Disease diagnosis system based on immunodetection

Abstract

The invention relates to the technical field of immunodetection, in particular to a disease diagnosis system based on immunodetection, which comprises a probe design module, a temperature control regulation module, a probe signal acquisition module, a probe signal judgment module and a diagnosis evaluation module. According to the invention, through the design of multiple probes, the system can simultaneously capture multiple markers in the same immune reaction system, optimize the quantification process of signals, and enhance the detection capability, particularly, the detection is more prominent than multi-marker detection and early-stage pathological detection, and the temperature gradient control can accurately adjust the reaction temperature and improve the binding efficiency of antibodies and antigens, so that the sensitivity of the system is improved. The problem of non-uniform efficiency at a traditional constant temperature is overcome, the specificity of immunoreaction is enhanced, the potential of non-specific binding is reduced, the sensitivity of the system in low-concentration marker detection is improved through multi-frequency signal detection, the limitation of small change recognition of traditional single-frequency detection is effectively avoided, and the detection accuracy is improved. Therefore, the accuracy of disease judgment and the early recognition capability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of immune detection, and in particular to a disease diagnosis system based on immune detection. Background Art

[0002] Immunoassay is a technology widely used in medical and biological research to detect and quantify specific proteins, antibodies, or pathogens in blood or other biological samples. This technology relies on the specific binding between antigens and antibodies, which can accurately identify and measure specific immune markers in individuals. Immunoassays include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), immunofluorescence assay, etc. Each method has its own specific application scenarios and sensitivity.

[0003] Among them, the disease diagnosis system of immune detection is an intelligent system that uses immunological principles to make disease judgments. The system determines whether an individual is infected with a certain pathogen or whether there is a certain immune response by detecting the presence and concentration of specific antigens or antibodies in individual biological samples. The system is particularly suitable for scenarios where rapid diagnostic results need to be obtained, such as rapid screening during epidemic outbreaks. It is also suitable for health monitoring to help medical professionals evaluate disease progression or treatment effectiveness.

[0004] Existing technologies have limitations in signal sensitivity and detection accuracy. In particular, when detecting multiple markers simultaneously, they are susceptible to background noise interference and find it difficult to accurately distinguish subtle changes in low-concentration markers. Fixed reaction temperatures can easily lead to uneven antibody-antigen binding efficiency, thereby affecting the reliability of test results. Single-frequency signal detection limits the ability to capture tiny signal changes. Especially in the detection of low-concentration markers, traditional systems cannot meet the requirements of high sensitivity, resulting in false positive or false negative results, affecting the accuracy of clinical judgments, and delaying the early identification of diseases. Summary of the Invention

[0005] In order to solve the technical problems that the existing technology is susceptible to background noise interference when detecting multiple markers simultaneously, making it difficult to accurately distinguish subtle changes in low-concentration markers, the fixed reaction temperature easily leads to uneven efficiency of antibody and antigen binding, thereby affecting the reliability of the detection results, and the single-frequency signal detection limits the ability to capture small signal changes. Especially in the detection of low-concentration markers, the traditional system cannot meet the high sensitivity requirements, resulting in false positive or false negative results, affecting the accuracy of clinical judgment, and delaying the early identification of diseases, the embodiment of the present invention provides a disease diagnosis system based on immunoassay. The technical solution is as follows: In one aspect, a disease diagnosis system based on immunoassay is provided, comprising: The probe design module obtains data on hepatitis virus core antigen, target marker type, and antibody affinity, analyzes the distribution density and number of binding sites of the target marker, monitors the binding time and signal intensity of the probe and target marker, adjusts the probe reaction time, and generates the probe reaction balance; The temperature control module sets the initial low-temperature reaction time based on the probe reaction equilibrium, gradually heats the reaction to the target temperature, evaluates the relationship between the heating rate and the binding efficiency, optimizes the heating temperature control process, and obtains the temperature response optimization configuration; The probe signal acquisition module is configured according to the temperature response optimization, analyzes the signal response time and the frequency band signal change amplitude, compares the phase offset and amplitude fluctuation of the frequency point, adjusts the signal acquisition time interval, and generates a signal frequency change index; The probe signal judgment module screens the key data points of response changes according to the signal frequency change index, makes judgments based on the signal response frequency and background noise interval, evaluates the coverage ratio of the effective signal, analyzes the signal credibility, and obtains the signal validity evaluation result.

[0006] On the other hand, the temperature response optimization configuration includes the optimal temperature setting and temperature control accuracy; the signal frequency change index includes the signal change matching result, signal strength difference information, and signal time interval optimization result; the signal validity evaluation result includes the signal coverage ratio optimization result, the signal credibility optimization result, and the background noise evaluation result.

[0007] On the other hand, the probe design module includes: The target recognition submodule obtains hepatitis virus core antigen, target marker type and antibody affinity data, detects target marker type, collects antibody affinity data, analyzes target marker distribution density and the number of potential binding sites, performs cross-judgment based on affinity parameters and marker distribution, and establishes a priority recognition site code; The antibody screening submodule selects monoclonal antibodies and antibody fragments that are structurally complementary to the preferred recognition site encoding, extracts and normalizes affinity parameters, compares and analyzes them with the recognition sequence, selects the antibody combination with the best affinity overlap, and labels its reactivity data to generate an antibody response characteristic set; Based on the antibody reaction characteristic set, the reaction regulation submodule calls the antibody combination for binding experiments, monitors the binding time and signal intensity during the reaction process, calculates the binding frequency and signal duration, compares the probe reaction time control benchmark, adjusts the probe reaction time parameters, and generates the probe reaction balance.

[0008] On the other hand, the temperature control and adjustment module includes: The time period setting submodule determines the initial binding time period of the antibody and antigen based on the probe reaction balance, sets the optimal validity period of the time period, compares the binding time period with the affinity data, identifies the substandard interval, and extends the low-temperature reaction time if it does not meet the standard. If it meets the standard, the original setting is maintained to obtain the low-temperature reaction time configuration; The temperature rise control submodule calls the low-temperature reaction period configuration, sets the starting point for gradual temperature rise, monitors the difference between the current and target temperatures in real time during the temperature rise process, calculates the temperature rise rate, analyzes the relationship with the binding efficiency, selects the temperature rise rate with the optimal fluctuation range, and obtains the temperature rise rate stability index; The response analysis submodule analyzes the effect of temperature in different stages on the binding efficiency based on the heating rate stability index, identifies the deviation of the change in binding efficiency in each stage from the efficiency benchmark, and normalizes it with the temperature change gradient to obtain the temperature response optimization configuration.

[0009] On the other hand, the probe signal acquisition module includes: The signal period evaluation submodule analyzes the correlation between the signal response time and the signal change amplitude according to the temperature response optimization configuration, determines whether the amplitude change rate meets the response time change standard, and extends the acquisition period range if it exceeds, otherwise maintains the original period setting, and generates a signal response period indicator; The synchronization strength acquisition submodule calls the signal response period indicator, collects the synchronization signal strength under the frequency band, analyzes the phase and amplitude of the frequency point, determines the synchronization between the phase and amplitude, selects the signal frequency band with the best synchronization, and obtains the frequency band synchronization analysis result; The frequency response adjustment submodule compares the signal change trend with the frequency offset trend within the acquisition time based on the frequency band synchronization analysis results, evaluates whether the signal change trend is consistent with the response range, adjusts the signal acquisition time interval, optimizes the signal frequency capture accuracy, and generates a signal frequency change index.

[0010] On the other hand, the correlation between the analysis signal response time and the signal change amplitude is calculated using the formula: ; Calculating signal correlation If it exceeds the limit, the acquisition period range is extended, otherwise the original period setting is maintained and the signal response period index is generated, where Representative The time of the sampling point, Represents the average value of all sampling point times, Representative The signal amplitude of each sampling point, Represents the average value of the signal amplitude of all sampling points, Represents the total number of sampling points.

[0011] On the other hand, the probe signal determination module includes: The key point screening submodule extracts the change data of the key frequency segment according to the signal frequency change index, analyzes the response change cycle within the frequency segment, determines the peak and valley turning points, checks whether the turning points exceed the change standard, and screens the key data points that exceed the standard to obtain the frequency change key points; The signal coverage evaluation submodule calls the frequency change key points, analyzes the signal response frequency and the background noise amplitude, calculates the coverage ratio of the signal in the noise interval, compares the coverage ratio with the benchmark value, evaluates the effectiveness of the signal, and obtains the signal coverage ratio index; The interference denoising processing submodule marks the signal frequency band with high noise interference according to the signal coverage ratio index, removes high noise data, and evaluates the stability and duration of the denoised signal to obtain a signal effectiveness evaluation result.

[0012] On the other hand, the analysis signal response frequency and background noise amplitude are calculated using the formula: ; Get the signal-to-background noise ratio index ,in, Representative The signal response, represents the average amplitude of background noise, Representative The background noise of the sub-measurement, Represents the number of measurements.

[0013] In another aspect, the system further comprises: The diagnostic assessment module, based on the signal validity assessment result, organizes the marker combination numbers according to the pathological reference concentration interval and the pathological state weight, analyzes the relationship between the marker concentration deviation and the pathological state, determines whether the disease determination criteria are met, and obtains the pathological state determination result; The pathological state determination results include the correlation between marker concentration and pathological state, pathological state determination criteria, and marker combination effects.

[0014] In another aspect, the diagnostic assessment module comprises: The marker sorting submodule classifies the markers by concentration based on the signal validity evaluation results and the pathological reference concentration range, analyzes the relationship between the concentration of each marker and the weight of the pathological state, and establishes a marker combination number; The status determination submodule compares the matching degree of the marker concentration deviation and the pathological status weight according to the marker combination number, calculates the pathological risk level of each marker, analyzes the impact of the level deviation on the disease status, determines whether the disease determination criteria are met, and obtains the pathological status determination result.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: The multiple probe design enables the system to simultaneously capture multiple markers in the same immune response system, optimize the signal quantification process, and enhance detection capabilities, especially in multi-marker detection and early pathology detection. Temperature gradient control improves the binding efficiency of antibodies and antigens by precisely adjusting the reaction temperature, overcoming the problem of uneven efficiency at traditional constant temperature, enhancing the specificity of the immune response, and reducing the potential for non-specific binding. Multi-frequency signal detection improves the system's sensitivity in low-concentration marker detection, effectively avoiding the limitations of traditional single-frequency detection in identifying small changes, thereby improving the accuracy of disease judgment and early identification capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A schematic diagram of the system of the present invention; Figure 2 Schematic diagram of the system framework of the present invention; Figure 3 A flow chart of the probe design module of the present invention; Figure 4 This is a flow chart of the temperature control and adjustment module of the present invention; Figure 5 This is a flow chart of the probe signal acquisition module of the present invention; Figure 6 This is a flow chart of the probe signal judgment module of the present invention; Figure 7 Flowchart of the diagnostic evaluation module of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0020] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0023] The present invention provides a disease diagnosis system based on immune detection, such as Figure 1 As shown, the system includes: The probe design module obtains hepatitis virus core antigen, target marker type, and antibody affinity data, analyzes the distribution density and number of binding sites of the target marker, selects matching monoclonal antibodies and antibody fragments based on the structural differences of the markers, adjusts antibody reactivity through affinity parameters, constructs immune probe sets with multiple recognition sites, monitors the binding time and signal intensity of the probes and target markers, compares the reaction frequency and time coverage of differential probes, adjusts the probe reaction time, and generates probe reaction balance; The temperature control module sets the initial low-temperature reaction time based on the probe reaction equilibrium, the antibody-antigen binding period, and the initial antibody affinity. It gradually heats the reaction to the target reaction temperature and collects efficiency change data during the binding process in real time. It evaluates the relationship between the heating rate and the binding efficiency, optimizes the heating temperature control process, and analyzes the impact of temperature changes on the binding efficiency to obtain the optimal temperature response configuration. The probe signal acquisition module optimizes the configuration based on the temperature response, analyzes the signal response time and the frequency band signal change amplitude, collects the synchronous signal strength of the different frequency bands, compares the phase offset and amplitude fluctuation of the frequency point, adjusts the signal acquisition time interval, matches the signal change trend, and generates the signal frequency change index; The probe signal judgment module selects key data points of response changes based on the signal frequency change index, makes judgments based on the signal response frequency and background noise interval, evaluates the coverage ratio of the effective signal, analyzes the signal credibility, and performs denoising based on the interference of noise on the marker signal to obtain the signal validity evaluation result; The diagnostic evaluation module is based on the signal validity evaluation results, the pathological reference concentration range and the pathological state weight, and organizes the marker combination number, analyzes the relationship between the marker concentration deviation and the pathological state, determines whether the disease determination criteria are met, and obtains the pathological state determination result.

[0024] Temperature response optimization configuration includes optimal temperature setting and temperature control accuracy; signal frequency change indicators include signal change matching results, signal strength difference information, and signal time interval optimization results; signal effectiveness evaluation results include signal coverage ratio optimization results, signal credibility optimization results, and background noise evaluation results; and pathological state determination results include the correlation between marker concentration and pathological state, pathological state determination criteria, and marker combination effects.

[0025] The affinity parameter refers to the affinity of the binding between the antibody and the antigen (affinity constant), which measures the strength of the binding between the antibody and its target antigen. The affinity parameter is a key factor in determining whether the antibody can effectively recognize and bind to a specific antigen. By adjusting the antibody affinity parameters, the performance of the immunoprobe can be optimized to ensure that the probe can specifically recognize the target marker in a complex biological environment and reduce non-specific binding; the number of binding sites refers to the area on the antibody or immunoprobe that can bind to the target molecule; the immunoprobe refers to the probe molecule used to specifically recognize and bind to the target substance (such as antigens, markers, cells, etc.) in immunological testing. The immunoprobe is usually composed of antibodies or antibody derivatives (such as monoclonal antibodies or antibody fragments).

[0026] like Figure 2 and Figure 3 As shown, the probe design module includes: The target recognition submodule obtains hepatitis virus core antigen, target marker type and antibody affinity data, detects target marker type, collects antibody affinity data, analyzes target marker distribution density and the number of potential binding sites, performs cross-judgment based on affinity parameters and marker distribution, and establishes a priority recognition site code; The collected hepatitis virus samples are identified in the form of numbers, and the protein conformation information of their core antigens is extracted through imaging and detection equipment and input into the system in the form of sequence coding. Subsequently, the marker types such as virus surface protein, core protein and envelope protein stored in the system are called, and their corresponding molecular weight, isoelectric point and configuration parameters are extracted. They are compared one by one with the detection data in the sample to identify all the marker types of the sample and generate a unique code. On this basis, a dilution reaction method is used to perform a concentration graded reaction experiment on multiple antibodies, and the binding state of each antibody with the above markers is collected. The time and number of binding points required to reach a stable binding state at different concentrations are recorded, and the affinity value corresponding to each antibody is inferred. This is repeated three times. The experiment is to obtain the average value for subsequent comparison. In the marker distribution density analysis link, the marker distribution is image-scanned by chip fluorescence imaging, and the number of molecules per unit area is used as the statistical standard. The density division standard is set as high-density area >200 molecules / μm², medium-density area is 50~200 molecules / μm², and low-density area is <50 molecules / μm². The distribution frequency and number of binding points of each marker in each area are recorded. If the number of binding points of a marker in the high-density area accounts for >60% of the total points of the marker, and its affinity value is greater than the specified benchmark value range, it is marked as a priority identification object. The markers that meet the conditions and their corresponding binding points are numbered and integrated to generate a priority identification site code.

[0027] The antibody screening submodule selects monoclonal antibodies and antibody fragments that are structurally complementary to the preferred recognition site encoding, extracts and normalizes affinity parameters, compares and analyzes them with the recognition sequence, selects the antibody combination with the best affinity overlap, and labels its reactivity data to generate an antibody response characteristic set; According to the structural data provided in the encoding of the priority recognition site, the full sequence information in the antibody structure database is imported one by one, and the spatial configuration of each priority recognition site is compared with the complementary region in the antibody structure to screen out antibody candidates whose spatial layout matches the recognition groove. The matching standard is set to a residue spacing of less than 5Å between the antibody binding region and the recognition site. After the preliminary screening is completed, the affinity data of each antibody and the corresponding marker are extracted and normalized uniformly for cross-group comparison. During the normalization process, the maximum and minimum affinity values ​​within the group are used as the standard, and all affinity values ​​are uniformly converted to the range of 0~1. The normalized antibody affinity value is then compared with the recognition The original affinity values ​​corresponding to the sites were compared one-to-one. If the difference between the two values ​​was less than 0.05, the affinity was considered to overlap. All antibody combinations formed under this standard were selected, and the following reactivity parameters were extracted from each combination: binding rate (bound number / total antibody number), binding duration (time from the start to the signal plateau), and signal intensity peak (maximum fluorescence value). Combinations with binding rate ≥80% and signal intensity peak ≥5000 RLU were marked as preferred antibody groups. All data were recorded by a real-time monitoring system in the experimental platform, and the reaction data of each antibody group were integrated to form an antibody reaction characteristic set for subsequent regulatory processing.

[0028] The reaction control submodule uses the antibody reaction characteristic set to call the antibody combination for binding experiments, monitor the binding time and signal intensity during the reaction, calculate the binding frequency and signal duration, compare the probe reaction time control benchmark, adjust the probe reaction time parameters, and generate the probe reaction balance; Based on the antibody combination with centralized record of antibody reaction characteristics, a combination with a binding rate ≥ 80% and a signal peak ≥ 5000RLU was selected to perform the binding experiment. The experimental platform used an automatic loading system to drip antibodies in the order of priority recognition sites. During the experiment, the fluorescence imaging system recorded the signal changes of each site throughout the experiment, and the maximum signal time Tmax of each site was extracted as the indicator for judging the completion time of binding. If Tmax < 300 seconds, it was considered a rapid reaction, and Tmax > 600 seconds was classified as a slow reaction. At the same time, the number of times the signal showed obvious changes per unit time (1 minute) was counted during the entire binding cycle. If the frequency was > 5 times, it was determined to be a high-frequency reaction. The signal intensity at each site was further recorded to maintain at the background. The duration is more than twice the scene value. If the duration is greater than 600 seconds, it is marked as a stable signal; if it is less than 300 seconds, it is marked as an unstable signal. All reaction time indicators will be compared with the experimental benchmark data item by item. The benchmark value is set as the sum of the average value of the maximum signal time in the reference standard experiment and the fluctuation range. If the Tmax of a combination exceeds the upper limit of this benchmark value by more than 20%, the current probe reaction time setting parameter will be shortened by 10% and the experiment will be re-executed to correct the response offset. The test will be repeated until the reaction time is concentrated in the range of ±10% above and below the reference value. The ratio of the reaction time fluctuation of each combination to the average time is calculated. When the ratio is less than 0.1, it is judged that the reaction equilibrium state has been reached. This ratio is the probe reaction equilibrium and is recorded and saved.

[0029] like Figure 2 and Figure 4 As shown, the temperature control module includes: The time period setting submodule determines the initial binding time period of the antibody and antigen based on the probe reaction balance, sets the optimal validity period of the time period, compares the binding time period with the affinity data, identifies the substandard interval, and extends the low-temperature reaction time if it does not meet the standard. If it meets the standard, maintain the original setting and obtain the low-temperature reaction time configuration; First, read the historical record value of the probe reaction balance, and combine it with the reaction time fluctuation ratio of each group of reactions in the current experiment, extract the average fluctuation ratio and the reaction start time of each recognition site, and summarize the reaction start time points of all sites to establish the initial sequence of the reaction start time period. This sequence is obtained by comparing the binding response data of multiple sample groups. During the comparison process, the binding rate and signal intensity corresponding to the starting time point are screened, and only data entries with a binding rate ≥ 80% and a signal intensity peak ≥ 5000RLU are retained. The average time of the first signal surge is calculated. For example, in sample group A, 7 of the 10 reaction sites have a signal surge at 120 seconds, and the average time point is recorded as 120 seconds. This time is used as the center point of the initial binding time period of the sample group. Then, the optimal effective period range is determined according to the standard deviation of the signal change. If the standard deviation is 30 seconds, the optimal effective period is set to 90 seconds to 150 seconds. Then, this time period is compared with the affinity data corresponding to each antibody combination to determine if the affinity is higher than 10 7 Check whether all combinations of M⁻¹ generate signal responses within the validity period. If there are samples with signal response times delayed beyond 150 seconds and the binding rate of the samples is lower than 60%, they are marked as non-compliant intervals, and their start and end times are recorded. For example, if the signal response time of a combination is 180 seconds and the binding rate is 55%, this interval is marked as non-compliant. Determine whether the low-temperature reaction time needs to be extended. The extension criteria are that the response time is more than 30 seconds later than the upper limit and the binding rate is lower than 60%. If so, the original low-temperature reaction time is extended to 1.5 times the original setting time. For example, if the original setting is 5 minutes, it is extended to 7.5 minutes. Otherwise, the original setting is maintained. After completing the judgment and time period correction for all combinations, the low-temperature reaction duration of each group of experiments is summarized to establish the low-temperature reaction time configuration.

[0030] The temperature rise control submodule calls the low-temperature reaction period configuration, sets the starting point for gradual temperature increase, monitors the difference between the current and target temperatures in real time during the temperature rise process, calculates the temperature rise rate, analyzes its relationship with the binding efficiency, selects the temperature rise rate with the optimal fluctuation range, and obtains the temperature rise rate stability index; First, read the duration of the reaction period set in the previous stage. For example, if a combination sets the low-temperature reaction to 7.5 minutes, this time point is used as the starting point for heating. The system sets the heating cycle to 3 minutes, and the total temperature difference is from 25°C to 37°C. During the heating process, the current temperature value recorded by the temperature sensor is collected in real time and compared with the target temperature second by second. The temperature difference per second is recorded, and the temperature change per unit time is statistically calculated based on this. Then, the heating rate is calculated. For example, if the temperature rises from 25°C to 28°C in the first minute, the heating rate is 3°C / minute, and if it rises to 32°C in the second minute, it is 4°C / minute. The heating rate records in each time period form a complete rate sequence, and then the rate sequence is matched and analyzed with the binding efficiency data. The binding rate data is obtained every 30 seconds during the heating process, and the correlation between the rate change amplitude and the binding rate improvement value is compared. , determine whether rate fluctuations cause fluctuations in the binding rate. The fluctuation amplitude is calculated by counting the difference between the maximum and minimum values ​​in the rate sequence. If the fluctuation amplitude of a certain combination is 1°C / minute between 3 and 4°C / minute and the binding rate remains above 85%, it is considered the optimal fluctuation amplitude combination. If the fluctuation amplitude exceeds 2°C / minute and the binding rate drops below 70%, it is a non-optimal combination. All combinations with a fluctuation amplitude less than 1°C / minute and a binding rate above 80% are recorded, and their temperature difference stability is calculated as the heating rate stability index. This index is the proportion of time during the entire heating cycle that the temperature difference fluctuation is less than the set range (±0.5°C). For example, if the temperature difference fluctuation is controlled within ±0.5°C for 2 minutes during the 3-minute heating, the stability index is 66%. The heating rate stability index of each combination is obtained for subsequent analysis.

[0031] The response analysis submodule analyzes the effect of temperature on the binding efficiency at different stages based on the heating rate stability index, identifies the deviation of the binding efficiency change at each stage from the efficiency benchmark, and normalizes it with the temperature change gradient to obtain the temperature response optimization configuration; Read the time series data of the binding efficiency collected during the heating process of each combination, and record the efficiency changes in each 30-second period. For example, the first section is the period from 25°C to 28°C, when the binding efficiency increases from 60% to 68%. The second section is the period from 28°C to 32°C, when the binding efficiency increases from 68% to 75%. The efficiency change range of each section is compared with the temperature change range before and after, and the ratio of the efficiency improvement value to the temperature difference is calculated. This ratio preliminarily reflects the impact of temperature on efficiency. Then, using the binding efficiency benchmark value of 80% as a reference, the efficiency of each section is compared with the benchmark value. If the deviation is within ±5%, it is judged as a stable stage. If it exceeds ±10%, it is marked as an abnormal response segment. During the normalization process, the temperature difference and efficiency deviation values ​​of each segment are scaled by ratio, and the data in different intervals are converted to the interval of 0~1. For example, if the temperature difference is 3℃ and the efficiency deviation is 6%, the normalization value is 0.5. All normalized values ​​are mapped to the time series to draw a heat map trend, and the change pattern of reaction efficiency with rising temperature is identified. The time period with the smallest deviation and the normalized value close to 0 is recorded as the optimal response period to form the temperature response optimization configuration.

[0032] like Figure 2 and Figure 5 As shown, the probe signal acquisition module includes: The signal period evaluation submodule is configured based on the temperature response optimization, analyzes the correlation between the signal response time and the signal change amplitude, and determines whether the amplitude change rate meets the response time change standard. If it exceeds, the acquisition period range is extended; otherwise, the original period setting is maintained to generate the signal response period indicator; Analyze the correlation between signal response time and signal change amplitude, using the formula: ; Calculating signal correlation If it exceeds the limit, the acquisition period range is extended, otherwise the original period setting is maintained and the signal response period index is generated, where Representative The time of the sampling point, Represents the average value of all sampling point times, Representative The signal amplitude of each sampling point, Represents the average value of the signal amplitude of all sampling points, represents the total number of sampling points; Five data points were collected within a certain time window ( ), the specific time and signal amplitude are as follows: Second; ; Calculate the average and : Second; ; Calculate using the above data : ; ; ; The results show that within the monitoring time window, the correlation between the signal response time and the signal change amplitude is 5.8, which expresses the average product absolute deviation between the response time and the signal amplitude change. The measurement index is used to determine whether the acquisition period needs to be adjusted. If If the value exceeds the set threshold, it means that the signal change speed or amplitude is highly correlated with time, and the acquisition period needs to be extended to capture more relevant data.

[0033] The synchronization strength acquisition submodule uses the signal response period indicator to collect the synchronization signal strength under the frequency band, analyzes the phase and amplitude of the frequency point, determines the synchronization between the phase and amplitude, selects the signal frequency band with the best synchronization, and obtains the frequency band synchronization analysis result; For each time period, a frequency step value of 0.5 MHz was set. Signal strength was scanned sequentially from the low frequency end to the high frequency end. The phase and amplitude values ​​of the signal were obtained at each frequency point, and a frequency-phase-amplitude triplet sequence was constructed. The phase variation period and amplitude variation trend of each frequency point were then analyzed one by one to see if they were synchronized. In the comparative analysis, the phase and amplitude differences between two consecutive points before and after each frequency point were extracted, and the consistency of their change directions was recorded. If the directions were consistent, the signal was marked as synchronized; if the directions were opposite, the signal was marked as asynchronous. The frequency of this synchronization judgment was counted across the entire frequency band, and the proportion of synchronized change points within the band was calculated as the synchronization index for the frequency band. For example, if 20 frequency points were collected in a frequency band and 15 of them were determined to be synchronized, the synchronization was 75%. Frequency bands with synchronization ≥80% were marked as high synchronization segments, those with synchronization between 60% and 80% were marked as medium synchronization segments, and those with synchronization below 60% were marked as low synchronization segments. All high synchronization segments were screened out, and their frequency start and end points were recorded. The results of the frequency band synchronization analysis were summarized.

[0034] The frequency response adjustment submodule compares the signal change trend with the frequency offset trend during the acquisition period based on the frequency band synchronization analysis results, evaluates whether the signal change trend is consistent with the response range, adjusts the signal acquisition time interval, optimizes the signal frequency capture accuracy, and generates a signal frequency change index. First, read the trend curve of signal strength change in each frequency band and mark the rising, falling and platform interval positions of each segment to establish a complete signal change trend sequence. Then, compare the frequency offset data of each frequency segment in the acquisition time interval, extract the change speed and direction of the frequency offset at different time points, and judge whether the trend is consistent with the signal change trend. Specifically, compare whether the direction of signal strength change is the same as the direction of frequency offset. If they are in the same direction and the offset amplitude is within the range of ±2MHz, they are considered consistent. If they are in opposite directions or the offset exceeds ±2MHz, they are judged to be inconsistent. Then, combine the inflection point position of the frequency offset trend to judge whether there is a timing misalignment between the signal change and the frequency offset. If the misalignment exceeds 30 seconds, move the current acquisition time interval forward or backward by 30 seconds for correction. Repeat the detection of the adjusted response trend and frequency change relationship until the synchronization rate is improved to more than 80%. After completion, record the mapping relationship between the acquisition time interval and the frequency point.

[0035] like Figure 2 and Figure 6 As shown, the probe signal judgment module includes: The key point screening submodule extracts the change data of the key frequency band according to the signal frequency change index, analyzes the response change cycle within the frequency band, determines the turning points of the peak and valley values, checks whether the turning points exceed the change standard, and screens the key data points that exceed the standard to obtain the key points of frequency change; First, the signal change data for each frequency band over different acquisition times is read. The signal strength and time axis are constructed into a two-dimensional response curve. Based on this, the signal strength extreme points of each frequency band are extracted. Whether the extreme points are local peaks or valleys in the continuous change is determined. The positions and times of all local extreme points are recorded. Each signal change period is divided into sections, and the time interval from one peak to the next is calculated as the response change period of the frequency band. The intensity difference between the peak and valley within the period is further analyzed, and the amplitude change is recorded. For example, in a certain frequency band, the amplitude change from a valley of 300 RLU to a peak of 1200 RLU is 900 RLU. At the same time, the maximum amplitude change of each frequency band is compared with a set change standard, which is set to 500 RLU. If the amplitude change value within a certain section exceeds the standard, the corresponding extreme point and the signal points within 10 seconds before and after it are marked as key data points. Then, based on the density and repetition frequency of these key data points, points with a recurrence of ≥3 times are selected as the main reference points of the change within the key frequency band. Finally, a frequency-key point mapping table is established, and the key points of frequency change are obtained.

[0036] The signal coverage assessment submodule calls the key points of frequency change, analyzes the signal response frequency and background noise amplitude, calculates the coverage ratio of the signal in the noise interval, compares the coverage ratio with the benchmark value, evaluates the effectiveness of the signal, and obtains the signal coverage ratio index; Analyze the signal response frequency and background noise amplitude using the formula: ; Get the signal-to-background noise ratio index , the ratio index is the significance of the reaction signal in the background noise, which is used to measure the strength of the signal relative to the background noise and determine whether it will be covered by the background noise. Representative The signal response is the amplitude of the signal in a single measurement. The average amplitude of the background noise is the average of multiple measurements of the background noise value. Representative The background noise of a single measurement is the amplitude of the background noise in a single measurement. Represents the number of measurements, that is, the total number of times signal and noise measurements are performed; carried out The following data were obtained: The signal response values ​​are [-70dBm, -68dBm, -71dBm, -69dBm, -70dBm]; The average background noise level is -90dBm; The background noise values ​​are [-90dBm, -91dBm, -89dBm, -90dBm, -90dBm]; Calculate the absolute value of the difference between the signal and the background noise each time: |-70-(-90)|=20dB; |-68-(-90)|=22dB; |-71-(-90)|=19dB; |-69-(-90)|=21dB; |-70-(-90)|=20dB; Summing to get =20+22+19+21+20=102dB; Calculate the square root of the sum of the squares of the background noise values: ; ; Substituting the calculation results into the equation, we get: ; This result indicates that the signal-to-background noise ratio index It is 0.507, reflecting the ratio of the signal strength to the background noise in the considered measurement environment. The result shows that the signal is more obvious in this environment, but there is still a certain amount of noise interference.

[0037] The interference denoising submodule marks the signal frequency bands with high noise interference based on the signal coverage ratio index, removes the high-noise data, and evaluates the stability and duration of the denoised signal to obtain the signal effectiveness evaluation result; First, all frequency bands with coverage ratios greater than 70% are marked as high-noise frequency bands. All signal data points with amplitude changes less than 100 RLU in the corresponding frequency bands are removed from the original data to form a denoised signal sequence. The total number of denoised signal responses is then counted, and the duration of each signal segment is recorded to determine whether it still has a stable response. The stability standard is set as the response frequency maintaining above 80% of the original response frequency. If the original frequency is 0.067 Hz, the denoised frequency is required to be ≥0.054 Hz for stability. In addition, the signal duration ≥180 seconds is used as the shortest valid time threshold. Frequency bands that meet the above two conditions are marked as stable segments, otherwise they are marked as unstable segments. The responses of each segment in the stable segment are numbered and classified, and a list of denoised signal validity identifications is established, and the signal validity evaluation results are output.

[0038] like Figure 2 and Figure 7 As shown, the diagnostic assessment module includes: The marker organization submodule classifies markers by concentration based on the signal validity evaluation results and the pathological reference concentration range, analyzes the relationship between each marker concentration and the pathological status weight, and establishes a marker combination number; From the signal validity evaluation results, a set of markers that are consistent with the response of the antigen recognition site is screened out, and the signal intensity and frequency of each marker are extracted as the basis for concentration estimation. The concentration value is obtained by comparing the signal intensity with the experimental calibration curve. For example, the signal intensity of a marker is 7500RLU, and the corresponding calibration concentration is 20ng / mL. This value is recorded in the concentration list, and then all marker concentrations are divided into intervals. Referring to the pathological reference concentration interval, each marker is classified into four levels: "normal", "mildly high", "moderately high", and "severely abnormal". The specific concentration thresholds are set as follows: 0.25ng / mL is slightly high, 25~50ng / mL is moderately high, and >50 ng / mL is considered a severe abnormality. The concentration category of each marker is recorded, and then the correlation between each marker and the established pathological state in different concentration ranges is analyzed. A weight value is assigned between the marker and the pathological state based on clinical statistical data. The weight is set based on the proportion of the marker appearing in the reference sample group under a specific pathological state. For example, the frequency of antigen A in patients with cirrhosis is 60%, so the corresponding state is assigned a weight of 0.6. The weight value and concentration category are then combined and encoded. The encoding of each group of markers consists of a concentration range label and a weight range number. For example, a marker with a concentration of "moderately high" and a weight of 0.6 is numbered "C3-W2". In this way, the combination number of all markers is constructed.

[0039] The status determination submodule compares the matching degree of marker concentration deviation and pathological status weight according to the marker combination number, calculates the pathological risk level of each marker, analyzes the impact of the level deviation on the disease status, determines whether the disease determination criteria are met, and obtains the pathological status determination result; First, read the concentration range label and pathological status weight value in each number, and calculate the pathological risk level for each marker. The level classification standard is as follows: if the concentration is "normal", the level is 1, "mildly high" is 2, "moderately high" is 3, and "severely abnormal" is 4. The risk weighting coefficients corresponding to each level are 0.25, 0.5, 0.75, and 1.0, respectively. At the same time, group them according to the weight value. Weight ≤ 0.3 is low correlation, 0.3~0.6 is medium correlation, and > 0.6 is high correlation, corresponding to weight coefficients of 0.5, 1.0, and 1.5, respectively. Finally, the concentration level is multiplied by the weight coefficient to obtain the risk index. For example, the concentration of antigen B is The degree is "severe abnormality", the level is 4, the corresponding weight is 0.7, the weight coefficient is 1.5, and the risk index is 4×1.5=6.0. All marker risk indices are classified and sorted, and then the risk indices of individual markers are averaged as the basis for judging the overall pathological state. The range of risk level deviation is then compared with the established disease state reference value for judgment. If the overall average risk index is ≥4.0 and the risk index of at least two markers is ≥5.0, it is considered that the criteria for confirming the disease state are met. If not, it is judged to be an unclear state, and the judgment result is marked as "confirmed" or "unconfirmed" to generate a pathological state confirmation result.

[0040] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0041] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0042] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0043] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0044] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0045] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0046] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0047] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0048] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0049] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A disease diagnosis system based on immune detection, characterized in that: The system comprises: The probe design module obtains data on hepatitis virus core antigen, target marker type, and antibody affinity, analyzes the distribution density and number of binding sites of the target marker, monitors the binding time and signal intensity of the probe and target marker, and generates the probe reaction balance; The temperature control module gradually heats the reaction to the target temperature based on the probe reaction equilibrium, evaluates the relationship between the heating rate and the binding efficiency, optimizes the heating temperature control process, and obtains the temperature response optimization configuration; The probe signal acquisition module is configured according to the temperature response optimization, analyzes the signal response time and the frequency band signal change amplitude, compares the phase offset and amplitude fluctuation of the frequency point, adjusts the signal acquisition time interval, and generates a signal frequency change index; The probe signal judgment module screens the key data points of the response change according to the signal frequency change index, evaluates the coverage ratio of the effective signal, analyzes the signal credibility, and obtains the signal validity evaluation result; The diagnostic evaluation module organizes the marker combination numbers based on the signal validity evaluation results, analyzes the relationship between the marker concentration deviation and the pathological state, determines whether the disease determination criteria are met, and obtains the pathological state determination result.

2. The disease diagnosis system based on immune detection according to claim 1, characterized in that: The temperature response optimization configuration includes the optimal temperature setting and temperature control accuracy; the signal frequency change index includes the signal change matching result, signal strength difference information, and signal time interval optimization result; the signal validity evaluation result includes the signal coverage ratio optimization result, the signal credibility optimization result, and the background noise evaluation result; the pathological state determination result includes the correlation between the marker concentration and the pathological state, the pathological state determination standard, and the marker combination effect.

3. The disease diagnosis system based on immune detection according to claim 1, characterized in that: The probe design module includes: The target recognition submodule obtains hepatitis virus core antigen, target marker type and antibody affinity data, detects target marker type, collects antibody affinity data, analyzes target marker distribution density and the number of potential binding sites, performs cross-judgment based on affinity parameters and marker distribution, and establishes a priority recognition site code; The antibody screening submodule selects monoclonal antibodies and antibody fragments that are structurally complementary to the preferred recognition site encoding, extracts and normalizes affinity parameters, compares and analyzes them with the recognition sequence, selects the antibody combination with the best affinity overlap, and labels its reactivity data to generate an antibody response characteristic set; Based on the antibody reaction characteristic set, the reaction regulation submodule calls the antibody combination for binding experiments, monitors the binding time and signal intensity during the reaction process, calculates the binding frequency and signal duration, compares the probe reaction time control benchmark, adjusts the probe reaction time parameters, and generates the probe reaction balance.

4. The disease diagnosis system based on immune detection according to claim 1, characterized in that: The temperature control and adjustment module includes: The time period setting submodule determines the initial binding time period of the antibody and antigen based on the probe reaction balance, sets the optimal validity period of the time period, compares the binding time period with the affinity data, identifies the substandard interval, and extends the low-temperature reaction time if it does not meet the standard. If it meets the standard, the original setting is maintained to obtain the low-temperature reaction time configuration; The temperature rise control submodule calls the low-temperature reaction period configuration, sets the starting point for gradual temperature rise, monitors the difference between the current and target temperatures in real time during the temperature rise process, calculates the temperature rise rate, analyzes the relationship with the binding efficiency, selects the temperature rise rate with the optimal fluctuation range, and obtains the temperature rise rate stability index; The response analysis submodule analyzes the effect of temperature in different stages on the binding efficiency based on the heating rate stability index, identifies the deviation of the change in binding efficiency in each stage from the efficiency benchmark, and normalizes it with the temperature change gradient to obtain the temperature response optimization configuration.

5. The disease diagnosis system based on immunoassay according to claim 1, characterized in that: The probe signal acquisition module includes: The signal period evaluation submodule analyzes the correlation between the signal response time and the signal change amplitude according to the temperature response optimization configuration, determines whether the amplitude change rate meets the response time change standard, and extends the acquisition period range if it exceeds, otherwise maintains the original period setting, and generates a signal response period indicator; The synchronization strength acquisition submodule calls the signal response period indicator, collects the synchronization signal strength under the frequency band, analyzes the phase and amplitude of the frequency point, determines the synchronization between the phase and amplitude, selects the signal frequency band with the best synchronization, and obtains the frequency band synchronization analysis result; The frequency response adjustment submodule compares the signal change trend with the frequency offset trend within the acquisition time based on the frequency band synchronization analysis results, evaluates whether the signal change trend is consistent with the response range, adjusts the signal acquisition time interval, optimizes the signal frequency capture accuracy, and generates a signal frequency change index.

6. The disease diagnosis system based on immunoassay according to claim 5, characterized in that: The correlation between the analysis signal response time and the signal change amplitude is analyzed using the formula: ; Calculating signal correlation If it exceeds the limit, the acquisition period range is extended, otherwise the original period setting is maintained and the signal response period index is generated, where Representative The time of the sampling point, Represents the average value of all sampling point times, Representative The signal amplitude of each sampling point, Represents the average value of the signal amplitude of all sampling points, Represents the total number of sampling points.

7. The disease diagnosis system based on immunoassay according to claim 1, characterized in that: The probe signal judgment module includes: The key point screening submodule extracts the change data of the key frequency segment according to the signal frequency change index, analyzes the response change cycle within the frequency segment, determines the peak and valley turning points, checks whether the turning points exceed the change standard, and screens the key data points that exceed the standard to obtain the frequency change key points; The signal coverage evaluation submodule calls the frequency change key points, analyzes the signal response frequency and the background noise amplitude, calculates the coverage ratio of the signal in the noise interval, compares the coverage ratio with the benchmark value, evaluates the effectiveness of the signal, and obtains the signal coverage ratio index; The interference denoising processing submodule marks the signal frequency band with high noise interference according to the signal coverage ratio index, removes high noise data, and evaluates the stability and duration of the denoised signal to obtain a signal effectiveness evaluation result.

8. The disease diagnosis system based on immunoassay according to claim 7, characterized in that: The analysis signal response frequency and background noise amplitude are calculated using the formula: ; Get the signal-to-background noise ratio index ,in, Representative The signal response, represents the average amplitude of background noise, Representative The background noise of the sub-measurement, Represents the number of measurements.

9. The disease diagnosis system based on immunoassay according to claim 1, characterized in that: The diagnostic assessment module includes: The marker sorting submodule classifies the markers by concentration based on the signal validity evaluation results and the pathological reference concentration range, analyzes the relationship between the concentration of each marker and the weight of the pathological state, and establishes a marker combination number; The status determination submodule compares the matching degree of the marker concentration deviation and the pathological status weight according to the marker combination number, calculates the pathological risk level of each marker, analyzes the impact of the level deviation on the disease status, determines whether the disease determination criteria are met, and obtains the pathological status determination result.

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