Autoimmune disease antibody detection system based on biosensing technology

Abstract

The invention provides an autoimmune disease antibody detection system based on a biosensing technology, which is characterized by comprising a data acquisition module, a data processing module, a detection analysis module and an interaction management module, the data acquisition module is used for acquiring comprehensive detection information of the autoimmune disease of a patient, the data processing module is used for preprocessing the comprehensive detection information, and the detection analysis module is used for analyzing and evaluating the autoimmune disease risk of the patient in combination with the preprocessed comprehensive detection information; the interaction management module is used for managing information data in the system and completing information interaction between the system and the outside; by integrating a biosensing technology, intelligent data analysis and dynamic health management, high-sensitivity detection, accurate risk assessment and personalized health intervention of autoimmune diseases are realized, and the early diagnosis rate and the health management level are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical detection systems, and in particular to an autoimmune disease antibody detection system based on biosensor technology. Background Art

[0002] Autoimmune diseases are a type of disease in which the body's own tissues are attacked due to abnormal immune systems, including systemic lupus erythematosus, rheumatoid arthritis, Sjögren's syndrome, etc. These diseases are usually characterized by complex course, large individual differences, and atypical early symptoms, which makes clinical diagnosis difficult, and misdiagnosis and missed diagnosis are relatively common.

[0003] At present, the detection of autoimmune diseases mainly relies on autoantibody detection, immune function assessment and physiological indicator monitoring, but traditional methods often have problems such as limited detection sensitivity, high false positive rate and lack of comprehensive evaluation system. In addition, existing detection methods usually statically evaluate the patient's condition, making it difficult to achieve dynamic tracking and personalized management of the 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 of the invention

[0004] The purpose of the present invention is to propose an autoimmune disease antibody detection system based on biosensor technology to address the current deficiencies.

[0005] The present invention adopts the following technical solution: A biosensor-based autoimmune disease antibody detection system, the system comprising a data acquisition module, a data processing module, a detection and analysis module and an interactive management module; the data acquisition module is used to obtain comprehensive detection information of a patient's autoimmune disease, the data processing module is used to preprocess the comprehensive detection information, the detection and 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.

[0006] The data acquisition module includes a sample collection unit, a sample separation unit, a biosensor 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 impurities and enrich the blood sample through a microfluidic chip to obtain serum samples and cell component samples; the biosensor detection unit is used to perform biosensor detection on the serum sample and the cell component sample respectively to obtain multiple immune-related biomarker data; the detection information acquisition unit is used to process the multiple immune-related biomarker data and integrate them to generate comprehensive detection information.

[0007] Furthermore, the comprehensive detection information of 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.

[0008] 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 in combination with the autoantibody detection information; the comprehensive immune status assessment unit is used to combine the analysis results of the autoantibody analysis unit with the patient's autoimmune disease-related physiological information to complete a comprehensive assessment of the patient's condition.

[0009] Furthermore, the autoantibody analysis unit completes the analysis of the possibility of the patient having an autoimmune disease by inputting the autoantibody detection information into a pre-established statistical analysis model, and the statistical analysis model is established by combining the patient group immune characteristic data and clinical case data in a medical database using mathematical modeling.

[0010] Furthermore, the comprehensive evaluation method of the comprehensive immune status evaluation unit is as follows: S11: Establish a correlation model between physiological information related to autoimmune diseases and autoimmune diseases; S12: Comprehensively evaluate the patient's immune status using the association model: ; in, Comprehensively assess the probability of disease for patients. It is the first physiological information related to autoimmune diseases The influence weight of the dimensional information on the patient's illness is obtained through the association model. is the total number of dimensional information types in physiological information related to autoimmune diseases, The first physiological information related to the patient's autoimmune disease Quantitative processing value of dimensional information; The initial probability of the patient's disease obtained through the autoantibody analysis unit; is the preset reduction factor, the value range is .

[0011] Furthermore, in step S1, the association model is established in the following manner: S111: acquiring sample medical record information with a time series from a medical database, wherein each sample medical record information includes physiological information related to an autoimmune disease and its corresponding disease information, and arranging 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: Obtain the influence of each dimension of physiological information related to autoimmune diseases on the patient's illness through logistic regression method and perform quantitative processing; S113: Calculate the influence weight of each dimensional information on the patient's illness, and integrate the influence weights corresponding to all types of dimensional information to construct an association model between autoimmune disease-related physiological information and autoimmune diseases; the specific calculation method of the influence weight of each dimensional information on the patient's illness is as follows: ; in, For the The weighted influence coefficient of each time period. The closer the time period is to the current time, the The larger the value of; is the total number of preset time periods; For the In the time period, The influence coefficient of the dimensional information on the patient's illness is obtained through step S112; satisfy: ; in, It is the time attenuation coefficient, which is used to control the contribution of the time of historical data to the model calculation results and is set through pre-experimental settings.

[0012] Furthermore, the interactive management module includes 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 data information detected and analyzed by the system; the interactive unit is used to complete data information interaction between the system and the outside world; the dynamic health tracking unit is used to analyze the patient's disease development trend in combination with the patient's long-term illness assessment information and provide personalized health management suggestions for the patient.

[0013] The beneficial effects achieved by the present invention are: The present invention combines biosensor technology with microfluidic chips to efficiently acquire and detect biomarkers related to patients' autoimmune diseases, thereby improving the sensitivity and accuracy of detection. It constructs a dynamic immune assessment system in combination with medical big data through statistical analysis models and logistic regression methods, thereby improving the accuracy of patient disease assessments. It optimizes the association model's focus on recent data through time-weighted analysis, thereby enhancing the timeliness and dynamic adaptability of disease assessments. It provides continuous disease monitoring and optimized intervention strategies by combining dynamic health tracking with personalized health management recommendations, thereby improving the early detection rate of autoimmune diseases and the level of health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0015] Figure 1 It is a schematic diagram of the overall module of the present invention.

[0016] Figure 2 Schematic diagram of the workflow of the comprehensive immune status assessment unit of the present invention.

[0017] Figure 3 The figure is a flow chart of the method for establishing the association model of the present invention.

[0018] Figure 4 Schematic diagram of the workflow of the dynamic health tracking unit of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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 invention and are not used to limit the present invention; for those skilled in the art, other systems, methods and / or features of the present embodiment will become apparent after reviewing the following detailed description; it is intended that all such additional systems, methods, features and advantages are included in this specification; included within the scope of the present invention and protected by the appended claims; additional features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0020] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances. Example

[0021] like Figure 1 As shown, this embodiment provides an autoimmune disease antibody detection system based on biosensor technology, the system includes 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 pre-process 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 pre-processed 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; The data acquisition module includes a sample collection unit, a sample separation unit, a biosensor 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 impurities and enrich the blood sample through a microfluidic chip to obtain a serum sample and a cell component sample; the biosensor detection unit is used to perform biosensor detection on the serum sample and the cell component sample respectively to obtain a variety of immune-related biomarker data; the detection information acquisition unit is used to process the data of the various immune-related biomarkers and integrate them to generate comprehensive detection information; Furthermore, the comprehensive detection information of the patient's autoimmune disease includes autoantibody detection information and physiological information related to the autoimmune disease; the autoantibody detection information specifically includes the presence and concentration level of specific autoantibodies in the patient's blood; other physiological information related to the autoimmune disease includes but is not limited to the patient's inflammatory state, immune cell function, cytokine and metabolic and organ function-related marker information; Furthermore, the preprocessing operations of the data processing module on the comprehensive detection information include signal denoising and filtering, data formatting, and outlier detection and compensation processing; Furthermore, the detection and analysis module includes an autoantibody analysis unit and a comprehensive immune status evaluation unit; the autoantibody analysis unit is used to preliminarily analyze the possibility of the patient having an autoimmune disease in combination with the autoantibody detection information; the comprehensive immune status evaluation unit is used to complete a comprehensive evaluation of the patient's condition in combination with the analysis results of the autoantibody analysis unit and the physiological information related to the patient's autoimmune disease; Furthermore, the autoantibody analysis unit completes the analysis of the possibility of the patient having an autoimmune disease by inputting the autoantibody detection information into a pre-established statistical analysis model, wherein the statistical analysis model is established by combining the patient group immune characteristic data and clinical case data in a medical database using a mathematical modeling method; Further, such as Figure 2 As shown, the comprehensive evaluation method of the comprehensive immune status evaluation unit is as follows: S11: Establish a correlation model between physiological information related to autoimmune diseases and autoimmune diseases; S12: Comprehensively evaluate the patient's immune status using the association model: ; in, Comprehensively assess the probability of disease for patients. It is the first physiological information related to autoimmune diseases The influence weight of the dimensional information on the patient's illness is obtained through the association model. is the total number of dimensional information types in physiological information related to autoimmune diseases, The first physiological information related to the patient's autoimmune disease Quantitative processing value of dimensional information; The initial probability of the patient's disease obtained through the autoantibody analysis unit; is the preset reduction factor, the value range is ; Further, such as Figure 3 As shown, in step S1, the association model is established in the following manner: S111: acquiring sample medical record information with a time series from a medical database, wherein each sample medical record information includes physiological information related to an autoimmune disease and its corresponding disease information, and arranging 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: Obtain the influence of each dimension of physiological information related to autoimmune diseases on the patient's illness through logistic regression method and perform quantitative processing; S113: Calculate the influence weight of each dimensional information on the patient's illness, and integrate the influence weights corresponding to all types of dimensional information to construct an association model between autoimmune disease-related physiological information and autoimmune diseases; the specific calculation method of the influence weight of each dimensional information on the patient's illness is as follows: ; in, For the The weighted influence coefficient of each time period. The closer the time period is to the current time, the The larger the value of; is the total number of preset time periods; For the In the time period, The influence coefficient of the dimensional information on the patient's illness is obtained through step S112; satisfy: ; in, is the time attenuation coefficient, which is used to control the contribution of the time of historical data to the model calculation results and is set through pre-experimental settings; This program combines autoantibody detection information with multidimensional physiological information related to the patient's autoimmune diseases, adopts logistic regression modeling and time-weighted analysis methods to construct a dynamic immune assessment system, thereby improving the detection accuracy and risk prediction ability of autoimmune diseases; compared with traditional methods based only on autoantibody detection, this program integrates multidimensional physiological information, including physiological information such as immune function, inflammatory status, and metabolic indicators, and optimizes the time decay weight in the setting of the association model to ensure that recent data contributes more to risk assessment, thereby improving the real-time and accuracy of diagnosis. Example

[0022] This embodiment should be understood to include at least all the features of any of the above embodiments, and further improve upon them; The present embodiment provides an autoimmune disease antibody detection system based on biosensor technology, the system comprising a data acquisition module, a data processing module, a detection and 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 pre-process the comprehensive detection information, the detection and 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 within the system and complete information interaction between the system and the outside world; Furthermore, the interactive management module includes 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 data information detected and analyzed by the system; 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 patient's disease development trend in combination with the patient's long-term disease assessment information and provide personalized health management suggestions for the patient; Further, such as Figure 4 As shown, the specific working process of the dynamic health tracking unit is as follows: S21: setting a continuous time period, and obtaining evaluation information of the patient's condition within the continuous time period, wherein the evaluation information includes the patient's comprehensive evaluation probability of illness and external intervention information affecting the patient's condition, wherein the external intervention information includes but is not limited to treatment intervention information, lifestyle information, and complication information; S22: Calculate the evaluation parameters of the patient's disease development trend: ; in, It is an evaluation parameter for the patient's disease development trend, reflecting the progression or remission of the patient's disease; is the total number of time periods, The closest to the current time Comprehensive assessment of the probability of illness of patients within a time period; is the normalized adjustment coefficient, For the The degree of change in the patient's comprehensive assessment of the probability of illness within a time period meets the following requirements: ; in, For the The comprehensive evaluation probability of the patient in a time period is For the Comprehensive assessment of the probability of illness of patients within a time period; S23: extracting negative influencing factors on the patient's disease from the external intervention information, and comparing the evaluation parameter of the patient's disease development trend with a preset evaluation threshold; if the evaluation parameter of the patient's disease development trend is greater than the evaluation threshold, sorting the negative influencing factors on the patient's disease, and obtaining corresponding health management suggestions according to the sorted negative influencing factors through a preset health intervention rule library and providing them to the patient; Furthermore, the health intervention rule base is specifically a set of rules built based on medical big data and expert experience, which is used to guide the generation of personalized health management suggestions; Specifically, in the step S23, the negative influencing factors extracted from the patient's external intervention information are as follows: Lack of treatment compliance: failure to take medication or receive treatment at the designated times; Bad living habits: staying up late for a long time and not getting enough sleep every day; Nutritional imbalance: a diet high in salt and sugar leads to weight gain; Increased chronic inflammatory response: recent tests show an increase in the inflammatory marker CRP; Complications affected: recently diagnosed mild diabetes; When the patient's development trend evaluation parameter is greater than the evaluation threshold, the impact of each negative influencing factor on the patient's condition is obtained in combination with medical big data, and a comparison and ranking is performed; combined with the health intervention rule library, the health management suggestions generated for each negative influencing factor are as follows: Improve treatment compliance: set medication reminders and monitor medication compliance rates; communicate with doctors to optimize treatment plans, reduce side effects, and improve patient acceptance; Reduce inflammation: Increase anti-inflammatory diet, including foods rich in omega-3, such as fish and nuts; exercise regularly, control weight, and reduce systemic inflammation; Improve your lifestyle: get enough sleep, avoid staying up late, improve immune stability and reduce mental stress, try meditation or psychological counseling; Control the impact of diabetes: adjust diet, reduce high sugar intake, and avoid the impact of blood sugar fluctuations on the immune system; monitor blood sugar changes and regularly check glycosylated hemoglobin; Optimize nutritional structure: Supplement vitamin D, zinc and other trace elements in appropriate amounts to enhance immune regulation; adopt a low-salt and low-sugar diet and reduce the intake of processed foods; This solution continuously monitors the patient's disease development trend through a dynamic health tracking unit combined with the patient's long-term disease assessment information. By setting disease development trend assessment parameters, the system can quantitatively assess the patient's disease progression. By obtaining external intervention information related to the patient's condition and combining it with a health intervention rule library, it can provide patients with personalized and actionable health management suggestions, thereby improving the accuracy of disease management and reducing the risk of worsening of the patient's condition.

[0023] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. An autoimmune disease antibody detection system based on biosensor technology, characterized in that: The system includes a data acquisition module, a data processing module, a detection and 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 and 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; The data acquisition module includes a sample collection unit, a sample separation unit, a biosensor 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 impurities and enrich the blood sample through a microfluidic chip to obtain serum samples and cell component samples; the biosensor detection unit is used to perform biosensor detection on the serum sample and the cell component sample respectively to obtain multiple immune-related biomarker data; the detection information acquisition unit is used to process the multiple immune-related biomarker data and integrate them to generate comprehensive detection information.

2. The autoimmune disease antibody detection system based on biosensor technology according to claim 1, characterized in that: The comprehensive detection information of 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.

3. The autoimmune disease antibody detection system based on biosensor technology according to claim 1, characterized in that: 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 in combination with the autoantibody detection information; the comprehensive immune status assessment unit is used to combine the analysis results of the autoantibody analysis unit with the physiological information related to the patient's autoimmune disease to complete a comprehensive assessment of the patient's condition.

4. The autoimmune disease antibody detection system based on biosensor technology according to claim 3, characterized in that: The autoantibody analysis unit completes the analysis of the possibility of the patient having an autoimmune disease by inputting the autoantibody detection information into a pre-established statistical analysis model, and the statistical analysis model is established by combining the patient group immune characteristic data and clinical case data in a medical database using mathematical modeling.

5. The autoimmune disease antibody detection system based on biosensor technology according to claim 3, characterized in that: The comprehensive evaluation method of the comprehensive immune status evaluation unit is as follows: S11: Establish a correlation model between physiological information related to autoimmune diseases and autoimmune diseases; S12: Comprehensively evaluate the patient's immune status using the association model: ； in, Comprehensively assess the probability of disease for patients. It is the first physiological information related to autoimmune diseases The influence weight of the dimensional information on the patient's illness is obtained through the association model. is the total number of dimensional information types in physiological information related to autoimmune diseases, The first physiological information related to the patient's autoimmune disease Quantitative processing value of dimensional information; The initial probability of the patient's disease obtained through the autoantibody analysis unit; is the preset reduction factor, the value range is .

6. The autoimmune disease antibody detection system based on biosensor technology according to claim 5, characterized in that: In step S1, the association model is established in the following manner: S111: acquiring sample medical record information with a time series from a medical database, wherein each sample medical record information includes physiological information related to an autoimmune disease and its corresponding disease information, and arranging 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: Obtain the influence of each dimension of physiological information related to autoimmune diseases on the patient's illness through logistic regression method and perform quantitative processing; S113: Calculate the influence weight of each dimensional information on the patient's illness, and integrate the influence weights corresponding to all types of dimensional information to construct an association model between autoimmune disease-related physiological information and autoimmune diseases; the specific calculation method of the influence weight of each dimensional information on the patient's illness is as follows: ； in, For the The weighted influence coefficient of each time period. The closer the time period is to the current time, the The larger the value of; is the total number of preset time periods; For the In the time period, The influence coefficient of the dimensional information on the patient's illness is obtained through step S112; satisfy: ； in, It is the time attenuation coefficient, which is used to control the contribution of the time of historical data to the model calculation results and is set through pre-experimental settings.

7. The autoimmune disease antibody detection system based on biosensor technology according to claim 1, 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 detected and analyzed by the system; the interaction unit is used to complete data information interaction between the system and the outside world; the dynamic health tracking unit is used to analyze the patient's disease development trend in combination with the patient's long-term disease assessment information and provide personalized health management suggestions for the patient.

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