseases of a patient, the data processing module is used for preprocessing the comprehensive detection information, the detection analysis module is used for analyzing and evaluating the risk of autoimmune diseases of the patient in combination with the preprocessed comprehensive detection information; and the interactive management module is used for managing information data in the system and completing information interaction between the system and the outside world; through integration of the biosensing technology, intelligent data analysis and dynamic health management, the application realizes high-sensitivity detection, accurate risk evaluation and personalized health intervention of autoimmune diseases, and improves the early diagnosis rate and the health management level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomedical detection systems, and particularly relates to an autoimmune disease antibody detection system based on biosensing technology. BACKGROUND

[0002] Autoimmune diseases are a class of diseases caused by abnormal immune systems attacking self-tissues, including systemic lupus erythematosus, rheumatoid arthritis, Sjogren's syndrome, etc. Such diseases usually have complex disease courses, large individual differences and atypical early symptoms, leading to difficulties in clinical diagnosis, and misdiagnosis and missed diagnosis are common.

[0003] Currently, the detection of autoimmune diseases mainly relies on self-antibody detection, immune function evaluation and physiological index monitoring, but traditional methods often have limited detection sensitivity, high false positive rate and lack of comprehensive evaluation system, etc. In addition, existing detection methods usually assess the patient's condition statically, making it difficult to achieve dynamic tracking and personalized management of immune status, limiting the accuracy of early intervention and disease progression prediction. Therefore, there is an urgent need for a more accurate, efficient and dynamic detection and evaluation system to improve the early diagnosis rate of autoimmune diseases and optimize long-term health management strategies. SUMMARY

[0004] The present application aims to address the existing problems and provides an autoimmune disease antibody detection system based on biosensing technology.

[0005] The present application adopts the following technical solutions:

[0006] An autoimmune disease antibody detection system based on biosensing technology, the system comprising a data acquisition module, a data processing module, a detection analysis module and an interactive management module; the data acquisition module is used to obtain comprehensive detection information of the patient's autoimmune disease, the data processing module is used to preprocess the comprehensive detection information, and the detection analysis module is used to analyze and evaluate the patient's autoimmune disease risk in combination with the preprocessed comprehensive detection information; the interactive management module is used to manage information data within the system and complete information interaction between the system and the outside world.

[0007] The data acquisition module comprises a sample acquisition unit, a sample separation unit, a biosensing detection unit and a detection information acquisition unit; the sample acquisition unit is configured to acquire blood sample information of a patient; the sample separation unit is configured to separate, decontaminate and enrich the blood sample by using a microfluidic chip to obtain serum samples and cell component samples; the biosensing detection unit is configured to perform biosensing detection on the serum samples and the cell component samples to obtain a plurality of immune-related biomarker data; and the detection information acquisition unit is configured to perform data processing on the plurality of immune-related biomarker data and integrate the plurality of immune-related biomarker data to generate comprehensive detection information.

[0008] Further, the comprehensive detection information of the patient's autoimmune disease comprises autoimmune antibody detection information and physiological information related to the autoimmune disease; and the autoimmune antibody detection information specifically refers to the presence of specific autoimmune antibodies in the patient's blood and the concentration level of the specific autoimmune antibodies.

[0009] Further, the detection analysis module comprises an autoimmune antibody analysis unit and a comprehensive immune state evaluation unit; the autoimmune antibody analysis unit is configured to preliminarily analyze the possibility of the patient having an autoimmune disease based on the autoimmune antibody detection information; and the comprehensive immune state evaluation unit is configured to complete comprehensive evaluation of the patient's disease condition based on the analysis result of the autoimmune antibody analysis unit and the physiological information related to the autoimmune disease of the patient.

[0010] Further, the autoimmune antibody analysis unit completes the analysis of the possibility of the patient having an autoimmune disease by inputting the autoimmune antibody detection information into a pre-established statistical analysis model; and the statistical analysis model is established by using a mathematical modeling method based on the immune feature data and the clinical case data of the patient population in a medical database.

[0011] Further, the comprehensive evaluation method of the comprehensive immune state evaluation unit is specifically as follows:

[0012] S11: establishing a correlation model between the physiological information related to the autoimmune disease and the autoimmune disease;

[0013] S12: comprehensively evaluating the immune state of the patient based on the correlation model:

[0014] ;

[0015] wherein, is the comprehensive evaluation probability of the patient having the disease, is the influence weight of the i-th dimension information in the physiological information related to the autoimmune disease on the patient's disease, which is obtained by using the correlation model, is the total number of the dimension information in the physiological information related to the autoimmune disease, and is the total number of the dimension information in the physiological information related to the autoimmune disease, and The first in the physiological information related to the patient's autoimmune disease Quantitative processing values ​​of information in various dimensions; This represents the initial disease probability of patients obtained through the autoantibody analysis unit. The preset reduction factor has a range of values. .

[0016] Furthermore, in step S1, the association model is established in the following manner:

[0017] S111: Obtain time-series sample medical record information from a medical database. Each sample medical record includes physiological information related to autoimmune diseases and their corresponding disease information. The sample medical record information is organized and stored according to time series to form multiple consecutive datasets within a preset time period.

[0018] S112: The impact of each dimension of physiological information related to autoimmune diseases on the patient's disease is obtained and quantified by logistic regression.

[0019] S113: Calculate the influence weight of each dimension of information on the patient's disease, and integrate the influence weights corresponding to all types of dimension information to construct a correlation model between physiological information related to autoimmune diseases and autoimmune diseases; the specific calculation method for the influence weight of each dimension of information on the patient's disease is as follows:

[0020] ;

[0021] in, For the first The weighted influence coefficient for each time period, with the time period being closer to the current time. The larger the value; The total number of preset time periods; In the first Within the first time period, the first The influence coefficient of each dimension of information on the patient's disease is obtained through step S112; for satisfy:

[0022] ;

[0023] in, This is the time decay coefficient, used to control the contribution of historical data at a given time to the model's calculation results. It is set through pre-experimentation.

[0024] Further, the interaction management module comprises a data storage management unit, an interaction unit, a dynamic health tracking unit and a personalized suggestion unit; the data storage management unit is used for managing and storing data information detected and analyzed by the system; the interaction unit is used for completing data information interaction between the system and the outside world; the dynamic health tracking unit is used for analyzing the disease development trend of the patient in combination with the evaluation information of the long-term disease condition of the patient and providing personalized health management suggestions for the patient.

[0025] The present application has the following beneficial effects:

[0026] The present application efficiently acquires and detects the autoimmune disease related biomarkers of the patient by combining biosensing technology with microfluidic chips, improves the sensitivity and accuracy of detection, constructs a dynamic immune evaluation system by combining statistical analysis model and logistic regression method with medical big data, improves the accuracy of disease evaluation of the patient, optimizes the attention degree of the recent data of the correlation model by time weighted analysis, enhances the timeliness and dynamic adaptability of the disease evaluation, provides continuous disease monitoring and optimized intervention strategy by combining dynamic health tracking with personalized health management suggestions, and thus improves the early detection rate and health management level of autoimmune diseases. BRIEF DESCRIPTION OF DRAWINGS

[0027] The present application can be further understood from the following description in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is placed on showing the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0028] Figure 1 It is a schematic diagram of the overall module of the present application.

[0029] Figure 2 It is a schematic diagram of the working process of the comprehensive immune state evaluation unit of the present application.

[0030] Figure 3 It is a schematic diagram of the correlation model establishment method flow of the present application.

[0031] Figure 4 It is a schematic diagram of the working process of the dynamic health tracking unit of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present application clearer and more apparent, the present application will be further described in detail below in conjunction with its embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application; for those skilled in the art, other systems, methods and / or features of the embodiments will become apparent after reading the following detailed description; all such additional systems, methods, features and advantages are intended to be included within the scope of the present application; included within the scope of the present application and protected by the appended claims; additional features of the disclosed embodiments are described in the following detailed description, and will be apparent from the following detailed description.

[0033] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right" and the like are based on the orientations or positional relationships shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or components referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationships in the drawings are only used for exemplary illustration, and cannot be understood as a limitation on the present patent, for those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances. Embodiments

[0034] As Figure 1 shown, the present embodiment provides an autoimmune disease antibody detection system based on biosensing technology, which comprises a data acquisition module, a data processing module, a detection analysis module and an interactive management module; the data acquisition module is used to acquire comprehensive detection information of autoimmune diseases of patients, the data processing module is used to preprocess the comprehensive detection information, the detection analysis module is used to analyze and evaluate the risk of autoimmune diseases of patients in combination with the preprocessed comprehensive detection information; the interactive management module is used to manage information data in the system and complete information interaction between the system and the outside world;

[0035] The data acquisition module comprises a sample collection unit, a sample separation unit, a biosensing detection unit and a detection information acquisition unit; the sample collection unit is used to collect blood sample information of patients; the sample separation unit is used to separate, remove impurities and enrich blood samples through a microfluidic chip to obtain serum samples and cell component samples; the biosensing detection unit is used to perform biosensing detection on the serum samples and the cell component samples respectively to obtain a plurality of immune-related biomarker data; the detection information acquisition unit is used to process and integrate the plurality of immune-related biomarker data to generate comprehensive detection information;

[0036] Furthermore, the comprehensive detection information for the patient's autoimmune disease includes autoantibody detection information and physiological information related to the autoimmune disease; the autoantibody detection information specifically refers to the presence and concentration level of specific autoantibodies in the patient's blood; the other physiological information related to the autoimmune disease includes, but is not limited to, the patient's inflammatory status, immune cell function, cytokines, and biomarkers related to metabolism and organ function.

[0037] Furthermore, the data processing module performs preprocessing operations on the comprehensive detection information, including signal denoising and filtering, data formatting, and outlier detection and compensation.

[0038] Furthermore, the detection and analysis module includes an autoantibody analysis unit and a comprehensive immune status assessment unit; the autoantibody analysis unit is used to preliminarily analyze the possibility of the patient having an autoimmune disease by combining autoantibody detection information; the comprehensive immune status assessment unit is used to complete a comprehensive assessment of the patient's condition by combining the analysis results of the autoantibody analysis unit with the physiological information related to the patient's autoimmune disease.

[0039] Furthermore, the autoantibody analysis unit completes the analysis of the possibility of a patient having an autoimmune disease by inputting autoantibody detection information into a pre-established statistical analysis model. The statistical analysis model is established by combining patient herd immunity characteristic data and clinical case data in the medical database using mathematical modeling.

[0040] Furthermore, such as Figure 2 As shown, the comprehensive assessment method of the comprehensive immune status assessment unit is as follows:

[0041] S11: Establish a correlation model between physiological information related to autoimmune diseases and autoimmune diseases;

[0042] S12: Comprehensive assessment of the patient's immune status using correlation models:

[0043] ;

[0044] in, To comprehensively assess the patient's probability of developing the disease, The first in the physiological information related to autoimmune diseases The weights of each dimension of information in relation to the patient's disease are obtained through an association model. This refers to the total number of dimensional information types in physiological information related to autoimmune diseases. The first in the physiological information related to the patient's autoimmune disease Quantitative processing values ​​of information in various dimensions; The preliminary disease probability of the patient acquired by the autoantibody analysis unit; The preset reduction coefficient, which is in the range of ;

[0045] Further, as shown in Figure 3 , in the step S1, the correlation model is established by the following way:

[0046] S111: Acquire sample medical record information with time sequence from the medical database, each sample medical record information includes physiological information related to autoimmune diseases and corresponding disease information, and store the sample medical record information according to time sequence to form a plurality of continuous data sets in a preset time period;

[0047] S112: Obtain the influence degree of each dimension information in the physiological information related to autoimmune diseases on the patient's disease by the logistic regression method and perform quantitative processing;

[0048] S113: Calculate the influence weight of each dimension information on the patient's disease, and integrate the influence weights of all kinds of dimension information to construct the correlation model between the physiological information related to autoimmune diseases and autoimmune diseases; the influence weight of each dimension information on the patient's disease is calculated in the following way:

[0049] ;

[0050] Wherein, is the weighted influence coefficient of the th time period, the closer the time period is to the current time, the larger the value is; ; is the total number of preset time periods; is the influence coefficient of the th dimension information on the patient's disease in the th time period, which is acquired by the step S112; for , it satisfies:

[0051] ;

[0052] Wherein, is the time decay coefficient, which is used to control the contribution degree of the time of the historical data to the calculation result of the model, and is set by pre-experiment;

[0053] The scheme combines the self-antibody detection information with the multi-dimensional physiological information related to the patient's autoimmune disease, uses the logistic regression modeling and time-weighted analysis method to construct a dynamic immune evaluation system, thereby improving the detection accuracy and risk prediction ability of autoimmune diseases; compared with the traditional method based only on self-antibody detection, the scheme integrates multi-dimensional physiological information, including immune function, inflammatory state, metabolic indicators and other physiological information, and optimizes the time decay weight in the setting of the correlation model, ensuring that recent data contribute more to risk assessment, thereby improving the real-time and accuracy of diagnosis. Embodiments

[0054] The present embodiment should be understood as at least including all the features of any one of the preceding embodiments, and further improving on the basis thereof;

[0055] The present embodiment provides an autoimmune disease antibody detection system based on biosensing technology, which comprises a data acquisition module, a data processing module, a detection analysis module and an interactive management module; the data acquisition module is used to acquire comprehensive detection information of the patient's autoimmune disease, the data processing module is used to preprocess the comprehensive detection information, and the detection analysis module is used to analyze and evaluate the risk of the patient's autoimmune disease in combination with the preprocessed comprehensive detection information; the interactive management module is used to manage the information data in the system and complete the information interaction between the system and the outside world;

[0056] Further, the interactive management module comprises a data storage management unit, an interactive unit, a dynamic health tracking unit and a personalized suggestion unit; the data storage management unit is used to manage and store the data information of the system detection analysis; the interactive unit is used to complete the data information interaction between the system and the outside world; the dynamic health tracking unit is used to analyze the disease development trend of the patient in combination with the evaluation information of the long-term disease condition of the patient and provide personalized health management suggestions for the patient;

[0057] Further, as shown in Figure 4 The specific workflow of the dynamic health tracking unit is as follows:

[0058] S21: Set a continuous time period and acquire evaluation information of the patient's disease condition in the continuous time period, the evaluation information comprising the comprehensive evaluation probability of the patient's disease and external intervention information affecting the patient's disease condition, the external intervention information including but not limited to treatment intervention information, lifestyle information and complication information;

[0059] S22: Calculate the evaluation parameters of the patient's disease development trend:

[0060] ;

[0061] Among them, an evaluation parameter of a disease development trend of the patient, reflecting progress or remission of the disease of the patient; a total number of time periods, a comprehensive evaluation probability of the patient in the first time period closest to the current time; a normalization adjustment coefficient, a change degree of the comprehensive evaluation probability of the patient in the first time period, satisfying:

[0062] ;

[0063] wherein, a comprehensive evaluation probability of the patient in the first time period, a comprehensive evaluation probability of the patient in the first time period;

[0064] S23: extracting negative influence factors on the disease of the patient from the external intervention information, and comparing the evaluation parameter of the disease development trend of the patient with a preset evaluation threshold value, if the evaluation parameter of the disease development trend of the patient is greater than the evaluation threshold value, sorting the negative influence factors on the disease of the patient, and providing the corresponding health management suggestions to the patient according to the sorted negative influence factors through a preset health intervention rule base;

[0065] Further, the health intervention rule base is specifically a rule set constructed based on medical big data and expert experience, for guiding generation of individualized health management suggestions;

[0066] Specifically, in the S23 step, suppose that the negative influence factors extracted from the external intervention information of the patient are as follows:

[0067] insufficient treatment compliance: not taking medicine on time or not receiving treatment at the specified time;

[0068] unhealthy living habits: long-term sleep deprivation, insufficient sleep every day;

[0069] nutritional imbalance: high-salt and high-sugar diet, weight gain;

[0070] enhanced chronic inflammatory response: recent detection shows that the inflammatory marker CRP is rising;

[0071] complications: mild diabetes was diagnosed recently;

[0072] When the patient's development trend evaluation parameter is greater than the evaluation threshold, the influence degree of each negative influence factor on the patient's condition is obtained in combination with medical big data, and is compared and sorted; in combination with a health intervention rule library, a health management suggestion generated for each negative influence factor is as follows:

[0073] Improve treatment compliance: set medication reminders, monitor drug compliance; communicate with doctors to optimize treatment plans, reduce side effects, and improve patient acceptance;

[0074] Reduce inflammation: increase anti-inflammatory diet, including foods rich in Omega-3 such as fish and nuts; moderate exercise, control weight, reduce systemic inflammation;

[0075] Improve lifestyle: ensure adequate sleep, reduce overtime, improve immune stability, reduce stress, and try meditation or counseling;

[0076] Control diabetes: adjust diet, reduce high-sugar intake, avoid blood sugar fluctuations affecting the immune system; monitor blood sugar changes and regularly review glycated hemoglobin;

[0077] Optimize nutritional structure: supplement vitamins D, zinc and other trace elements to enhance immune regulation; adopt low-salt and low-sugar diet, reduce processed food intake;

[0078] The scheme continuously monitors the patient's disease development trend through the dynamic health tracking unit combined with the evaluation information of the patient's long-term illness, sets a disease development trend evaluation parameter, and the system can quantitatively evaluate the patient's disease progression; by obtaining external intervention information related to the patient's condition and combining a health intervention rule library, personalized and operable health management suggestions can be provided for the patient, thereby improving the accuracy of disease management and reducing the risk of the patient's condition worsening.

[0079] The above disclosed content is only the preferred feasible embodiment of the present application, and does not limit the protection scope of the present application, so any equivalent technical changes made according to the content of the present application specification and drawings are included in the protection scope of the present application, and in addition, the elements can be updated as technology develops.

Claims

1. An autoimmune disease antibody detection system based on biosensing technology, characterized by, The system comprises a data acquisition module, a data processing module, a detection analysis module and an interactive management module; the data acquisition module is used to acquire comprehensive detection information of the patient's autoimmune disease, the data processing module is used to pre-process the comprehensive detection information, the detection analysis module is used to analyze and evaluate the patient's autoimmune disease risk in combination with the pre-processed comprehensive detection information; the interactive management module is used to manage information data in the system and complete information interaction between the system and the outside world. The data acquisition module comprises a sample collection unit, a sample separation unit, a biological sensing detection unit and a detection information acquisition unit; the sample collection unit is used to collect blood sample information of the patient; the sample separation unit is used to separate, remove and enrich blood samples through a microfluidic chip to obtain serum samples and cell component samples; the biological sensing detection unit is used to perform biological sensing detection on the serum samples and the cell component samples to obtain a plurality of immune-related biomarker data; and the detection information acquisition unit is used to process and integrate the plurality of immune-related biomarker data to generate comprehensive detection information. The detection analysis module comprises an autoantibody analysis unit and a comprehensive immune state evaluation unit; the autoantibody analysis unit is used to preliminarily analyze the possibility of the patient having an autoimmune disease in combination with autoantibody detection information; and the comprehensive immune state evaluation unit is used to complete comprehensive evaluation of the patient's disease condition in combination with the analysis result of the autoantibody analysis unit and physiological information related to the patient's autoimmune disease. The comprehensive evaluation method of the comprehensive immune state evaluation unit is specifically as follows: S11: establishing a correlation model between the physiological information related to the autoimmune disease and the autoimmune disease; S12: comprehensively evaluating the immune state of the patient in combination with the correlation model: ； in, To comprehensively assess the patient's probability of developing the disease, The first in the physiological information related to autoimmune diseases The weights of each dimension of information in relation to the patient's disease are obtained through an association model. This refers to the total number of dimensional information types in physiological information related to autoimmune diseases. The first in the physiological information related to the patient's autoimmune disease Quantitative processing values ​​of information in various dimensions; This represents the initial disease probability of patients obtained through the autoantibody analysis unit. The preset reduction factor has a range of values. ; In step S11, the correlation model is established by the following method: S111: acquiring sample medical record information with time series from a medical database, each sample medical record information comprising physiological information related to the autoimmune disease and corresponding disease information, and storing the sample medical record information according to the time series to form a plurality of continuous data sets within a preset time period; S112: obtaining the influence degree of each dimension information in the physiological information related to the autoimmune disease on the patient's disease and performing quantitative processing by a logistic regression method; S113: calculating the influence weight of each dimension information on the patient's disease, and integrating the influence weights of all kinds of dimension information to construct a correlation model between the physiological information related to the autoimmune disease and the autoimmune disease; the influence weight of each dimension information on the patient's disease is calculated as follows: ； in, For the first The weighted influence coefficient for each time period, with the time period being closer to the current time. The larger the value; The total number of preset time periods; In the first Within the first time period, the first The influence coefficient of each dimension of information on the patient's disease is obtained through step S112; for satisfy: ； wherein, is a time decay coefficient, used to control the degree of contribution of the time at which the historical data is located to the model calculation result, set by pre-experiment.

2. The system for detecting autoimmunity disease antibodies based on biosensing technology according to claim 1, wherein, The comprehensive detection information of the patient's autoimmune disease comprises autoantibody detection information and physiological information related to the autoimmune disease; and the autoantibody detection information specifically refers to the presence and concentration level of specific autoantibodies in the patient's blood.

3. The system for detecting autoimmunity disease antibodies based on biosensing technology according to claim 2, characterized in that, The autoantibody analysis unit analyzes the possibility of the patient having an autoimmune disease by inputting autoantibody detection information into a pre-established statistical analysis model established by combining patient population immune feature data and clinical case data in a medical database using a mathematical modeling method.

4. The system for detecting autoimmunity disease antibodies based on biosensing technology according to claim 3, characterized in that, The interaction management module includes a data storage management unit, an interaction unit, a dynamic health tracking unit, and a personalized suggestion unit. The data storage management unit is used to manage and store data information analyzed by the system; the interaction unit is used to complete data information interaction between the system and the outside world; and the dynamic health tracking unit is used to analyze the disease development trend of the patient in combination with the evaluation information of the long-term illness of the patient and provide personalized health management suggestions for the patient.

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