Assessment method for measuring uncertainty of immune factors in serum by ELISA (Enzyme-Linked Immunosorbent Assay) method

Abstract

The invention belongs to the technical field of detection, and particularly discloses a measurement uncertainty evaluation method for measuring immune factors in serum by an ELISA (enzyme-linked immuno sorbent assay) method. According to the method, ELISA detection is carried out on immune factors (such as IL-1beta) in serum of an experimental animal in contact with a medical instrument according to standards such as GB / T16886.20-2015, system analysis comprises nine types of uncertainty sources such as repeated detection, standard substance preparation and sample dilution, the standard uncertainty of each component is calculated through mathematical methods such as rectangular distribution and a least square method, and the standard uncertainty of each component is calculated according to the standard uncertainty. And synthesizing the standard uncertainty and calculating the expansion uncertainty, and finally outputting a standard detection result. The method comprehensively covers the uncertainty source, is rigorous in computational logic and clear in key quality control factors, provides a scientific and complete uncertainty evaluation scheme for medical instrument immunogenicity evaluation and immune factor determination in other experimental scenes, is beneficial to laboratory quality control and detection result optimization, and has wide practicability.

Description

Technical Field

[0001] This invention belongs to the field of detection technology, specifically relating to a method for evaluating the measurement uncertainty of immune factors in serum by ELISA. It is applicable to the quality control of serum immune factor detection in the immunogenicity evaluation of medical devices, and is particularly applicable to the immunogenicity assessment of animal-derived medical products, allogeneic products and tissue-engineered medical products. Background Technology

[0002] The immune response is a crucial defense mechanism of the body. Medical devices, as exogenous substances, influence the body's immune response through various pathways after contact with the human body, particularly animal-derived medical products, allogeneic products, and tissue-engineered medical devices. While the interaction between medical devices / materials and the immune system can produce different immune responses, they can be broadly categorized into two types: humoral immune responses and cell-mediated immune responses. Currently, it is impossible to determine whether the immune response stimulated by medical devices or materials is beneficial or harmful to the host; therefore, conducting immune response studies using medical devices / materials to obtain relevant information is extremely important.

[0003] Serum immune factor levels can reveal the body's humoral immune status and reflect the level of humoral immune response. This can be used to predict whether medical devices contain potential immunogens, whether they will cause changes in immune-related cytokines in mouse serum, and subsequent immune responses. Currently, methods for evaluating the immunogenicity of medical devices are relatively mature; however, research on methods for assessing the measurement uncertainty of immune factors in serum is limited, and the analysis of uncertainty sources is incomplete. Therefore, this application provides a method for assessing the measurement uncertainty of immune factors in serum using an ELISA method. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method for assessing the measurement uncertainty of immune factors in serum using the ELISA method, thereby solving the technical problems of incomplete uncertainty source analysis and non-standard calculation logic in existing assessment methods.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for evaluating the measurement uncertainty of immune factors in serum using an ELISA method includes the following steps: (1) Serum sample preparation: According to YY / T1465.2-2016, GB / T16886.20-2015 or ISO / TS10993-20:2006 standards, the experimental animals were immunized with the tested medical device samples. After immunization, the serum was collected and diluted for later use. (2) Preparation of standard solutions: Prepare a series of concentrations of immune factor standards, wherein the immune factors include IL-1β; (3) ELISA detection: Following the procedure in the ELISA kit instructions, the diluted serum sample was incubated with a series of standards in sequence, followed by antibody binding, color development, and termination of the reaction. The absorbance value was then detected using an enzyme-linked immunosorbent assay (ELISA) reader. (4) Analysis of sources of uncertainty: Establishing a mathematical model ρ xi =ρ x0 ×N(ρ xi ρ represents the concentration of immune factors in the original sample. x0 To detect the concentration of immune factors in a sample (N is the dilution factor), we analyzed Type A uncertainty (repeated sample detection) and Type B uncertainty. The Type B uncertainty includes at least the uncertainty introduced by standard preparation, sample dilution, pipette loading, incubation conditions, microplate reader error, and standard curve fitting. (5) Uncertainty calculation: Calculate the standard uncertainty of each uncertainty component, combine the standard uncertainty, determine the coverage factor according to the confidence level, and calculate the expanded uncertainty; (6) Results output: The content of immune factors in serum is output in the form of "mean ± expanded uncertainty", and the main influencing factors are analyzed.

[0006] Preferably, the experimental animals in step (1) are mice, the immunization cycle is 21-45 days, the serum dilution factor is 5-20 times, the number of parallel samples is ≥10, and the serum is collected by orbital blood collection.

[0007] Preferably, in step (2), the standard solution is prepared using a combination of a 1mL pipette and a 100μL pipette. The maximum permissible error of the 1mL pipette is ≤100μL, and the maximum permissible error of the 100μL pipette is ≤2μL. The laboratory temperature is controlled at 18-25℃, and the temperature fluctuation is ≤±4℃.

[0008] Preferably, the ELISA detection process in step (3) includes: adding 100 μL of sample / standard → incubating at 37℃ for 90 min → discarding the solution → adding 100 μL of antibody → incubating at 37℃ for 60 min → washing the plate → adding 100 μL of ABC working solution → incubating at 37℃ for 30 min → washing the plate → adding 90 μL of TMB chromogenic solution → incubating at 37℃ for 20 min → adding 100 μL of TMB stop solution → detection by microplate reader.

[0009] Preferably, the Type B uncertainty mentioned in step (4) includes: the uncertainty u introduced by the preparation of the standard. B1 Uncertainty u introduced by sample dilution B2 Uncertainty u introduced by pipette addition during incubation process B3 Uncertainty u introduced by incubation timeB4 Uncertainty u introduced by incubation temperature B5 Uncertainty u introduced by the error of the ELISA reader reading B6 Uncertainty u introduced by standard curve fitting B7 Uncertainty u introduced by absorbance fluctuations in the blank control B8 and the uncertainty u introduced by data rounding B9 .

[0010] Preferably, in step (5), each Type B uncertainty component is calculated according to a rectangular distribution, with a coverage factor k = The standard curve fitting uses the least squares method, and the linear regression equation is Y = bρ + a. The standard uncertainty u(L) introduced by the standard curve fitting is calculated using the following formula: Where S is the standard deviation of the standard curve variability, p is the number of measurements of the sample, and n is the total number of measurements at each calibration point of the standard curve. ρ represents the average concentration of immune factors in a serum sample. i These are the measured values ​​of immune factor concentrations in a series of standard solutions. Yi represents the average concentration at each calibration point of the standard curve, and Yi represents the absorbance value at each point of the series of standard working solutions.

[0011] Preferably, in step (5), the relative standard uncertainty u is synthesized. rel Calculate using the following formula: Where u rel (A) represents the type A relative standard uncertainty, u rel B1) to u rel (B9) represents the relative standard uncertainty of each Type B uncertainty component; with a confidence level of 95%, coverage factor k=2, and expanded uncertainty U=k×u(ρ xi ), u(ρ xi ) represents the combined standard uncertainty.

[0012] Preferably, the preparation of the series of standards in step (2) includes: preparing a 10000 pg / mL stock solution, and then diluting it sequentially to obtain a series of concentrations of 800 pg / mL, 200 pg / mL, 100 pg / mL, 50 pg / mL, 25 pg / mL, and 12.5 pg / mL. During the dilution process, a pipette is used to transfer the solution, and the transfer volume includes 1000 μL, 920 μL, 300 μL, and 80 μL.

[0013] Preferably, in step (5), the Type A standard uncertainty u A Calculate using the following formula: Where S ρi denoted as , where is the standard deviation of the concentration of immune factors in the original sample, and n is the number of parallel samples.

[0014] Preferably, the tested medical device sample includes injectable cross-linked sodium hyaluronate gel, with an absorbance reading error of ≤0.01A for the microplate reader, an incubator expansion uncertainty U=0.4℃ (k=2), and a timer expansion uncertainty U=1s (k=2).

[0015] Compared with the prior art, the present invention has at least the following advantages: 1. This invention comprehensively analyzes nine sources of uncertainty in the ELISA detection of immune factors in serum. Compared with existing methods, it is more systematic and complete, avoiding distortion of evaluation results due to omission of key error factors. It clarifies the calculation logic and formulas of each uncertainty component, adopts scientific mathematical methods such as rectangular distribution and least squares method, and the calculation process is standardized and traceable, improving the accuracy and reliability of measurement uncertainty evaluation.

[0016] 2. This invention identifies key influencing factors through component contribution analysis, providing targeted guidance for experimental operation optimization and laboratory quality control. It can effectively reduce detection errors and improve the comparability and credibility of test results. It is adapted to the core requirements of immunogenicity evaluation of medical devices and can be widely applied to ELISA detection scenarios for various immune factors, possessing broad practicality and promotional value. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0018] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0019] The present invention will now be described in further detail. It should be noted that the following specific embodiments are only used to further illustrate the present invention and should not be construed as limiting the scope of protection of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention provides a general and / or specific description of the materials and experimental methods used in the experiments. Unless otherwise specified, all experimental or testing methods are conventional methods; all reagents and instruments used, unless otherwise specified, are commercially available conventional products prepared or used using conventional methods, and unless otherwise specified, the raw materials used in parallel experiments are from the same batch.

[0021] A method for evaluating the measurement uncertainty of immune factors in serum using an ELISA method includes the following steps: I. Serum Sample Preparation Following the principles of YY / T1465.2-2016, GB / T16886.20-2015, or ISO / TS10993-20:2006 standards, mice were selected as experimental animals. The tested medical device sample (such as cross-linked sodium hyaluronate gel for injection) was subcutaneously injected into the back of each mouse, with a total of 10 replicates. After 30 days of immunization, orbital blood was collected from each mouse, serum was separated, and 100 μL of the original serum solution was diluted to 1000 μL (dilution factor N=10) with sample diluent for later use.

[0022] II. Preparation of Standard Solutions 1. To prepare a 10000 pg / mL standard: Use a 1 mL pipette to add 1 mL of sample diluent to the standard tube, cover it, and let it stand for at least 10 minutes. Repeatedly invert / rub the tube to aid dissolution. 2. Prepare 800 pg / mL standard: Use a 100 μL pipette to add 0.08 mL of 10000 pg / mL standard to an Ep tube, and use a 1 mL pipette to add 0.92 mL of sample diluent, mix well and label. 3. Preparation of 200 pg / mL → 12.5 pg / mL standard: Prepare 5 Eppendorf tubes. Use a 1 mL pipette to add 0.3 mL of sample diluent to each tube and label them as 200 pg / mL, 100 pg / mL, 50 pg / mL, 25 pg / mL, and 12.5 pg / mL, respectively. Use a 1 mL pipette to add 0.3 mL of the 800 pg / mL standard to the tube labeled 200 pg / mL. Mix well, then use a 1 mL pipette to add 0.3 mL to the next tube, and so on until the last sample tube.

[0023] During preparation, a 1 mL pipette and a 100 μL pipette were used together. The maximum permissible error for the 1 mL pipette was 10 μL, and the maximum permissible error for the 100 μL pipette was 2 μL. The solvent for the sample diluent was water, and the coefficient of thermal expansion of water at 20°C was 2.1 × 10⁻⁻⁻⁻⁶. 4 / ℃, and the laboratory temperature is controlled within the range of (20±4)℃.

[0024] III. ELISA Testing Perform the test according to the instructions in the ELISA kit (IL-1β). The specific steps are as follows: 1. Sample addition: Add 100 μL of sample / standard to the wells of the ELISA plate using a 100 μL pipette; 2. First incubation: Incubate at 37℃ for 90 minutes, then discard the solution; 3. Add antibody: Add 100 μL of antibody using a 100 μL pipette, incubate at 37°C for 60 min, and then wash the plate; 4. Add ABC working solution: Add 100 μL of ABC working solution using a 100 μL pipette, incubate at 37°C for 30 min, and then wash the plate; 5. Color development: Add 90 μL of TMB color development solution using a 100 μL pipette and incubate at 37°C for 20 min; 6. Termination and detection: Add 100 μL of TMB stop solution using a 100 μL pipette, and detect the absorbance value using an ELISA reader.

[0025] Therefore, the standard sample testing results are shown in Table 1 below; Table 1. Test Results of Standard Samples The sample test results are shown in Table 2 below; To obtain the repeatability measurement uncertainty, 10 parallel samples were set up, and the absorbance measurement value of the sample was OD. x0 The absorbance value after subtracting the blank well is OD. xi The IL-1β content in the tested sample was ρ x0 (pg / mL), the IL-1β content in the original sample was ρ xi (pg / mL), average IL-1β content in the original sample (pg / mL), standard deviation S ρi Standard uncertainty u A and relative standard uncertainty u A1 .

[0026] Table 2 Serum sample test results IV. Analysis of Uncertainty Sources 1. Mathematical model: ρ xi =ρ x0 ×N; where ρ xi The concentration of IL-1β in the original sample (pg / mL), ρ x0 To detect the concentration (pg / mL) of IL-1β in the sample, N is the sample dilution factor; 2. Type A uncertainty: Uncertainty introduced by repeated sample testing (u) A ), calculated based on the test results of 10 parallel samples; 3. Type B uncertainties: These include 9 components, namely: (1) Uncertainty u introduced by the preparation of standard products B1 ; (2) Uncertainty u introduced by sample dilution B2 ; (3) Uncertainty u introduced by pipette addition during incubation B3 ; (4) Uncertainty u introduced by incubation time B4 ; (5) Uncertainty u introduced by incubation temperature B5 ; (6) Uncertainty u introduced by the ELISA reader B6 ; (7) Uncertainty u introduced by standard curve fitting B7 ; (8) Uncertainty u introduced by blank control B8 ; (9) Uncertainty u introduced by data rounding B9 .

[0027] V. Evaluation of Standard Uncertainty of Each Component 1. Type A Uncertainties The relative standard uncertainty introduced by repeated sample testing is calculated as follows: Table 1 shows the average IL-1β content in the original samples from the 10 parallel samples. =176.6666641 pg / mL, standard deviation S ρxi =8.6315 pg / mL, number of parallel samples n=10, then: Standard uncertainty relative standard uncertainty 2. Type B Uncertainties (1) The relative standard uncertainty u introduced by the preparation of a series of standard products B1 The uncertainty in the preparation of standard solutions is introduced by the volumetric error of the pipette and the temperature effect, following a rectangular distribution with a coverage factor k = √3. The maximum permissible error for a 1 mL pipette is 10 μL, and the maximum permissible error for a 100 μL pipette is 2 μL. The solvent for the sample diluent is water, and the coefficient of thermal expansion of water at 20°C is 2.1 × 10⁻⁶. -4 / ℃, the laboratory temperature fluctuates within the range of (20±4)℃. During the preparation process, 1000μL of solution is transferred once using a 1mL pipette, 920μL of solution is transferred once using a 1mL pipette, 300μL of solution is transferred 10 times using a 1mL pipette, and 80μL of solution is transferred twice using a 100μL pipette.

[0028] Uncertainty introduced by using a 1 mL pipette to transfer 1000 μL of solution in a single transaction: The relative standard uncertainty caused by capacity error: Relative standard uncertainty caused by temperature effect: The relative standard uncertainty introduced by using a 1 mL pipette to transfer 1000 μL of solution once is: Uncertainty introduced by using a 1 mL pipette to transfer 920 μL of solution in a single transaction: The relative standard uncertainty caused by capacity error: Relative standard uncertainty caused by temperature effect: The relative standard uncertainty introduced by using a 1 mL pipette to transfer 920 μL of solution once is: Uncertainty introduced by using a 1 mL pipette to transfer 300 μL of solution in a single transaction: The relative standard uncertainty caused by capacity error: Relative standard uncertainty caused by temperature effect: The relative standard uncertainty introduced by using a 1 mL pipette to transfer 300 μL of solution once is: Uncertainty introduced by using a 100 μL pipette to transfer 80 μL of solution in a single operation: The relative standard uncertainty caused by capacity error: Relative standard uncertainty caused by temperature effect: The relative standard uncertainty introduced by transferring 80 μL of solution once using a 100 μL pipette is: In summary, the preparation of the series of standards involved using a 1mL pipette to transfer 1000μL of solution once, 920μL of solution once, and 300μL of solution ten times, and using a 100μL pipette to transfer 80μL of solution twice. Therefore, uB1 is: (2) Uncertainty u introduced by sample dilution B2 The uncertainty in the sample dilution process is introduced by pipette volume error and temperature effect, and follows a rectangular distribution with a coverage factor k= The procedure involved adding 100 μL of sample using a 100 μL pipette, with a maximum permissible error of 2 μL for the 100 μL pipette. A 900 μL sample diluent was added using a 1 mL pipette, with a maximum permissible error of 10 μL for the 1 mL pipette. The serum sample and sample diluent were primarily composed of water, which has a coefficient of thermal expansion of 2.1 × 10⁻⁶ at 20°C. -4 / ℃, the laboratory temperature fluctuates within the range of (20±4)℃.

[0029] Uncertainty introduced by using a 100 μL pipette to transfer 100 μL of solution in a single operation The relative standard uncertainty caused by its capacity error is: The relative standard uncertainty caused by its temperature effect is: The relative standard uncertainty introduced by using a 100 μL pipette to transfer 100 μL of solution once is: ② Uncertainty introduced by using a 1 mL pipette to transfer 900 μL of solution in one operation The relative standard uncertainty caused by its capacity error is: The relative standard uncertainty caused by its temperature effect is: The relative standard uncertainty introduced by using a 1 mL pipette to transfer 900 μL of solution once is: In summary, the relative standard uncertainty u introduced by sample dilution B2 for: (3) Uncertainty u introduced by pipette addition during incubation B3 The uncertainty during incubation is mainly introduced by the volume error of the pipette and the temperature effect, both of which are considered to be rectangular distributions with a coverage factor k= The maximum permissible error for a 1 mL pipette is 10 μL, and for a 100 μL pipette, it is 2 μL. The solvent for the sample diluent is water, whose coefficient of thermal expansion at 20°C is 2.1 × 10⁻⁶. -4 / ℃, and the laboratory temperature fluctuated within the range of (20±4)℃. The procedure involved using a 100μL pipette to transfer 100μL of solution four times, and using a 100μL pipette to transfer 90μL of solution once.

[0030] ① Uncertainty introduced by using a 100μL pipette to transfer 100μL of solution in one transaction The relative standard uncertainty caused by its capacity error is The relative standard uncertainty caused by its temperature effect is The relative standard uncertainty introduced by using a 100 μL pipette to transfer 100 μL of solution once is: ② Uncertainty introduced by using a 100μL pipette to transfer 90μL of solution in one transaction The relative standard uncertainty caused by its capacity error is The relative standard uncertainty caused by its temperature effect is The relative standard uncertainty introduced by transferring 90 μL of solution once using a 100 μL pipette is: In summary, the relative standard uncertainty u introduced by the preparation of a series of standard products B3 for (4) Uncertainty u introduced by incubation time B4 ; This experiment consisted of four incubation processes, with incubation times of 90 min, 60 min, 30 min, and 20 min, respectively. The expanded uncertainty of the calibrated timer was U = 1 s (k = 2).

[0031] ① The relative standard uncertainty introduced by incubation for 90 minutes is: ②The relative standard uncertainty introduced by incubation for 60 minutes is ③ The relative standard uncertainty introduced by incubation for 30 minutes is ④ The relative standard uncertainty introduced by incubation for 20 minutes is The relative standard uncertainty u introduced by incubation time B4 for (5) Uncertainty u introduced by incubation temperature B5 This experiment consisted of four incubation processes, all at a temperature of 37℃. The calibrated incubator's expanded uncertainty was U=0.4℃ (k=2).

[0032] The relative standard uncertainty u introduced by the incubation temperature B5 for (6) Uncertainty u introduced by the ELISA reader B6 The absorbance reading error stated on the ELISA reader calibration certificate is -0.01A. Considering a rectangular distribution, its relative standard uncertainty is... (7) Uncertainty u introduced by standard curve fitting B7 A series of standard solutions were tested using an ELISA reader, with each concentration point measured once, for a total of 5 concentration points. One blank solution was also measured simultaneously. The concentration was plotted on the x-axis, and the absorbance of each concentration point minus the absorbance of the blank solution was plotted on the y-axis. The least squares method was used to fit the linear regression equation, yielding Y = bρ + a, where b = 0.0036, a = 0.0345, and r = 0.9987.

[0033] The formula for calculating the standard deviation S of the standard curve variability is as follows: Substituting the data, we get S≈0.0086; Average concentration at each calibration point of the standard curve The calculation formula is as follows: The formula for calculating the standard uncertainty u(L) introduced by standard curve fitting is as follows: In the formula: The standard deviation introduced by the variability of the S-standard curve; p - the number of measurements of the sample being tested, p=10; n - Total number of measurements at each calibration point of the standard curve, n=5; -The average concentration of IL-1β in serum samples, pg / mL; =176.6666641 pg / mL; ρi - The measured concentration of IL-1β in a series of standard solutions, pg / mL; - The average concentration at each calibration point of the standard curve, in pg / mL; =77.5 pg / mL; The absorbance values ​​at various points corresponding to the Yi-series standard working solutions; ∑i=1n(ρ i - ) 2 =28125 Substituting the data into Table 1, we calculate u(L) = 13.7245 pg / mL, with a relative standard uncertainty of [missing value]. (8) Uncertainty u introduced by blank control B8 The absorbance value of the blank control was 0.0935, with a fluctuation range of ±0.001A, a rectangular distribution, and a coverage factor k= Relative standard uncertainty (9) Uncertainty u introduced by data rounding B9 The test results are rounded to four significant figures, with a rounding error of ±0.00005. The results are distributed in a rectangular pattern, with a coverage factor k= Relative standard uncertainty VI. Combined Standard Uncertainty The uncertainty components are summarized in the following table: The experimental results were combined, and the composite relative standard uncertainty was: =0.121456091 The combined standard uncertainty is: u(ρ xi )= ×u rel =176.6666641×0.121456091≈21.46pg / mL VII. Expanded Uncertainty The measurement results conform to a normal distribution. At a confidence level of 95% and a coverage factor k=2, the expanded uncertainty is: U=k×u(ρ xi = 2 × 21.46 ≈ 42.92 pg / mL VIII. Results Output and Analysis of Influencing Factors The serum IL-1β level was determined by enzyme-linked immunosorbent assay (ELISA), and the result was expressed as (176.67±42.32) pg / mL, k=2.

[0034] A comparison of the uncertainty components reveals that the factors significantly impacting uncertainty are the uncertainty introduced by standard curve fitting (relative contribution ≈ 44%) and the uncertainty introduced by standard preparation (relative contribution ≈ 29%). In subsequent testing, measures such as increasing the number of calibration points and rationally distributing concentrations, performing repeated measurements at each calibration point, strictly controlling the standard preparation process, correctly calculating the fitting uncertainty, and using long-term data to determine whether batch-to-batch variability has been introduced can reduce the uncertainty introduced by standard curve fitting.

[0035] The beneficial effects of this invention are: 1. This invention comprehensively analyzes nine sources of uncertainty in the ELISA detection of immune factors in serum. Compared with existing methods, it is more systematic and complete, avoiding distortion of evaluation results due to omission of key error factors. It clarifies the calculation logic and formulas for each uncertainty component, adopts scientific mathematical methods such as rectangular distribution and least squares method, and the calculation process is standardized and traceable, improving the accuracy and reliability of measurement uncertainty evaluation. 2. This invention identifies key influencing factors through component contribution analysis, providing targeted guidance for experimental operation optimization and laboratory quality control. It can effectively reduce detection errors and improve the comparability and credibility of test results. It is adapted to the core requirements of immunogenicity evaluation of medical devices and can be widely applied to ELISA detection scenarios for various immune factors, possessing broad practicality and promotional value.

[0036] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for evaluating the measurement uncertainty of immune factors in serum using an ELISA method, characterized in that, Includes the following steps: (1) Serum sample preparation: According to YY / T1465.2-2016, GB / T16886.20-2015 or ISO / TS10993-20:2006 standards, the experimental animals were immunized with the tested medical device samples. After immunization, the serum was collected and diluted for later use. (2) Preparation of standard solutions: Prepare a series of concentrations of immune factor standards, wherein the immune factors include IL-1β; (3) ELISA detection: Following the procedure in the ELISA kit instructions, the diluted serum sample was incubated with a series of standards in sequence, followed by antibody binding, color development, and termination of the reaction. The absorbance value was then detected using an enzyme-linked immunosorbent assay (ELISA) reader. (4) Analysis of sources of uncertainty: Establishing a mathematical model ρ xi =ρ x0 ×N, analyze Type A and Type B uncertainties, wherein the Type B uncertainties include at least the uncertainties introduced by standard preparation, sample dilution, pipette loading, incubation conditions, microplate reader error, and standard curve fitting; (5) Uncertainty calculation: Calculate the standard uncertainty of each uncertainty component, combine the standard uncertainty, determine the coverage factor according to the confidence level, and calculate the expanded uncertainty; (6) Results output: The serum immune factor content is output in the form of "mean ± expanded uncertainty" and the main influencing factors are analyzed.

2. The measurement uncertainty evaluation method according to claim 1, characterized in that, The experimental animals mentioned in step (1) are mice, the immunization cycle is 21-45 days, the serum dilution factor is 5-20 times, the number of parallel samples is ≥10, and the serum collection method is orbital blood collection.

3. The measurement uncertainty evaluation method according to claim 1, characterized in that, In step (2), the standard solution is prepared using a 1mL pipette and a 100μL pipette. The maximum permissible error of the 1mL pipette is ≤10μL, and the maximum permissible error of the 100μL pipette is ≤2μL. The laboratory temperature is controlled at 18-25℃, and the temperature fluctuation is ≤±4℃.

4. The measurement uncertainty evaluation method according to claim 1, characterized in that, The ELISA detection process in step (3) includes the following steps in sequence: adding 100 μL of sample / standard; incubating at 37℃ for 90 min; discarding the solution; adding 100 μL of antibody; incubating at 37℃ for 60 min; washing the plate; adding 100 μL of ABC working solution; incubating at 7℃ for 30 min; washing the plate; adding 90 μL of TMB chromogenic solution; incubating at 37℃ for 20 min; adding 100 μL of TMB stop solution; and detecting with an enzyme-linked immunosorbent assay (ELISA) reader.

5. The measurement uncertainty evaluation method according to claim 1, characterized in that, The Type B uncertainty mentioned in step (4) includes: the uncertainty u introduced by the preparation of the standard sample. B1 Uncertainty u introduced by sample dilution B2 Uncertainty u introduced by pipette addition during incubation process B3 Uncertainty u introduced by incubation time B4 Uncertainty u introduced by incubation temperature B5 Uncertainty u introduced by the error of the ELISA reader reading B6 Uncertainty u introduced by standard curve fitting B7 Uncertainty u introduced by absorbance fluctuations in the blank control B8 and the uncertainty u introduced by data rounding B9 .

6. The measurement uncertainty evaluation method according to claim 1, characterized in that, In step (5), each Type B uncertainty component is calculated according to a rectangular distribution, and the coverage factor k = The standard curve fitting uses the least squares method, and the linear regression equation is Y = bρ + a. The standard uncertainty u(L) introduced by the standard curve fitting is calculated using the following formula: Where S is the standard deviation of the standard curve variability, p is the number of measurements of the sample, and n is the total number of measurements at each calibration point of the standard curve. ρ represents the average concentration of immune factors in a serum sample. i These are the measured values ​​of immune factor concentrations in a series of standard solutions. Yi represents the average concentration at each calibration point of the standard curve, and Yi represents the absorbance value at each point of the series of standard working solutions.

7. The measurement uncertainty evaluation method according to claim 1, characterized in that, In step (5), the relative standard uncertainty u is synthesized. rel Calculate using the following formula: Where u rel (A) represents the type A relative standard uncertainty, u rel( B1) to u rel (B9) represents the relative standard uncertainty of each Type B uncertainty component; with a confidence level of 95%, coverage factor k=2, and expanded uncertainty U=k×u(ρ xi ), u(ρ xi ) represents the combined standard uncertainty.

8. The measurement uncertainty evaluation method according to claim 3, characterized in that, The preparation of the series of standards in step (2) includes: preparing a 10000 pg / mL stock solution, and then diluting it sequentially to obtain a series of concentrations of 800 pg / mL, 200 pg / mL, 100 pg / mL, 50 pg / mL, 25 pg / mL, and 12.5 pg / mL. During the dilution process, a pipette is used to transfer the solution, and the transfer volumes include 1000 μL, 920 μL, 300 μL, and 80 μL.

9. The method for evaluating measurement uncertainty according to claim 1, characterized in that, In step (5), the Type A standard uncertainty u A Calculate using the following formula: Where S ρi denoted as , where is the standard deviation of the concentration of immune factors in the original sample, and n is the number of parallel samples.

10. The method for evaluating measurement uncertainty according to claim 1, characterized in that, The tested medical device samples included injectable cross-linked sodium hyaluronate gel, with an absorbance reading error of ≤0.01A for the microplate reader, an incubator expansion uncertainty U=0.4℃ (k=2), and a timer expansion uncertainty U=1s (k=2).

Images