Preparation method of outer vesicle functional marker rapid detection kit substrate

Abstract

The invention provides a preparation method of a substrate of an external vesicle functional marker rapid detection kit, and belongs to the technical field of external vesicle functional marker detection.The preparation method comprises the steps that a high-purity coating working solution is prepared, and precise spraying is achieved through a piezoelectric type ink jet system; substrate functional modification is completed through confining liquid treatment and labeled antibody working solution preparation, a double-layer game model containing detection sensitivity optimization and process stability optimization is established, and an optimal parameter combination is obtained through game iterative calculation by combining the synergistic effect of a quality evaluation function and a stability control function. A characteristic parameter database is established to store historical production data and quality detection results, a multi-parameter joint evaluation method is adopted to calculate a comprehensive score and carry out unified product grading, and a quality control system and a standardized parameter optimization mechanism are driven through full-process data. The technical problem that the quality consistency difference is large in the batch production process is solved.

Description

Technical Field

[0001] This invention belongs to the field of detection technology of functional biomarkers of external vesicles, and more specifically, relates to a method for preparing a substrate for a rapid detection kit of functional biomarkers of external vesicles. Background Technology

[0002] Extravesicular vesicles play a crucial role in disease diagnosis and treatment monitoring as important biomarkers. Traditional extravesicular vesicle detection kit substrate preparation technology mainly adopts conventional antibody coating methods. By directly coating the surface of a solid-phase carrier with captured antibodies, combined with the principle of enzyme-linked immunosorbent assay (ELISA), specific identification and quantitative detection of biomarkers on the surface of extravesicular vesicles are achieved. This technology is widely used in clinical testing, scientific research experiments, and in vitro diagnostic product development. However, traditional preparation methods have significant shortcomings in batch-to-batch quality control. Different batches of products often show significant differences in detection sensitivity, reproducibility, and stability, mainly manifested in inconsistent antibody coating uniformity, fluctuations in reagent formulation stability, and insufficient precision in process parameter control. In existing technologies, due to the lack of a systematic quality control system and standardized production process, the quality of different batches of products is often affected by various factors such as operator skill level, changes in environmental conditions, and fluctuations in raw material quality, resulting in significant differences in product performance parameters between batches. In other words, existing technologies suffer from the technical problem of large variations in quality consistency during the production process. Summary of the Invention

[0003] In view of this, the present invention provides a method for preparing a substrate for a rapid detection kit of functional biomarkers of external vesicles, which can solve the technical problem of large quality inconsistencies in batch production of substrates for external vesicle detection kits.

[0004] This invention is implemented as follows: A method for preparing a substrate for a rapid detection kit of exovesicular functional biomarkers includes: preparing exovesicular capture antibodies, evaluating the quality of exovesicular capture antibodies, preparing a coating solution, mixing and preparing a coating working solution, precisely spraying the coating working solution, preparing a blocking solution to treat the substrate, preparing a labeled antibody working solution, substrate assembly and quality testing, iterative optimization using a game model, establishing a feature parameter database, and product grading. The iterative optimization of the game model involves inputting antibody activity retention rate, specific binding rate, antibody concentration, spraying precision, drying time, blocking effect, and assembly pressure into the game model for iterative calculation. The upper-level objective function optimizes detection sensitivity, and the lower-level objective function optimizes process stability. Iterative calculations are performed based on the coupling relationship until convergence, and the optimal parameter combination is output to guide production parameter adjustments. A multi-parameter joint evaluation method is used to calculate a comprehensive score for product grading, resulting in the prepared substrate for the rapid detection kit of exovesicular functional biomarkers.

[0005] The step of preparing the outer vesicle capture antibody specifically involves dissolving the outer vesicle surface marker-specific antibody in phosphate buffer, adjusting the pH to 7.2 to 7.4 to achieve a final antibody concentration of 10 to 20 μg / mL, using low-temperature dissolution to avoid antibody denaturation, measuring the antibody concentration using a spectrophotometer to obtain the antibody concentration value, and inputting the antibody concentration value into a quality assessment function to calculate the initial quality score.

[0006] The steps for evaluating the quality of external vesicle capture antibodies specifically involve: using enzyme-linked immunosorbent assay (ELISA) to detect antibody activity and obtain antibody activity retention rate; using Western blotting to detect antibody specificity and obtain specific binding rate; establishing a gradient dilution series and calculating the half-maximal effective concentration using recombinant antigen as a standard; processing antibody activity retention rate and specific binding rate through an antibody activity fitness index calculation model to output antibody quality grade scores; and selecting antibody solutions with an activity retention rate greater than 95% and a specific binding rate greater than 90%.

[0007] The preparation of the coating solution involves adding sucrose and albumin to a phosphate buffer solution at a mass ratio of 2:1, so that the sucrose mass fraction is 1% to 3% and the albumin mass fraction is 0.5% to 1%. The solution is then magnetically stirred at a constant temperature for more than 30 minutes to ensure that all components are fully dissolved. The transmittance of the solution is measured to obtain the clarity parameter of the coating solution. The clarity parameter of the coating solution is then input into a stability control function to calculate the formulation optimization rate.

[0008] The step of preparing the coating working solution involves mixing the outer vesicle capture antibody and the coating solution at a volume ratio of 1:9, vortexing at low speed for 1 to 2 minutes, centrifuging at 5000 r / min for 10 minutes to remove insoluble matter, measuring the turbidity index of the supernatant, and filtering the supernatant through a 0.22 μm filter membrane when the turbidity index is less than 0.1 to obtain the coating working solution.

[0009] The precision spraying of the coating working fluid involves applying the coating working fluid to the substrate using a piezoelectric inkjet system. The operating voltage is controlled at 3 to 5V, the frequency is set at 2 to 4kHz, the droplet volume is controlled at 2 to 3pL, the distance between the printhead and the substrate is maintained at 1.5 to 2.0mm, the spraying amount is set at 10 to 15μL per hole, the spraying spacing is 1.0 to 1.2mm, and drying is carried out at 37℃ for 45 to 60 minutes.

[0010] The step of preparing the blocking solution for substrate treatment specifically involves dissolving 2% to 5% albumin in phosphate buffer to adjust the pH to 7.2 to 7.4, using ultrasound to treat for 2 to 5 minutes to improve the dissolution effect, immersing the substrate at 20 to 25°C for 90 to 120 minutes, rinsing three times with phosphate buffer for 5 to 10 minutes each time, and vacuum drying at 37°C for 30 to 60 minutes.

[0011] The step of preparing the labeled antibody working solution specifically involves dissolving the biotin-labeled detection antibody in phosphate buffer to a final concentration of 5 to 10 μg / mL, adding horseradish peroxidase-labeled avidin to the labeled antibody solution to a final concentration of 1 to 2 μg / mL, incubating at 4 to 8°C in the dark for 150 to 180 min, mixing it with the labeled antibody dilution solution at a volume ratio of 1:9, filtering through a 0.22 μm filter membrane, and then spraying it onto a substrate using an inkjet system and drying it at 37°C for 60 to 90 min.

[0012] The substrate assembly and quality inspection steps involve sequentially pressing the substrate and glass pad together at a pressure of 0.3 to 0.5 MPa for 3 to 5 seconds. The overlap index is obtained by detecting the overlap between components, the integrity coefficient is obtained by detecting the integrity of components, and the position accuracy is obtained by detecting the position accuracy. The overlap index, integrity coefficient, position accuracy, assembly pressure value, and marking efficiency parameter are input into the quality evaluation function to calculate the final quality score.

[0013] The steps of establishing a feature parameter database and classifying products include storing the final quality score and optimal parameter combination in the feature parameter database, calculating the comprehensive score using a multi-parameter joint evaluation method, determining the product as Grade A when the comprehensive score is greater than or equal to 90, Grade B when the comprehensive score is between 75 and 90, and unqualified when the comprehensive score is less than 75. Qualified products are stored at 4 to 8°C in the dark.

[0014] Among them, extracellular vesicles refer to membrane vesicles with a diameter ranging from 30 to 150 nm. They contain bioactive substances such as protein markers, lipids, and nucleic acids, and have surface markers such as CD63, CD9, and CD81. They are secreted by cells and participate in intercellular communication processes.

[0015] The antibody activity fitness index calculation model is used to comprehensively evaluate the binding ability and stability of antibodies. The inputs include antibody activity retention rate, specific binding rate, half maximum effect concentration, cross-reactivity coefficient, and antibody stability index. The output is the antibody quality grade score used to screen qualified antibodies.

[0016] Among them, the piezoelectric inkjet system utilizes the principle that piezoelectric materials undergo mechanical deformation under the action of an electric field. By precisely controlling the voltage pulse, it achieves on-demand jetting of droplets, featuring high precision and high reproducibility, and is used to accurately apply reagents to the substrate surface.

[0017] The game theory model includes an upper-level model aimed at maximizing detection sensitivity and a lower-level model aimed at maximizing process stability. The coupling terms of the two objective functions are the antibody concentration value and the spraying accuracy value. The objective function of the upper-level model is the detection sensitivity optimization function, which is calculated by combining the product of antibody activity retention rate and specific binding rate, the logarithmic function of antibody concentration value, the exponential decay term of drying time parameter, the square root term of sealing effect parameter, and the linear term of assembly pressure value.

[0018] The upper-level model has the following constraints: antibody activity retention rate ≥ 0.95, specific binding rate ≥ 0.90, antibody concentration in the range of 10 to 20 μg / mL, drying time in the range of 45 to 60 min, and blocking effect ≥ 0.85. The lower-level model's objective function is a process stability optimization function, which is calculated by combining the product of spraying accuracy and reproducibility coefficient, the exponential function of coating solution clarity, the square root of temperature control accuracy, the logarithmic term of batch-to-batch consistency index, and the linear term of equipment accuracy stability.

[0019] The lower-level model is constrained by the following conditions: spraying accuracy value greater than or equal to 0.95, coating solution clarity parameter greater than or equal to 0.90, temperature control accuracy less than or equal to ±0.5℃, batch-to-batch consistency index greater than or equal to 0.90, and equipment accuracy stability greater than or equal to 0.95. The quality assessment function is used to comprehensively evaluate the influence of each key parameter in the substrate preparation process. The inputs include antibody concentration value, turbidity index, drying time parameter, blocking effect parameter, and overlap index. The output is the final quality score and the predicted pass rate value for quality control decision-making.

[0020] This invention constructs a comprehensive quality management system covering the entire process from raw material control to final product by establishing a feature parameter database and a multi-parameter joint evaluation method. It employs quality assessment functions and stability control functions to monitor and dynamically adjust key process parameters in real time. Utilizing a two-layer optimization strategy based on a game theory model, this invention establishes standardized parameter control ranges and operating procedures through the synergistic effect of upper-level detection sensitivity optimization and lower-level process stability optimization. It iterative optimization yields the optimal parameter combination, ensuring that each batch is produced under the same optimized parameters, thereby significantly reducing batch-to-batch quality variations. Furthermore, by establishing a comprehensive scoring system and product grading standards, this invention achieves quantitative assessment and unified control of product quality. A comprehensive score of 90 or higher is classified as Grade A, 75 to 90 as Grade B, and less than 75 as unqualified. This standardized quality grading system ensures the consistency and predictability of product quality across different batches. In summary, this invention solves the technical problem of large quality consistency variations during production batches mentioned in the background art. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention.

[0022] Figure 2 This is a convergence diagram of the iterative optimization process of the game model in Example 2.

[0023] Figure 3 This is a trend chart of the optimization of quality assessment parameters in Example 2.

[0024] Figure 4 This is a distribution diagram of the multi-parameter joint evaluation in Example 2. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0026] like Figure 1 The diagram shown is a flowchart of a method for preparing a substrate for a rapid detection kit for functional biomarkers of exovesicles provided by this invention. This method includes the following steps:

[0027] S01. Preparation of external vesicle capture antibody: Dissolve the specific antibody for the external vesicle surface marker in phosphate buffer, adjust the pH to 7.2 to 7.4, so that the final antibody concentration reaches 10 to 20 micrograms per milliliter. Low temperature dissolution is used to avoid antibody denaturation. The antibody concentration is measured by spectrophotometer to obtain the antibody concentration value. The antibody concentration value is input into the quality assessment function to calculate the initial quality score.

[0028] S02. Quality assessment of extravesicle capture antibodies: Antibody activity retention rate was obtained by enzyme-linked immunosorbent assay (ELISA), and antibody specificity was obtained by Western blotting. A gradient dilution series was established to calculate the half-maximal effective concentration (MCI) using recombinant antigen as a standard. The antibody activity retention rate and specific binding rate were processed by an antibody activity fitness index calculation model to output an antibody quality grade score. The antibody activity retention rate, specific binding rate, and antibody concentration were input into the upper objective function of a game model for detection sensitivity optimization calculation. Antibody solutions with an activity retention rate greater than 95% and a specific binding rate greater than 90% were selected.

[0029] S03. Preparation of coating solution: Add sucrose and albumin to phosphate buffer at a mass ratio of 2:1, so that the mass fraction of sucrose is 1% to 3% and the mass fraction of albumin is 0.5% to 1%. Stir magnetically for more than 30 minutes under constant temperature to ensure that all components are fully dissolved. Measure the transmittance of the solution to obtain the clarity parameter of the coating solution. Input the clarity parameter of the coating solution into the stability control function to calculate the formulation optimization rate.

[0030] S04. Preparation of coating working solution: Mix the outer vesicle capture antibody and coating solution at a volume ratio of 1:9, vortex at low speed for 1 to 2 minutes, centrifuge at 5000 rpm for 10 minutes to remove insoluble matter, measure the turbidity index of the supernatant, and when the turbidity index is less than 0.1, take the supernatant and filter it through a 0.22-micron filter membrane to obtain the coating working solution. Input the turbidity index into the quality assessment function to update the quality score.

[0031] S05, Precision Spray Coating Working Fluid: A piezoelectric inkjet system is used to apply the coating working fluid to the substrate. The working voltage is controlled at 3 to 5V, the frequency is set at 2 to 4kHz, the droplet volume is controlled at 2 to 3 picoliters, the distance between the printhead and the substrate is maintained at 1.5 to 2.0 mm, the spraying amount is set at 10 to 15 microliters per hole, the spraying spacing is 1.0 to 1.2 mm, the spraying accuracy value is recorded, and the spraying accuracy value is input into the lower objective function of the game model to calculate the process stability. The drying time parameter is obtained by drying at 37°C for 45 to 60 minutes.

[0032] S06. Preparation of blocking solution for substrate treatment: Dissolve 2% to 5% albumin in phosphate buffer and adjust the pH to 7.2 to 7.4. Use sonication for 2 to 5 minutes to improve the dissolution effect. Soak the substrate at 20 to 25°C for 90 to 120 minutes. Rinse three times with phosphate buffer for 5 to 10 minutes each time. Measure the blocking effect parameters. Input the blocking effect parameters and the drying time parameters into the quality evaluation function to calculate the blocking quality score. Vacuum dry at 37°C for 30 to 60 minutes.

[0033] S07. Preparation of labeled antibody working solution: Dissolve biotin-labeled detection antibody in phosphate buffer to a final concentration of 5 to 10 μg / mL. Add horseradish peroxidase-labeled avidin to the labeled antibody solution to a final concentration of 1 to 2 μg / mL. Incubate at 4 to 8°C in the dark for 150 to 180 minutes. Mix with labeled antibody dilution solution at a volume ratio of 1:9. Filter through a 0.22 μm filter membrane. Spray the solution onto a substrate using an inkjet system and dry at 37°C for 60 to 90 minutes. Record the labeled antibody binding efficiency as the labeling efficiency parameter.

[0034] S08. Perform substrate assembly and quality inspection: Press the substrate and glass pad sequentially in the order of substrate and glass pad, with a pressing pressure of 0.3 to 0.5 MPa and a pressing time of 3 to 5 seconds. Record the assembly pressure value, detect the overlap between components to obtain the overlap index, detect the integrity of components to obtain the integrity coefficient, and detect the position accuracy to obtain the position accuracy. Input the overlap index, the integrity coefficient, the position accuracy, the assembly pressure value, and the marking efficiency parameter into the quality evaluation function to calculate the final quality score.

[0035] S09. Perform iterative optimization of the game model: Input the antibody activity retention rate, specific binding rate, antibody concentration, spraying accuracy, drying time, sealing effect, and assembly pressure obtained in steps S02 to S08 into the game model for the first iteration calculation. Optimize the detection sensitivity through the upper-level objective function to obtain the sensitivity optimization result, and optimize the process stability through the lower-level objective function to obtain the stability optimization result. Based on the coupling relationship between the sensitivity optimization result and the stability optimization result, perform the second iteration calculation until convergence, and output the optimal parameter combination to guide the adjustment of production parameters.

[0036] S10. Establish a feature parameter database and classify products: Store the final quality score and the optimal parameter combination in the feature parameter database, calculate the comprehensive score using a multi-parameter joint evaluation method, determine the product as Grade A when the comprehensive score is greater than or equal to 90, determine the product as Grade B when the comprehensive score is between 75 and 90, and determine the product as unqualified when the comprehensive score is less than 75. Store qualified products at 4 to 8°C in the dark.

[0037] The extracellular vesicles refer to membrane vesicles with a diameter in the range of 30 to 150 nanometers, containing bioactive substances such as protein markers, lipids, and nucleic acids, and having surface markers such as CD63, CD9, and CD81. They are secreted by cells and participate in intercellular communication processes.

[0038] The antibody activity fitness index calculation model is used to comprehensively evaluate the binding ability and stability of antibodies. The inputs include antibody activity retention rate, specific binding rate, half maximum effect concentration, cross-reactivity coefficient, and antibody stability index. The output is an antibody quality grade score used to screen qualified antibodies.

[0039] The turbidity index is a physical parameter characterizing the clarity of a solution. It is calculated by measuring the ratio of transmitted light intensity to incident light intensity. The smaller the value, the clearer the solution, and it is used to evaluate the purity of the coating working solution.

[0040] The piezoelectric inkjet system utilizes the principle of mechanical deformation of piezoelectric materials under the action of an electric field. By precisely controlling the voltage pulse, it achieves on-demand jetting of droplets, featuring high precision and high reproducibility, and is used to accurately apply reagents to the substrate surface.

[0041] The overlap index is obtained by calculating the ratio of the actual overlap length to the theoretical overlap length. An overlap index in the range of 0.8 to 1.2 is considered acceptable and is used to evaluate the assembly accuracy of components.

[0042] The integrity coefficient is obtained by inspecting the edge integrity and calculating the defect rate using a digital microscope at 100x magnification. An integrity coefficient greater than 0.95 is considered acceptable and is used to evaluate the substrate preparation quality.

[0043] The position accuracy is obtained by calculating the deviation between the actual position and the standard position. A position deviation of less than 0.1 mm corresponds to a position accuracy of more than 0.9, which is considered qualified and is used to evaluate the positioning accuracy of the component.

[0044] The multi-parameter joint evaluation method is an evaluation system that comprehensively considers multiple quality indicators such as detection sensitivity, batch-to-batch difference coefficient, and repeatability error. It calculates a comprehensive score through weighted calculation and classifies products for final quality determination.

[0045] The feature parameter database contains historical production data and quality inspection results, which are used to establish quality control standards and predict product performance, providing a reference for subsequent production.

[0046] The quality assessment function is used to comprehensively evaluate the influence of various key parameters in the substrate preparation process. The inputs include antibody concentration, turbidity index, drying time parameter, blocking effect parameter, and overlap index. The output is the final quality score and the predicted pass rate for quality control decision-making.

[0047] The stability control function is used to optimize reagent formulation and improve preparation stability. The inputs include coating liquid clarity parameters, spraying accuracy values, temperature control parameters, humidity control parameters, and storage stability indicators. The outputs are the formulation optimization rate and stability prediction values ​​for process control.

[0048] The constructed game theory model includes an upper-level model that aims to maximize detection sensitivity and a lower-level model that aims to maximize process stability.

[0049] The objective function of the upper-level model is the detection sensitivity optimization function, which is calculated by combining the product of antibody activity retention rate and specific binding rate, the logarithmic function of antibody concentration, the exponential decay term of drying time, the square root term of blocking effect, and the linear term of assembly pressure. The constraints are: antibody activity retention rate ≥ 0.95, specific binding rate ≥ 0.90, antibody concentration in the range of 10 to 20 μg / mL, drying time in the range of 45 to 60 minutes, and blocking effect ≥ 0.85.

[0050] The objective function of the lower-level model is the process stability optimization function, which is calculated by combining the product of the spraying accuracy value and the repeatability coefficient, the exponential function of the coating liquid clarification parameter, the square root of the temperature control accuracy, the logarithmic term of the batch consistency index, and the linear term of the equipment accuracy stability. The constraints are: the spraying accuracy value is greater than or equal to 0.95, the coating liquid clarification parameter is greater than or equal to 0.90, the temperature control accuracy is less than or equal to ±0.5℃, the batch consistency index is greater than or equal to 0.90, and the equipment accuracy stability is greater than or equal to 0.95.

[0051] The coupling terms of the two objective functions are the antibody concentration value and the spraying accuracy value. When the upper-level model pursues higher detection sensitivity, it needs to increase the antibody concentration value and improve the spraying accuracy value. In order to ensure process stability, the lower-level model will optimize the antibody dosage ratio and improve the equipment accuracy requirements. Through game iteration, the optimal balance between detection performance and process stability is found.

[0052] The detection sensitivity optimization function is used to balance the relationship between detection performance and technical parameters. The inputs include antibody activity retention rate, specific binding rate, antibody concentration value, drying time parameter, and blocking effect parameter. The output is the sensitivity optimization result and the detection performance prediction value for upper-level game decision-making.

[0053] The process stability optimization function is used to optimize the manufacturing process and quality consistency. The inputs include spraying accuracy values, coating liquid clarification parameters, temperature control accuracy, batch consistency index, and equipment accuracy stability. The outputs are the stability optimization results and process reliability assessment values ​​for lower-level game decision-making.

[0054] The specific implementation methods of the above steps are described in detail below.

[0055] The specific implementation of step S01 involves preparing exovesicle capture antibodies by precisely controlling the antibody dissolution conditions. First, monoclonal antibodies targeting exovesicle surface markers such as CD63, CD9, or CD81 are removed from their frozen state and slowly thawed at a low temperature of 2°C to 8°C to avoid denaturation of the antibody's spatial structure due to sudden temperature changes. Then, cryoprotectants and other impurities are removed using molecular sieve chromatography. The purified antibody is dissolved in phosphate buffer with a pH of 7.2 to 7.4, and antibody stability is ensured by adjusting the ionic strength. The absorbance is measured at a wavelength of 280 nm using a spectrophotometer, and the antibody concentration is calculated according to Beer-Lambert's law, adjusting the final concentration to the range of 10 to 20 μg / mL. The measured antibody concentration value is input into a quality assessment function, which calculates an initial quality score based on a linear relationship model between concentration and activity, providing a benchmark parameter for subsequent quality control.

[0056] The specific implementation of step S02 involves establishing a multi-dimensional antibody quality evaluation system to screen high-quality capture antibodies. Antibody activity is detected using enzyme-linked immunosorbent assay (ELISA). A standard curve is established to determine the binding ability of the antibody to the target antigen, and the threshold requirement of an activity retention rate of over 95% is calculated. Simultaneously, Western blotting is used to verify antibody specificity. By detecting the binding of the antibody to the target protein and non-specific proteins, the standard requirement of a specific binding rate exceeding 90% is calculated. A gradient dilution series is established, using recombinant exovesicle marker protein as a standard. A four-parameter logistic regression model is used to fit the dose-response curve, and the half-maximal effective concentration (MCD) value is calculated. An antibody activity fitness index calculation model is constructed. This model comprehensively considers the activity retention rate, specific binding rate, MCD, cross-reactivity coefficient, and stability index, and outputs an antibody quality grade score using a weighted average algorithm. Parameters meeting the quality standards are input into the upper-level objective function of the game model for detection sensitivity optimization calculation, ensuring that the selected antibody solution meets the high-sensitivity detection requirements.

[0057] The specific implementation of step S03 involves preparing a coating solution formulation with enhanced stability. Sucrose and bovine serum albumin are added sequentially to phosphate buffer at a precise mass ratio of 2:1. Sucrose acts as a protective agent to prevent protein aggregation, with a mass fraction controlled between 1% and 3%. Albumin acts as a stabilizer and carrier protein, with a mass fraction maintained between 0.5% and 1%. The solution is continuously stirred at 300 rpm for at least 30 minutes at a constant temperature of 25°C to ensure complete dissolution and a homogeneous solution. The transmittance of the solution is measured at a wavelength of 600 nm using a spectrophotometer. The clarity parameter of the coating solution is calculated according to Beer-Lambert's law; a transmittance of 95% or higher indicates a clear solution. The clarity parameter is input into a stability control function. This function, based on a mathematical model established by colloidal stability theory, calculates the formulation optimization rate by analyzing particle distribution and aggregation trends, guiding further improvements to the coating solution formulation.

[0058] The specific implementation of step S04 involves preparing a high-quality coating working solution through precise mixing and purification. The vesicle-capturing antibody prepared in step S01 is mixed with the coating solution prepared in step S03 at a volume ratio of 1:9, determined based on an optimized balance between antibody concentration and coating effect. A vortex mixer is used to oscillate at 1000 rpm for 1 to 2 minutes to avoid generating bubbles and shear force that could damage the antibody structure. Subsequently, the mixture is centrifuged at 5000 rpm for 10 minutes at 4°C to remove undissolved particles and aggregates using the density difference principle. The turbidity index of the supernatant is measured using a turbidimeter; an index less than 0.1 turbidity units indicates that the solution purity meets the requirements. The qualified supernatant is then aseptically filtered through a 0.22 μm pore size sterile filter membrane to remove bacteria and particulate contaminants, yielding the final coating working solution. The turbidity index is input into a quality assessment function to update the quality score, providing real-time feedback for process control.

[0059] The specific implementation of step S05 involves using piezoelectric inkjet technology to precisely apply the coating working fluid. The piezoelectric inkjet system is based on the inverse piezoelectric effect principle, precisely controlling the voltage pulses applied to the piezoelectric ceramic element to induce minute deformations that drive droplet ejection. The operating voltage is controlled within the range of 3 to 5V; too low a voltage leads to unstable ejection, while too high a voltage may damage the piezoelectric element. The frequency is set to 2 to 4kHz to ensure the continuity and stability of the ejection. The droplet volume is precisely controlled within the range of 2 to 3 picoliters by adjusting the pulse width and amplitude to ensure the consistency of each droplet. The distance between the printhead and the substrate is maintained at 1.5 to 2.0 mm; too close a distance may cause droplet merging, while too far a distance affects ejection accuracy. The coating amount per orifice is set to 10 to 15 μL, and the spraying spacing is controlled at 1.0 to 1.2 mm. A position feedback system ensures spraying accuracy. The spraying accuracy values ​​are recorded and input into the lower-level objective function of a game theory model to calculate the process stability index. After spraying, the substrate was dried at a constant temperature of 37°C for 45 to 60 minutes to allow the solvent to evaporate and the antibody to bind firmly to the substrate surface. The drying time parameters were recorded for subsequent optimization.

[0060] The specific implementation of step S06 involves eliminating non-specific binding sites through a blocking treatment. A 2% to 5% (w / w) bovine serum albumin blocking solution is prepared and dissolved in phosphate buffer at a pH of 7.2 to 7.4. Albumin molecules occupy vacant binding sites on the substrate surface through physical adsorption. An ultrasonic processor is used at a frequency of 40 kHz for 2 to 5 minutes to improve the uniformity and activity of albumin dissolution through cavitation. The coated substrate is then immersed in the blocking solution and treated at room temperature (20 to 25°C) for 90 to 120 minutes, ensuring that the blocking solution fully contacts all areas of the substrate surface. Subsequently, the substrate is rinsed with phosphate buffer for 5 to 10 minutes each time, for a total of three times, to remove unbound blocking proteins. The blocking effect parameters are determined using a fluorescent labeling method to detect the reduction in non-specific binding; the blocking efficiency should reach 85% or higher. The blocking effect parameters and drying time parameters are input into a quality assessment function to calculate the blocking quality score and evaluate the effectiveness of the blocking treatment. Finally, the substrate is dried in a vacuum drying oven at 37°C for 30 to 60 minutes to remove residual moisture and solidify the blocking layer.

[0061] The specific implementation of step S07 involves preparing a highly active labeled antibody working solution for signal detection. The biotin-labeled detection antibody is removed from refrigeration and dissolved in phosphate buffer to a final concentration of 5 to 10 μg / mL. The biotin molecules are covalently bound to the antibody molecules via amide bonds, which does not affect their binding activity. Horseradish peroxidase-labeled avidin is added to the labeled antibody solution to a final concentration of 1 to 2 μg / mL, utilizing the high affinity between biotin and avidin to form a detection complex. The mixed solution is incubated at 4 to 8°C in the dark for 150 to 180 minutes to ensure sufficient binding of biotin and avidin to form a stable complex. Subsequently, it is diluted with labeled antibody diluent at a volume ratio of 1:9 to the working concentration, and filtered through a 0.22 μm filter to remove aggregates and impurities. The labeled antibody working solution is precisely sprayed onto the detection area of ​​the substrate using an inkjet system, with the spraying parameters identical to those in step S05 to ensure consistency. The labeled antibody was fixed on the substrate surface by drying at 37°C for 60 to 90 minutes. The binding efficiency of the labeled antibody was measured by enzyme activity detection method as the labeling efficiency parameter, which was used to evaluate the signal intensity of the detection system.

[0062] The specific implementation of step S08 ensures substrate product quality through precision assembly and multi-dimensional quality inspection. The substrate, sample pad, bonding pad, and absorbent pad are laminated sequentially, with each layer precisely aligned to ensure the continuity of the inspection channel. A pneumatic laminator is used with a pressing pressure of 0.3 to 0.5 MPa. Insufficient pressure results in weak interlayer bonding, while excessive pressure may damage the functional coating. The pressing time is controlled within 3 to 5 seconds to ensure full adhesion of each layer while avoiding over-pressing. Assembly pressure values ​​are recorded in real time for process monitoring. The overlap between components is detected using a digital microscope, and the ratio of the actual overlap length to the theoretical overlap length is calculated to obtain the overlap index, with a acceptable range of 0.8 to 1.2. The integrity of the edges of each component is checked using 100x magnification, and the number of defects is counted to calculate the integrity coefficient, which must be greater than 0.95. The deviation between the actual position and the standard position of each component is detected using a coordinate measuring system, and the positional accuracy is calculated. A positional deviation of less than 0.1 mm corresponds to a positional accuracy greater than 0.9. The overlap index, integrity coefficient, positional accuracy, assembly pressure value, and marking efficiency parameter are input into the quality assessment function to calculate the final quality score, providing a quantitative basis for product quality judgment.

[0063] The specific implementation of step S09 involves using a two-layer game model to achieve synergistic optimization of detection performance and process stability. The game model includes an upper-layer detection sensitivity optimization model and a lower-layer process stability optimization model, which are coupled through parameters to form a Stackelberg game structure. The upper-layer objective function is the detection sensitivity optimization function, primarily composed of the product of antibody activity retention rate and specific binding rate, reflecting detection specificity; the logarithmic function of antibody concentration, reflecting the saturation characteristics of the concentration effect; the exponential decay term of drying time, simulating the negative impact of excessive drying on activity; the square root term of the blocking effect, characterizing the marginal diminishing law of the blocking effect; and the linear term of assembly pressure, reflecting the direct impact of mechanical stress on detection performance. The lower-layer objective function is the process stability optimization function, composed of the product of spraying accuracy and reproducibility coefficient, reflecting equipment stability; the exponential function of coating solution clarity, reflecting the importance of solution stability; the square root term of temperature control accuracy, characterizing the nonlinear impact of temperature fluctuations on the process; the logarithmic term of batch-to-batch consistency index, reflecting the marginal effect of consistency improvement; and the linear term of equipment accuracy stability, directly related to equipment reliability. The two objective functions are coupled through antibody concentration and spraying precision values. When the upper-level model seeks higher detection sensitivity and requires increasing antibody concentration, the lower-level model needs to correspondingly increase spraying precision to ensure process stability, forming a mutual constraint relationship between the parameters. The game-theoretic solution process uses an iterative algorithm. First, the lower-level variables are fixed to solve for the optimal solution of the upper-level model. Then, the upper-level variables are fixed to solve for the optimal solution of the lower-level model. This process is repeated until the change in the values ​​of the two objective functions is less than the set convergence precision of 0.001, at which point the process stops and the optimal parameter combination is output to guide the adjustment of production parameters.

[0064] The specific implementation of step S10 involves establishing an intelligent quality management system to achieve product grading and traceability management. The final quality score and optimal parameter combination obtained in step S09 are stored in a feature parameter database. This database uses a relational structure to store historical production data, quality inspection results, process parameters, and environmental conditions. A comprehensive scoring system is established using a multi-parameter joint evaluation method. This method is based on fuzzy comprehensive evaluation theory, comprehensively considering multiple quality indicators such as detection sensitivity, batch-to-batch difference coefficient, repeatability error, and stability index. The weight coefficients of each indicator are determined using an expert weighting method, and a weighted average algorithm is used to calculate the comprehensive score. A three-level product grading standard is established based on the comprehensive score. A comprehensive score greater than or equal to 90 points is classified as Grade A, indicating a high-quality product; a comprehensive score between 75 and 90 points is classified as Grade B, indicating a qualified product; and a comprehensive score less than 75 points is classified as a non-qualified product requiring rework. Qualified products are sealed in aluminum foil packaging and stored in a light-protected environment at 4-8℃ to ensure stable performance within their shelf life.

[0065] It should be noted that the game theory model solution process in this invention is as follows: The game theory model adopts a two-layer iterative solution mechanism. First, key parameters such as antibody activity retention rate, specific binding rate, antibody concentration, spraying accuracy, drying time, blocking effect, and assembly pressure are input into the game theory model for the first iteration calculation. The upper-layer model calculates the sensitivity optimization result through a detection sensitivity optimization function. This function is composed of the product of antibody activity retention rate and specific binding rate, the logarithmic function of antibody concentration, the exponential decay term of drying time, the square root term of blocking effect, and the linear term of assembly pressure, while satisfying constraints such as antibody activity retention rate greater than or equal to 0.95 and specific binding rate greater than or equal to 0.90. The lower-layer model calculates the stability optimization result through a process stability optimization function. This function is composed of the spraying accuracy and reproducibility coefficient. The objective function is composed of a product term, an exponential function term for the coating liquid clarity parameter, a square root term for temperature control accuracy, a logarithmic term for batch-to-batch consistency index, and a linear term for equipment accuracy stability. It simultaneously satisfies constraints such as a coating accuracy value greater than or equal to 0.95 and a coating liquid clarity parameter greater than or equal to 0.90. The two objective functions are coupled through antibody concentration and coating accuracy values. When the upper-level model pursues higher detection sensitivity, it needs to optimize antibody concentration and coating accuracy. The lower-level model adjusts these parameters accordingly to ensure process stability. A second iteration is performed based on the coupling relationship between the sensitivity optimization result and the stability optimization result. This iteration continues until the two objective functions converge to the optimal equilibrium point. The final output includes the optimal parameter combination, encompassing antibody concentration, coating parameters, and process conditions, which guides the adjustment of actual production parameters, achieving synergistic optimization of detection performance and process stability.

[0066] It should be noted that the key technical ideas of this invention are mainly reflected in four aspects. The first key technical idea is a parameter collaborative optimization mechanism based on a two-layer game model. Traditional preparation methods usually adopt single-objective optimization or empirical parameter tuning, which is prone to getting trapped in local optima and makes it difficult to balance the contradiction between detection performance and process stability. The Stackelberg game model constructed in this invention achieves multi-objective collaborative optimization through a hierarchical decision structure of upper-level detection sensitivity optimization and lower-level process stability optimization. When the upper-level model pursues the maximization of detection performance, it increases the amount of antibody and raises the process requirements, while the lower-level model uses process stability constraints for feedback adjustment. The two-layer model finds the global optimal balance point through parameter coupling and iterative solution, avoiding the problem of conflict between performance and stability in traditional methods.

[0067] The second key technological approach is an intelligent quality control system based on a multi-dimensional quality assessment function. Existing technologies primarily rely on single indicators or simple weighted averages to evaluate product quality, which fails to accurately reflect the true performance of complex biological detection systems. The quality assessment function established in this invention comprehensively considers multiple parameters such as antibody activity, process precision, and stability. By modeling the interactions between these parameters through nonlinear functional relationships, it achieves precise quantitative assessment of product quality. This system can identify key quality-influencing factors, provide real-time quality feedback, and significantly improve the accuracy and timeliness of quality control.

[0068] The third key technological approach is the precision manufacturing process of piezoelectric inkjet technology. Traditional coating methods, such as screen printing or capillary coating, suffer from low precision and poor consistency, making it difficult to meet the requirements for precise application of trace reagents. The piezoelectric inkjet technology employed in this invention achieves on-demand jetting of picoliter-level droplets based on the inverse piezoelectric effect, and achieves high-precision control of the coating amount and position through precise control of voltage pulse parameters. This technology not only improves reagent utilization and product consistency but also provides a technological foundation for large-scale automated production.

[0069] The fourth key technological approach is predictive quality management driven by a feature parameter database. Traditional quality management mainly relies on post-event inspection and experience-based judgment, lacking predictability and systematicity. The feature parameter database established in this invention integrates historical production data, process parameters, and quality inspection results. Through data mining and machine learning algorithms, a quality prediction model is built, realizing the transformation from passive quality control to proactive quality prediction.

[0070] The synergistic effect of these four key technological approaches has yielded significant technical results. Game theory model optimization ensures optimal parameter configuration, the quality assessment function provides accurate quality quantification, precision inkjet technology guarantees process execution accuracy, and the database system enables intelligent management. These four elements support each other to form a complete intelligent preparation system. Compared to traditional experience-driven preparation methods, this invention achieves a significant improvement in product quality and a substantial increase in production efficiency, providing an advanced technical solution for the industrial production of exovesicle detection reagents.

[0071] Specifically, the principle of this invention is as follows: The key to solving the problem of large quality inconsistencies in production batches lies in establishing a data-driven, end-to-end quality control system and a standardized parameter optimization mechanism. The feature parameter database, as a core component, systematically collects and stores historical production data and quality inspection results. Through data mining and statistical analysis, it establishes quality control standards and performance prediction models, providing standardized references for each new batch. The quality assessment function, by comprehensively considering key parameters such as antibody concentration, turbidity index, drying time, sealing effect, and overlap index, establishes a quantitative mapping relationship between parameters and quality. It can calculate the final quality score and predicted pass rate in real time, providing a scientific basis for quality control decisions during production. The stability control function, by monitoring process parameters such as coating liquid clarity, spraying accuracy, and temperature control, calculates the formula optimization rate and stability prediction values, ensuring that the process conditions for each batch are maintained at their optimal state. The game theory model's two-layer optimization mechanism, through iterative calculation, automatically finds the optimal balance between detection performance and process stability. The output optimal parameter combination provides a unified parameter setting standard for each production batch, eliminating the impact of human factors and environmental changes on product quality. The multi-parameter joint evaluation method establishes a unified product grading standard by calculating a comprehensive score through weighted calculation, ensuring that different batches of products can be evaluated and graded according to the same quality standards. This quality control system based on mathematical models and data analysis fundamentally guarantees the quality consistency between production batches.

[0072] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0073] The specific implementation of step S01 involves accurately determining the antibody concentration using Beer-Lambert's Law and establishing a quality assessment system. The formula for calculating antibody concentration is: In the formula, This refers to antibody concentration, expressed in μg / mL. The absorbance value at a wavelength of 280 nm is dimensionless. denoted as the molar absorptivity of the antibody, with a value of 1.4. ; The optical path length is 1 cm. Antibody concentration was determined using a spectrophotometer. To obtain the antibody sample, dilute it to an appropriate concentration range and measure the absorbance at a wavelength of 280 nm. Repeat the measurement three times and take the average value. The initial quality score is calculated using the following formula: In the formula, The initial quality score is dimensionless. This is the logarithmic coefficient for the concentration term, with a value of 15.2; This is the coefficient for the square root of the concentration term, with a value of 3.8; This is a constant term with a value of 20.5.

[0074] The specific implementation of step S02 involves establishing an antibody activity fitness index calculation model for quality assessment. Antibody activity retention rate. The antibody was obtained by enzyme-linked immunosorbent assay (ELISA), including the following steps: Step 1: preparing a graded dilution of the standard antigen, with a concentration range of 0.1-10 μg / mL; Step 2: incubating the antibody to be tested with the standard antigen at 37°C for 60 minutes; Step 3: measuring the binding signal intensity and calculating the activity retention rate. Specific binding rate. The antibody activity fitness index is obtained through Western blotting, including step 1: preparing target and non-specific protein samples; step 2: hybridization reaction of antibody with protein samples; and step 3: calculating the ratio of specific binding signal to total binding signal. The formula for calculating the antibody activity fitness index is: In the formula, This is a dimensionless index representing the fitness index of antibody activity. These are weighting coefficients, with values ​​of 0.4, 0.3, and 0.3 respectively. The half-maximal effect concentration is expressed in μg / mL. This is a dimensionless indicator of antibody stability. This represents the random error term, ranging from -0.05 to 0.05. The upper-level objective function of the game theory model is: In the formula, To optimize the objective function value for detection sensitivity; The coefficients of the objective function are 25.6, 8.3, 12.7, 15.2, and 6.9, respectively. This is the drying time parameter, in minutes; The parameter for the closing effect is dimensionless; The value represents the assembly pressure, expressed in MPa.

[0075] The specific implementation of step S03 involves optimizing the coating solution formulation using a stability control function. The coating solution clarity parameter... The transmittance was obtained through transmittance measurement, and the calculation formula is as follows: In the formula, Intensity of transmitted light; Let be the incident light intensity. The stability control function is: In the formula, This is the value of the stability control function; The control parameters are set to values ​​of 0.8, 1.2, 0.6, and 0.4, respectively. This is a dimensionless numerical value representing the coating precision. These are temperature control parameters, in °C. These are humidity control parameters, expressed in %; This is the systematic error term, ranging from -0.1 to 0.1. The formula for calculating the formulation optimization rate is: In the formula, To optimize the formula rate; For reference stability, the value is 85.0.

[0076] The specific implementation of step S04 involves evaluating the quality of the coated working fluid through turbidity measurement. The formula for calculating the turbidity index is: In the formula, Turbidity index, measured in turbidity units (NTU). The intensity of the scattered light; The incident light intensity is used for turbidity measurement. The turbidity index is obtained by measuring with a turbidimeter, including step 1: placing the coated working solution sample in a standard cuvette; step 2: measuring the scattered light intensity at a 90-degree scattering angle; and step 3: calculating the turbidity index and comparing it with the standard value of 0.1 NTU. The quality assessment function update formula is: In the formula, The updated quality score; The update coefficients are set to 8.6 and 5.3 respectively. To update the error term, the range is -0.8 to 0.8.

[0077] The specific implementation of step S05 involves using a piezoelectric inkjet system to achieve precision spraying and establishing a lower-level game objective function. Spraying accuracy numerical values. Obtained through positional deviation measurement, the calculation formula is as follows: In the formula, For the first The actual coordinates of each spray point; The target coordinates; The number of measurement points is set to 20. The maximum permissible deviation is set to 0.5 mm. The lower-level objective function of the game model is: ; In the formula, Optimize the objective function value to ensure process stability; The coefficients of the objective function are 18.4, 22.1, 9.7, 13.6, and 11.2, respectively. The reproducibility coefficient is calculated from the coefficient of variation of 10 consecutive batches of products and is dimensionless. Temperature control accuracy is obtained by measuring the standard deviation of the temperature control system, with the unit being °C. It is a batch consistency index, obtained by calculating the ratio of batch-to-batch variation to intra-batch variation using analysis of variance, and is dimensionless. The accuracy and stability of the equipment are obtained through stability assessment of the equipment calibration data, and are dimensionless.

[0078] The specific implementation of step S06 involves calculating the closure quality score using a quality assessment function. Closure effect parameters. The fluorescence labeling method was used for determination, including step 1: preparing a 50 μg / mL fluorescently labeled bovine serum albumin solution as a non-specific protein; step 2: incubating the blocked substrate with the fluorescent protein at 25°C for 30 minutes; and step 3: measuring the fluorescence intensity using a fluorescence microscope at an excitation wavelength of 488 nm and an emission wavelength of 525 nm. Step 4: Using the fluorescence intensity of the unsealed substrate To compare and calculate the closure efficiency The formula for calculating the closure quality score is: In the formula, The score is the quality score for the closed loop. The evaluation coefficients were set to 28.5, 15.7, and 12.3, respectively. The fluorescence intensity of the substrate after sealing; The fluorescence intensity of the unsealed substrate; This is the measurement error term, ranging from -2.0 to 2.0.

[0079] The specific implementation method of step S07 is the same as described above, and will not be repeated in detail here.

[0080] The specific implementation of step S08 involves establishing a multi-dimensional quality inspection system and calculating the final quality score. (Overlap index) The calculation formula is: In the formula, This is the actual overlap length, in mm; The theoretical overlap length is in mm. Integrity factor. The calculation formula is: In the formula, The number of defects; Total number of detection points, valued at 100. Location accuracy. The calculation formula is: In the formula, Positional deviation, in mm; The positional tolerance is set to 0.1 mm. The final quality score is calculated using the following formula: In the formula, For the final quality score; These are quality assessment coefficients, with values ​​of 22.8, 35.6, 18.9, 8.7, and 12.4, respectively. The efficiency parameter is dimensionless. This is the comprehensive error term, ranging from -1.5 to 1.5.

[0081] The specific implementation of step S09 involves using the Stackelberg game iterative algorithm to solve for the optimal parameter combination. The coupling constraint matrix of the game model is represented as follows: In the formula, The elements of the coupling coefficient matrix are 0.85, 0.23, 0.67, and 0.91, respectively. The change in the objective function between the upper and lower layers; The elements of the bias vector are 5.2 and 0.8, respectively. The convergence criterion for the iteration is: In the formula, This represents the current iteration step number; Indicates the next iteration step; Indicates the current iteration step; For the first The value of the upper-level objective function of the step; For the first The value of the lower-level objective function in the next step; To ensure convergence accuracy, a value of 0.001 is used. The iterative update formula is: In the formula, For the first The parameter vector of the step; For the first The updated parameter vector; The learning rate is 0.02. For the first The gradient vector of the objective function corresponding to the step parameter vector.

[0082] The specific implementation of step S10 is to establish a multi-parameter joint evaluation system to achieve product grading. The comprehensive score calculation formula is: In the formula, For comprehensive scoring; The total number of evaluation indicators is 6; For the first Weighting coefficients for each indicator; For the first The standardized values ​​of each indicator; For the first The importance coefficients for each indicator range from 0.8 to 1.2. The weight coefficient vector is as follows: ; In the formula, each weight corresponds to the detection sensitivity, batch-to-batch variation coefficient, repeatability error, stability index, accuracy parameter, and consistency index, respectively. The standardization formula is: In the formula, These are the original indicator values; These are the minimum and maximum values ​​of the indicator.

[0083] It should be noted that the Lambert-Beer law formula for calculating antibody concentration is based on the principle of light absorption law, achieving precise quantification of antibody concentration by establishing a linear relationship between absorbance and concentration. Compared with traditional gravimetric or volumetric methods, this formula enables rapid and non-destructive detection of trace samples, significantly improving the accuracy and efficiency of concentration determination and providing reliable basic data for subsequent process parameter optimization.

[0084] The quality assessment function takes the form of a polynomial combination, where the logarithmic terms... This reflects the saturation characteristic of the concentration effect, the square root term. This function reflects the nonlinear relationship between concentration and mass. Compared to a single linear evaluation model, it can more accurately reflect the interactions between various parameters in complex biological systems, enabling a comprehensive quantitative assessment of product quality and providing a scientific basis for quality control decisions.

[0085] Turbidity Index Calculation Formula Based on the Rayleigh scattering principle, the particulate matter content in a solution is assessed by measuring the intensity ratio of the scattered light at 90 degrees to the incident light. Compared to traditional visual observation methods, this mathematical model can quantify the detection of submicron-sized particles, providing a precise numerical standard for controlling the purity of the coating working solution and ensuring the stability and reproducibility of subsequent preparation processes.

[0086] The antibody activity fitness index calculation model uses weighted product terms. And logarithmic transformation term This combination comprehensively considers multiple dimensions of antibody binding activity, specificity, and stability. The product term reflects the synergistic effect of activity and specificity, while the logarithmic term transforms the reciprocal of the half-maximal effect concentration logarithmically, amplifying the advantages of high-affinity antibodies. Compared to traditional single-index evaluation methods, this model can more comprehensively assess antibody quality and effectively screen for high-performance capture antibodies.

[0087] The Stackelberg game model optimizes detection sensitivity and process stability in its upper and lower objective functions, respectively, achieving multi-objective collaborative optimization through a combination of nonlinear function terms. The exponential decay term in the upper function... The negative impact of excessive drying on detection performance was simulated, and the exponential term in the lower-level function was also considered. The importance of solution clarity to process stability is emphasized. Compared with traditional single-objective optimization methods, this game theory model can find the optimal balance between performance improvement and stability assurance, achieving global optimization of the preparation process.

[0088] The coupling constraint matrix establishes the mathematical relationship between upper and lower level decision variables, and describes the interdependence between antibody concentration and spraying accuracy through matrix operations. Iterative convergence criteria. The use of the Euclidean norm to evaluate function value changes ensures the stable convergence of the algorithm. Compared with empirical parameter tuning methods, this mathematical framework can systematically find the optimal solution, significantly improving the efficiency and accuracy of parameter optimization.

[0089] The multi-parameter joint evaluation function adopts a weighted linear combination form. The importance of each quality indicator is assigned through a weight vector, and the process is standardized. This system eliminates the influence between indicators with different dimensions. Compared with single-indicator judgment methods, this evaluation system can comprehensively reflect the overall quality level of products, providing scientific quantitative standards for product grading and quality management, and realizing the transformation from qualitative evaluation to quantitative evaluation.

[0090] It should be noted that this embodiment also solves the following four key technical problems:

[0091] First, this invention addresses the technical problem of a lack of unified standards for assessing the quality of extravesicle capture antibodies. Traditional methods often evaluate antibody quality solely through simple concentration or activity assays, lacking a comprehensive evaluation system. This invention establishes an antibody activity fitness index calculation model that comprehensively considers multiple key indicators such as antibody activity retention rate, specific binding rate, half-maximal effective concentration, and cross-reactivity coefficient. It establishes a standardized detection process using a series of gradient dilutions and recombinant antigen standards, and optimizes detection sensitivity through a game theory model's upper-level objective function. This establishes a complete technical chain from antibody preparation to quality assessment, ensuring that only antibody solutions with an activity retention rate greater than 95% and a specific binding rate greater than 90% can be used for subsequent preparation, thus guaranteeing the quality stability and detection accuracy of the test kit from the source.

[0092] Secondly, this invention solves the technical problem of controlling the uniformity and reproducibility of droplet distribution in the substrate coating process. Traditional coating or impregnation methods struggle to achieve precise application and uniform distribution of trace reagents. This invention employs a piezoelectric inkjet system, precisely controlling the operating voltage between 3 and 5V and the frequency between 2 and 4kHz to achieve precise control of droplet volume between 2 and 3 picoliters. By optimizing key parameters such as the distance between the printhead and the substrate, the coating amount, and the spraying spacing, a standardized spraying process flow is established. Combined with turbidity index detection and membrane filtration, the purity and uniformity of the coating working solution are ensured. The process stability is optimized in real time using a game theory model with a lower-level objective function, achieving high-precision application of trace reagents and high batch-to-batch reproducibility.

[0093] Third, this invention solves the technical problem of controlling positional accuracy and integrity during the assembly of multi-component substrates. Traditional assembly methods often rely on manual operation or simple mechanical pressing, which makes it difficult to guarantee the precise positioning and integrity between components. This invention establishes a quantitative evaluation system for overlap index, integrity coefficient, and positional accuracy, and uses digital microscopy to accurately measure assembly quality. By controlling the pressing pressure within a precise range of 0.3 to 0.5 MPa and the pressing time within a precise range of 3 to 5 seconds, and combining this with a quality evaluation function, a comprehensive evaluation of key parameters such as overlap index, integrity coefficient, and positional accuracy is established. This creates a full-process quality control system from component preparation to final assembly, ensuring that the overlap index is within the range of 0.8 to 1.2, the integrity coefficient is greater than 0.95, and the positional accuracy is greater than 0.9, significantly improving the assembly accuracy and reliability of the product.

[0094] Fourth, this invention solves the technical problem of simultaneously optimizing detection sensitivity and process stability. Traditional methods often employ a single-objective optimization strategy. Pursuing higher detection sensitivity can compromise process stability, while focusing on process stability can limit the improvement of detection performance. This invention establishes a multi-parameter coupled optimization system by constructing an upper-level model aimed at maximizing detection sensitivity and a lower-level game-theoretic model aimed at maximizing process stability. The upper-level model comprehensively considers factors such as antibody activity retention rate, specific binding rate, and antibody concentration through a detection sensitivity optimization function. The lower-level model incorporates factors such as spraying accuracy, coating solution clarity, and temperature control accuracy into a unified framework through a process stability optimization function. The two objective functions are coupled through antibody concentration and spraying accuracy, and the optimal balance point is found through game-theoretic iteration, achieving synergistic optimization of detection performance and process stability.

[0095] To better understand and implement this invention, the following is a specific application scenario of the invention, Example 2: The research team needs to prepare external vesicles for detecting prostate cancer. The rapid detection kit substrate for biomarkers first involves the preparation of extravesicle capture antibodies. The frozen antibody... The monoclonal antibody was slowly thawed at 4°C for 30 minutes to avoid denaturation caused by sudden temperature changes. The antibody was dissolved in phosphate buffer (pH 7.3), and the absorbance was measured at 280 nm using a spectrophotometer; the result was 0.428. Based on Beer-Lambert's law, the antibody concentration was calculated to be 12.6 μg / mL, meeting the concentration range requirement of 10 to 20 μg / mL. Substituting the antibody concentration value into the quality assessment function, an initial quality score of 78.3 was obtained.

[0096] The research team then established an antibody quality assessment system. Enzyme-linked immunosorbent assay (ELISA) was used to detect antibody activity, and recombinant antibodies with concentrations ranging from 0.1 to 10 μg / mL were prepared. A protein gradient dilution buffer was used to incubate the test antibody and standard antigen at 37°C for 60 minutes. The binding signal intensity was measured, and the activity retention rate was calculated to be 0.967. Antibody specificity was verified by Western blotting, and the target antibody was prepared. Protein and nonspecific , The specific binding rate of the antibody to the protein sample after hybridization was calculated to be 0.923. A serial dilution assay was established with a half-maximal effective concentration (MCP) of 2.4 μg / mL, and the antibody stability index was 0.89. Substituting these parameters into the antibody activity fitness index calculation model, a fitness index of 0.842 was obtained, indicating excellent antibody quality. Table 1 shows the detailed data recorded by the research team for antibody activity testing.

[0097] Table 1 Antibody Activity Detection Data

[0098]

[0099] The research team then prepared the coating solution formulation. Sucrose and bovine serum albumin were precisely added to phosphate buffer at a mass ratio of 2:1, with the sucrose mass fraction controlled at 2.1% and the albumin mass fraction at 0.8%. The solution was stirred at 300 rpm for 45 minutes at a constant temperature of 25°C to ensure complete dissolution of all components. The transmittance was measured at 600 nm using a spectrophotometer, and the clarity parameter of the coating solution was calculated to be 96.8%, meeting the requirement of over 95%. Substituting the clarity parameter into the stability control function, a stability control value of 4.32 was obtained, achieving a formulation optimization rate of 101.6%.

[0100] When preparing the coating working solution, the research team mixed the outer vesicle capture antibody and the coating solution at a volume ratio of 1:9 and vortexed the mixture at 1000 rpm for 90 seconds. After centrifugation at 5000 rpm for 10 minutes at 4°C, the turbidity index of the supernatant was measured to be 0.086 turbidity units, meeting the requirement of less than 0.1. The qualified supernatant was then filtered through a 0.22 μm sterile filter to obtain the coating working solution, which received an updated score of 106.7 points on the quality assessment function.

[0101] The R&D team employed a piezoelectric inkjet system for precision coating. The operating voltage was set to 4.2V, the frequency to 3.1kHz, the droplet volume to 2.6 picoliters, and the distance between the printhead and the substrate to be 1.8mm. The coating volume per aperture was set to 12μL, and the spraying interval to 1.1mm. The actual coordinate deviation of 20 spray points was measured using a position feedback system, and the coating accuracy was calculated to be 0.956. After coating, the coating was dried at a constant temperature of 37℃ for 52 minutes, and the drying time was recorded as 52 minutes.

[0102] The research team prepared a blocking solution for substrate treatment. 3.2% bovine serum albumin (BSA) was dissolved in phosphate buffer (pH 7.3) and treated with an ultrasonic processor at 40 kHz for 3 minutes. The substrate was then immersed at 22°C for 105 minutes, followed by rinsing three times with phosphate buffer for 8 minutes each time. The blocking effect was determined using a fluorescent labeling method. A 50 μg / mL fluorescently labeled BSA solution was prepared, and the blocked substrate and fluorescent protein were incubated at 25°C for 30 minutes. Fluorescence intensity was measured at an excitation wavelength of 488 nm and an emission wavelength of 525 nm. The fluorescence intensity of the blocked substrate was 1256, while that of the unblocked substrate was 8934. The blocking effect parameter was calculated to be 0.859. Substituting the relevant parameters into the blocking quality score formula, a blocking quality score of 45.8 was obtained.

[0103] The research team prepared the labeled antibody working solution. Biotin-labeled detection antibody was dissolved in phosphate buffer to a final concentration of 7.5 μg / mL. Horseradish peroxidase-labeled avidin was added to the labeled antibody solution to a final concentration of 1.6 μg / mL. The mixture was incubated at 6°C in the dark for 165 minutes, then mixed with the labeled antibody dilution buffer at a volume ratio of 1:9 and filtered through a 0.22 μm filter. The solution was then sprayed onto the detection area of ​​the substrate using an inkjet printing system and dried at 37°C for 75 minutes. The binding efficiency of the labeled antibody was determined by an enzyme activity assay, yielding a labeling efficiency parameter of 0.834.

[0104] The R&D team conducted substrate assembly and quality inspection. The substrate, sample pad, bonding pad, and absorbent pad were laminated sequentially using a pneumatic laminator at a pressure of 0.42 MPa for 4 seconds. The overlap of each component was inspected using a digital microscope; the actual overlap length was 4.7 mm, the theoretical overlap length was 5.0 mm, and the calculated overlap index was 0.94. Edge integrity was checked at 100x magnification, finding 3 defects out of a total of 100 inspection points, resulting in an integrity coefficient of 0.97. Positional deviation was detected using a coordinate measuring system, with a deviation of 0.067 mm and a calculated positional accuracy of 0.933. Table 2 shows the results of the process parameter optimization.

[0105] Table 2 Results of Process Parameter Optimization

[0106]

[0107] The R&D team implemented an iterative optimization process for the game theory model. The obtained parameter data was input into the game theory model for calculation; the upper-level objective function value was 78.4, and the lower-level objective function value was 89.6. By coupling the constraint matrix and using an iterative algorithm, the model converged after 7 iterations, yielding the optimal parameter combination. Figure 2 As shown, the iterative optimization process of the game theory model demonstrates the convergence trend of the objective functions at both the upper and lower levels. Figure 3 As shown, the variation of quality assessment parameters during the optimization process reflects the effectiveness of process improvement.

[0108] The R&D team established a multi-parameter joint evaluation system for product grading. Each quality indicator was standardized: detection sensitivity was standardized to 0.89, inter-batch variation coefficient to 0.92, repeatability error to 0.85, stability to 0.88, accuracy to 0.91, and consistency to 0.87. Importance coefficients were set to 1.1, 0.9, 1.0, 0.95, 1.05, and 0.85, respectively. The comprehensive score calculated using the multi-parameter joint evaluation method was 0.885, equivalent to 88.5 points on a percentage scale, classifying it as a Grade B qualified product. As shown in Table 3, the technical team compiled the final quality grading results.

[0109] Table 3 Statistical Table of Quality Grading Results

[0110]

[0111] The R&D team sealed qualified products in aluminum foil and stored them at 6°C in a light-protected environment. They also established a database of characteristic parameters to store production data and quality testing results. Through this implementation, the R&D team successfully prepared a high-quality substrate for the exovesicle detection kit, with product quality stability and detection sensitivity meeting the expected requirements. Figure 4 A distribution diagram of the multi-parameter joint evaluation is presented.

[0112] This invention represents a significant technological advancement over traditional preparation methods. Traditional methods rely primarily on empirical parameter tuning and single-objective optimization, easily falling into local optima and struggling to balance performance and stability. This invention, by constructing a Stackelberg game model, achieves synergistic optimization of detection sensitivity and process stability. While the upper-level model maximizes performance, the lower-level model uses stability constraints for feedback adjustment, resulting in a globally optimal parameter configuration. Traditional piezoelectric inkjet technology lacks precise mathematical modeling and real-time feedback control. This invention establishes a spraying accuracy evaluation system based on positional deviation measurement, achieving on-demand spraying of picoliter droplets through precise control of the piezoelectric effect, significantly improving the uniformity and reproducibility of reagent distribution. Traditional quality control relies mainly on post-processing detection and subjective judgment. This invention constructs a multidimensional quality evaluation function that models the interactions between parameters through nonlinear functional relationships, achieving precise quantitative evaluation of complex biological detection systems and providing a scientific mathematical basis for quality control decisions. Traditional preparation processes lack systematic data management and predictive capabilities. The feature parameter database established in this invention integrates historical production data and process parameters, enabling a shift from passive quality control to proactive quality prediction, providing data support for continuous process improvement.

[0113] It should be noted that the variables involved in this invention are explained in detail in Tables 4 and 5.

[0114] Table 4. Variable Explanation Table (Part 1)

[0115]

[0116] Table 5. Variable Explanation Table (Part Two)

[0117]

[0118] 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 changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for preparing a substrate for a rapid detection kit of functional biomarkers of exovesicles, characterized in that, include: The process includes: preparation of exovesicle capture antibodies, quality assessment of exovesicle capture antibodies, preparation of coating solutions, mixing and preparation of coating working solutions, precision spraying of coating working solutions, preparation of blocking solutions for substrate treatment, preparation of labeled antibody working solutions, substrate assembly and quality inspection, iterative optimization of a game model, establishment of a feature parameter database, and product grading. The iterative optimization of the game model involves inputting antibody activity retention rate, specific binding rate, antibody concentration, spraying accuracy, drying time, blocking effect, and assembly pressure into the game model for iterative calculation. The upper-level objective function optimizes detection sensitivity, while the lower-level objective function optimizes process stability. Iterative calculations are performed based on the coupling relationship until convergence, and the optimal parameter combination is output to guide production parameter adjustments. A multi-parameter joint evaluation method is used to calculate a comprehensive score for product grading, resulting in the preparation of substrates for a rapid detection kit of exovesicle functional biomarkers.

2. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 1, characterized in that, The steps for preparing the outer vesicle capture antibody are as follows: dissolve the specific antibody against the outer vesicle surface marker in phosphate buffer, adjust the pH to 7.2 to 7.4 to achieve a final antibody concentration of 10 to 20 μg / mL, use low temperature dissolution to avoid antibody denaturation, measure the antibody concentration using a spectrophotometer to obtain the antibody concentration value, and input the antibody concentration value into the quality assessment function to calculate the initial quality score.

3. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 2, characterized in that, The steps for quality assessment of extravesicle capture antibodies are as follows: antibody activity retention rate is obtained by detecting antibody activity using enzyme-linked immunosorbent assay (ELISA); specific binding rate is obtained by detecting antibody specificity using Western blotting; a gradient dilution series is established to calculate the half-maximal effective concentration using recombinant antigen as a standard; antibody activity retention rate and specific binding rate are processed by an antibody activity fitness index calculation model to output antibody quality grade score; and antibody solutions with an activity retention rate greater than 95% and a specific binding rate greater than 90% are selected.

4. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 3, characterized in that, The steps for preparing the coating solution are as follows: sucrose and albumin are added to phosphate buffer at a mass ratio of 2:1, so that the mass fraction of sucrose is 1% to 3% and the mass fraction of albumin is 0.5% to 1%. The mixture is magnetically stirred at a constant temperature for more than 30 minutes to ensure that all components are fully dissolved. The transmittance of the solution is measured to obtain the clarity parameter of the coating solution. The clarity parameter of the coating solution is then input into the stability control function to calculate the formulation optimization rate.

5. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 4, characterized in that, The steps for preparing the coating working solution are as follows: the outer vesicle capture antibody and the coating solution are mixed at a volume ratio of 1:9, vortexed at low speed for 1 to 2 minutes, centrifuged at 5000 r / min for 10 minutes to remove insoluble matter, and the turbidity index of the supernatant is measured. When the turbidity index is less than 0.1, the supernatant is filtered through a 0.22 μm filter membrane to obtain the coating working solution.

6. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 5, characterized in that, The steps for precision spraying of the coating working fluid are as follows: a piezoelectric inkjet system is used to apply the coating working fluid to the substrate, the working voltage is controlled at 3 to 5V, the frequency is set at 2 to 4kHz, the droplet volume is controlled at 2 to 3pL, the distance between the printhead and the substrate is maintained at 1.5 to 2.0mm, the spraying amount is set at 10 to 15μL per hole, the spraying spacing is 1.0 to 1.2mm, and drying is carried out at 37℃ for 45 to 60 minutes.

7. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 6, characterized in that, The steps for preparing the blocking solution to treat the substrate are as follows: dissolve 2% to 5% albumin in phosphate buffer and adjust the pH to 7.2 to 7.4; use ultrasound to treat for 2 to 5 minutes to improve the dissolution effect; soak the substrate at 20 to 25°C for 90 to 120 minutes; rinse three times with phosphate buffer for 5 to 10 minutes each time; and vacuum dry at 37°C for 30 to 60 minutes.

8. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 7, characterized in that, The steps for preparing the labeled antibody working solution are as follows: dissolve the biotin-labeled detection antibody in phosphate buffer to a final concentration of 5 to 10 μg / mL, add horseradish peroxidase-labeled avidin to the labeled antibody solution to a final concentration of 1 to 2 μg / mL, incubate at 4 to 8°C in the dark for 150 to 180 min, mix with the labeled antibody dilution solution at a volume ratio of 1:9, filter through a 0.22 μm filter membrane, spray onto a substrate using an inkjet system, and then dry at 37°C for 60 to 90 min.

9. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 8, characterized in that, The steps for substrate assembly and quality inspection are as follows: the substrate and glass pad are pressed together in sequence, with a pressing pressure of 0.3 to 0.5 MPa and a pressing time of 3 to 5 seconds. The overlap index is obtained by detecting the overlap between components, the integrity coefficient is obtained by detecting the integrity of components, and the position accuracy is obtained by detecting the position accuracy. The overlap index, integrity coefficient, position accuracy, assembly pressure value, and marking efficiency parameter are input into the quality evaluation function to calculate the final quality score.

10. The method for preparing the substrate of the rapid detection kit for functional biomarkers of exovesicles according to claim 9, characterized in that, The steps for establishing a feature parameter database and classifying products are as follows: the final quality score and the optimal parameter combination are stored in the feature parameter database; a multi-parameter joint evaluation method is used to calculate the comprehensive score; when the comprehensive score is greater than or equal to 90 points, it is judged as a grade A product; when the comprehensive score is between 75 and 90 points, it is judged as a grade B product; when the comprehensive score is less than 75 points, it is judged as a substandard product; and qualified products are stored at 4 to 8℃ in the dark.

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