Automatic anti-sugar biomolecule activity detection method and system

Abstract

The invention discloses an automatic anti-sugar biomolecule activity detection method and system, relates to the field of automatic anti-sugar biomolecule activity detection, is used for improving the reliability of a detection result, and comprises the following steps: obtaining initial state characteristic data of biomolecules in a sample and transportation and treatment process information of the sample; the transportation and processing process information comprises sample collection time and a temperature change curve in the transportation process; based on the initial state characteristic data and the transportation and processing process information, evaluating the activity state of the biomolecules in the sample; adjusting a biomolecule activity detection strategy according to the activity state of the biomolecules in the sample; and executing the adjusted biomolecule activity detection strategy, and outputting a biomolecule activity detection result.

Description

Technical Field

[0001] This invention relates to the field of automated detection of anti-glycation biomolecule activity, and more particularly to an automated method and system for detecting anti-glycation biomolecule activity. Background Technology

[0002] Traditional automated anti-glycation biomolecular activity assay systems often face challenges during sample collection, transportation, storage, and internal processing due to the loss of activity or structural changes in the analyte biomolecules. Simultaneously, the reaction reagents upon which the system relies may gradually degrade during use. Existing automated systems often lack the ability to perceive and adaptively adjust to these biochemical changes in real time, thus affecting the accuracy of the test results and their clinical guidance value. Summary of the Invention

[0003] This application discloses an automated method and system for detecting anti-glycation biomolecule activity, aiming to solve the technical problems of existing automated anti-glycation biomolecule activity detection systems, such as loss of biomolecule activity or structural changes during sample collection, transportation, storage and internal system processing, as well as reagent decay, which affect the accuracy of detection results and clinical guidance value.

[0004] In a first aspect, this application discloses an automated method for detecting anti-glycation biomolecular activity, comprising the following steps: Acquire initial state characteristic data of biomolecules in the sample and information on sample transportation and processing; transportation and processing information includes sample collection time and temperature change curve during transportation; based on initial state characteristic data and transportation and processing information, assess the activity state of biomolecules in the sample; adjust the biomolecule activity detection strategy according to the activity state of biomolecules in the sample; execute the adjusted biomolecule activity detection strategy and output biomolecule activity detection results.

[0005] Optionally, based on initial state characteristic data and transportation and processing information, the active state of biomolecules in the sample is assessed, including: The sample is divided into a first subsample and a second subsample; The first subsample is scanned by a sensor to generate the first feature data; A chemical reagent is injected into the second subsample, and a sensor scan is performed to generate second feature data; Based on the first and second feature data, the interfering substances and their corresponding contributions are determined; Based on the interfering substances and their corresponding contributions, the influence of the interfering substances is removed from the initial state characteristic data to restore the true signal of the target anti-glycation biomolecules; Based on the real signals and transport and processing information of the target anti-glycation biomolecules, the activity status of biomolecules in the sample is assessed.

[0006] Optionally, a chemical reagent is injected into the second subsample, and a sensor scan is performed to generate second feature data, including: The second sample after being injected with chemical reagents was divided into two micro-quantum samples, resulting in the first micro-quantum sample and the second micro-quantum sample. The first micro-quantum sample is scanned by sensors to generate processed biomolecular state characteristic data. The second micro-quantum sample was subjected to target anti-glycation biomolecule removal treatment and sensor scanning to generate characteristic data on the effects of chemical reagents. The second characteristic data is obtained by subtracting the characteristic data of the influence of chemical reagents from the characteristic data of the state of biomolecules after processing.

[0007] Optionally, the second micro-quantum sample undergoes targeted anti-glycation biomolecule removal treatment and sensor scanning to generate characteristic data on the effects of chemical reagents, including: The second micro-quantum sample is filtered through a micro-physical separation module. The micro-physical separation module integrates a microfluidic channel with a nanoporous membrane. The pore size of the nanoporous membrane is preset to be smaller than the hydrodynamic diameter of the target anti-glycation biomolecule, and the pore size of the nanoporous membrane is preset to be larger than the hydrodynamic diameter of the chemical reagent and interfering substances, thereby selectively filtering and removing the target anti-glycation biomolecule from the second micro-quantum sample. The rate of change in conductivity of the effluent is detected by a conductivity sensor inside the micro-physical separation module. When the rate of change in conductivity is less than a preset threshold, the second filtered micro-quantum sample is scanned by a sensor to generate characteristic data on the influence of chemical reagents.

[0008] Optionally, the method also includes: Before filtering the second micro-quantum sample, the optical density, pH value, and concentration of specific ions of the second micro-quantum sample were measured. The preset threshold is dynamically adjusted based on optical density, pH value, and the concentration of specific ions.

[0009] Optionally, the method also includes: When the rate of change in conductivity is greater than or equal to a preset threshold, it is determined that the removal of the target anti-glycation biomolecules is insufficient. If the removal of the target anti-glycation biomolecules is deemed insufficient, a self-test procedure is initiated. The self-test procedure includes: detecting the working status of the conductivity sensor, detecting the latency and packet loss rate of the communication link; the working status includes calibration parameters and response time. Based on the results of the self-test procedure, a system status report is generated; When any parameter in the system status report exceeds the system health threshold, the internal parameters are adjusted for adaptive correction. The internal parameters include the driving voltage or pulse duration of the micro piezoelectric pump. The micro piezoelectric pump is used to periodically generate fluid pulses to prevent blockage of the nanoporous membrane and to promote the passage of target anti-glycation biomolecules. The parameters in the system status report include the operating status of the conductivity sensor, the latency of the communication link, and the packet loss rate.

[0010] Optionally, the method also includes: Obtain the preset initial system health threshold; Acquire historical performance data of the automated antiglycation biomolecular activity detection system; historical performance data includes performance drift trends, performance data at different ambient temperatures, and response characteristics when processing different sample matrices; Obtain current environmental information; environmental information includes temperature and humidity information; Based on historical performance data and current environmental information, the initial system health threshold is adjusted to obtain the system health threshold.

[0011] Optionally, based on the actual signal and transport and processing information of the target anti-glycation biomolecules, the activity state of the biomolecules in the sample is assessed, including: Determine the fingerprint deviation; the fingerprint deviation is determined based on the Euclidean distance between the true signal of the target anti-glycation biomolecule and the pre-stored reference data. Logistics impact assessment indicators are determined based on transportation and processing information; these indicators are used to assess the impact of transportation and processing information on the active state of biomolecules. The activity status of biomolecules in the sample is assessed based on fingerprint deviation and logistics impact assessment indicators.

[0012] Optionally, the activity state of biomolecules in the sample can be assessed based on fingerprint deviation and logistical impact assessment indicators, including: The activity status score is obtained by weighted summation of fingerprint deviation and logistics impact assessment indicators; The activity state of biomolecules in a sample is assessed based on the activity state score and the activity mapping relationship; the activity mapping relationship includes the correspondence between different activity state scores and different activity states.

[0013] Secondly, this application also discloses an automated anti-glycation biomolecular activity detection system, which includes: The initial state characteristic data acquisition module is used to acquire the initial state characteristic data of biomolecules in the sample and the information on the sample transportation and processing; the information on the transportation and processing includes the sample collection time and the temperature change curve during transportation. The activity state assessment module is used to assess the activity state of biomolecules in a sample based on initial state characteristic data and transportation and processing information. The detection strategy adjustment module is used to adjust the biomolecule activity detection strategy according to the activity state of biomolecules in the sample. The detection execution and result output module is used to execute the adjusted biomolecular activity detection strategy and output the biomolecular activity detection results. Beneficial effects

[0014] This application discloses an automated method for detecting the activity of anti-glycation biomolecules. It acquires initial state characteristic data of biomolecules in a sample, along with information on sample transportation and processing. Based on this data, it assesses the activity state of the biomolecules, adjusts the biomolecule activity detection strategy, and finally executes the adjusted strategy to output the detection results. This method effectively solves the technical challenges in existing technologies where the accuracy and clinical guidance value of detection results are affected by activity loss or structural changes during sample collection, transportation, storage, and internal system processing, as well as reagent decay. By tracking and evaluating the activity of the sample throughout its entire lifecycle, this application can perceive the biochemical changes of biomolecules in real time and dynamically adjust the detection strategy accordingly, avoiding the limitations of traditional systems' mechanized sample processing and fixed algorithms. Therefore, this application significantly improves the accuracy, reliability, and clinical guidance value of the detection results, providing more precise data support for clinical diagnosis. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of an automated method for detecting the activity of anti-glycation biomolecules provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of another automated method for detecting anti-glycation biomolecule activity provided in this embodiment of the invention; Figure 3 This is a schematic diagram of an automated anti-glycation biomolecular activity detection system provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0017] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0018] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0019] "Initial state characteristic data of biomolecules" refers to data obtained non-invasively or minimally invasively after sample collection but before any processing or transportation, which reflects the original structure and functional state of biomolecules. This data may include, but is not limited to, spectral data (such as UV-Vis absorption spectroscopy, fluorescence spectroscopy, circular dichroism spectroscopy), mass spectrometry data, nuclear magnetic resonance data, electrochemical signals, and other physicochemical parameters that can characterize the conformation, concentration, purity, or preliminary activity of biomolecules.

[0020] "Transportation and processing information" refers to all environmental and operational data that may affect the activity of biomolecules from the sample collection point to the receiving point of the detection system, and during the pre-processing process within the detection system. This includes the sample collection time, temperature change profiles during transportation (e.g., continuous temperature data acquired via temperature recorders or smart tags), light intensity, vibration levels, storage duration, and specific parameters for operations such as dispensing, mixing, and incubation within the automated system.

[0021] The "active state" of a biomolecule refers to its ability to perform its biological functions under specific conditions. For anti-glycosylation biomolecules, this may involve their efficiency in binding to glycosylation products, catalyzing specific reactions, or inhibiting glycosylation processes. Assessing the active state is a comprehensive judgment that considers not only the current physicochemical state of the biomolecule but also the potential impacts it may have during transport and processing.

[0022] "Biomolecular activity detection strategy" refers to a series of experimental steps and parameters designed for the accurate measurement of biomolecular activity. This may include, but is not limited to, reagent concentration, reaction temperature and time, pH value, detection wavelength, data acquisition frequency, and algorithmic models used for data analysis.

[0023] "Adjusting the biomolecular activity detection strategy" refers to dynamically modifying one or more parameters in the above detection strategy based on the assessed biomolecular activity status to compensate for the impact of biomolecular activity loss or reagent decay, thereby ensuring the accuracy of the final detection results.

[0024] The following specific embodiments will provide a detailed introduction and explanation of an automated method for detecting anti-glycation biomolecular activity provided in this application.

[0025] Reference Figure 1 This invention provides an automated method for detecting anti-glycation biomolecular activity, comprising the following steps: S1, obtain the initial state characteristic data of biomolecules in the sample and the information on the sample transportation and processing.

[0026] The transportation and processing information includes the sample collection time and the temperature change curve during transportation.

[0027] For example, initial state characteristic data can be obtained by rapidly scanning the sample at the sample collection point using a portable spectrometer, generating a preliminary spectral fingerprint. Alternatively, electrochemical sensors integrated into microfluidic chips can be used to immediately detect the redox potential or enzymatic reaction current of biomolecules after sample collection, obtaining initial activity indicators. For transportation and processing information, the sample collection time can be directly recorded by medical personnel or automated collection equipment at the time of collection. During transportation, disposable temperature recording tags or smart IoT sensors can be attached to the sample container. These sensors can continuously monitor and record the ambient temperature of the sample and generate detailed temperature change curves. This data can be transmitted wirelessly or read using scanning equipment after the sample arrives at the laboratory.

[0028] S2. Based on initial state characteristic data and transportation and processing information, assess the activity state of biomolecules in the sample.

[0029] For example, a machine learning model can be built that takes initial state feature data (such as spectral fingerprints) and transport and processing information (such as temperature profiles and acquisition time) as input. This model is trained on a large amount of sample data with known activity states, learning the complex mapping relationship between different input features and the biomolecular activity state. When new sample data is input, the model can output a predicted activity state score. Alternatively, a series of mathematical models for biomolecular activity decay can be pre-defined, describing the theoretical decay patterns of biomolecular activity under different temperature, time, and processing conditions. Then, the actual transport and processing information is substituted into these models to calculate the theoretical activity loss. This is then combined with the initial state feature data to correct the theoretical activity loss, thereby assessing the current biomolecular activity state.

[0030] S3. Adjust the biomolecule activity detection strategy according to the activity state of biomolecules in the sample.

[0031] For example, if the assessment results show a slight loss of biomolecule activity, the concentration of the reaction reagent can be increased accordingly to compensate for the reduction in the number of active biomolecules, ensuring the reaction rate remains within the detectable range. Alternatively, the reaction incubation time can be extended to give the biomolecules more time to complete the reaction. If the assessment results show a more severe loss of activity, the detector sensitivity settings can be adjusted, such as increasing the voltage or integration time of the photomultiplier tube to capture weak signals. Furthermore, the parameters of the data analysis algorithm can be adjusted based on the activity state. For example, if the biomolecule activity is low, a more complex curve fitting algorithm can be used to extract the effective signal from the noise, or the background subtraction parameters can be adjusted.

[0032] S4. Execute the adjusted biomolecular activity detection strategy and output the biomolecular activity detection results.

[0033] For example, if the strategy is adjusted to increase reagent concentration, the automated pipetting system will precisely aspirate and dispense a larger amount of reagent into the reaction wells. If the strategy is adjusted to extend the incubation time, the system will correspondingly extend the timer of the incubation module. After all adjusted steps have been performed, the detector will acquire the final signal data. This data will then be transmitted to the data processing unit, processed according to the adjusted data analysis algorithm, and finally output as biomolecular activity detection results. The detection results can be presented to the user in numerical form (e.g., activity units / mL), qualitative classification (e.g., high, medium, low activity), or graphical report (e.g., activity versus time curve).

[0034] The automated method for detecting the activity of anti-glycation biomolecules proposed in this application operates on the principle of constructing a closed-loop feedback system to address potential changes in biomolecule activity throughout the detection process. First, initial state characteristic data of the biomolecules are acquired at the initial stage of sample collection, providing a benchmark for subsequent activity assessment. Simultaneously, information on sample transportation and processing is tracked throughout the process, as this information is crucial for changes in biomolecule activity. Subsequently, the system combines this initial state characteristic data with the transportation and processing information, utilizing a pre-set model or algorithm to assess the current activity state of the biomolecules. This assessment process considers the potential attenuation or alteration of biomolecules under different environmental and operational conditions. Once the biomolecule's activity state is assessed, the system dynamically adjusts the subsequent biomolecule activity detection strategy accordingly. This adjustment can involve changes in reagent concentration, extension of reaction time, improvement of detector sensitivity, or even optimization of data analysis algorithm parameters, aiming to compensate for the impact of biomolecule activity loss or reagent attenuation. Finally, the system executes the adjusted detection strategy and outputs calibrated, more accurate biomolecule activity detection results. In this way, the method of this application can ensure reliable test results even when the sample undergoes complex environmental changes or uncertainties in the internal processing of the system, thereby significantly improving the accuracy and guidance value of clinical diagnosis.

[0035] The core innovation of this application lies in the introduction of real-time sensing of sample transportation and processing information, and an adaptive detection strategy adjustment mechanism based on this information. Traditional automated systems primarily focus on physical separation and dispensing, lacking the ability to real-time assess the biochemical integrity and activity of target biomolecules in the sample. They mechanically process samples and assume their biological activity remains stable throughout the process. This means that a result reported as "low activity" may not fully reflect the patient's true physiological state, but rather be due to sample degradation during transportation or partial failure of reagents on the instrument. This application, by acquiring initial state characteristic data of the sample and information on the transportation and processing process, and assessing the activity state of biomolecules based on this information, can accurately identify potential activity losses of biomolecules before and during detection. More importantly, this application can dynamically adjust the detection strategy based on the assessment results, such as adjusting reagent concentration, reaction time, or data analysis parameters, thereby effectively compensating for activity losses and ensuring the accuracy of the final detection results. This adaptive adjustment capability is not available in existing technologies. It enables the detection system to dynamically optimize the detection process according to the actual biochemical environment, significantly improving the reliability of the detection results and the clinical guidance value, and avoiding misjudgments caused by sample degradation or reagent decay.

[0036] like Figure 2As shown, based on initial state characteristic data and transportation and processing information, the activity state of biomolecules in the sample is evaluated, specifically including the following steps: S101. Divide the sample into a first subsample and a second subsample.

[0037] Specifically, the sample is divided into a first subsample and a second subsample. The purpose of this is to provide a basis for subsequent differential analysis, making it possible to detect the sample under different treatment conditions. The first subsample is usually used as a control group to obtain the original comprehensive signal of the sample; while the second subsample is used to introduce specific treatments to distinguish target biomolecules from interfering substances.

[0038] S102. Perform sensor scanning on the first sub-sample to generate the first feature data.

[0039] The process of scanning the first subsample with a sensor to generate first feature data involves using a suitable biosensor to detect the first subsample, which has not undergone any chemical treatment, to obtain a comprehensive signal containing the target biomolecules, interfering substances, and the sample matrix. This first feature data reflects the overall initial state of the sample.

[0040] S103. Inject chemical reagents into the second subsample and perform sensor scanning to generate second feature data.

[0041] This step involves adding one or more pre-defined chemical reagents to the second subsample. These reagents can be designed to specifically react with target anti-glycation biomolecules (e.g., to inactivate, degrade, or generate a detectable label) or to react with specific interfering substances. After the chemical reagents are injected, the second subsample is scanned by a sensor, and the resulting second feature data reflects the signal changes in the sample after chemical treatment.

[0042] S104. Based on the first feature data and the second feature data, determine the interfering substances and their corresponding contributions.

[0043] Specifically, this step involves comparing the differences between the first and second feature data, and combining this with the known mechanisms of action of the injected chemical reagents to analyze and identify interfering substances present in the sample and their specific impact on the sensor signal. For example, if the chemical reagent specifically acts on the target biomolecule, then the difference between the two subsample data will primarily reflect the signal of the target biomolecule. Conversely, if the chemical reagent acts on an interfering substance, the difference will reflect the signal of the interfering substance.

[0044] S105. Based on the interfering substances and their corresponding contributions, the influence of the interfering substances is removed from the initial state characteristic data to restore the true signal of the target anti-glycation biomolecules.

[0045] Specifically, this step involves using data processing algorithms (e.g., signal deconvolution, multivariate statistical analysis, or machine learning models) to remove the signal contributions of identified interfering substances from the original initial state feature data. This process effectively eliminates interference, resulting in a purer and more accurate signal that reflects the true state of the target anti-glycation biomolecules.

[0046] S106. Based on the real signals and transport and processing information of the target anti-glycation biomolecules, assess the activity status of biomolecules in the sample.

[0047] Specifically, this step involves obtaining undisturbed, authentic signals and then combining this information with details of the transportation and processing, such as sample collection time and temperature changes during transport, to comprehensively assess the activity level of the target anti-glycation biomolecules. Authentic signals provide accurate information about the intrinsic state of the biomolecules, while information about the transportation and processing provides clues about the influence of the external environment on their activity.

[0048] This application's solution effectively addresses the impact of interfering substances on the accuracy of biomolecular activity assessment in traditional methods by introducing the principle of differential detection. Specifically, by dividing the sample into a first subsample and a second subsample, and treating the second subsample with chemical reagents, the signals of the target anti-glycation biomolecule or specific interfering substances exhibit discernible differences in the sensor scan results of the two subsamples. The first feature data provides the overall signal spectrum of the sample, while the second feature data provides the signal spectrum after specific chemical intervention. By comparing and analyzing these two sets of data, the signal characteristics of the interfering substances and their contribution to the overall signal can be accurately identified. Once the interfering substances are quantified, their influence can be stripped from the initial state feature data, thereby restoring the true signal of the target anti-glycation biomolecule. This true signal eliminates external interference and can more accurately reflect the active state of the biomolecule itself. Combined with information on transport and processing, it ultimately achieves a precise assessment of the biomolecule's active state.

[0049] Through the above technical solution, this application can significantly improve the accuracy and reliability of automated detection of anti-glycation biomolecule activity. By refining sample processing and data analysis, it effectively eliminates the interference of various interfering substances in the sample that may obscure the signal of the target anti-glycation biomolecule, avoiding misjudgments caused by interfering signals. This makes the assessment of the activity state of the target anti-glycation biomolecule closer to its true state, thus providing a more solid and reliable basis for subsequent adjustments to the detection strategy and ensuring the scientific validity and effectiveness of the entire detection process.

[0050] In some preferred embodiments, a specific example is illustrated below. Suppose it is necessary to detect the activity of a specific anti-glycation protein in a sample. First, the sample is divided into a first subsample and a second subsample. The first subsample is directly subjected to fluorescence spectroscopy to obtain its comprehensive fluorescence signal containing all fluorescent substances (including the target anti-glycation protein and potential endogenous fluorescent interfering substances), as the first characteristic data. Subsequently, an enzyme or small molecule inhibitor known to specifically degrade or quench the fluorescence signal of the target anti-glycation protein is injected into the second subsample. After sufficient reaction, the treated second subsample is subjected to fluorescence spectroscopy again to obtain its fluorescence signal, as the second characteristic data. By comparing the first and second characteristic data, for example, by calculating the difference in fluorescence intensity between the two within a specific wavelength range, the contribution of the target anti-glycation protein to the fluorescence signal can be quantified. Simultaneously, using a pre-established database of interfering substances and algorithms, the influence of other non-specific fluorescent interfering substances can be identified and removed from the first characteristic data. Finally, the signals of these interfering substances are subtracted from the initial characteristic data to restore the true fluorescence signal representing only the target anti-glycation protein. By combining information such as the sample collection time and temperature fluctuation records during transportation, the system can accurately assess the activity status of the anti-glycation protein based on this real signal, such as determining whether it has been inactivated due to improper transportation.

[0051] In some embodiments described above in this application, a second subsample is injected with a chemical reagent, and then scanned by a sensor to generate second feature data. However, during implementation, the signal of the chemical reagent itself may affect the sensor scanning results, making the directly obtained second feature data unable to accurately reflect the true interaction between biomolecules and the chemical reagent. This may introduce measurement errors and affect the accurate identification of subsequent interfering substances and the assessment of the biomolecule's active state.

[0052] In this regard, this application further proposes the following steps for injecting chemical reagents into the second subsample and performing sensor scanning to generate second feature data: S201. Divide the second subsample after injecting chemical reagents into two micro-quantum samples to obtain the first micro-quantum sample and the second micro-quantum sample.

[0053] S202. Perform sensor scanning on the first micro-quantum sample to generate processed biomolecular state characteristic data.

[0054] S203. The second micro-quantum sample is subjected to target anti-glycation biomolecule removal treatment and sensor scanning to generate characteristic data of chemical reagent effects.

[0055] S204. Subtract the influence data of chemical reagents from the characteristic data of the state of biomolecules after processing to obtain the second characteristic data.

[0056] Specifically, after the second subsample containing the injected chemical reagent is divided into two micro-quantum samples, the first micro-quantum sample is used to acquire a comprehensive signal including biomolecules, chemical reagents, and any potential interfering substances—that is, the post-treatment biomolecule state characteristic data. This data reflects the overall state of the biomolecules under the influence of the chemical reagent. Simultaneously, the second micro-quantum sample is designed to specifically assess the impact of the chemical reagent itself on the sensor signal. By removing the target anti-glycation biomolecules from the second micro-quantum sample, the target anti-glycation biomolecules can be effectively separated from the sample, allowing subsequent sensor scanning to primarily capture the signals of the chemical reagent and other non-target substances, thus generating chemical reagent influence characteristic data. Finally, by subtracting the chemical reagent influence characteristic data from the post-treatment biomolecule state characteristic data, the background signal of the chemical reagent itself can be accurately stripped away, resulting in purer and more accurate second characteristic data that more realistically reflects the state changes of biomolecules under the influence of the chemical reagent.

[0057] This application's solution effectively solves the problem of interference from the chemical reagent's own signal on the detection of biomolecular activity by refining the processing of the second subsample after chemical reagent injection. Specifically, the second subsample after chemical reagent injection is divided into a first micro-quantum sample and a second micro-quantum sample, allowing for the acquisition of sensor data in two states: one containing the target biomolecule and the other not. The sensor scan result of the first micro-quantum sample, i.e., the processed biomolecular state characteristic data, includes the combined signal of the target biomolecule, the chemical reagent, and other interfering substances. The sensor scan result of the second micro-quantum sample, after the removal of the target anti-glycation biomolecule, i.e., the chemical reagent influence characteristic data, mainly reflects the signal of the chemical reagent itself and other non-target interfering substances. By subtracting the processed biomolecular state characteristic data from the chemical reagent influence characteristic data, the background influence of the chemical reagent can be accurately separated from the combined signal, thereby restoring the true signal of the target biomolecule under the action of the chemical reagent and ensuring the accuracy of the second characteristic data. This separation and processing mechanism makes the subsequent identification of interfering substances and the determination of their contribution more accurate, thus improving the reliability of biomolecular activity state assessment.

[0058] Through the above technical solution, this application can significantly improve the accuracy and reliability of the second feature data. By effectively separating the background signal of chemical reagents from the response signal of biomolecules, potential errors caused by the characteristics of the chemical reagents themselves are avoided. Therefore, the types of interfering substances in the sample and their contribution to the initial state feature data can be more accurately determined, providing a solid foundation for subsequently restoring the true signal of the target anti-glycation biomolecules. This refined data processing method ultimately helps to achieve a more accurate and reliable assessment of the activity state of biomolecules in the sample, improving the overall performance and reliability of the automated anti-glycation biomolecule activity detection method.

[0059] The second micro-quantum sample underwent targeted anti-glycation biomolecule removal treatment and sensor scanning to generate characteristic data on the influence of chemical reagents, including: S301. Filter the second micro-quantum sample through the micro-physical separation module.

[0060] The micro-physical separation module integrates a microfluidic channel of a nanoporous membrane. The pore size of the nanoporous membrane is preset to be smaller than the hydrodynamic diameter of the target anti-glycation biomolecule, and the pore size of the nanoporous membrane is preset to be larger than the hydrodynamic diameter of the chemical reagent and interfering substances, thereby selectively filtering and removing the target anti-glycation biomolecule from the second micro-quantum sample.

[0061] S302. The conductivity change rate of the effluent is detected by the conductivity sensor inside the micro physical separation module.

[0062] S303. When the rate of change of conductivity is less than the preset threshold, the second micro-quantum sample after filtering is scanned by a sensor to generate characteristic data on the influence of chemical reagents.

[0063] Changes in conductivity can reflect changes in ion concentration or composition in the effluent, thus indirectly indicating whether the target anti-glycation biomolecules have been sufficiently removed. For example, when the target anti-glycation biomolecules are effectively removed, the rate of change in conductivity of the effluent will tend to stabilize or reach a certain value. When the detected rate of change in conductivity is less than a preset threshold, it indicates that the target anti-glycation biomolecules have been sufficiently removed. Only then can the second micro-quantum sample after filtration be scanned by the sensor to generate accurate data on the influence of chemical reagents. This preset threshold can be set based on experimental calibration data or theoretical models to ensure the reliability of the removal effect.

[0064] This application's solution effectively addresses the potential problem of insufficient removal of target anti-glycation biomolecules during the removal process by introducing a micro-physical separation module and a conductivity sensor. Specifically, the unique pore size design of the nanoporous membrane—smaller than the hydrodynamic diameter of the target anti-glycation biomolecules but larger than the hydrodynamic diameters of chemical reagents and interfering substances—allows for the efficient and selective retention of the target anti-glycation biomolecules, while allowing other components to pass through. This ensures that the interference of the target anti-glycation biomolecules in the sample is eliminated to the greatest extent possible before the generation of chemical reagent influence characteristic data. Furthermore, by monitoring the rate of change in conductivity of the effluent in real time using the conductivity sensor within the micro-physical separation module, the system can dynamically determine whether the target anti-glycation biomolecules have been sufficiently removed. When the rate of change in conductivity falls below a preset threshold, it indicates that the composition of the effluent has stabilized and the target anti-glycation biomolecules are no longer significantly present. At this point, the chemical reagent influence characteristic data obtained by sensor scanning will be purer and more accurate. This real-time feedback mechanism avoids subsequent data deviations caused by incomplete removal, thereby ensuring the reliability of the entire detection process.

[0065] Through the above technical solution, this application ensures that the target anti-glycation biomolecules are sufficiently and effectively removed when generating the chemical reagent-affected characteristic data. This significantly improves the purity and accuracy of the chemical reagent-affected characteristic data, avoiding measurement errors caused by residual target anti-glycation biomolecules. Therefore, when subtracting the chemical reagent-affected characteristic data from the processed biomolecule state characteristic data, the second characteristic data can be obtained more accurately, providing a solid data foundation for accurately assessing the activity state of biomolecules in the sample. This solution not only improves the reliability of the detection results but also enhances the robustness of the entire automated anti-glycation biomolecule activity detection method.

[0066] In some preferred embodiments, it is assumed that the hydrodynamic diameter of the target anti-glycation biomolecule is 10 nanometers, while the hydrodynamic diameters of the chemical reagents and interfering substances are both less than 5 nanometers. In this case, the pore size of the nanoporous membrane integrated in the micro-physical separation module can be preset to 7 nanometers. When the second micro-quantum sample is filtered through this micro-physical separation module, the target anti-glycation biomolecule will be retained, while the chemical reagents and interfering substances will pass through with the fluid. During the filtration process, the conductivity sensor inside the micro-physical separation module continuously monitors the rate of change in the conductivity of the effluent. For example, a preset threshold can be set; when the rate of change in conductivity is less than 0.01 μS / cm for 10 consecutive seconds, it is considered that the target anti-glycation biomolecule has been sufficiently removed. Once this condition is met, the system will initiate a sensor scan of the filtered second micro-quantum sample to generate accurate data on the influence of the chemical reagents.

[0067] In some embodiments described above, when removing target anti-glycation biomolecules from a second micro-quantum sample, the conductivity change rate of the effluent is detected by a conductivity sensor within the micro-physical separation module and compared with a preset threshold to determine whether the target anti-glycation biomolecules have been sufficiently removed. However, factors such as matrix complexity, initial concentration, and pH value of different samples may lead to variations in the response characteristics of the conductivity change rate, making a single preset threshold difficult to accurately adapt to all situations, potentially affecting the accuracy of the removal efficiency assessment. If this problem is not addressed, it may result in inaccurate evaluation of the influence of chemical reagents on characteristic data, thereby affecting the reliability of the final biomolecule activity detection results.

[0068] In response, this application proposes an automated method for detecting anti-glycation biomolecular activity, the method further comprising: S401. Before filtering the second micro-quantum sample, detect the optical density, pH value, and concentration of specific ions in the second micro-quantum sample.

[0069] Optical density refers to the degree of light absorption or scattering by the sample, reflecting the overall concentration of biomolecules or interfering substances in the sample. pH value is an indicator of the sample's acidity or alkalinity, affecting the conformational stability of biomolecules, the reactivity of chemical reagents, and the surface charge of nanoporous membranes, thus influencing filtration efficiency and conductivity response. The concentration of specific ions directly affects the sample's conductivity background value and rate of change. By detecting these parameters before filtration, the real-time physicochemical properties of the sample can be obtained.

[0070] S402. Dynamically adjust the preset threshold based on optical density, pH value, and the concentration of specific ions.

[0071] Specifically, a mapping relationship can be established in advance between optical density, pH value, concentration of specific ions, and preset threshold. Based on the optical density, pH value, concentration of specific ions, and the mapping relationship, the preset threshold can be determined and adjusted accordingly.

[0072] This application's solution provides a comprehensive understanding of the sample's current state by acquiring the optical density, pH value, and concentration of specific ions in a second micro-quantum sample before filtration. These parameters directly affect the filtration efficiency of the nanoporous membrane and the response characteristics of the conductivity sensor. For example, the ionic strength of the sample influences the baseline and magnitude of conductivity changes, while pH can alter the charge state of biomolecules, thus affecting their interaction with the nanoporous membrane. By incorporating these real-time parameters and dynamically adjusting a preset threshold accordingly, it ensures that the threshold more accurately reflects the actual removal status of the current sample, thereby avoiding misjudgments caused by sample differences.

[0073] In some embodiments described above, a micro-physical separation module is used to remove target anti-glycation biomolecules from a second micro-quantum sample, and the removal effect is judged based on the rate of change in conductivity. However, in actual operation, various factors, such as blockage of the nanoporous membrane, sensor performance drift, or communication link anomalies, may result in insufficient removal of the target anti-glycation biomolecules. If this inadequate removal is not detected and corrected in time, it will directly affect the accuracy of subsequent chemical reagent influence characteristic data, thereby negatively impacting the evaluation results of the biomolecule's activity state and reducing the reliability of the detection method. To address this, this application further proposes a mechanism for self-checking and adaptive correction when the removal of target anti-glycation biomolecules is insufficient, to ensure the accuracy of the detection process and the stability of the system.

[0074] The above methods also include: S501. When the rate of change of conductivity is greater than or equal to a preset threshold, it is determined that the removal of the target anti-glycation biomolecules is insufficient.

[0075] S502. If it is determined that the removal of the target anti-glycation biomolecules is insufficient, a self-test procedure is initiated.

[0076] The self-test procedure includes: detecting the working status of the conductivity sensor, detecting the delay and packet loss rate of the communication link; the working status includes calibration parameters and response time.

[0077] S503. Based on the results of the self-test procedure, generate a system status report.

[0078] The report summarizes key parameters such as the operating status of conductivity sensors, communication link latency, and packet loss rate.

[0079] S504. When any parameter in the system status report exceeds the system health threshold, adjust the internal parameters for adaptive correction.

[0080] The internal parameters include the driving voltage or pulse duration of the micro piezoelectric pump; the micro piezoelectric pump is used to periodically generate fluid pulses to prevent blockage of the nanoporous membrane and to promote the passage of target anti-glycation biomolecules; the parameters in the system status report include the operating status of the conductivity sensor, the latency of the communication link, and the packet loss rate.

[0081] This application's solution effectively addresses the potential problem of insufficient removal of target anti-glycation biomolecules by introducing an intelligent self-checking and adaptive correction mechanism. When the rate of change in conductivity indicates insufficient removal of target anti-glycation biomolecules, the system no longer simply stops or reports an error, but actively initiates a self-checking procedure. This self-checking procedure comprehensively diagnoses the operating status of the conductivity sensor and the performance of the communication link, thereby identifying the specific reasons for the decline in removal efficiency, such as sensor calibration deviation, response hysteresis, or unstable data transmission. The system status report generated based on the self-checking results allows the system to clearly understand its own operating status. Once any critical parameter in the report exceeds a health threshold, the system immediately adjusts the driving voltage or pulse duration of the micro-piezoelectric pump. This adjustment optimizes the intensity and frequency of the fluid pulses generated by the micro-piezoelectric pump, thereby more effectively clearing blockages on the nanoporous membrane and enhancing the dynamics of target anti-glycation biomolecules through the membrane, ultimately ensuring that the target anti-glycation biomolecules are sufficiently removed, providing accurate samples for subsequent detection steps.

[0082] Through the above technical solution, this application can significantly improve the reliability and accuracy of automated methods for detecting the activity of anti-glycation biomolecules. This solution, by real-time monitoring of the removal effect and the introduction of self-checking and adaptive correction mechanisms, effectively avoids detection errors caused by insufficient removal of target anti-glycation biomolecules, ensuring the authenticity of subsequent chemical reagent-induced characteristic data and the accuracy of biomolecule activity state assessment. Furthermore, this mechanism enables early warning and automated intervention for potential system failures, reducing the need for manual intervention, improving the system's automation level and operational stability, thereby providing a more stable and reliable technical guarantee for biomolecule activity detection.

[0083] In some preferred embodiments, assuming that during the removal of target anti-glycation biomolecules from the second micro-quantum sample, the system detects that the rate of change in the conductivity of the effluent is consistently higher than a preset threshold. At this point, the system immediately determines that the removal of the target anti-glycation biomolecules is insufficient and initiates a self-test procedure. The self-test procedure first checks the operating status of the conductivity sensor, finding that its response time is slightly prolonged and the calibration parameters have a slight drift. Simultaneously, the communication link delay is also slightly higher than normal. Based on these self-test results, the system generates a system status report, showing that the response time parameter of the conductivity sensor and the delay parameter of the communication link are both close to the upper limit of the system health threshold. To prevent further decrease in removal efficiency, the system automatically adjusts the driving voltage of the micro-piezoelectric pump according to the report results, slightly increasing it from the standard value and appropriately extending the pulse duration. Through this adaptive correction, the intensity of the fluid pulse generated by the micro-piezoelectric pump increases, more effectively clearing any micro-blockages that may exist on the nanoporous membrane and accelerating the passage of the target anti-glycation biomolecules. After adjustment, the system re-detected the rate of change of conductivity of the effluent and found that it had dropped below the preset threshold, indicating that the target anti-glycation biomolecules had been fully removed, thus ensuring the accuracy of subsequent detection data.

[0084] In some embodiments described above, this application proposes a scheme to adaptively correct internal parameters when any parameter in the system status report exceeds the system health threshold. However, in practical applications, if the system health threshold is fixed, it may not adequately adapt to the complex operating conditions of the automated anti-glycation biomolecular activity detection system during long-term operation, caused by performance drift, environmental changes, or processing different sample matrices. This could lead to inaccurate judgment of abnormal states by the system, affecting the effectiveness of adaptive correction. Therefore, this application further proposes a method for dynamically adjusting the system health threshold to improve the accuracy and robustness of the system's adaptive correction.

[0085] The method also includes: S601. Obtain the preset initial system health threshold.

[0086] Specifically, the preset initial system health threshold refers to the baseline allowable range set for various key parameters (such as the operating status of the conductivity sensor, communication link delay, and packet loss rate) under ideal or standard operating conditions. This threshold is usually determined during system design or at the factory, serving as the starting point for determining the system's health status.

[0087] S602. Obtain historical performance data of the automated anti-glycation biomolecular activity detection system.

[0088] Historical performance data includes performance drift trends, performance data at different ambient temperatures, and response characteristics when processing different sample matrices.

[0089] Performance drift trend: refers to the slow, gradual changes in various performance parameters of a system over time, such as a decrease in sensor sensitivity or an increase in response time. By analyzing these trends, it is possible to predict when the system may deviate from its normal operating range.

[0090] Performance data at different ambient temperatures: This refers to the performance of various parameters of the system when it operates at different ambient temperatures (e.g., 0°C to 40°C). Biomolecular detection systems are sensitive to temperature, so understanding their response characteristics at different temperatures helps to more accurately assess their health status.

[0091] Response characteristics when processing different sample matrices: This refers to the changes in the system's performance parameters (such as detection accuracy and stability) when processing samples of different types or complexities (e.g., serum, urine, tissue extracts, etc.). Different sample matrices may cause different interferences or loads to the system.

[0092] S603. Obtain current environment information.

[0093] The environmental information includes temperature and humidity information.

[0094] S604. Based on historical performance data and current environmental information, adjust the initial system health threshold to obtain the system health threshold.

[0095] Specifically, a dynamic model or algorithm can be established to comprehensively analyze historical performance data (including performance drift trends, performance data under different ambient temperatures, and response characteristics when processing different sample matrices) and current real-time environmental information (temperature and humidity) to calculate a system health threshold that better reflects the current system operating status and environmental conditions. For example, when the system exhibits a certain performance drift trend, or when the ambient temperature deviates significantly from the standard operating temperature, this threshold can be appropriately relaxed or tightened to ensure that the judgment of the system health status is neither too sensitive nor too insensitive.

[0096] This application's solution effectively addresses the potential for misjudgments or missed detections caused by fixed thresholds in complex and variable operating environments by introducing a dynamic adjustment mechanism for system health thresholds. Specifically, firstly, a preset initial system health threshold is obtained as a benchmark, providing a basis for preliminary judgment of the system's health status. Secondly, by continuously acquiring and analyzing historical performance data of the automated anti-glycation biomolecular activity detection system, including performance drift trends, performance data at different ambient temperatures, and response characteristics when processing different sample matrices, the system can learn and understand its behavioral patterns and performance evolution under long-term operation, environmental changes, and different sample loads. Simultaneously, real-time environmental information, such as temperature and humidity, provides the immediate context of the system's current operating condition. Finally, based on this comprehensive historical performance data and real-time environmental information, an intelligent algorithm adaptively adjusts the initial system health threshold. For example, if historical data shows a slight decrease in system performance under high temperatures, the system health threshold will be appropriately relaxed under the current high-temperature environment to avoid false alarms caused by environmental factors; conversely, if historical data shows that the system is abnormally sensitive under specific conditions, the threshold may be tightened. This dynamic adjustment ensures that the system health threshold always matches the actual operating state of the system and the external environment, thereby improving the accuracy and robustness of the system's judgment of abnormal states.

[0097] In some preferred embodiments, a specific example is given below. Assume that the automated anti-glycation biomolecular activity detection system is set with an initial system health threshold at the factory, for example, the response time threshold of the conductivity sensor is 100ms. After the system is put into use, its historical performance data is continuously recorded. For example, after one year of operation, analysis of historical performance data reveals that due to sensor aging, its average response time has drifted to 95ms, and the response time increases by an additional 5ms when the ambient temperature is above 30°C. Simultaneously, the packet loss rate of the communication link increases slightly when processing high-viscosity samples. When the system is running in a high-temperature environment (e.g., 32°C) during summer and processing high-viscosity samples, the system acquires the current temperature and humidity information and combines it with historical performance data. Based on this information, the system dynamically adjusts the response time threshold of the conductivity sensor, for example, adjusting it to 100ms (initial) + 5ms (drift) + 5ms (high-temperature effect) = 110ms. Simultaneously, the packet loss rate threshold of the communication link is also appropriately relaxed based on historical data from processing high-viscosity samples. In this way, the system can dynamically adjust the health threshold according to the actual operating conditions and its own aging status, thereby avoiding unnecessary alarms under normal aging or specific environmental influences, while also being able to detect real system faults in a timely manner and ensure the accurate triggering of adaptive correction.

[0098] In some of the embodiments described above in this application, although the assessment of the biomolecule activity state in a sample based on the real signals and transport and processing information of the target anti-glycation biomolecules has been proposed, in practical applications, effectively integrating these two types of information to provide a comprehensive and accurate activity assessment may still face challenges. If assessment is based solely on data from a single dimension, it may not adequately reflect the multiple influences on biomolecule activity, thus limiting the accuracy and reliability of the assessment results.

[0099] In this regard, this application further proposes that the steps for assessing the active state of biomolecules in the above-mentioned sample include: S701, Determine fingerprint deviation.

[0100] The fingerprint deviation is determined based on the Euclidean distance between the actual signal of the target anti-glycation biomolecule and the pre-stored reference data.

[0101] Specifically, fingerprint deviation refers to the degree of change in the structure or function of a biomolecule by quantifying the difference between the actual signal of the target anti-glycation biomolecule and pre-stored reference data. This difference can be quantified using Euclidean distance, which visually represents the spatial distance between multidimensional data points, thus effectively capturing subtle changes in the actual signal of the biomolecule. The pre-stored reference data typically refers to the characteristic signal of the target anti-glycation biomolecule obtained under ideal conditions (e.g., fresh, undegraded samples), serving as a benchmark for activity assessment.

[0102] S702. Determine logistics impact assessment indicators based on transportation and processing information.

[0103] Among them, the logistics impact assessment index is used to assess the impact of transportation and processing information on the active state of biomolecules.

[0104] This indicator aims to transform these external factors into an assessable numerical value in order to comprehensively consider their contribution to the active state of biomolecules. For example, temperature change curves can be used to calculate cumulative temperature stress, and sampling time can be used to calculate sample storage time; these can all serve as input parameters for constructing a logistic impact assessment indicator.

[0105] S703. Assess the activity status of biomolecules in the sample based on fingerprint deviation and logistics impact assessment indicators.

[0106] Specifically, the fingerprint deviation and logistics impact assessment indicators can be weighted and summed to obtain the activity status score; based on the activity status score and the activity mapping relationship, the activity status of biomolecules in the sample can be assessed.

[0107] The activity mapping relationship includes the correspondence between different activity state scores and different activity states.

[0108] This application's solution organically integrates two key parameters—fingerprint deviation and logistics impact assessment indicators—into a unified activity status score by introducing a weighted summation mechanism. This integration overcomes the potential ambiguity of simply evaluating based on two indicators, providing a quantitative and comparable comprehensive assessment value. Furthermore, by establishing an activity mapping relationship, the continuous activity status score is transformed into discrete, easily understood, and operable activity status categories, making the assessment results more instructive. It is precisely because of this quantification and classification mechanism that the assessment process of biomolecular activity status becomes more standardized and objective, and provides a clear basis for subsequent adjustments to detection strategies.

[0109] Through the above technical solution, this application provides a more accurate and objective method for assessing the biomolecular activity state. By weighted summing of fingerprint deviation and logistics impact assessment indicators, both intrinsic changes in biomolecules and external environmental influences can be comprehensively considered, resulting in a more representative activity state score. Furthermore, by establishing an activity mapping relationship, the complex quantitative score is transformed into an intuitive activity state category, greatly improving the interpretability and practicality of the assessment results. This helps users quickly and accurately understand the activity status of samples and make more rational decisions, avoiding resource waste or misjudgments caused by inaccurate assessments.

[0110] In some preferred embodiments, a specific example is given below. Assume that when evaluating the activity state of an anti-glycation biomolecule, the fingerprint deviation is first calculated to be 0.25 based on the actual signal of the target anti-glycation biomolecule and reference data. Simultaneously, based on information about the sample's transportation and processing (e.g., the sample underwent prolonged high-temperature exposure during transportation), the logistics impact assessment index is determined to be 0.7. For comprehensive evaluation, the weight of the fingerprint deviation can be set to 0.6, and the weight of the logistics impact assessment index to 0.4. Through weighted summation, the activity state score is obtained as: (0.25 * 0.6) + (0.7 * 0.4) = 0.15 + 0.28 = 0.43. Further, the pre-established activity mapping relationship can be defined as follows: activity state score between 0 and 0.2: high activity; activity state score between 0.2 and 0.5: moderate activity; activity state score between 0.5 and 0.8: low activity; activity state score between 0.8 and 1.0: inactivation. Based on the above mapping relationship, since the calculated activity state score of 0.43 falls between 0.2 and 0.5, the activity state of the biomolecules in this sample can be assessed as "moderate activity". This quantification and mapping process makes the activity assessment results clear and operable, and can guide the adjustment of subsequent detection strategies.

[0111] like Figure 3 As shown, this embodiment of the invention also provides an automated anti-glycation biomolecular activity detection system. The system includes: The initial state characteristic data acquisition module is used to acquire the initial state characteristic data of biomolecules in the sample and the information on the sample transportation and processing; the information on the transportation and processing includes the sample collection time and the temperature change curve during transportation. The activity state assessment module is used to assess the activity state of biomolecules in a sample based on initial state characteristic data and transportation and processing information. The detection strategy adjustment module is used to adjust the biomolecule activity detection strategy according to the activity state of biomolecules in the sample. The detection execution and result output module is used to execute the adjusted biomolecular activity detection strategy and output the biomolecular activity detection results.

[0112] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0115] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. An automated method for detecting anti-glycation biomolecular activity, characterized in that, include: Acquire initial state characteristic data of biomolecules in the sample and information on the transportation and processing of the sample; the information on the transportation and processing includes the sample collection time and the temperature change curve during transportation; Based on the initial state characteristic data and the transportation and processing information, the activity state of biomolecules in the sample is evaluated; The biomolecule activity detection strategy is adjusted based on the activity state of the biomolecules in the sample. Implement the adjusted biomolecular activity detection strategy and output the biomolecular activity detection results.

2. The automated method for detecting anti-glycation biomolecular activity according to claim 1, characterized in that, The assessment of the biomolecule activity state in the sample based on the initial state characteristic data and the transportation and processing information includes: The sample is divided into a first subsample and a second subsample; The first subsample is scanned by a sensor to generate first feature data; The second subsample is injected with chemical reagents and scanned by a sensor to generate second feature data; Based on the first feature data and the second feature data, the interfering substances and their corresponding contributions are determined; Based on the interfering substances and their corresponding contributions, the influence of the interfering substances is removed from the initial state feature data to restore the true signal of the target anti-glycation biomolecules; The activity state of the biomolecules in the sample is evaluated based on the real signals of the target anti-glycation biomolecules and the information on the transport and processing process.

3. The automated method for detecting anti-glycation biomolecular activity according to claim 2, characterized in that, The step of injecting chemical reagents into the second subsample and performing sensor scanning to generate second feature data includes: The second subsample after being injected with chemical reagents is divided into two micro-quantum samples to obtain the first micro-quantum sample and the second micro-quantum sample. The first micro-quantum sample is scanned by a sensor to generate processed biomolecular state characteristic data. The second micro-quantum sample was subjected to target anti-glycation biomolecule removal treatment and sensor scanning to generate characteristic data on the influence of chemical reagents; The second feature data is obtained by subtracting the chemical reagent influence feature data from the processed biomolecular state feature data.

4. The automated method for detecting anti-glycation biomolecular activity according to claim 3, characterized in that, The process of removing the target anti-glycation biomolecules from the second micro-quantum sample and performing sensor scanning to generate chemical reagent influence characteristic data includes: The second micro-quantum sample is filtered through a micro-physical separation module. The micro-physical separation module integrates a microfluidic channel of a nanoporous membrane. The pore size of the nanoporous membrane is preset to be smaller than the hydrodynamic diameter of the target anti-glycation biomolecule, and the pore size of the nanoporous membrane is preset to be larger than the hydrodynamic diameter of the chemical reagent and the interfering substance, thereby selectively filtering and removing the target anti-glycation biomolecule from the second micro-quantum sample. The rate of change in conductivity of the effluent is detected by a conductivity sensor inside the micro-physical separation module. When the rate of change of conductivity is less than a preset threshold, the second filtered micro-quantum sample is scanned by a sensor to generate the characteristic data of the influence of the chemical reagent.

5. The automated method for detecting anti-glycation biomolecular activity according to claim 4, characterized in that, The method further includes: Before filtering the second micro-quantum sample, the optical density, pH value, and concentration of specific ions of the second micro-quantum sample are detected; The preset threshold is dynamically adjusted based on the optical density, the pH value, and the concentration of the specific ion.

6. The automated method for detecting anti-glycation biomolecular activity according to claim 4, characterized in that, The method further includes: When the rate of change of conductivity is greater than or equal to the preset threshold, it is determined that the removal of the target anti-glycation biomolecule is insufficient; If the removal of the target anti-glycation biomolecules is deemed insufficient, a self-test procedure is initiated. The self-test procedure includes: detecting the operating status of the conductivity sensor and detecting the delay and packet loss rate of the communication link. The operating status includes calibration parameters and response time. Based on the results of the self-test procedure, a system status report is generated; When any parameter in the system status report exceeds the system health threshold, the internal parameters are adjusted for adaptive correction. The internal parameters include the driving voltage or pulse duration of the micro piezoelectric pump. The micro piezoelectric pump is used to periodically generate fluid pulses to prevent the nanoporous membrane from becoming blocked and to promote the passage of the target anti-glycation biomolecules. The parameters in the system status report include the operating status of the conductivity sensor, the latency of the communication link, and the packet loss rate.

7. The automated method for detecting anti-glycation biomolecular activity according to claim 6, characterized in that, The method further includes: Obtain the preset initial system health threshold; Acquire historical performance data of the automated antiglycation biomolecular activity detection system; the historical performance data includes performance drift trends, performance data at different ambient temperatures, and response characteristics when processing different sample matrices; Obtain current environmental information; the environmental information includes temperature and humidity information; Based on the historical performance data and the current environmental information, the initial system health threshold is adjusted to obtain the system health threshold.

8. The automated method for detecting anti-glycation biomolecular activity according to claim 2, characterized in that, The assessment of the biomolecule activity state in the sample based on the real signal of the target anti-glycation biomolecule and the transport and processing information includes: The fingerprint deviation is determined based on the Euclidean distance between the actual signal of the target anti-glycation biomolecule and pre-stored reference data. Based on the transportation and processing information, a logistics impact assessment index is determined; the logistics impact assessment index is used to assess the impact of the transportation and processing information on the active state of the biomolecules. The activity state of biomolecules in the sample is assessed based on the fingerprint deviation and the logistics impact assessment index.

9. The automated method for detecting anti-glycation biomolecular activity according to claim 8, characterized in that, The step of assessing the activity state of biomolecules in the sample based on the fingerprint deviation and the logistics impact assessment index includes: The activity status score is obtained by weighted summation of the fingerprint deviation and the logistics impact assessment index. The activity state of biomolecules in the sample is assessed based on the activity state score and the activity mapping relationship; the activity mapping relationship includes the correspondence between different activity state scores and different activity states.

10. An automated anti-glycation biomolecular activity detection system, characterized in that, The system includes: The initial state characteristic data acquisition module is used to acquire the initial state characteristic data of biomolecules in the sample and the transportation and processing information of the sample; the transportation and processing information includes the sample collection time and the temperature change curve during transportation. An activity state assessment module is used to assess the activity state of biomolecules in the sample based on the initial state characteristic data and the transportation and processing information. The detection strategy adjustment module is used to adjust the biomolecule activity detection strategy according to the activity state of biomolecules in the sample. The detection execution and result output module is used to execute the adjusted biomolecular activity detection strategy and output the biomolecular activity detection results.

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