Medical simulation experiment method and device for acting apoptosis vesicles on primary cartilage cells based on cell line culture

Abstract

The invention relates to a medical simulation experiment method and device for acting apoptosis vesicles on primary cartilage cells based on cell line culture, and the method mainly comprises the following steps: S1, co-culture system construction, S2, multi-dimensional observation and analysis, and S3, operation risk assessment. In the step S1, primary cartilage cells, primary immune cells and cell line culture apoptosis vesicles are co-cultured; s2, carrying out multi-dimensional observation and analysis on the influence of apoptosis vesicles on cells; and S3, mainly constructing a multi-dimensional risk scoring system, and carrying out risk grade division by applying a biological network model. The invention aims at simulating and evaluating the influence of cell line culture apoptosis vesicles on primary cartilage cells in vitro and the interaction between the cell line culture apoptosis vesicles and primary immune cells, so that an experimental basis is provided for subsequent clinical application.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical engineering, and particularly to a medical simulation experiment method and device for culturing apoptotic vesicles from cell lines and acting on primary chondrocytes. Background Art

[0002] Osteoarthritis (OA) is a common degenerative joint disease that seriously endangers human health and is characterized by synovial inflammation and cartilage degeneration. Due to its complex pathogenesis that has not been fully elucidated, current clinical treatments mainly focus on alleviating symptoms and it is difficult to stop the progression of the disease, and ultimately joint replacement is often required. Cell therapy, especially chondrocyte transplantation, is a new direction for OA treatment. However, the transplanted chondrocytes are prone to apoptosis and release apoptotic vesicles (ApoVs). ApoVs were initially considered as by-products of cell apoptosis, but studies have shown that they have potential application value in the treatment of osteoarthritis. ApoVs are a special type of extracellular vesicles with advantages such as simple preparation and high yield. The sources of apoptotic vesicles are diverse, but there are limitations in the yield of ApoVs from primary and stem cells. In contrast, ApoVs from cell lines can be prepared on a large scale to meet the needs of research and clinical applications. However, ApoVs from cell lines may differ from primary ApoVs, and long-term in vitro culture may lead to changes in cell lines, thereby affecting the biological characteristics of ApoVs. This difference has raised concerns about safety and effectiveness. Currently, there is a lack of systematic and comprehensive in vitro evaluation methods for the application of ApoVs from cell lines in the treatment of osteoarthritis, especially in terms of safety risk assessment. Summary of the Invention

[0003] In order to solve the technical problems of the above-mentioned prior art, the present invention provides a medical simulation experiment method and device for culturing apoptotic vesicles from cell lines and acting on primary chondrocytes, aiming to simulate and evaluate in vitro the effects of apoptotic vesicles cultured from cell lines on primary chondrocytes and their interactions with primary immune cells, so as to provide an experimental basis for subsequent clinical applications.

[0004] The present invention discloses a medical simulation experiment method for culturing apoptotic vesicles from cell lines and acting on primary chondrocytes, comprising the following steps:

[0005] S1. Steps for constructing the co - culture system: Co - culture primary chondrocytes, primary immune cells with apoptotic vesicles cultured from cell lines. During this process, set the cell ratio, concentration gradient of apoptotic vesicles, control groups, and factors simulating the osteoarthritis microenvironment; S2. Steps for multi - dimensional observation and analysis: Conduct multi - dimensional observation and analysis of the effects of apoptotic vesicles on cells. That is, collect and process samples at different time points, and perform cell viability detection, detection of chondrocyte function indicators, detection of immune cell function indicators, cell migration experiments, and cell uptake experiments, providing multi - parameter data through dynamic monitoring at different time points; S3. Steps for surgical risk assessment: Construct a multi - dimensional risk scoring system, apply a biological network model, and conduct risk level classification. Establish a quantitative scoring criterion based on the multi - parameter data in step S2, form a closed - loop feedback system, and realize the process evaluation from in vitro simulation to clinical decision - making support.

[0006] In a medical simulation experiment method based on apoptotic vesicles cultured from cell lines acting on primary chondrocytes according to the present invention, in step S1: Set the ratio of primary chondrocytes to primary immune cells to be 1:0.5 to 1:5, for simulating different degrees of inflammatory infiltration states; When setting the concentration gradient of apoptotic vesicles, set control groups, low - dose groups, medium - dose groups, and high - dose groups within the concentration range of 0 - 150 μg / mL; When setting the control groups, set a cell control group, a blank vesicle control group, an irrelevant vesicle control group, and a culture medium control group respectively. Among them, in the cell control group, only primary chondrocytes and primary immune cells are cultured without adding apoptotic vesicles; in the blank vesicle control group, blank vesicles prepared from the cell line culture supernatant without apoptosis induction are added; in the irrelevant vesicle control group, apoptotic vesicles prepared from a cell line with a different source or unrelated function to the experimental cell line are added; in the culture medium control group, only the culture medium is used without adding any cells and vesicles; When simulating the osteoarthritis microenvironment, add the inflammatory factor IL - 1β, set the final concentration to be 8 - 12 ng / mL, apply periodic compressive stress through a mechanical loading device, conduct hypoxic culture in a hypoxic incubator, add an oxidative stress inducer, and use a low - glucose and low - serum culture medium to simulate the state of nutrient deprivation.

[0007] In a medical simulation experiment method based on apoptotic vesicles cultured from cell lines acting on primary chondrocytes according to the present invention: The parameters for applying periodic compressive stress through a mechanical loading device are: pressure intensity 0.3 - 0.7 MPa, loading frequency 0.3 - 0.7 Hz, loading time 2 - 6 hours per day, and the waveform pattern is a sine wave; The oxygen concentration during hypoxic culture in a hypoxic incubator is 3% - 7%; The oxidative stress inducer added is set to H2O2, and the final concentration is 80 - 100 μM; The glucose concentration in the low - glucose and low - serum culture medium is 0.8 - 1.2 g / L, and the serum concentration is 1 - 3% FBS.

[0008] A medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes according to the present invention: When constructing the in vitro co - culture system step, a chondrocyte - immune cell metabolic interaction network (CIMN) model is introduced. Based on the metabolite exchange and signal pathway association between chondrocytes and immune cells, the metabolic environmental homeostasis under different cell ratio conditions is quantitatively evaluated, and its calculation formula is:

[0009]

[0010] Where G is the glucose uptake rate, L is the lactic acid production rate, O is the oxygen consumption rate, N is the cell number, α and β are weight coefficients, and the subscript c represents chondrocytes and i represents immune cells.

[0011] A medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes according to the present invention. In step S2, through the micro - environmental signal integration (MEIS) model, it is used to systematically monitor and standardize the comprehensive effect of osteoarthritis micro - environment simulation, and its calculation formula is:

[0012]

[0013] Where I is the concentration of inflammatory factors, M is the intensity of mechanical stress, O is the oxygen concentration, R is the level of reactive oxygen species, N is the nutrient level; I o 、M o 、O o 、R o 、N o are the reference values of the concentration of inflammatory factors, the intensity of mechanical stress, the oxygen concentration, the level of reactive oxygen species, and the nutrient level respectively; w1 to w5 are weight coefficients, and the sum of the weight coefficients is 1.

[0014] A medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes according to the present invention. In step S2, when setting the apoptotic vesicle concentration gradient, the apoptotic vesicle effective concentration - response curve (AEDC) threshold relationship formula is applied:

[0015]

[0016] Where R is the overall response value, R max is the maximum effective response, C is the apoptotic vesicle concentration, EC 50 is the apoptotic vesicle concentration that produces 50% of the maximum effective response, TC 50 is the apoptotic vesicle concentration that produces 50% of the maximum toxic response, k is the toxicity weight coefficient, and n and m are Hill coefficients.

[0017] According to a medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes of the present invention, in step S2, the steps of performing cell viability detection include: conducting a CCK8 experiment to evaluate cell proliferation activity; performing flow cytometry to detect the apoptosis rate of cells, differentiating early apoptosis, late apoptosis, and necrotic cells through Annexin V / PI double staining; conducting an LDH release experiment to evaluate cell cytotoxicity; and conducting an MTT experiment to detect cell metabolic activity.

[0018] According to a medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes of the present invention, in step S2, the steps of performing detection of chondrocyte function indicators include: detecting the expression levels of chondromatrix anabolism marker genes COL2A1, ACAN, SOX9 and catabolism marker genes ADAMTS5, MMP13, MMP3 by qPCR, and calculating the synthesis / catabolism balance index (SDBI); detecting the corresponding protein expression levels by Western Blotting; observing the distribution of related proteins by cell immunofluorescence; and evaluating the chondromatrix components by histochemical staining.

[0019] According to a medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes of the present invention, in step S2, the steps of performing detection of immune cell function indicators include: detecting immune cell surface markers by flow cytometry, including macrophage M1 type marker CD86 and M2 type marker CD206, and calculating the M1 / M2 phenotype ratio; detecting the concentrations of pro-inflammatory cytokines TNF-α, IL-6, IL-1β and anti-inflammatory cytokines IL-10, IL-4, TGF-β in the culture supernatant by ELISA or Cytokine Array detection technology, and calculating the pro-inflammatory / anti-inflammatory cytokine ratio to evaluate the degree of inflammatory response.

[0020] According to a medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes of the present invention, the calculation formula for calculating the pro-inflammatory / anti-inflammatory cytokine ratio (PIR) is:

[0021]

[0022] Wherein, C1, C2, C3, D1, D2, D3 are weight coefficients, and C1 + C2 + C3 = 1, D1 + D2 + D3 = 1; the concentration of each factor is in pg / mL, multiplied by the corresponding weight coefficient. The higher the PIR value, the stronger the inflammatory response, and the lower the value, the more obvious the anti-inflammatory effect.

[0023] A medical simulation experiment method based on culturing apoptotic vesicles from cell lines and acting on primary chondrocytes according to the present invention. In step S2, the steps of performing a cell migration experiment include: performing a scratch healing experiment, making a scratch on a monolayer of cells, adding serum-free medium containing apoptotic vesicles at different concentrations, observing and calculating the healing rate at different time points; performing a Transwell experiment: adding chondrocytes or immune cells to the upper chamber of the Transwell, and adding complete medium containing apoptotic vesicles at different concentrations to the lower chamber as a chemotactic agent, fixing and staining the migrated cells after culturing for 12 - 24 hours, and calculating the migration index.

[0024] A medical simulation experiment method based on culturing apoptotic vesicles from cell lines and acting on primary chondrocytes according to the present invention. In step S2, the steps of performing a cell uptake experiment include: fluorescently labeling apoptotic vesicles by mixing apoptotic vesicles with a lipophilic fluorescent dye PKH26 or PKH67; observing by fluorescence microscopy, adding fluorescently labeled apoptotic vesicles to the co-culture system, fixing the cells at different time points, differentiating different cell types by immunofluorescence, and observing the distribution of apoptotic vesicles in the cells; quantitatively analyzing by flow cytometry, measuring the proportion and fluorescence intensity of PKH26 fluorescent positive cells in different cell types, and calculating the uptake rate and uptake intensity; observing by confocal microscopy, combining with organelle-specific fluorescent probes, and analyzing the precise localization and transport pathway of apoptotic vesicles in the cells.

[0025] A medical simulation experiment method based on culturing apoptotic vesicles from cell lines and acting on primary chondrocytes according to the present invention. In step S2, a multi-omics data integration analysis framework (MIDAS) is introduced to systematically integrate data from multiple dimensions including cell activity, cartilage function, immune regulation, cell migration, and vesicle uptake. Its calculation formula is:

[0026] MIDAS = Σ(W i ×N i )

[0027] Wherein, W i is the weight coefficient of each dimension, and N i is the standardized score of each dimension. The scores of each dimension are calculated based on indicators such as the apoptosis rate, proliferation rate, LDH release rate, synthesis / degradation metabolism balance index, matrix staining intensity, pro-inflammatory / anti-inflammatory cytokine ratio, immune balance index, migration index, vesicle uptake rate, and uptake intensity.

[0028] According to a medical simulation experiment method based on culturing apoptotic vesicles from cell lines and acting on primary chondrocytes of the present invention, in step S2, it further includes: establishing a dynamic effect topological network model of apoptotic vesicles (DETN) to describe the direct effects of apoptotic vesicles on chondrocytes and immune cells and the indirect effects of cell-cell interactions. The model adopts a weighted directed graph structure, where nodes represent cell types or key biological processes, and edges represent the action relationship and intensity. The effect transfer function is:

[0029] E(i→j) = w i→j ×A i ×f(t)

[0030] where w i→j is the edge weight from node i to j, A i is the activity value of node i, and f(t) is a time function.

[0031] According to a medical simulation experiment method based on culturing apoptotic vesicles from cell lines and acting on primary chondrocytes of the present invention, in step S2, it further includes: applying an apoptosis vesicle safety-benefit evaluation (ASBE) model, and its threshold relationship is:

[0032] ASBE = [E b ×a - S i ×β] × [1 - γ × (C - C opt ) 2

[0033] where E b is the benefit score, S i is the safety risk score, C is the concentration of apoptotic vesicles, Copt is the optimal concentration, and α, β, γ are weight coefficients; and it is evaluated according to the criteria that ASBE > 6 is highly recommended for clinical translational research, 3 ≤ ASBE ≤ 6 is considered for clinical translational research but needs further optimization, and ASBE < 3 is not recommended for clinical translational research.

[0034] According to a medical simulation experiment method based on culturing apoptotic vesicles from cell lines and acting on primary chondrocytes of the present invention, in step S3, the steps of constructing a multi-dimensional risk scoring system include: key index selection and weight assignment: assigning weights to the indexes of the cell activity dimension, cartilage matrix metabolism dimension, inflammatory immune response dimension, and cell behavior dimension; dimension risk score calculation: calculating the dimension risk scores according to the index changes of each dimension; comprehensive risk score calculation: integrating the risk scores of the four dimensions to obtain a comprehensive risk score (CRS); risk correction factor introduction: considering the impacts of factors such as cell line origin, apoptotic vesicle preparation method, and clinical application dose on the risk, and calculating the final risk score (FRS).

[0035] ​According to a medical simulation experiment method based on culturing apoptotic vesicles from a cell line to act on primary chondrocytes of the present invention, in step S3: The biological network model application adopts a biological network integrated risk assessment model (BNRIM). By constructing a dynamic network of the interaction between apoptotic vesicles - chondrocytes - immune cells, the overall assessment of the risk of a complex biological system is realized, and its risk assessment algorithm is:

[0036] R i (t + 1) = R i (t) + α × ∑[R j (t) × w j→i - R i (t) × w i→j

[0037] Where R i (t) represents the risk value of node i at time t, and R j (t) represents the risk value of node j at time t; w j→i represents the influence weight of node j on node i; w i→j represents the influence weight of node i on node j, and α is the learning rate parameter;

[0038] And, the risk level division adopts the following threshold relationship:

[0039] CRV = β1 × FRS + β2 × BNR

[0040] Where, FRS is the final risk score, BNR is the network risk value output by the biological network integrated risk assessment model, β1 and β2 are weight coefficients and β1 + β2 = 1; and based on the CRV value, the risk level is divided into low risk (CRV < X1), medium risk (X1 ≤ CRV < X2) and high risk (CRV ≥ X2), where the range of X1 is 0.25 - 0.35 and the range of X2 is 0.55 - 0.65.

[0041] Based on the above method, the present invention discloses an experimental device, including:

[0042] ​A co-culture module, configured to construct an in vitro co-culture system, co-culturing primary chondrocytes, primary immune cells and apoptotic vesicles from cell line cultures through the co-culture module, and supporting the setting of different cell ratios, apoptotic vesicle concentration gradients, control groups and osteoarthritis microenvironment simulation factors; a detection module, configured to perform multi-dimensional observation and analysis on samples in the co-culture system, performing cell viability detection, chondrocyte function index detection, immune cell function index detection, cell migration experiments and cell uptake experiments through the detection module to generate multi-parameter data; a data processing module, configured to receive and process the multi-parameter data generated by the detection module, analyzing the multi-parameter data through the data processing module and generating an analysis result for risk assessment; a risk assessment module, configured to perform surgical risk assessment based on the analysis result generated by the data processing module, constructing a multi-dimensional risk scoring system, applying a biological network model and performing risk level classification through the risk assessment module; a control module, configured to coordinate the operations of the co-culture module, the detection module, the data processing module and the risk assessment module, controlling the experimental process, data collection and data transfer between modules through the control module to achieve a closed-loop process from in vitro simulation to risk assessment.

[0043] A medical simulation experiment method based on cell line-cultured apoptotic vesicles acting on primary chondrocytes of the present invention can achieve the following technical effects: 1. Improve the authenticity of in vitro simulation and more accurately reflect in vivo biological effects: The in vitro co-culture system constructed through step S1 integrates primary chondrocytes, primary immune cells, and cell line-cultured apoptotic vesicles, and sets various experimental parameters such as cell ratios, apoptotic vesicle concentration gradients, control groups, and osteoarthritis microenvironment simulation factors, enabling a more comprehensive and refined simulation of the complex microenvironment of in vivo osteoarthritis, thereby improving the authenticity of in vitro simulation and more accurately reflecting the biological effects of apoptotic vesicles in vivo. Compared with traditional models induced by a single inflammatory factor, the OA microenvironment simulated by this method has a significantly improved degree of agreement with the real pathological state. 2. Achieve a comprehensive characterization of the biological effects of apoptotic vesicles. Then, through the multi-dimensional observation and analysis carried out in step S2, covering multiple biological dimensions such as cell viability, chondrocyte function, immune cell function, cell migration, and cell uptake, and combined with dynamic monitoring at different time points, it is possible to comprehensively and dynamically investigate the biological effects of apoptotic vesicles on cells, thereby more deeply understanding their action mechanisms and potential risks. Compared with traditional single-index evaluation, this method improves the comprehensiveness and reliability of the evaluation. 3. Establish a quantitative risk assessment standard to provide an objective basis for clinical decision-making. Finally, through the multi-dimensional risk scoring system, biological network model, and risk level classification constructed in step S3, the multi-parameter data obtained in step S2 is converted into a quantifiable risk score, and risk grading is carried out based on a preset threshold relationship, thereby establishing an objective and quantitative risk assessment standard, providing a more scientific and reliable basis for clinical decision-making. Quantitative evaluation significantly reduces the influence of subjective factors in risk judgment. 4. Form a closed-loop feedback system to achieve an effective connection from in vitro simulation to clinical decision-making; the risk assessment system established in step S3 is based on the multi-parameter data in step S2, forming a closed-loop feedback system, realizing a complete process evaluation from in vitro simulation to clinical decision-making support, helping to effectively transform in vitro experimental results into clinical application guidance, and accelerating the clinical transformation process of cell line-cultured apoptotic vesicles in the treatment of osteoarthritis. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flowchart of the method steps of the present invention.

[0045] Reference Signs:

[0046] None. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following is combined with Figure 1 to describe the embodiments of the present invention in detail.

[0048] Example 1

[0049] A medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes in this embodiment includes step S1 - the construction step of a co - culture system, step S2 - the multi - dimensional observation and analysis step, and step S3 - the surgical risk assessment step. Among them, in step S1, primary chondrocytes, primary immune cells and apoptotic vesicles cultured from cell lines are co - cultured. During this process, cell ratios, apoptotic vesicle concentration gradients, control groups and osteoarthritis microenvironment simulation factors are set. In step S2, the effects of apoptotic vesicles on cells are observed and analyzed in multiple dimensions. That is, samples at different time points are collected and processed, and cell viability detection, chondrocyte function index detection, immune cell function index detection, cell migration experiments and cell uptake experiments are performed. Multi - parameter data are provided through dynamic monitoring at different time points. In step S3, a multi - dimensional risk scoring system is constructed, a biological network model is applied, and risk levels are classified. Based on the multi - parameter data in step S2, a quantitative scoring standard is established to form a closed - loop feedback system, realizing the process evaluation from in vitro simulation to clinical decision - making support.

[0050] I. The specific implementation process of constructing an in vitro co - culture system in step S1 is as follows:

[0051] The construction of the in vitro co - culture system in step S1 aims to detail a method for constructing an in vitro co - culture system, which is used to simulate the pathological microenvironment of osteoarthritis and to evaluate the effects of apoptotic vesicles cultured from cell lines on the co - culture system of primary chondrocytes and primary immune cells. When implementing step S1 in this embodiment, first, cell ratios and concentrations are set to determine the co - culture ratio of primary chondrocytes and primary immune cells, as well as the concentration gradient of apoptotic vesicles in the co - culture system, so as to simulate different pathological states and treatment intervention levels in vivo.

[0052] When setting the cell ratio in step S1, the following cell ratio groups are set:

[0053] 1:1 group (simulation group of moderate inflammatory infiltration): The number ratio of primary chondrocytes to primary immune cells is set to 1:1, aiming to simulate the common degree of inflammatory infiltration in the course of osteoarthritis;

[0054] 1:2 group (simulation group of severe inflammatory infiltration): The number ratio of primary chondrocytes to primary immune cells is set to 1:2, aiming to simulate the pathological state with a more intense inflammatory response;

[0055] 2:1 group (simulation group of mild inflammatory infiltration): The number ratio of primary chondrocytes to primary immune cells is set to 2:1, aiming to simulate the pathological stage with a relatively mild inflammatory response.

[0056] Regarding the cell seeding density, the cell seeding density is determined according to the surface area of the culture carrier used. In this example, a commonly used 24-well plate is taken as an example for illustration. First, primary chondrocytes are seeded into the 24-well plate at a density of 5×10 4 cells per well. After the primary chondrocytes adhere to the wall, according to the settings of the above different ratio groups, the corresponding number of primary immune cells is added to each well:

[0057] Group 1:1: Add 5×10 4 primary immune cells to each well;

[0058] Group 1:2: Add 1×10 5 primary immune cells to each well;

[0059] Group 2:1: Add 2.5×10 4 primary immune cells to each well.

[0060] Technical effect explanation: The above settings of cell ratios are aimed at simulating the interaction relationship between chondrocytes and immune cells under the pathological state of osteoarthritis. By setting different cell ratios, the differences in the degree of inflammatory infiltration can be simulated, so as to more comprehensively evaluate the effect of apoptotic vesicles in different inflammatory microenvironments, providing a basis for subsequent research on the therapeutic effects of apoptotic vesicles at different pathological stages.

[0061] In addition, to further optimize the chondrocyte-immune cell metabolic interaction network model and more precisely set the cell ratios, this example introduces the chondrocyte-immune cell metabolic interaction network (CIMN) model. This model is based on the metabolite exchange and signal pathway association between chondrocytes and immune cells, quantitatively evaluating the metabolic environmental homeostasis under different cell ratio conditions, so as to guide the optimized setting of cell ratios in the co-culture system. The core parameter calculation formula of the CIMN model is as follows:

[0062]

[0063] Among them, G represents the glucose uptake rate, with the unit of picomoles per minute per cell (pmol / min / cell); L represents the lactate production rate, with the unit of picomoles per minute per cell (pmol / min / cell); O represents the oxygen consumption rate, with the unit of picomoles per minute per cell (pmol / min / cell), N represents the cell number; α and β are weight coefficients, which are set to 0.6 and 0.4 respectively in this example. These weight coefficients are determined based on pre-experimental data and literature research, reflecting the relative importance of lactate production and oxygen consumption in the metabolic interaction network; the subscript c represents chondrocytes, and the subscript i represents immune cells.

[0064] In specific operations, first, metabolic parameters such as glucose uptake rate, lactate production rate, and oxygen consumption rate of primary chondrocytes and primary immune cells at different ratios are measured through preliminary experiments. Then, these parameters are substituted into the CIMN model formula to calculate the CIMN values at different cell ratios. According to the results of the preliminary experiments, when the CIMN value is within the range of 0.85 - 1.15, the cell metabolic microenvironment is considered to be closest to the physiological state, which is conducive to observing the true biological effects of apoptotic vesicles. Therefore, in this embodiment, the final determination of the cell ratio needs to refer to the calculation results of the CIMN model to ensure that the metabolic microenvironment of the co-culture system is closer to the in vivo physiological state.

[0065] Explanation of technical effects: The introduction of the CIMN model helps to overcome the empirical and subjective nature of setting cell ratios in traditional co-culture systems. By quantifying metabolic interactions, the co-culture ratio is optimized, making the in vitro microenvironment closer to the physiological state and improving the physiological relevance and predictive value of the simulation system. Compared with the traditional fixed-ratio setting method, this method can more accurately simulate the in vivo cell metabolic microenvironment, thus more truly reflecting the biological effects of apoptotic vesicles and providing guarantee for the accuracy and reliability of subsequent experimental results.

[0066] When setting the apoptotic vesicle concentration gradient in step S1, in this embodiment, four apoptotic vesicle concentration gradient groups are selected to simulate different treatment intervention levels and investigate the dose-effect relationship of apoptotic vesicles, specifically as follows:

[0067] (1) Formulate concentration gradient grouping.

[0068] 0 μg / mL group (control group): No apoptotic vesicles are added to the culture medium, serving as the basic control group to evaluate the cell state under the condition of no apoptotic vesicle intervention;

[0069] 10 μg / mL group (low-dose group): Simulate the early or mild treatment intervention level and preliminarily investigate the biological effects of low-concentration apoptotic vesicles;

[0070] 50 μg / mL group (medium-dose group): Simulate the standard treatment intervention level and evaluate the therapeutic effect of apoptotic vesicles at conventional doses;

[0071] 100 μg / mL group (high-dose group): Simulate the intensive treatment intervention level and investigate the potential effects and possible toxicity risks of high-concentration apoptotic vesicles.

[0072] (2) Formulate the timing and method of adding apoptotic vesicles

[0073] The timing of adding apoptotic vesicles is as follows: After co - culturing primary chondrocytes and primary immune cells for 24 hours, apoptotic vesicles at a set concentration are added to the culture system. To ensure the uniform distribution of apoptotic vesicles in the culture system, before addition, the vesicle suspension is first mixed with an equal volume of culture medium, gently mixed evenly, and then added to the culture wells.

[0074] (3) Establish the threshold relationship of the apoptotic vesicle effective concentration - response curve for optimization

[0075] To further optimize the concentration setting of apoptotic vesicles, in this example, a threshold relationship of the apoptotic vesicle effective concentration - response curve (AEDC) is established. This relationship aims to comprehensively consider the effectiveness and potential toxicity of apoptotic vesicles, determine the optimal dose window, and provide a theoretical basis for the selection of experimental concentrations in the follow - up. The AEDC threshold relationship is as follows:

[0076]

[0077] Where: R is the overall response value, representing the comprehensive biological effect of apoptotic vesicles; R max is the maximum effective response value, representing the maximum therapeutic effect that apoptotic vesicles may achieve; C is the concentration of apoptotic vesicles, with the unit of micrograms per milliliter (μg / mL); EC 50 is the concentration of apoptotic vesicles that produces 50% of the maximum effective response, representing the first threshold for effective treatment; TC 50 is the concentration of apoptotic vesicles that produces 50% of the maximum toxic response, representing the second threshold for toxic reactions; k is the toxicity weight coefficient, used to adjust the influence of toxic reactions on the overall response. In this example, it is set to 0.7, indicating that the toxic reaction has a certain negative regulatory effect on the overall response; n and m are the Hill coefficients, respectively characterizing the steepness of the dose - effective response curve and the dose - toxic response curve, reflecting the sensitivity of the response to dose changes.

[0078] Based on the pre - experimental data, the parameter values of the AEDC threshold relationship in this example are set as follows: EC 50 = 35 ± 5 μg / mL; TC 50 = 125 ± 15 μg / mL; n = 1.8; m = 3.2; k = 0.7.

[0079] According to the above - mentioned threshold relationship, the overall response value R at different apoptotic vesicle concentrations can be calculated. By analyzing the AEDC curve, the optimal dose window can be determined, that is, the concentration range that minimizes the toxic reaction while ensuring the therapeutic effect. In this example, according to the AEDC curve analysis, the optimal dose window of apoptotic vesicles is 10 - 100 μg / mL.

[0080] Explanation of Technical Results: The establishment of the AEDC threshold relationship addresses the critical issue of selecting apoptotic vesicle concentration. By integrating both effective and toxic responses, an optimal dose window is determined, ensuring therapeutic efficacy while avoiding toxicity, thereby enhancing the clinical translational value of experimental results. Compared with traditional concentration setting methods based on experience or single-effect observations, this method is more scientific and rational, providing more accurate guidance for apoptotic vesicle dose selection, laying the foundation for the reliability of subsequent experimental results and clinical translation.

[0081] When setting up control groups in step S1, in order to accurately evaluate the specific effects of apoptotic vesicles in cell line culture and eliminate the interference of various nonspecific factors, the following four control groups were set up in this example to form a multi-level, comprehensive control system.

[0082] (1) Cell control group

[0083] Setting: Only primary chondrocytes and primary immune cells were cultured without the addition of apoptotic vesicles;

[0084] Purpose: To eliminate the interference of cell-specific factors (such as natural changes in cell culture, the influence of culture medium components, etc.) on the experimental results, and to serve as a basic control to evaluate the baseline status of the co-culture system;

[0085] Grouping: Set up subgroups according to the three cell ratios described above (1:1, 1:2, and 2:1) to correspond to the cell ratio settings of the experimental group;

[0086] Conditions: Maintain exactly the same culture conditions and observation time points as the experimental group, including culture medium components, culture environment parameters, and osteoarthritis microenvironment simulation factors (such as the addition of inflammatory factors, etc.).

[0087] (2) Blank vesicle control group

[0088] Setup: “blank vesicles” prepared by adding culture supernatant of cell lines without apoptosis induction;

[0089] Purpose: To eliminate the nonspecific effects of other components in the cell culture supernatant (e.g., non-apoptotic vesicle components such as proteins, lipids, and nucleic acids secreted by cells) on the experimental results.

[0090] Preparation Method: Culture supernatant is collected from a normal cultured cell line and isolated using the same ultracentrifugation procedure as apoptotic vesicles (100,000 g, 70 minutes, 4°C). This method ensures that blank vesicles undergo the same physical treatment as apoptotic vesicles during preparation, minimizing the risk of nonspecific factors introduced during the preparation process.

[0091] (3) Unrelated vesicle control group

[0092] Setting: Add apoptotic vesicles prepared from cell lines that are different from the source or have no functional relevance to the experimental cell line;

[0093] Objective: To exclude non-specific vesicle effects, for example, the general biological effects of certain vesicles, rather than the specific effects of apoptotic vesicles derived from a specific cell line. By comparing with the irrelevant vesicle group, the specific biological effects of the apoptotic vesicles in the experimental group can be identified;

[0094] Source: Select cell lines that have no obvious biological association with cartilage tissue, such as human embryonic kidney cell line HEK293 or human hepatocyte cell line HepG2, etc. Selecting apoptotic vesicles from irrelevant cell lines can exclude the influence of cell type specificity;

[0095] Preparation method: The preparation method of the irrelevant vesicles is the same as that of the apoptotic vesicles in the main experimental group, using the same apoptosis inducer and preparation process to ensure that the two groups of vesicles are as similar as possible in the preparation process and physical and chemical properties, so as to highlight the difference in cell source.

[0096] (4) Medium control group

[0097] Setting: Only use the medium without adding any cells and vesicles;

[0098] Objective: As a basic control, to evaluate the background influence of the culture environment itself on the detection indicators, for example, the interference of certain components that may exist in the medium itself on the detection results. By comparing with the medium control group, the background signal of the medium can be deducted to improve the accuracy of the experimental results.

[0099] Technical effect explanation: By setting the above four control groups, this embodiment constructs a multi-level and comprehensive control system, which can systematically exclude the interference of various non-specific factors and ensure that the experimental results can accurately reflect the specific effects of apoptotic vesicles cultured from cell lines. Compared with the traditional single control group design, this method can more effectively exclude interference factors, improve the reliability and persuasiveness of the experimental results, and provide a strong guarantee for the accuracy and scientificity of the subsequent experimental conclusions.

[0100] When simulating the osteoarthritis microenvironment in step S1, in order to make the in vitro co-culture system closer to the in vivo pathological environment of osteoarthritis, this embodiment simulates the main microenvironment characteristics of osteoarthritis, including multiple pathological factors such as inflammation, mechanical stress, hypoxia, oxidative stress, and nutrient deprivation, and strives to maximize the restoration of the complex in vivo pathological microenvironment in vitro, as follows:

[0101] (1) Addition of inflammatory factors. Method: Recombinant human interleukin-1β (IL-1β) was added to the co-culture system at a final concentration of 10 ng / mL to simulate the inflammatory microenvironment of osteoarthritis. IL-1β is a key proinflammatory cytokine in the pathological process of osteoarthritis, which can induce chondrocyte catabolism and promote inflammatory response. Addition time: Add immediately after the primary chondrocytes and primary immune cells are mixed to simulate the inflammatory microenvironment as early as possible. Maintenance: Fresh IL-1β was added when the culture medium was changed every 24 hours to maintain the stability of the IL-1β concentration in the culture system and ensure continuous inflammatory stimulation.

[0102] (2) Mechanical stress simulation. Device: The co-culture system is placed in a special mechanical loading device that can provide controllable compressive stress to simulate the mechanical load that articular cartilage bears in the body. Parameters: Set periodic compressive stress parameters to simulate physiological or pathological mechanical stress. Pressure intensity: 0.5 MPa, simulating the physiological load borne by articular cartilage during normal walking. Loading frequency: 0.5 Hz, simulating the frequency of joint movement at normal human walking speed. Loading time: 4 hours per day can be selected to simulate the daily activity time of the human body. Waveform mode: Sine waveform, simulating the smooth periodic characteristics of joint movement.

[0103] (3) Hypoxic environment simulation. Equipment: Place the co-culture system in a hypoxic incubator to precisely control the oxygen concentration in the culture environment. Parameters: Set the following parameters to simulate the hypoxic microenvironment of the osteoarthritis lesion site:

[0104] Oxygen concentration: 5% (volume ratio) to simulate the oxygen partial pressure level in the joint cavity, which is significantly lower than atmospheric oxygen levels (21%) and closer to the physiological state in vivo. Carbon dioxide concentration: 5% (volume ratio) to maintain a stable pH value in the culture medium. Temperature: 37°C to maintain normal cell growth temperature. Relative humidity: 95% to prevent excessive evaporation of the culture medium.

[0105] (4) Oxidative stress simulation. Inducer: Add the oxidative stress inducer hydrogen peroxide (H2O2) to the co-culture system at a final concentration of 100 micromoles / liter (μM) to simulate the oxidative stress state in osteoarthritis lesion tissue. Oxidative stress is an important factor in the pathological process of osteoarthritis, which can damage chondrocytes and promote inflammatory response. Method: An intermittent addition scheme was adopted to simulate the fluctuating characteristics of oxidative stress in vivo: the cells were treated for 2 hours every 24 hours, and then fresh culture medium was replaced to remove H2O2, allowing the cells to recover within a certain period of time before the next round of oxidative stress stimulation.

[0106] (5) Nutrient deprivation simulation. Culture medium: A low-glucose and low-serum culture medium was used to simulate the nutrient-deprived state of osteoarthritis chondrocytes. In the lesion site of osteoarthritis, the nutrient supply to the cartilage tissue is reduced, and the cells are in a state of long-term malnutrition. Low-glucose DMEM medium: The glucose concentration is 1 gram per liter (g / L), which is about one-fourth of the glucose concentration in the standard DMEM medium, simulating the pathological state of reduced glucose concentration in synovial fluid. Low-serum condition: The concentration of fetal bovine serum (FBS) is 2%, which is about one-fifth of the standard culture condition, simulating the pathological state of reduced concentration of nutrient factors in synovial fluid.

[0107] Technical effect: Through the simulation of the above-mentioned various microenvironmental factors, in this example, an in vitro co-culture system that multi-dimensionally simulates the osteoarthritis microenvironment was constructed, which is closer to the pathological environment of in vivo osteoarthritis and improves the clinical relevance of experimental data. Compared with the traditional model induced by a single inflammatory factor, the degree of coincidence between the OA microenvironment simulated by this method and the real pathological state has been significantly improved, and it can more realistically reflect the biological effects of apoptotic vesicles in the complex in vivo microenvironment, providing a more reliable experimental basis for the clinical translational application of subsequent experimental results.

[0108] (6) Standardization of the microenvironment signal integration model

[0109] To systematically monitor and standardize the comprehensive effects of osteoarthritis microenvironment simulation, in this example, a microenvironment signal integration (MEIS) model was innovatively introduced. This model is based on the following key parameters to quantitatively evaluate the overall intensity of microenvironment simulation and ensure the consistency of microenvironment simulation intensity in different batches of experiments. The specific calculation formula model is:

[0110]

[0111] Among them, I is the concentration of inflammatory factors, M is the intensity of mechanical stress, O is the oxygen concentration, R is the level of reactive oxygen species, and N is the nutrient level; I o 、M o 、O o 、R o 、N oThey are the reference values of inflammatory factor concentration, mechanical stress intensity, oxygen concentration, reactive oxygen species level, and nutrient level respectively; w1 to w5 are weight coefficients, and the sum of all weight coefficients is 1. Specifically, among them, I is the inflammatory factor concentration, and I0 is the reference value of 10 ng / mL IL-1β; M is the mechanical stress intensity, and M0 is the reference value of 0.5 MPa; O is the oxygen concentration, and O0 is the reference value of 5%; R is the reactive oxygen species level, and R0 is the reference value of 100 μM H2O2; N is the nutrient level, and N0 is the reference value (1 g / L glucose, 2% FBS); w1 to w5 are weight coefficients, which are set to 0.3, 0.25, 0.15, 0.2, and 0.1 respectively in this embodiment. These weight coefficients are determined based on literature research and experiments, reflecting the relative importance of different microenvironment factors in the pathological process of osteoarthritis. In specific operations, by real-time monitoring the actual parameter values of each microenvironment factor in the co-culture system and substituting them into the MEIS model formula, the MEIS value is calculated. According to the pre-experiment results, when the MEIS value is within the range of 0.8 - 1.2, it can be considered that an effective simulation of the moderate osteoarthritis microenvironment has been achieved. Through the monitoring and regulation of the MEIS model, the consistency of the microenvironment simulation intensity of different batches of experiments can be ensured.

[0112] Technical effect: The introduction of the MEIS model solves the key problem of traditional in vitro simulation of osteoarthritis: the mutual influence and overall effect among different microenvironment factors are difficult to quantitatively evaluate. Through the MEIS model, standardized simulation of the multi-factor microenvironment, personalized microenvironment adjustment, and cross-laboratory result comparison can be achieved, greatly improving the similarity between the simulation system and the real disease environment. Compared with the traditional empirical and single-factor design methods, this method is more scientific, quantitative, and controllable, providing a guarantee for the repeatability and comparability of subsequent experimental results.

[0113] In addition, in step S1, it also includes the maintenance and monitoring of the co-culture system, specifically:

[0114] (1) Incubation time. Specifically: According to the experimental design, the co-culture system was maintained for an appropriate time, which was set to 24 hours, 48 hours, and 72 hours in this example to dynamically observe short-term, medium-term, and long-term effects and comprehensively evaluate the biological effects of apoptotic vesicles at different time points; (2) Medium replacement. Specifically: During the co-culture process, the medium was replaced every 24 hours, and fresh microenvironmental factors (such as IL-1β, H2O2, etc.) were supplemented to maintain the stability of the microenvironmental factor concentration and ensure the continuity and effectiveness of microenvironment simulation. (3) Morphological observation. Specifically: An inverted microscope was used for cell morphological observation, and information such as cell morphological changes, cell aggregation status, and cell-cell interactions was regularly recorded to preliminarily evaluate the effects of apoptotic vesicles on cell morphology and behavior. (4) Sample collection. Specifically: Culture supernatants and cell samples were collected at preset time points (24 hours, 48 hours, 72 hours), processed according to the predetermined sample processing procedure, and properly stored for subsequent analysis and evaluation such as cell viability detection, chondrocyte function index detection, immune cell function index detection, cell migration experiment, and cell uptake experiment.

[0115] Through the above various implementation methods for constructing the in vitro co - culture system in step S1, an in vitro co - culture system that can effectively simulate the multi - dimensional osteoarthritis microenvironment can be established. This system has the following technical effects: 1. Multi - dimensional simulation of the osteoarthritis microenvironment: It integrates various pathological factors such as inflammatory factors, mechanical stress, hypoxia, oxidative stress, and nutrient deprivation, more comprehensively and realistically simulates the in - vivo osteoarthritis microenvironment, and improves the physiological relevance of the in - vitro simulation system. 2. Quantitative parameter setting: Through the CIMN model and the MEIS model, the quantitative setting of key experimental parameters is realized, enabling precise control and repetition of experimental conditions, and improving the reliability and repeatability of experimental results. 3. Optimized apoptotic vesicle dosage range: Through the AEDC threshold relationship formula, the concentration window of apoptotic vesicles that can observe significant biological effects while avoiding toxic reactions is determined, providing a scientific basis for the selection of apoptotic vesicle concentrations in subsequent experiments. 4. Comprehensive control system: The multi - level control group design ensures the reliability of experimental results, effectively excludes the interference of non - specific factors, and improves the accuracy and persuasiveness of experimental conclusions. 5. Highly integrated simulation system: It integrates multidisciplinary technologies such as cell biology, mechanics, and biochemistry, constructs a highly integrated in - vitro osteoarthritis simulation system, and provides a comprehensive experimental platform for the evaluation of the application of apoptotic vesicles cultured from cell lines in the treatment of osteoarthritis. 6. Enhancement of clinical translation value: It more precisely simulates the in - vivo environment, improves the accuracy of predicting clinical effects from experimental results, and provides an important experimental platform and data support for the clinical translational application of apoptotic vesicles cultured from cell lines in the treatment of osteoarthritis. Generally speaking, the successful construction of the in - vitro co - culture system in step S1 is a key step in realizing the transition from in - vitro simulation to clinical decision - making support, and provides an important experimental platform for the application of apoptotic vesicles cultured from cell lines in the treatment of osteoarthritis.

[0116] II. The specific implementation process for the multi - dimensional observation and analysis of the effects of apoptotic vesicles on cells in step S2 is as follows:

[0117] This embodiment aims to describe in detail how to conduct multi - dimensional and multi - time - point observations and analyses on the interactions between apoptotic vesicles cultured from cell lines, primary chondrocytes, and primary immune cells, so as to comprehensively evaluate the biological effects of apoptotic vesicles and provide a data basis for subsequent surgical risk assessments. The specific steps are as follows:

[0118] 1. Sample collection and preparation: Obtain cell and cell culture supernatant samples for subsequent multi - dimensional analysis, and perform necessary pre - treatments to ensure sample quality and the reliability of experimental results.

[0119] (1) Sample collection time points: To comprehensively evaluate the dynamic effects of apoptotic vesicles, three sample collection time points were set in this example: 24 hours, 48 hours, and 72 hours after the start of co-culture. These three time points represent the early, middle, and late effects of the action of apoptotic vesicles respectively, and can dynamically reflect the long-term effects of apoptotic vesicles on cells. Technical effect: The multi-time point sample collection strategy overcomes the limitation of only focusing on the effects at a single time point in traditional research, can more comprehensively reveal the dynamic change process of the action of apoptotic vesicles, and provides necessary data support for in-depth understanding of its action mechanism and long-term effects. Compared with only performing single-time point detection, this method can more accurately evaluate the treatment timeliness and potential long-term effects of apoptotic vesicles.

[0120] (2) Sample separation method: At each preset time point, the sample separation is carried out according to the following steps:

[0121] (2-1) Collection of culture supernatant: Carefully aspirate the supernatant in the culture well and transfer it to a centrifuge tube. Centrifuge at a centrifugal force of 1000g for 10 minutes to remove suspended cells and cell debris. Aliquot the pure supernatant into a new centrifuge tube, label it, and store it in a -80°C ultra-low temperature freezer for subsequent lactate dehydrogenase (LDH) release experiments and cytokine detection;

[0122] (2-2) Collection of adherent cells: After removing the culture supernatant, gently wash the culture well twice with phosphate buffered saline (PBS) to remove residual supernatant and non-adherent cells. According to the requirements of subsequent experiments, select different cell collection methods:

[0123] RNA and protein extraction: Directly add cell lysate (such as Trizol reagent or RIPA lysate) to the culture well, fully lyse the cells, and collect the cell lysate for subsequent RNA extraction, reverse transcription and quantitative PCR analysis, as well as protein extraction and Western Blotting analysis;

[0124] Live cell experiments: For experiments that require live cell detection, such as flow cytometry to detect the apoptosis rate of cells, cell immunofluorescence staining, cell migration experiments and cell uptake experiments, trypsin digestion of adherent cells is required to prepare a single cell suspension;

[0125] (2-3) Preparation of mixed cell suspension: For experiments that require distinguishing primary chondrocytes and primary immune cells for analysis, such as flow cytometry for detecting cell apoptosis rate and immune cell surface markers, and quantitative PCR for analyzing gene expression of different cell types, a mixed cell suspension needs to be prepared. After using trypsin to digest adherent cells and preparing them into a single-cell suspension, according to the experimental requirements, cell sorting techniques (such as magnetic bead sorting or flow cytometry sorting) can be selected to separate primary chondrocytes and primary immune cells for subsequent analysis respectively.

[0126] Technical effect: This embodiment adopts a multi-step and refined sample separation method, ensuring the purity and integrity of different types of samples, and laying a foundation for the accuracy of subsequent various detection and analysis. For example, removing cell debris by low-speed centrifugation avoids the interference of cell debris in the supernatant on cytokine detection; using trypsin digestion to prepare a single-cell suspension ensures the accuracy of flow cytometry detection. Compared with traditional rough sample processing methods, this method can significantly improve the reliability and repeatability of experimental data.

[0127] (3) Sample preparation and preservation. According to different subsequent detection items, the separated samples are prepared and preserved accordingly: (3-1) RNA sample preparation and preservation: After collecting the cell lysate, operate according to the instructions of the RNA extraction kit (such as Trizol kit) to extract total cellular RNA. Use a nucleic acid quantifier to detect the concentration and purity of RNA. Dissolve the RNA in RNase-free water, aliquot it, and store it in an ultra-low temperature refrigerator at -80 °C for subsequent quantitative PCR analysis. (3-2) Protein sample preparation and preservation: After collecting the cell lysate, add a protease inhibitor mixture, fully lyse the cells, and extract total cellular protein. Use a BCA protein concentration assay kit to measure the protein concentration. Aliquot the protein samples and store them in an ultra-low temperature refrigerator at -80 °C for subsequent Western Blotting analysis. (3-3) Preparation and maintenance of live cell samples: For samples that require live cell experiments, after preparing them into a single-cell suspension, use a cell counter to count the cells and adjust the cell concentration to the concentration required for the experiment. For experiments that need to be carried out immediately, such as flow cytometry for detecting cell apoptosis rate and cell surface markers, directly proceed with the subsequent staining and detection steps. For live cell samples that need to be stored short-term, they can be resuspended in a freezing medium containing 10% fetal bovine serum and 10% dimethyl sulfoxide (DMSO), subjected to programmed freezing, and stored in liquid nitrogen for subsequent resuscitation and experiments.

[0128] Technical effect: In this embodiment, the extraction, quantification, and preservation of RNA and proteins, as well as the preparation and maintenance of live cell samples, are carried out strictly in accordance with the standard operating procedures, which maximally ensures the quality and biological activity of the samples and avoids the influence of sample degradation or inactivation on the experimental results. For example, protease inhibitors are used to prevent protein degradation, RNase inhibitors are used to prevent RNA degradation, and programmed freezing is used to preserve live cells, ensuring the stability of the samples during storage. Compared with non-standard sample preparation and preservation methods, this method can significantly improve the accuracy and reliability of experimental results.

[0129] 2. When performing the cell viability detection in step S2, through a variety of cell viability detection methods, comprehensively evaluate the effects of apoptotic vesicles on the viability of primary chondrocytes and primary immune cells, including multiple aspects such as cell proliferation, apoptosis, cytotoxicity, and metabolic activity, as follows:

[0130] (1) Evaluate the cell proliferation activity using the CCK8 assay. The Cell Counting Kit-8 (CCK8) assay is a rapid and highly sensitive method for detecting cell proliferation and cytotoxicity. Its principle is based on the fact that dehydrogenases in cells can reduce WST-8 in the CCK8 reagent to form an orange-yellow formazan compound, and the amount of the formed formazan compound is positively correlated with the number of live cells. The more specific steps are as follows: (1-1) Cell culture and drug addition: In a 96-well plate, inoculate primary chondrocytes and primary immune cells according to the same ratio as constructed in the in vitro co-culture system. After the cells adhere, add apoptotic vesicles at different concentrations (0 μg / mL, 10 μg / mL, 50 μg / mL, 100 μg / mL). (1-2) CCK8 reagent addition and incubation: At the time points of 24 hours, 48 hours, and 72 hours, add 10 μL of CCK8 reagent to each well. Place the culture plate in an incubator and incubate at 37 °C for 1-4 hours. The incubation time is optimized according to cell type and experimental conditions to ensure the best signal intensity. (1-3) Absorbance measurement and data analysis: Use a microplate reader to measure the absorbance value of each well at a wavelength of 450 nm. Zero with the medium control group (without cells). Calculate the cell proliferation rate of each apoptotic vesicle concentration group relative to the control group (0 μg / mL apoptotic vesicle group). The formula for calculating the cell proliferation rate is:

[0131] Cell proliferation rate (%) = (OD value of the experimental group / OD value of the control group) × 100%

[0132] Technical effect: The CCK8 assay can quickly and sensitively reflect the effect of apoptotic vesicles on cell proliferation activity. By detecting the cell proliferation rate after treatment with apoptotic vesicles at different time points and different concentrations, it is possible to evaluate whether apoptotic vesicles have a promoting or inhibitory effect on cell proliferation, as well as the dose-effect relationship and time-effect relationship. Compared with the traditional MTT assay, the CCK8 assay has higher sensitivity and a simpler operation process, and can more effectively evaluate cell proliferation activity.

[0133] (2) Detect the cell apoptosis rate by flow cytometry. Annexin V / PI double staining flow cytometry is a commonly used method for detecting cell apoptosis. Annexin V is a protein that can specifically bind to phosphatidylserine (PS) on the cell membrane, and PS will flip from the inner side to the outer side of the cell membrane in the early stage of external apoptosis. Propidium iodide (PI) is a nucleic acid dye that can only penetrate late apoptotic cells or necrotic cells with damaged cell membranes. By double staining with Annexin V and PI, live cells (Annexin V- / PI-), early apoptotic cells (Annexin V+ / PI-), late apoptotic cells (Annexin V+ / PI+), and necrotic cells (Annexin V- / PI+) can be distinguished. The more specific steps are as follows: (2-1) Cell collection and staining: Collect the cells in the co-culture system, wash them twice with PBS, and resuspend them in Annexin V binding buffer. According to the instructions of the Annexin V / PI double staining kit, add Annexin V-FITC and PI dyes in sequence, and incubate in the dark at room temperature for 15 minutes. (2-2) Flow cytometry detection and data analysis: Use flow cytometry for detection, with an excitation wavelength of 488 nm, an emission wavelength of 530 nm for Annexin V-FITC, and an emission wavelength of 585 nm for PI. Distinguish different cell types through cell surface markers (such as CD44 or CD105 for labeling chondrocytes, and CD45 for labeling immune cells), and at the same time detect the fluorescence signals of Annexin V-FITC and PI. Use flow cytometry analysis software (such as FlowJo) for data analysis, and calculate the early apoptosis rate (Annexin V+ / PI-), late apoptosis rate (Annexin V+ / PI+), and necrosis rate (Annexin V- / PI+) of the cells in each group.

[0134] Technical effect: Annexin V / PI double staining flow cytometry can quantitatively and accurately detect the effect of apoptotic vesicles on apoptosis of different types of cells. By differentiating early apoptosis, late apoptosis, and necrosis, the mechanism of apoptotic vesicle-induced cell death can be understood more deeply. Combining with cell surface markers, the effects of apoptotic vesicles on apoptosis of primary chondrocytes and primary immune cells can be evaluated separately, revealing their selective effects. Compared with traditional methods such as TUNEL staining, flow cytometry has higher throughput and quantitative analysis capabilities, and can more comprehensively evaluate the apoptotic effects of apoptotic vesicles.

[0135] (3) Evaluate cytotoxicity using the LDH release assay. The lactate dehydrogenase (LDH) release assay is a commonly used method for detecting cytotoxicity. LDH is a cytoplasmic enzyme that is released extracellularly when the cell membrane is damaged. By detecting the activity of LDH in the culture supernatant, the integrity of the cell membrane and the degree of cytotoxicity can be evaluated. The more specific steps are as follows: (3-1) Collection of culture supernatant and preparation of reagents: Collect the culture supernatants of each group and operate according to the instructions of the LDH detection kit. The kit usually contains a substrate solution, an enzyme solution, and a stop solution. (3-2) Enzymatic reaction and absorbance measurement: Mix the culture supernatant with the substrate solution and the enzyme solution, and incubate at room temperature for 30 minutes. Add the stop solution to terminate the reaction. Use an enzyme-linked immunosorbent assay (ELISA) reader to measure the absorbance value of each well at a wavelength of 490 nm. (3-3) Calculation of cytotoxicity and data analysis: Use the completely lysed cell group (cells treated with cell lysate) as the 100% LDH release control, and use the medium control group (without cells) as the spontaneous release control. Calculate the LDH release percentage of each apoptotic vesicle concentration group. The formula for calculating the LDH release rate is as follows:

[0136] LDH release rate (%) = (OD value of the experimental group - OD value of spontaneous release) / (OD value of maximum release - OD value of spontaneous release) × 100%;

[0137] Among them, an increase in the LDH release rate indicates damage to the cell membrane integrity and has a cytotoxic effect.

[0138] Technical effect: The LDH release assay can quantitatively evaluate the effect of apoptotic vesicles on cell membrane integrity and reflect their cytotoxic effects. By detecting the LDH release rate after treatment with different concentrations of apoptotic vesicles, the cytotoxicity magnitude and dose-effect relationship of apoptotic vesicles can be evaluated. Compared with traditional cell viability staining methods, the LDH release assay can more directly reflect the degree of cell membrane damage and more sensitively detect cytotoxicity.

[0139] (4) Detect the cell metabolic activity using the MTT assay. The MTT assay is a classic method for detecting cell metabolic activity. Its principle is based on the fact that succinate dehydrogenase in the mitochondria of living cells can reduce MTT (thiazolyl blue) to purple formazan crystals. The amount of formazan crystals generated is positively correlated with the number and metabolic activity of living cells. The more specific steps are as follows: (4-1) Cell culture and drug addition: In a 96-well plate, inoculate primary chondrocytes and primary immune cells according to the same ratio as constructed in the in vitro co-culture system. After the cells adhere, add apoptotic vesicles at different concentrations (0 μg / mL, 10 μg / mL, 50 μg / mL, 100 μg / mL). (4-2) Addition and incubation of MTT solution: At different time points (24 hours, 48 hours, 72 hours), add 20 μL of MTT solution (5 mg / mL) to each well. Place the culture plate in an incubator and incubate at 37 °C for 4 hours. (4-3) Dissolution of formazan and determination of absorbance: Carefully aspirate the supernatant in the culture wells, add 150 μL of dimethyl sulfoxide (DMSO) to each well, and shake for 10 minutes to fully dissolve the purple formazan crystals. Use an enzyme-linked immunosorbent assay (ELISA) reader to measure the absorbance value of each well at a wavelength of 570 nm. Analyze the differences in cell metabolic activity among groups. Cell metabolic activity is usually directly represented by the absorbance value. The higher the absorbance value, the stronger the cell metabolic activity.

[0140] Technical effect: The MTT assay can reflect the effect of apoptotic vesicles on cell metabolic activity. By detecting the cell metabolic activity after treatment with apoptotic vesicles at different time points and different concentrations, it is possible to evaluate whether apoptotic vesicles have an effect on cell metabolic function, as well as the dose-effect relationship and time-effect relationship. The MTT assay is a classic and mature method for detecting cell metabolic activity, with simple operation and reliable results, and is widely used in cell biology research.

[0141] 3. When detecting the functional indicators of chondrocytes in step S2, evaluate the effect of apoptotic vesicles on the function of primary chondrocytes from multiple levels such as genes, proteins, and histochemical staining, and focus on the balance of anabolic and catabolic metabolism of the cartilage matrix. Specifically as follows:

[0142] (1) Detect the expression level of key genes using quantitative PCR. Quantitative polymerase chain reaction (qPCR) is a nucleic acid quantification technique with high sensitivity and high specificity, which can accurately detect the expression level of genes. In this example, qPCR technology is used to detect the expression level of key genes related to anabolic and catabolic metabolism of the cartilage matrix in primary chondrocytes, and to evaluate the effect of apoptotic vesicles on chondrocyte function. The more specific steps are as follows:

[0143] (1-1), RNA extraction and reverse transcription: Total RNA was extracted from co-cultured cells, and the RNA was reverse transcribed into complementary deoxyribonucleic acid (cDNA) using a reverse transcription kit. The cDNA was used as the template for qPCR.

[0144] (1-2), Primer design and synthesis: Specific qPCR primers were designed according to the sequences of the target genes in the GenBank database. The primer sequences were subjected to Blast alignment to ensure their specificity and avoid primer dimers and non-specific amplification. The primers were synthesized by a professional company. The following key genes were selected for detection in this example: Markers of anabolic metabolism of cartilage matrix: COL2A1 (type II collagen), ACAN (aggrecan), SOX9 (transcription factor SOX9). Markers of catabolic metabolism of cartilage matrix: ADAMTS5 (ADAMTS metalloproteinase 5), MMP13 (matrix metalloproteinase 13), MMP3 (matrix metalloproteinase 3). Reference gene: GAPDH (glyceraldehyde-3-phosphate dehydrogenase) or β-actin (β-actin). The reference gene was used to correct the differences in the initial amount of RNA and reverse transcription efficiency among different samples.

[0145] (1-3), qPCR reaction and data analysis: The qPCR reaction was performed using a real-time fluorescence quantitative PCR instrument. The qPCR reaction system usually included cDNA template, primers, fluorescent dye (such as SYBR Green), and qPCR Master Mix. The qPCR reaction program usually included steps such as pre-denaturation, denaturation, annealing, and extension. The relative expression level of the gene was calculated using the 2 (-ΔΔCt) method, with the untreated control group as the reference. The calculation method of ΔCt was: ΔCt = Ct (target gene) - Ct (reference gene). The Ct value (cycle threshold) refers to the number of cycles experienced when the fluorescence signal intensity reaches the set threshold in the PCR reaction. The reference gene is a gene with relatively stable expression in various tissues or cells and is used to correct the differences in factors such as sample loading amount and reaction efficiency. The calculation method of ΔΔCt was: ΔΔCt = ΔCt (experimental group) - ΔCt (control group). By calculating ΔΔCt and then substituting it into the 2 (-ΔΔCt) formula, the relative expression level of the target gene in the experimental group relative to the control group could be obtained, thereby analyzing the expression changes of the gene under different conditions.

[0146] Technical effect: The qPCR technique can sensitively and accurately detect the effect of apoptotic vesicles on the expression of key genes in chondrocytes. By detecting the expression levels of genes related to the anabolic and catabolic metabolism of the cartilage matrix, it is possible to evaluate whether apoptotic vesicles have the effect of promoting cartilage matrix synthesis or inhibiting cartilage matrix degradation, as well as their impact on chondrocyte function. Changes in gene expression levels are early indicators of changes in cell function, and qPCR detection can early and sensitively reflect the biological effects of apoptotic vesicles.

[0147] (2) Detect the protein expression level using Western Blotting. Western Blotting (protein blotting) is a commonly used protein detection technique that can detect the expression level of specific proteins. In this example, the Western Blotting technique is used to detect the expression levels of key proteins related to the anabolic and catabolic metabolism of the cartilage matrix in primary chondrocytes, further verifying the qPCR results and evaluating the impact of apoptotic vesicles on chondrocyte function at the protein level. The more specific steps are as follows:

[0148] (2-1) Protein extraction and quantification: Extract total protein from co-cultured cells, and use a BCA protein concentration assay kit to measure the protein concentration, and adjust the protein concentrations of each sample to be the same.

[0149] (2-2) SDS-PAGE electrophoresis and membrane transfer: Take an equal amount of protein sample (30-50 μg) for separation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Transfer the separated protein to a polyvinylidene fluoride (PVDF) membrane.

[0150] (2-3) Blocking and primary antibody incubation: Use 5% skim milk powder or 5% bovine serum albumin (BSA) to block the PVDF membrane at room temperature for 1 hour to block non-specific binding sites on the membrane. Add the primary antibody and incubate overnight at 4°C. The primary antibody is an antibody that specifically recognizes the target protein. The following primary antibodies are used in this example: Cartilage matrix protein antibodies: anti-COL2A1, anti-Aggrecan. Catabolic enzyme antibodies: anti-ADAMTS5, anti-MMP13, anti-MMP3. Internal reference protein antibodies: anti-GAPDH or anti-β-actin. The internal reference protein is used to correct the difference in protein loading amounts between different samples.

[0151] (2-4) Washing and secondary antibody incubation: Wash the PVDF membrane 3 times with Tris-buffered saline Tween-20 (TBST), 10 minutes each time, to remove unbound primary antibody. Add the corresponding horseradish peroxidase (HRP)-labeled secondary antibody and incubate at room temperature for 1 hour. The secondary antibody is an antibody that specifically recognizes the primary antibody, and the HRP-labeled secondary antibody can catalyze the chemiluminescent substrate to emit light.

[0152] (2-5), Washing and ECL Development: Wash the PVDF membrane 3 times with TBST for 10 minutes each time to remove unbound secondary antibody. Add enhanced chemiluminescence reagent (ECL) dropwise onto the PVDF membrane to allow the chemiluminescent substrate to react with HRP to emit light. Use a gel imaging system to collect the chemiluminescent signal image.

[0153] (2-6), Image Analysis and Data Quantification: Use image analysis software (such as ImageJ) to measure the gray value of each band. Calculate the ratio of the gray value of the target protein band to the gray value of the internal reference protein band to reflect the relative expression level of the target protein.

[0154] Technical Effect: Western Blotting technology can detect the effect of apoptotic vesicles on the expression of key proteins in chondrocytes. The protein expression level is a direct manifestation of cell function execution. The Western Blotting results can further verify the qPCR results and more directly evaluate the effect of apoptotic vesicles on chondrocyte function at the protein level. The change in protein expression level usually lags behind the change in gene expression level, and Western Blotting detection can reflect the late effect of apoptotic vesicle action.

[0155] (3) Detection is carried out by immunofluorescence of cells. Immunofluorescence staining of cells is a commonly used in-situ protein detection technique for cells, which can detect the expression and distribution of specific proteins in cells. In this example, the immunofluorescence staining technique of cells is used to detect the expression and distribution of cartilage matrix proteins in primary chondrocytes, and to evaluate the effect of apoptotic vesicles on the function of chondrocytes from the level of cell morphology. The more specific steps are as follows: (3-1) Cell fixation and permeabilization: Co-cultured cells are cultured on slides. After washing with PBS, the cells are fixed with 4% paraformaldehyde for 20 minutes. The cells are permeabilized with 0.1% Triton X-100 for 10 minutes to increase the permeability of antibodies entering the cells. (3-2) Blocking and incubation with primary antibody: Blocking is carried out with 5% BSA for 1 hour to block non-specific binding sites. The primary antibodies (such as anti-COL2A1, anti-Aggrecan, etc.) are added and incubated overnight at 4°C. (3-3) Washing and incubation with secondary antibody: Wash 3 times with PBS for 5 minutes each time to remove the unbound primary antibody. Fluorescently labeled secondary antibodies (such as secondary antibodies labeled with fluorescein isothiocyanate (FITC)) are added and incubated in the dark at room temperature for 1 hour. The fluorescently labeled secondary antibodies can specifically bind to the primary antibodies and emit fluorescent signals. (4-4) Washing and DAPI nuclear staining: Wash 3 times with PBS for 5 minutes each time to remove the unbound secondary antibody. The nuclei are stained with 4’,6-diamidino-2-phenylindole (DAPI) for 5 minutes. DAPI can bind to DNA and emit blue fluorescence, which is used for the localization of cell nuclei. (4-5) Mounting and microscopic observation: Mount the slides with an anti-fluorescence quenching mounting medium. Observe and take pictures with a fluorescence microscope or a confocal microscope. The excitation wavelength and emission wavelength are selected according to the fluorescent dyes used. Analyze the expression and distribution of cartilage matrix proteins in cells.

[0156] Technical effect: The immunofluorescence staining technique of cells can visually observe the effect of apoptotic vesicles on the expression and distribution of cartilage matrix proteins in chondrocytes. Through observation with a fluorescence microscope, the localization of cartilage matrix proteins in cells, such as the cytoplasm, extracellular matrix, etc., can be understood, and whether apoptotic vesicles affect the normal secretion and assembly of cartilage matrix proteins can also be known. The results of immunofluorescence staining of cells can supplement the results of qPCR and Western Blotting from the level of cell morphology and more comprehensively evaluate the effect of apoptotic vesicles on the function of chondrocytes.

[0157] (4) Use histochemical staining. Histochemical staining is a commonly used method for qualitative and quantitative analysis of tissue and cell components. In this example, the histochemical staining method is used to detect the main components of the cartilage matrix in primary chondrocyte cultures, such as the content of proteoglycans and glycosaminoglycans, and to evaluate the effect of apoptotic vesicles on chondrocyte function at the histochemical level. The more specific steps are as follows: (4-1) Toluidine blue staining: Toluidine blue is a cationic dye that can bind to proteoglycans in the cartilage matrix and show a blue-violet color. After cell fixation, add 0.5% toluidine blue staining solution (pH 4.0), and stain at room temperature for 30 minutes. Wash with distilled water, dehydrate with gradient alcohol, clear with xylene, and mount with neutral gum. Observe under an optical microscope, and the blue-violet area represents the content of proteoglycans. (4-2) Alcian blue staining: Alcian blue is a copper phthalocyanine dye that can bind to glycosaminoglycans in the cartilage matrix and show a blue color. After cell fixation, add 1% alcian blue staining solution (pH 2.5), and stain at room temperature for 30 minutes. Wash with distilled water, dehydrate with gradient alcohol, clear with xylene, and mount with neutral gum. Observe under an optical microscope, and the blue area represents the content of glycosaminoglycans. (4-3) Safranin O / Fast Green staining: Safranin O is a cationic dye that can bind to proteoglycans in the cartilage matrix and show a red color. Fast Green is an anionic dye that can bind to cytoplasm and cell nuclei and show a green color. After cell fixation, add 0.1% safranin O staining solution and 0.5% fast green staining solution in sequence, and stain at room temperature. Wash with distilled water and mount. Observe under an optical microscope, the red area represents proteoglycans, and the green area represents proteins. (4-4) Staining quantitative analysis: Use image analysis software (such as ImageJ) to perform grayscale scanning and color separation on the stained images. Calculate the percentage of the specific stained area in the total cell area, or measure the grayscale value of the staining intensity. Compare the differences in staining intensity between different experimental groups to evaluate the effect of apoptotic vesicles on the components of the cartilage matrix.

[0158] Technical effect: The histochemical staining method can intuitively show the effect of apoptotic vesicles on the components of the cartilage matrix of chondrocytes. Through toluidine blue staining, alcian blue staining, and safranin O / fast green staining, the content changes of proteoglycans and glycosaminoglycans can be detected respectively to evaluate whether apoptotic vesicles affect the synthesis and deposition of the cartilage matrix. Combining image analysis software for staining quantitative analysis can more objectively and quantitatively evaluate the effect of apoptotic vesicles on the components of the cartilage matrix. The results of histochemical staining can supplement the detection results at the gene and protein levels at the tissue level and more comprehensively evaluate the effect of apoptotic vesicles on chondrocyte function.

[0159] 4. When detecting the immune cell function index in step S2, evaluate the effect of apoptotic vesicles on the function of primary immune cells in the co-culture system, and focus on the phenotype of immune cells, cytokine secretion, and related gene expression. The specific steps are as follows:

[0160] (1) Detect surface markers of immune cells using flow cytometry. Flow cytometry can not only be used to detect cell apoptosis, but also to detect the expression levels of cell surface markers, thereby analyzing the phenotype and functional status of cells. In this example, flow cytometry is used to detect the expression of primary immune cell surface markers and evaluate the effect of apoptotic vesicles on the phenotype of immune cells. The more specific steps are as follows:

[0161] (1-1) Cell collection and staining: Collect co-cultured cells, resuspend them in flow buffer after washing with PBS. Add a fluorescently labeled antibody mixture and incubate at 4°C in the dark for 30 minutes. The antibody mixture includes: 1. Macrophage phenotype markers: anti-CD86-FITC (marker for M1 macrophages), anti-CD206-PE (marker for M2 macrophages), anti-CD68-APC (general macrophage marker); 2. Synoviocyte activation markers: anti-CD55-FITC, anti-VCAM-1-PE, etc.; 3. Cell identity markers: anti-CD45-APC (immune cells), anti-CD44-FITC (chondrocytes), etc.

[0162] (1-2) Flow cytometry detection and data analysis: Wash twice with PBS and resuspend in flow buffer. Use flow cytometry for detection. Gate immune cells according to the cell identity marker (CD45+) and analyze the expression levels of each surface marker in immune cells. Calculate the following data: Data 1. M1 / M2 phenotype ratio: the ratio of the number of CD86+CD206+ cells. M1 macrophages are generally considered pro-inflammatory macrophages, and M2 macrophages are generally considered anti-inflammatory macrophages. An increase in the M1 / M2 ratio indicates a shift in the immune environment towards a pro-inflammatory direction, and a decrease indicates a shift towards an anti-inflammatory direction. Data 2. Proportion of activated synoviocytes: the proportion of CD55+VCAM-1+ cells in total CD45+ cells. CD55 and VCAM-1 are synoviocyte activation markers, and an increase in their expression indicates an increase in synoviocyte activation, which may be involved in the inflammatory response.

[0163] Technical effect: Flow cytometry can quantitatively analyze the effect of apoptotic vesicles on the phenotype of immune cells. By detecting the M1 / M2 phenotype ratio of macrophages and the expression levels of synoviocyte activation markers, it is possible to evaluate whether apoptotic vesicles have the effect of regulating immune cell polarization and synoviocyte activation, as well as their impact on the immune microenvironment. The phenotype of immune cells is an important determinant of immune cell function, and flow cytometry detection can reflect the immunomodulatory effect of apoptotic vesicles at the cell phenotype level.

[0164] (2) Detect the cytokine secretion level using ELISA or Cytokine Array. Enzyme-linked immunosorbent assay (ELISA) and Cytokine Array are commonly used methods for cytokine detection, which can quantitatively detect the concentration of cytokines in cell culture supernatants. In this example, ELISA or Cytokine Array is used to detect the cytokine secretion level in the co-culture supernatant to evaluate the effect of apoptotic vesicles on the cytokine secretion function of immune cells. The more specific steps are as follows:

[0165] (2-1) Collection of culture supernatant and preparation of reagents: Collect the co-culture supernatant and perform the detection according to the ELISA kit instructions or the Cytokine Array operation guide. ELISA kits usually contain coated antibodies, standards, enzyme-labeled antibodies, substrate solutions, and stop solutions. Cytokine Arrays usually contain a variety of cytokine antibodies pre-spotted on the membrane.

[0166] (2-2) ELISA detection steps: Operate according to the ELISA kit instructions, including steps such as coating, blocking, sample addition, incubation, washing, enzyme reaction, color development, and termination. Use an enzyme-linked immunosorbent assay reader to measure the absorbance value at a specific wavelength. Calculate the cytokine concentration according to the standard curve.

[0167] (2-3) Cytokine Array detection steps: Operate according to the Cytokine Array operation guide, including steps such as membrane blocking, sample addition, incubation, washing, secondary antibody incubation, color development, and image acquisition. Use image analysis software to analyze the dot signal intensity and calculate the cytokine concentration according to the standards.

[0168] (2-4) Cytokine selection and data analysis: Detect the concentrations of the following key cytokines: 1 - Pro-inflammatory cytokines: Tumor necrosis factor-α (TNF-α), Interleukin-6 (IL-6), Interleukin-1β (IL-1β). 2 - Anti-inflammatory cytokines: Interleukin-10 (IL-10), Interleukin-4 (IL-4), Transforming growth factor-β (TGF-β). Calculate the pro-inflammatory / anti-inflammatory cytokine ratio (PIR):

[0169]

[0170] where C1, C2, C3, D1, D2, D3 are weight coefficients, and C1 + C2 + C3 = 1, D1 + D2 + D3 = 1; the concentration of each factor is in pg / mL, multiplied by the corresponding weight coefficient. The higher the PIR value, the stronger the inflammatory response, and the lower the value, the more obvious the anti-inflammatory effect.

[0171] Technical effect: ELISA and Cytokine Array technologies can quantitatively detect the effect of apoptotic vesicles on the cytokine secretion function of immune cells. By detecting the secretion levels of pro-inflammatory cytokines and anti-inflammatory cytokines, and calculating the pro-inflammatory / anti-inflammatory cytokine ratio, it is possible to evaluate whether apoptotic vesicles have the effect of regulating the inflammatory response of immune cells and their impact on the inflammatory microenvironment. Cytokines are important molecules for the execution of immune cell functions, and changes in cytokine secretion levels can directly reflect the functional state of immune cells.

[0172] (3) Detect the expression of immune cell-related genes using quantitative PCR. Similar to the detection of chondrocyte function indicators, in this example, quantitative PCR technology is also used to detect the expression levels of key genes related to immune function in primary immune cells to evaluate the effect of apoptotic vesicles on immune cell function. The more specific steps are as follows:

[0173] (3-1) Isolation of immune cells and RNA extraction: Use magnetic bead sorting or flow cytometry sorting technology to isolate primary immune cells from the co-culture system. Extract the total RNA of immune cells and synthesize cDNA using a reverse transcription kit.

[0174] (3-2) Primer design and qPCR reaction: Design specific qPCR primers for detecting the expression levels of the following key genes: 1 - Pro-inflammatory genes: TNF, IL6, IL1B, CXCL10 (CXCL chemokine 10), NOS2 (inducible nitric oxide synthase). 2 - Anti-inflammatory genes: IL10, IL4, TGFB1, ARG1 (arginase 1), MRC1 (mannose receptor type C 1). 3 - Chemokines and their receptors: CCL2 (C-C motif chemokine ligand 2), CCR2 (C-C chemokine receptor 2), CXCL12 (CXCL chemokine 12), CXCR4 (CXCL chemokine receptor 4). 4 - Reference gene: GAPDH or β-actin.

[0175] (3-3) qPCR data analysis and calculation of immune balance index: Perform qPCR reactions using a real-time fluorescence quantitative PCR instrument. The relative expression levels of genes are calculated using the 2 (-ΔΔCt) method to calculate the relative expression amount, with the untreated control group as the benchmark. Calculate the immune balance index (IBP, Immune Balance Profile):

[0176] IBP = (mean relative expression of pro-inflammatory genes) / (mean relative expression of anti-inflammatory genes);

[0177] IBP > 1 indicates that pro-inflammation is dominant, and IBP < 1 indicates that anti-inflammation is dominant.

[0178] Technical effect: The qPCR technique can sensitively and accurately detect the impact of apoptotic vesicles on the expression of key genes in immune cells. By detecting the expression levels of pro-inflammatory genes, anti-inflammatory genes, chemokines, and their receptor genes, and calculating the immune balance index, it is possible to evaluate whether apoptotic vesicles have the effect of regulating the inflammatory response and chemotactic migration ability of immune cells, as well as their impact on the immune microenvironment. Changes in gene expression levels are early indicators of changes in immune cell function, and qPCR detection can early and sensitively reflect the immunomodulatory effect of apoptotic vesicles.

[0179] 4. When performing the cell migration experiment in step S2, evaluate the impact of apoptotic vesicles on the migration ability of primary chondrocytes and primary immune cells. Use two methods, the scratch wound healing assay and the Transwell assay, to evaluate the changes in cell migration ability from different perspectives. The specific steps are as follows:

[0180] (1). Perform the scratch wound healing assay. The scratch wound healing assay is a commonly used method for detecting cell migration ability. Its principle is to artificially create a scratch on a monolayer of cells and observe the ability of the cells to migrate into the scratched area and repair the scratch. The stronger the cell migration ability, the faster the scratch wound healing rate. The more specific steps are as follows: (1-1). Cell culture and scratch making: Culture primary chondrocytes or primary immune cells in a 6-well plate until 80-90% confluence. Use a 200 μL sterile pipette tip to vertically scratch a straight line on the monolayer of cells. Gently wash twice with PBS to remove detached cells. (1-2). Adding drugs and taking pictures: Add serum-free medium containing different concentrations of apoptotic vesicles (0 μg / mL, 10 μg / mL, 50 μg / mL, 100 μg / mL) (to inhibit cell proliferation and ensure that the observed effect is the migration effect). At 0 hours, 12 hours, 24 hours, and 48 hours after scratch making, take pictures at a fixed position using an inverted microscope. (1-3). Image analysis and calculation of the healing rate: Use image analysis software (such as ImageJ) to measure the scratch width. Calculate the healing rate:

[0181] Healing rate (%) = (Scratch width at 0 hour - Scratch width at Th hour) / Scratch width at 0 hour × 100%

[0182] Among them, compare the differences in the healing rates of different apoptotic vesicle concentration groups to evaluate their impact on cell migration ability.

[0183] Technical effect: The scratch wound healing assay can intuitively and simply evaluate the impact of apoptotic vesicles on the two-dimensional migration ability of cells. By observing the scratch wound healing rate, it is possible to understand whether apoptotic vesicles have the effect of promoting or inhibiting cell migration, as well as the dose-effect relationship and time-effect relationship. The scratch wound healing assay simulates the cell migration process on a two-dimensional plane and is suitable for evaluating the migration ability of cells on the tissue surface or vascular endothelium.

[0184] (2) Perform the Transwell experiment. The Transwell experiment is a commonly used method for detecting the chemotactic migration ability of cells. Its principle is to use a Transwell chamber. Cells are placed in the upper chamber, and a chemokine or the substance to be tested is added to the lower chamber. Observe the ability of cells to migrate through the pores of the Transwell membrane into the lower chamber. The stronger the chemotactic migration ability of the cells, the more cells migrate into the lower chamber. The more specific steps are as follows: (2-1) Preparation of the Transwell chamber and cell seeding: Use a Transwell chamber with an 8 μm pore size. Add 1×10 5 primary chondrocytes or primary immune cells (suspended in serum-free medium) to the upper chamber. (2-2) Addition of chemotactic agent and culture: Add complete medium containing apoptotic vesicles (0 μg / mL, 10 μg / mL, 50 μg / mL, 100 μg / mL) to the lower chamber as the chemotactic agent. Culture at 37 °C for 12 - 24 hours. (2-3) Cell fixation and staining: Carefully remove the non-migrated cells in the upper chamber. Fix the cells that have migrated to the lower surface of the membrane and stain them with crystal violet. (2-4) Cell counting and calculation of migration index: Randomly select 5 fields of view (200×) under an optical microscope to take pictures and count the number of migrated cells, or measure the OD value after dissolving the dye. Calculate the migration index (Migration Index):

[0185] Migration Index = Number of migrated cells in the experimental group / Number of migrated cells in the control group

[0186] Among them, the change in the migration index of different apoptotic vesicle concentration groups reflects the intensity of their chemotactic effect.

[0187] Technical effect: The Transwell experiment can quantitatively evaluate the effect of apoptotic vesicles on the chemotactic migration ability of cells. By detecting the number of cells that have migrated to the lower chamber of the Transwell membrane, it is possible to understand whether apoptotic vesicles have the effect of chemotaxis or inhibition of cell migration, as well as the dose-effect relationship. The Transwell experiment simulates the migration process of cells in three-dimensional space and is suitable for evaluating the migration ability of cells in tissue spaces or inflammatory sites. Compared with the scratch wound healing experiment, the Transwell experiment can more precisely control the concentration gradient of chemokines and more quantitatively evaluate the cell migration ability.

[0188] 4. When performing the cell uptake experiment in step S2, evaluate the uptake ability and intracellular localization of apoptotic vesicles by primary chondrocytes and primary immune cells. Use methods such as fluorescently labeled apoptotic vesicles, fluorescence microscopy observation, flow cytometry quantitative analysis, and confocal microscopy observation to reveal the interaction between cells and apoptotic vesicles from different levels. The specific steps are as follows:

[0189] (1) Fluorescent labeling of apoptotic vesicles. To trace the distribution and uptake process of apoptotic vesicles in cells, it is necessary to fluorescently label the apoptotic vesicles. In this example, the lipophilic fluorescent dyes PKH26 (red) or PKH67 (green) are used to label apoptotic vesicles. The more specific steps are as follows: (1-1) Dye dilution and vesicle mixing: Mix the apoptotic vesicle suspension (1 mg / mL) with PKH26 or PKH67 dye at a volume ratio of 1:1. The dye is pre-diluted to the working concentration with a dye diluent. (1-2) Incubation and termination of the reaction: Incubate in the dark at room temperature for 5 minutes to allow the dye to fully insert into the apoptotic vesicle membrane. Add an equal volume of 1% BSA solution to terminate the labeling reaction. BSA can bind unbound dye and reduce non-specific labeling. (1-3) Ultracentrifugation and resuspension: Ultracentrifuge at 100,000 g for 70 minutes to remove free dye. Collect the precipitate and resuspend it with PBS to obtain fluorescently labeled apoptotic vesicles. (1-4) Vesicle characterization: Use nanoparticle tracking analysis (NTA) or dynamic light scattering (DLS) technology to verify the integrity and particle size distribution of the vesicles after labeling. Ensure that the labeling process does not affect the structure and biological activity of the vesicles.

[0190] Technical effect: Fluorescently labeling apoptotic vesicles is a key step in cell uptake experiments. PKH26 and PKH67 are commonly used lipophilic fluorescent dyes that can stably insert into cell membranes or vesicle membranes, have high fluorescence intensity and good photostability, and are suitable for cell tracing and live cell imaging. Through fluorescent labeling, the distribution and uptake process of apoptotic vesicles in cells can be clearly observed.

[0191] (2) Observation of the uptake process using a fluorescence microscope. Fluorescence microscope observation can visually show the distribution and uptake process of apoptotic vesicles in cells. In this example, a fluorescence microscope is used to observe the distribution of fluorescently labeled apoptotic vesicles in primary chondrocytes and primary immune cells. The more specific steps are as follows: (2-1) Cell co-culture and addition of labeled vesicles: Add fluorescently labeled apoptotic vesicles (50 μg / mL) to the co-culture system. (2-2) Observation at time points and washing: At 2 hours, 6 hours, 12 hours, and 24 hours after addition, remove the culture medium and wash twice with PBS to remove unuptaken vesicles. (2-3) Cell fixation and nuclear staining: Fix the cells with 4% paraformaldehyde for 20 minutes, and stain the nuclei with DAPI after permeabilization. (2-4) Immunofluorescent staining: Simultaneously perform immunofluorescent staining to distinguish different cell types. Use anti-CD44 antibody (labeling chondrocytes) and anti-CD45 antibody (labeling immune cells). (2-5) Fluorescence microscope observation and image analysis: Observe and take pictures under a fluorescence microscope. Analyze the distribution of apoptotic vesicles in different cell types at different time points. Use image analysis software to calculate the fluorescence intensity and semi-quantitatively evaluate the uptake efficiency.

[0192] Technical effect: Fluorescence microscopy can visually display the distribution differences and temporal dynamic changes of apoptotic vesicles in different cell types. By observing the intensity and distribution of fluorescence signals, the uptake ability of apoptotic vesicles by different cell types and the time-dependence of the uptake process can be preliminarily judged. Combining immunofluorescence staining can distinguish the uptake differences of apoptotic vesicles by different cell types.

[0193] (3) Use flow cytometry to quantitatively analyze the uptake efficiency. Flow cytometry can not only be used to detect cell phenotypes and apoptosis, but also to quantitatively analyze the uptake efficiency of cells for fluorescent markers. In this example, flow cytometry was used to quantitatively analyze the uptake efficiency of primary chondrocytes and primary immune cells for fluorescently labeled apoptotic vesicles. The more specific steps are as follows: (3-1) Cell co-culture and addition of labeled vesicles: Add PKH26-labeled apoptotic vesicles to the co-culture system and incubate for different times (2 hours, 6 hours, 12 hours, 24 hours). (3-2) Cell collection and washing: Collect the cells and wash them 2 times with PBS to remove un-uptaken vesicles. (3-3) Cell surface labeling: Stain with fluorescently labeled anti-CD44 antibody (chondrocytes) and anti-CD45 antibody (immune cells) to distinguish different cell types. (3-4) Flow cytometry detection and data analysis: Detect with a flow cytometer. Distinguish different cell types according to cell surface labeling and measure the PKH26 fluorescence intensity in each type of cell. Calculate the uptake rate (UR) and uptake intensity (UI):

[0194] Uptake rate (%) = number of PKH26-positive cells / total number of cells × 100%

[0195] Uptake intensity = mean fluorescence intensity (MFI) of PKH26-positive cells

[0196] Among them, compare the uptake ability of apoptotic vesicles by different cell types and the influence of apoptotic vesicles at different concentrations on the uptake efficiency.

[0197] Technical effect: Flow cytometry can quantitatively and high-throughput analyze the uptake efficiency of cells for apoptotic vesicles. By calculating the uptake rate and uptake intensity, the differences in the uptake ability of apoptotic vesicles by different cell types and the influence of apoptotic vesicle concentration and incubation time on the uptake efficiency can be quantitatively compared. The quantitative analysis results of flow cytometry can supplement the semi-quantitative results of fluorescence microscopy and more accurately evaluate the ability of cells to uptake apoptotic vesicles.

[0198] (4) Use a confocal microscope to observe the intracellular localization of vesicles. The confocal microscope has an optical sectioning function and can clearly observe the internal structure of cells and the three-dimensional distribution of fluorescence signals. In this example, a confocal microscope is used to observe the precise localization of fluorescently labeled apoptotic vesicles within cells and their co-localization with different intracellular organelles, revealing the intracellular transport pathway and mechanism of action of apoptotic vesicles. The more specific steps are as follows:

[0199] (4-1) Cell culture and addition of labeled vesicles: Culture cells in a glass-bottomed dish and add PKH26-labeled apoptotic vesicles (50 μg / mL).

[0200] (4-2) Observation at different time points and labeling of organelles: After incubation at different time points (2 hours, 6 hours, 12 hours, 24 hours), wash with PBS. Use the following specific fluorescent probes to label different intracellular organelles: 1 - LysoTracker Green (labels lysosomes, green fluorescence); 2 - MitoTracker Green (labels mitochondria, green fluorescence); 3 - ER-Tracker Green (labels endoplasmic reticulum, green fluorescence); 4 - Hoechst 33342 (labels nuclei, blue fluorescence).

[0201] (4-3) Confocal microscopy imaging and three-dimensional reconstruction: Perform live cell confocal microscopy imaging and collect consecutive optical sections along the Z-axis. Analyze the precise localization of apoptotic vesicles within cells and the co-localization rate with each organelle by three-dimensional reconstruction.

[0202] (4-4) Co-localization analysis and calculation of co-localization coefficient: Use image analysis software (such as the JACoP plugin of ImageJ) for co-localization analysis. Calculate the vesicle-organelle co-localization coefficient:

[0203] Co-localization coefficient (%) = Number of co-localized pixels / Total number of apoptotic vesicle pixels × 100%

[0204] Among them, based on the co-localization analysis results, infer the main transport pathway and possible mechanism of action of apoptotic vesicles within cells. For example, a high co-localization rate with lysosomes suggests that apoptotic vesicles may be degraded through the lysosomal pathway; a high co-localization rate with mitochondria suggests that apoptotic vesicles may affect mitochondrial function.

[0205] Technical effect: Confocal microscopy observation can display the precise localization of apoptotic vesicles within cells and their interaction with organelles at high resolution. Through co-localization analysis, the intracellular transport pathway and mechanism of action of apoptotic vesicles can be revealed. For example, whether they enter cells through endocytosis and whether they interact with organelles such as lysosomes, mitochondria, or endoplasmic reticulum. The results of confocal microscopy observation can provide an in-depth understanding of the cell biological effects of apoptotic vesicles at the organelle level.

[0206] In addition, in step S2, in order to integrate and analyze the above multi-dimensional experimental data systematically, this embodiment introduces a multi-omics data integration and analysis framework (MIDAS). The MIDAS framework can standardize and weight the data in multiple dimensions such as cell viability, functional indicators, immune responses, and cell interactions to form a comprehensive evaluation index for comprehensively evaluating the biological effects of apoptotic vesicles. The core calculation formula of the MIDAS framework is:

[0207] MIDAS = Σ(W i ×N i )

[0208] where W i is the weight coefficient of each dimension, N i is the standardized score of each dimension, and the scores of each dimension are calculated based on indicators such as the apoptosis rate, proliferation rate, LDH release rate, synthesis / decomposition metabolism balance index, matrix staining intensity, pro-inflammatory / anti-inflammatory cytokine ratio, immune balance index, migration index, vesicle uptake rate, and uptake intensity index respectively.

[0209] Technical effect: The introduction of the MIDAS framework solves the problems of scattered data and difficulty in comprehensive evaluation in traditional analysis methods. By integrating multi-dimensional data into a comprehensive score, the MIDAS framework can more comprehensively and objectively evaluate the biological effects of apoptotic vesicles, improving the reliability and comparability of evaluation results. The MIDAS framework also has scalability and can flexibly adjust the dimension weights and score calculation methods according to research needs to adapt to different experimental purposes and data types.

[0210] In addition, in step S2, in order to understand the dynamic effects and interaction relationships of apoptotic vesicles on chondrocytes and immune cells more deeply, this embodiment establishes an apoptotic vesicle dynamic effect topological network model (DETN) to describe the direct effects of apoptotic vesicles on chondrocytes and immune cells and the indirect effects of cell-cell interactions. The model adopts a weighted directed graph structure, where nodes represent cell types or key biological processes, and edges represent the action relationships and intensities. The effect transfer function is:

[0211] E(i→j) = w i→j ×A i ×f(t)

[0212] where w i→j is the edge weight from node i to j, A i is the activity value of node i, and f(t) is a time function.

[0213] The DETN model can regard apoptotic vesicles, chondrocytes, immune cells, and key biological processes as network nodes. By defining the interaction relationships and intensities between nodes, it simulates the process of biological effect transmission mediated by apoptotic vesicles, and reveals the dynamic characteristics of complex biological systems.

[0214] Technical effects: The introduction of the DETN model overcomes the limitation of traditional linear analysis methods that cannot reveal the network characteristics of complex biological systems. By constructing a dynamic effect topological network, the DETN model can intuitively display the interaction relationships between apoptotic vesicles, chondrocytes, and immune cells, reveal the dynamic change process and long-term effects of the actions of apoptotic vesicles, and provide a new analysis tool for in-depth understanding of their action mechanisms and long-term effects. The DETN model also has predictive ability, and can predict the long-term effects and key influencing factors of apoptotic vesicles, providing guidance for optimizing the preparation and application of apoptotic vesicles.

[0215] In addition, in step S2, in order to comprehensively evaluate the balance between the safety and effectiveness of apoptotic vesicles and provide a basis for subsequent surgical risk assessment, this embodiment establishes an Apoptotic Vesicle Safety-Benefit Evaluation (ASBE) model, and its threshold relational expression is:

[0216] ASBE = [E b × a - S i × β] × [1 - γ × (C - C opt ) 2

[0217] Among them, E b is the benefit score, S i is the safety risk score, C is the concentration of apoptotic vesicles, Copt is the optimal concentration, and α, β, and γ are weight coefficients;

[0218] And it is evaluated according to the criteria that ASBE > 6 is highly recommended for clinical translational research, 3 ≤ ASBE ≤ 6 is considered for clinical translational research but needs further optimization, and ASBE < 3 is not recommended for clinical translational research.

[0219] The ASBE model can comprehensively consider the effectiveness and potential toxicity of apoptotic vesicles, and through the threshold relational expression, quantitatively evaluate the safety-benefit balance of apoptotic vesicles, providing a reference for clinical translational decision-making.

[0220] Technical effect: The introduction of the ASBE model solves the problems of the separation of effectiveness and safety evaluations and the lack of a comprehensive evaluation standard in traditional evaluation methods. By integrating effectiveness and safety into a comprehensive score, the ASBE model can more comprehensively and objectively evaluate the safety-benefit balance of apoptotic vesicles, providing a more scientific and reasonable basis for clinical translation decisions. The ASBE model is also adjustable and can flexibly adjust the weight coefficients and threshold settings according to different clinical application scenarios to adapt to different disease severities and treatment requirements.

[0221] Through the above multi-dimensional and multi-time-point observations and analyses of step S2, this embodiment can construct a comprehensive and systematic evaluation system for the effects of apoptotic vesicles. This system has the following technical effects: 1. Multi-dimensional evaluation system: It comprehensively evaluates the biological effects of apoptotic vesicles from multiple dimensions such as cell viability, chondrocyte function, immune cell function, cell migration, and cell uptake, improving the comprehensiveness and reliability of the evaluation. 2. Dynamic observation strategy: Adopting a multi-time-point sampling and analysis strategy, it realizes the dynamic monitoring of the effects of apoptotic vesicles, revealing the differences and transformation laws of short-term, medium-term, and long-term effects, providing a basis for the timeliness evaluation of clinical applications. 3. Quantitative evaluation standard: By introducing the MIDAS framework, DETN model, and ASBE threshold relationship, it converts qualitative observations into quantitative scores, providing an objective and standardized evaluation method, making different research results comparable, and improving the scientificity and reliability of the evaluation results. 4. Predictive analysis ability: The DETN model constructed based on experimental data has the ability to predict the long-term effects of apoptotic vesicles, breaking through the limitations of the experimental observation time limit, providing long-term safety and effectiveness predictions for clinical applications, and improving the prospectiveness of the evaluation results. 5. Integrated analysis advantage: The MIDAS framework realizes the integrated analysis of multi-omics data, systematically integrating transcriptome, proteome, and cell function phenotype data, and mining the correlations and consistencies between the data, improving the analysis depth and conclusion reliability. 6. Personalized evaluation flexibility: By adjusting the weight coefficients in each evaluation model, it can flexibly adjust the evaluation focus according to specific disease stages, patient individual differences, and treatment goals, supporting personalized treatment decisions, and improving the adaptability and clinical value of the evaluation method. Generally speaking, the specific implementation method of the multi-dimensional observation and analysis of the effects of apoptotic vesicles on cells in step S2 provides the necessary data basis for subsequent surgical risk assessments and is a key step in realizing the transition from in vitro simulation to clinical decision support.

[0222] III. The specific implementation process of the surgical risk assessment method for step S3 is as follows:

[0223] Step S3 aims to describe in detail how to conduct a surgical risk assessment of applying apoptotic vesicles cultured from cell lines to the treatment of osteoarthritis based on the multi-dimensional experimental data obtained in step S2. In this embodiment, a multi-dimensional risk scoring system is constructed, a biological network integration risk assessment model is applied, and a risk level classification standard is established to achieve a quantitative assessment of the potential risks of apoptotic vesicles cultured from cell lines and clinical decision-making support. Specifically as follows:

[0224] 1. Perform multi-dimensional data collection and preprocessing. This step aims to standardize and integrate the multi-dimensional experimental data obtained in Example 2, laying a data foundation for subsequent risk scoring and model analysis. The specific steps are as follows:

[0225] (1). Data standardization processing. To eliminate the differences in the dimensions and value ranges of different detection indicators and make the data comparable, the following standardization methods are used in this embodiment to preprocess the experimental data: (1-1). Change rate / magnitude conversion: For some indicators, such as cell proliferation rate, cell apoptosis rate, etc., their values are converted into the change rate or change multiple relative to the control group. For example, the change rate of cell proliferation rate = (proliferation rate of the experimental group - proliferation rate of the control group) / proliferation rate of the control group. (1-2). Z-score standardization: For non-ratio type data, such as gene expression level, protein expression level, etc., the Z-score standardization method is used. The calculation formula is:

[0226] Z = (X - μ) / σ

[0227] where X is the original data value, μ is the average value of the control group, and σ is the standard deviation of the control group. The data after Z-score standardization has a mean of 0 and a standard deviation of 1, which can effectively eliminate the influence of dimensions and make the data conform to the standard normal distribution, facilitating subsequent statistical analysis and model construction.

[0228] Technical effect: Data standardization processing is a key step in multi-dimensional data analysis. Through change rate / magnitude conversion and Z-score standardization, this embodiment effectively eliminates the dimensional differences and value range differences between different indicators, enabling different types of experimental data to be compared and analyzed on the same scale, ensuring the accuracy and reliability of subsequent risk scoring and model analysis. Compared with the original data without standardization, the standardized data is more suitable for constructing a comprehensive risk assessment model, improving the generalization ability and prediction accuracy of the model.

[0229] (2) Data grouping and integration. To comprehensively evaluate risks from different biological dimensions, in this embodiment, the standardized experimental data is grouped and integrated according to biological significance and divided into the following four main evaluation dimensions: 1 - Cell activity dimension (CA): This dimension mainly evaluates the impact of apoptotic vesicles on the overall cell activity, including: cell proliferation activity evaluated by CCK8 assay, apoptosis rate detected by flow cytometry, cytotoxicity evaluated by LDH release assay, and cell metabolic activity detected by MTT assay; 2 - Cartilage matrix metabolism dimension (CM): This dimension mainly evaluates the impact of apoptotic vesicles on the metabolic balance of cartilage cell matrix, including: expression levels of cartilage matrix anabolism marker genes (COL2A1, ACAN, SOX9) detected by qPCR, expression levels of cartilage matrix catabolism marker genes (ADAMTS5, MMP13, MMP3) detected by qPCR, and cartilage matrix staining intensity evaluated by histochemical staining (such as toluidine blue staining, alcian blue staining, safranin O / fast green staining); 3 - Inflammatory immune response dimension (IR): This dimension mainly evaluates the impact of apoptotic vesicles on the inflammatory immune microenvironment, including: secretion levels of pro-inflammatory cytokines (such as TNF-α, IL-6, IL-1β) and anti-inflammatory cytokines (such as IL-10, IL-4, TGF-β) detected by ELISA or CytokineArray, expression levels of immune cell surface markers (such as CD86, CD206, CD55, VCAM-1) detected by flow cytometry, and expression levels of immune cell-related genes (such as TNF, IL6, IL10, ARG1) detected by qPCR; 4 - Cell behavior dimension (CB): This dimension mainly evaluates the impact of apoptotic vesicles on cell behavior functions, including: cell migration ability evaluated by scratch wound healing assay, chemotactic migration ability of cells evaluated by Transwell assay, and uptake efficiency of apoptotic vesicles evaluated by cell uptake assay.

[0230] Technical effect: The data grouping and integration strategy structures complex multi-dimensional data, facilitating the systematic evaluation of the risks of apoptotic vesicles from different biological levels. By classifying relevant indicators into different dimensions, the specific impacts of apoptotic vesicles on cell activity, cartilage matrix metabolism, inflammatory immune response, and cell behavior can be more clearly understood, providing a logically clear data framework for subsequent risk scoring and comprehensive evaluation. Compared with the ungrouped data, the grouped data is easier to analyze and interpret, improving the readability and understandability of the risk assessment results.

[0231] (3) Outlier Detection and Handling. To ensure data quality and avoid the interference of outliers on the risk assessment results, this embodiment uses the box plot method (IQR method) to detect and handle outliers in the data. The specific steps are as follows: (3-1) Calculate quartiles: For the data of each indicator, calculate the lower quartile (Q1) and the upper quartile (Q3); (3-2) Calculate the interquartile range: Calculate the interquartile range (IQR = Q3 - Q1); (3-3) Determine the outlier range: Define the upper limit of outliers as Q3 + 1.5IQR, and the lower limit of outliers as Q1 - 1.5IQR; (3-4) Outlier Marking and Handling: Mark the data points outside the outlier range as outliers. For the marked outliers, perform the following handling according to the specific situation:

[0232] Repeated Verification: For suspicious outliers, conduct experiments or detections again to verify the authenticity of the data;

[0233] Removal Handling: If it is confirmed that the outliers are caused by experimental errors or operation mistakes, then remove the outliers and do not participate in the subsequent analysis;

[0234] Retention Handling: For a small number of outliers that cannot be verified or removed, if their impact on the overall analysis results is not significant, they can be selected for retention, but attention should be paid to their potential impact in the subsequent analysis.

[0235] Technical Effect: Outlier detection and handling are important links in data preprocessing. The box plot method (IQR method) can effectively identify outliers in the data and provide an objective basis for subsequent outlier handling. By reasonably handling outliers, data quality can be improved, the interference of outliers on the risk assessment results can be reduced, and the robustness and reliability of the risk assessment results can be ensured. Compared with the data without outlier handling, the data after outlier handling is cleaner and more reliable, and can improve the prediction accuracy and generalization ability of the risk assessment model.

[0236] 2. Execute the construction of a multi-dimensional risk scoring system. Construct a multi-dimensional risk scoring system to convert the preprocessed multi-dimensional data into a quantified risk score, providing a basis for subsequent risk level classification and clinical decision-making. The specific steps are as follows:

[0237] (1) Selection of key indicators and weight assignment. Based on the known correlations between each indicator and the surgical risk of osteoarthritis, the key indicators in each dimension were screened in this embodiment, and weights were assigned according to their importance and clinical significance. The key indicators and weight assignments in each dimension are as follows: (1-1) Cell activity dimension (CA): Apoptosis rate - weight 35%; Cell proliferation activity - weight 25%; LDH release rate - weight 25%; Cell metabolic activity (Metabolic activity, Met): weight 15%. (1-2) Cartilage matrix metabolism dimension (CM): Expression level of type II collagen (COL2A1) (COL) - weight 25%; Expression level of aggrecan (ACAN) (ACAN) - weight 20%; Expression level of transcription factor SOX9 (SOX9) - weight 15%; Expression level of matrix metalloproteinase MMP13 (MMP13) - weight 20%; Expression level of ADAMTS metalloproteinase ADAMTS5 (ADAMTS5) - weight 20%. (1-3) Inflammatory and immune response dimension (IR): Tumor necrosis factor-α (TNF-α) / interleukin-10 (IL-10) ratio (TNF-α / IL-10 ratio, T / I) - weight 25%; Ratio of M1 macrophages to M2 macrophages (M1 / M2 macrophage ratio, M1 / 2) - weight 25%; Level of interleukin-1β (IL-1β) (IL-1β level, IL1) - weight 20%; Level of interleukin-6 (IL-6) (IL-6 level, IL6) - weight 15%; Proportion of CD86-positive cells (CD86+ cell ratio, CD86) - weight 15%. (1-4) Cell behavior dimension (CB): Scratch wound closure rate (Scratch wound closure rate, SCR) - weight 30%; Transwell migration index (Transwell migration index, TMI) - weight 30%; Apoptotic vesicle uptake rate (Apoptotic vesicle uptake rate, VUR) - weight 40%.

[0238] Technical effect: The selection of key indicators and weight assignment are the core links in constructing an effective risk scoring system. Based on biological correlations and clinical significance, this embodiment selects key indicators closely related to the pathological process and surgical risk of osteoarthritis and assigns weights according to their importance. This weighted scoring strategy can more reasonably reflect the contribution degree of different indicators to the overall risk, improving the accuracy and clinical guiding value of the risk score. Compared with simple average or equal-weight scoring methods, this method can more effectively highlight key risk factors and improve the sensitivity and specificity of risk assessment.

[0239] (2) Dimension risk score calculation. Based on the selected key indicators and weight allocation, this embodiment designs the calculation formula for the risk score of each dimension. The calculation formula for the dimension risk score is as follows:

[0240] (2-1) Cell viability risk score (CA_Score), and the scoring model is as follows:

[0241] CA_Score = 0.35×Ap_N + 0.25×(1 - Pr_N) + 0.25×LDH_N + 0.15×(1 - Met_N)

[0242] Among them, Ap_N, Pr_N, LDH_N, and Met_N are the normalized values (in the range of 0 - 1) of the apoptosis rate, cell proliferation activity, LDH release rate, and cell metabolism activity, respectively. In the formula, the cell proliferation activity and cell metabolism activity indicators are calculated using (1 - normalized value), indicating that a decrease in activity increases the risk.

[0243] (2-2) Cartilage matrix metabolism risk score (CM_Score), and the scoring model is as follows: CM_Score = 0.25×(1 - COL_N) + 0.2×(1 - ACAN_N) + 0.15×(1 - SOX_N) + 0.2×MMP13_N + 0.2×ADAM_N

[0244] Among them, COL_N, ACAN_N, SOX_N, MMP13_N, and ADAM_N are the normalized values (in the range of 0 - 1) of the expression levels of type II collagen, aggrecan, transcription factor SOX9, matrix metalloproteinase MMP13, and ADAMTS metalloproteinase ADAMTS5, respectively. In the formula, the expression levels of the anabolic marker genes of the cartilage matrix (COL2A1, ACAN, SOX9) are calculated using (1 - normalized value), indicating that a decrease in expression increases the risk; the expression levels of the catabolic marker genes of the cartilage matrix (MMP13, ADAMTS5) are directly calculated using the normalized value, indicating that an increase in expression increases the risk.

[0245] (2-3) Inflammatory immune response risk score (IR_Score), and the scoring model is as follows: IR_Score = 0.25×T / I_N + 0.25×M1 / 2_N + 0.2×IL1_N + 0.15×IL6_N + 0.15×CD86_N

[0246] Among them, T / I_N, M1 / 2_N, IL1_N, IL6_N, and CD86_N are the standardized values (in the range of 0 - 1) of the TNF-α / IL-10 ratio, M1 / M2 macrophage ratio, IL-1β level, IL-6 level, and CD86 positive cell ratio, respectively. In the formula, all indicators directly use the standardized values, indicating that an increase in the indicator increases the risk of inflammation and immunity.

[0247] (2 - 4), Cell Behavior Risk Score (CB_Score), and the scoring model is as follows:

[0248] CB_Score = 0.3×|SCR_N - 0.5|×2 + 0.3×|TMI_N - 0.5|×2 + 0.4×|VUR_N - VUR_Opt|×2

[0249] Among them, SCR_N, TMI_N, and VUR_N are the standardized values (in the range of 0 - 1) of the scratch healing rate, Transwell migration index, and apoptotic vesicle uptake rate, respectively. VUR_Opt is the standardized value of the optimal apoptotic vesicle uptake rate, usually set to 0.6. In the formula, for the cell migration ability indicators (SCR, TMI), it is calculated as |standardized value - 0.5|×2, indicating that a deviation of the migration ability from the normal level (0.5) increases the risk; for the apoptotic vesicle uptake rate indicator (VUR), it is calculated as |standardized value - VUR_Opt|×2, indicating that a deviation of the uptake efficiency from the optimal value increases the risk.

[0250] Technical effect: The design of the dimensional risk score calculation formula fully considers the biological significance and risk direction of different indicators within each dimension. By means of weighted summation, multiple indicators within the dimension are integrated into a comprehensive score, which can more comprehensively and accurately reflect the risk level of this dimension. In the formula, for different types of indicators, different calculation methods are adopted. For example, for indicators where a decrease in activity increases the risk, it is calculated as (1 - standardized value); for indicators where a deviation from the normal level increases the risk, it is calculated as the absolute value deviation. This refined formula design improves the rationality and effectiveness of the dimensional risk score.

[0251] (3) Comprehensive risk score calculation. To obtain the final surgical risk assessment result, in this embodiment, the risk scores of the four dimensions are weighted and summed to calculate the comprehensive risk score (Comprehensive Risk Score, CRS). The calculation formula is as follows:

[0252] CRS = w1×CA_Score + w2×CM_Score + w3×IR_Score + w4×CB_Score

[0253] Among them, CA_Score, CM_Score, IR_Score, and CB_Score are the cell activity risk score, cartilage matrix metabolism risk score, inflammatory immune response risk score, and cell behavior risk score respectively. w1, w2, w3, and w4 are the weight coefficients of each dimension, which are set to 0.3, 0.3, 0.3, and 0.1 respectively in the standard protocol. The weight coefficients of each dimension can be adjusted according to specific application scenarios and risk preferences. For example, for application scenarios that focus on the cartilage protection effect, the weight of the cartilage matrix metabolism dimension (CM) can be appropriately increased; for application scenarios that focus on immune safety, the weight of the inflammatory immune response dimension (IR) can be appropriately increased.

[0254] Technical effect: The calculation of the comprehensive risk score (CRS) realizes the effective integration of multi-dimensional risk information. By means of weighted summation, the risk scores of multiple dimensions such as cell activity, cartilage matrix metabolism, inflammatory immune response, and cell behavior are integrated into a total score, which can more comprehensively and systematically evaluate the surgical risk of culturing apoptotic vesicles in cell lines. The setting of the dimension weight coefficients enables the flexible adjustment of the contribution ratio of each dimension according to different application scenarios and risk concerns, improving the flexibility and adaptability of risk assessment. Compared with the assessment method that only focuses on the risk of a single dimension, the comprehensive risk score can more accurately and reliably reflect the overall surgical risk level.

[0255] (4) Introduction of risk correction factors. To more comprehensively evaluate the surgical risk, this embodiment also considers the potential impact of factors such as cell line source, apoptotic vesicle preparation method, and clinical application dose on the risk, and introduces risk correction factors. The calculation formula of the final risk score (FRS) is as follows:

[0256] FRS = CRS × (1 + Σ correction factor value)

[0257] Common risk correction factors and their values include: 1. Heterologous cell line source: If the apoptotic vesicles are derived from a cell line different from the primary chondrocytes and primary immune cells, the risk correction factor value is +0.2. Apoptotic vesicles from a heterologous cell line source may pose potential immunogenic risks. 2. Non-standardized preparation process: If the preparation process of apoptotic vesicles does not conform to the standardized operation procedure and there are potential quality control risks, the risk correction factor value is +0.15. 3. Higher than the recommended clinical dose: If the clinical application dose is higher than the safe dose range recommended by pre-experiment or literature, the risk correction factor value is +0.1×(actual dose / recommended dose - 1). Excessive dose may increase the risk of toxic and side effects. 4. Combined use of other biological agents: If other biological agents are combined in clinical application, there may be risks of drug interactions. The risk correction factor value is ±0.1, and the plus or minus sign is determined according to the nature of the known drug interactions.

[0258] Technical effects: The introduction of the risk correction factor further improves the risk assessment system, enabling it to more comprehensively consider various potential risk factors. By correcting risks for factors such as cell line source, preparation process, clinical dose, and combination medication, the actual surgical risks of apoptotic vesicles cultured from cell lines can be more accurately evaluated, improving the comprehensiveness and clinical practical value of risk assessment. The setting of the risk correction factor makes the risk assessment results more personalized and context-specific, and can better serve clinical decision-making.

[0259] 3. Apply the Biological Network Integration Risk Assessment Model (BNRIM). Apply the Biological Network Integration Risk Assessment Model (BNRIM) to conduct a more in-depth and comprehensive assessment of the surgical risks of apoptotic vesicles cultured from cell lines from the perspective of systems biology. The core principle of the Biological Network Integration Risk Assessment Model (BNRIM) is to regard the key indicators in the multi-dimensional risk scoring system as nodes in the biological network. By constructing a network of interaction relationships between the nodes, the biological effect transmission process mediated by apoptotic vesicles is simulated, thereby realizing the overall assessment of the risks of complex biological systems. The BNRIM model is based on the theory of systems biology, believing that biological systems are complex network systems where each component interacts and influences each other, and the overall function and state of the system depend on the interaction relationships between the components. The specific steps are as follows:

[0260] (3-1) Network construction. The network construction of the BNRIM model mainly includes the following steps: 1. Node definition: Each key indicator in the multi-dimensional risk scoring system is defined as a network node. For example, the apoptosis rate, the expression level of type II collagen, the TNF-α / IL-10 ratio, etc. are all used as network nodes. Each node is assigned an initial weight value, which can be set according to the importance or risk level of the indicator. 2. Edge connection: Based on known biological knowledge and literature reports, connection edges are established between nodes. The connection edge represents the interaction relationship between nodes. For example, if IL-1β can promote the expression of MMP13, a directed edge is established between the IL-1β node and the MMP13 node. 3. Interaction strength: According to scientific literature data and experimental verification results, the interaction strength between nodes is defined. The interaction strength can be represented by a numerical value. For example, the promotion strength of IL-1β on MMP13 expression can be set as a positive value.

[0261] (3-2) Risk assessment algorithm. The BNRIM model uses the iterative propagation algorithm to calculate the equilibrium state of the risk network, so as to evaluate the overall risk level. The calculation formula of the iterative propagation algorithm is as follows:

[0262] R i (t + 1) = R i (t) + α × ∑[R j (t) × w j→i -R i (t) × w i→j

[0263] Where: R i (t) represents the risk value of node i at time t, and R j (t) represents the risk value of node j at time t. The risk value at the initial time (t = 0) can be set as the standardized value of the indicator corresponding to the node or the dimension risk score; w j→i represents the influence weight of node j on node i, that is, the interaction strength of the connection edge from node j to node i, and w i→j represents the influence weight of node i on node j, that is, the interaction strength of the connection edge from node i to node j; α is the learning rate parameter, which controls the speed and amplitude of information propagation, and is usually set as a small positive value, such as 0.1.

[0264] Through continuous iterative calculation, the iterative propagation algorithm enables the risk values of each node in the network to influence and adjust each other, and finally reaches an equilibrium state. The risk values of each node in the equilibrium state can reflect their risk contribution degree in the whole network, and the average risk value or the maximum risk value of the whole network can be used as the overall risk assessment result.

[0265] ​(3-3) Auxiliary decision-making support. The BNRIM model can not only provide the final risk score, but also offer the following auxiliary decision-making information through network analysis to provide more comprehensive support for clinical applications: 1. Identification of risk key nodes: By using network centrality analysis methods such as degree centrality, betweenness centrality, and closeness centrality, identify the key nodes that contribute the most to the overall risk. Key nodes are usually the important nodes in the network that are more connected to other nodes and on the information dissemination path. Identifying risk key nodes helps clarify the main sources of risk and provides targets for risk control and intervention. 2. Sensitivity analysis: By simulating the intervention of specific nodes (such as reducing the risk value of key risk nodes), predict the possible risk reduction paths and effects. Sensitivity analysis can evaluate the effectiveness of different risk control strategies and provide a basis for optimizing the risk management plan. 3. Indication recommendation: Based on the network state characteristics, for example, which nodes in the network have higher risk values and which nodes have stronger interactions with each other, recommend the osteoarthritis subtypes or patient populations that are most suitable for using the apoptotic vesicles. Indication recommendation helps achieve precision medicine, improve treatment effects, and reduce risks.

[0266] Technical effects: The application of the Biological Network Integrated Risk Assessment Model (BNRIM) elevates the surgical risk assessment from linear weighted scoring to the level of systematic network analysis. The BNRIM model can capture the complex network characteristics of apoptotic vesicle-cell interactions and evaluate surgical risks more comprehensively and accurately. Compared with traditional linear weighted scoring methods, the BNRIM model has the following technical effects: 1. Systematic assessment: The BNRIM model assesses risks from the perspective of the overall system, considering the interactions and influences among various risk factors, avoiding the limitations of isolating risk factors in the linear weighted scoring method. 2. Dynamic assessment: The BNRIM model uses an iterative propagation algorithm to simulate the dynamic transmission process of biological effects, which can reflect the dynamic changes and evolution trends of risks, improving the timeliness and forward-looking of risk assessment. 3. Enhanced interpretability: Through network visualization and network analysis, the BNRIM model can intuitively display the interaction relationships between risk factors and risk transmission paths, improving the interpretability and understandability of risk assessment results. 4. Enhanced decision-making support: The BNRIM model not only provides the final risk score, but also can provide auxiliary decision-making information such as risk key node identification, sensitivity analysis, and indication recommendation to provide more comprehensive support for clinical applications.

[0267] 4. Risk level classification and decision-making suggestions. Based on the output results of the Comprehensive Risk Score (FRS) and the Biological Network Integrated Risk Assessment Model (BNRIM), establish risk level classification criteria and provide corresponding clinical transformation decision-making suggestions according to the risk levels. The specific steps include:

[0268] (4-1) Set the risk threshold relationship. To comprehensively consider the FRS score and the network risk value output by the BNRIM model, this embodiment establishes a threshold relationship for the combined risk value (CRV):

[0269] CRV = β1 × FRS + β2 × BNR

[0270] Among them, FRS is the final risk score, BNR is the network risk value output by the BNRIM model, and β1 and β2 are weight coefficients used to balance the contribution ratios of FRS and BNR. The standard settings are β1 = 0.6 and β2 = 0.4. The weight coefficients can be adjusted according to specific application scenarios and risk preferences. Based on the CRV value, the surgical risk is divided into three levels: Low risk: CRV < 0.3; Medium risk: 0.3 ≤ CRV < 0.6; High risk: CRV ≥ 0.6.

[0271] (4-2) Definition of risk level characteristics. To more clearly describe the characteristics of different risk levels, this embodiment defines the typical characteristics of each risk level: 1 - Low risk characteristics: The increase in apoptosis rate does not exceed 20% of the control group; The decline in chondromatrix anabolism-related indicators (COL2A1, ACAN, SOX9) does not exceed 30%; The increase in the pro-inflammatory / anti-inflammatory cytokine ratio does not exceed 50%; The change in cell migration ability is within the normal physiological range (±30%). 2 - Medium risk characteristics: The increase in apoptosis rate is 20% - 50% of the control group; The decline in chondromatrix anabolism-related indicators is 30% - 60%; The increase in the pro-inflammatory / anti-inflammatory cytokine ratio is 50% - 100%; The change in cell migration ability exceeds the normal physiological range (±30% - 60%). 3 - High risk characteristics: The increase in apoptosis rate exceeds 50% of the control group; The decline in chondromatrix anabolism-related indicators exceeds 60%; The increase in the pro-inflammatory / anti-inflammatory cytokine ratio exceeds 100%; The change in cell migration ability seriously deviates from the normal physiological state (>±60%).

[0272] (4-3) Clinical transformation suggestions. According to the results of risk level classification, this embodiment provides corresponding clinical transformation decision suggestions. 1 - Low risk: It is recommended to consider entering the preclinical animal experiment and early clinical trial stages. The clinical trial is recommended to adopt the standard dose regimen and conduct routine safety monitoring. 2 - Medium risk: It is recommended to further optimize the apoptotic vesicle preparation process or adjust the clinical application plan. The following optimization strategies can be considered: reducing the clinical application dose; optimizing the preparation process to reduce potential harmful components; combining anti-inflammatory or cartilage protection drugs to reduce risks; increasing the frequency and monitoring indicators of safety monitoring in clinical trials and closely paying attention to safety issues. 3 - High risk: It is not recommended to directly enter clinical application. It is necessary to re-evaluate and optimize the apoptotic vesicle preparation plan, cell line source or treatment strategy. The following measures can be considered: redesigning the apoptotic vesicle preparation plan to reduce risks; changing the starting cell line source and selecting a safer cell line; considering alternative treatment strategies, for example, using other types of extracellular vesicles or drugs; deeply analyzing the risk sources, clarifying the specific reasons for high risks, and targeting to solve the risk problems.

[0273] Technical effects: The establishment of risk level classification and clinical transformation suggestions transforms the quantitative risk assessment results into actionable clinical decision guidance. The application of the CRV threshold relationship comprehensively considers the results of linear weighted scoring and network model analysis, improving the accuracy and reliability of risk level classification. The definition of risk level characteristics makes the risk characteristics of different risk levels clearer and more intuitive. The provision of clinical transformation suggestions provides clear decision guidance for the clinical application of apoptotic vesicles cultured from cell lines with different risk levels, helping to promote the clinical transformation of apoptotic vesicles cultured from cell lines in the treatment of osteoarthritis on the premise of ensuring patient safety.

[0274] Through the above multi-step and systematic surgical risk assessment methods for step S3, this embodiment can construct a comprehensive, objective, and quantitative surgical risk assessment system for cell line culture apoptotic vesicles. This system has the following technical effects: 1. Systematic risk assessment system: It establishes a complete risk assessment process from multi-dimensional data collection and preprocessing, construction of a multi-dimensional risk scoring system, application of a biological network model to risk level classification and decision-making recommendations, forming a systematic risk assessment technical solution. 2. Quantitative risk grading standard: By introducing a multi-dimensional risk scoring system, a biological network integrated risk assessment model, and a risk threshold relationship formula, qualitative risk descriptions are transformed into quantitative risk scores and risk levels, achieving the quantification and standardization of risk assessment. 3. Personalized risk assessment ability: Based on the network analysis of the biological network integrated risk assessment model (BNRIM), it can conduct personalized risk assessment for different cell line sources, preparation processes, and clinical application scenarios, improving the refinement and clinical applicability of risk assessment. 4. Accelerated clinical translation: The standardized surgical risk assessment method simplifies the clinical translation process of cell line culture apoptotic vesicles and accelerates the transformation process from laboratory to clinical application. Generally speaking, the steps of this embodiment regarding S3 achieve quantitative assessment of surgical risks based on in vitro simulation experiment data, providing important decision-making support for the clinical translation of cell line culture apoptotic vesicles in the treatment of osteoarthritis and being a key step in realizing the transition from in vitro simulation to clinical decision-making support.

[0275] Example Two

[0276] This embodiment aims to further improve Example One, and the specific steps are as follows:

[0277] S1. Co-culture system construction step: Co-culture primary chondrocytes, primary immune cells with cell line culture apoptotic vesicles; S2. Multi-dimensional observation and analysis step: Conduct multi-dimensional observation and analysis of the effects of apoptotic vesicles on cells to obtain multi-parameter data; S3. Surgical risk assessment step: Construct a multi-dimensional risk scoring system, apply a biological network model, and conduct risk level classification; among them, in step S3, the application of the biological network model uses a biological network integrated risk assessment model with an adaptive learning rate, and the learning rate parameter α in its risk assessment algorithm is adaptively adjusted based on the fluctuation degree of the multi-parameter data obtained in step S2, specifically realized through the following mathematical model:

[0278] α(t) = a0 × exp(-λ × Var(D(t)))

[0279] Among them, α(t) is the learning rate parameter at time t; a0 is the preset initial learning rate parameter; λ is the sensitivity coefficient and is a positive real number; Var(D(t)) is the variance of the multi-parameter data set D(t) obtained in step S2 at time t, used to characterize the fluctuation degree of the multi-parameter data.

[0280] It can be understood that when the data obtained from the multi-dimensional observation and analysis step fluctuates greatly, it indicates that there may be instability in the experimental system or the complexity of the biological response. At this time, the learning rate parameter α should be decreased to slow down the information propagation speed and avoid overfitting of the model in the unstable data. On the contrary, when the data fluctuation is small, it indicates that the experimental system is relatively stable and the biological response is relatively clear. The learning rate parameter α can be appropriately increased to accelerate information propagation and model convergence, and improve the evaluation efficiency. To achieve this technical purpose, in this embodiment, an information relationship and a control relationship are established between the degree of multi-parameter data fluctuation obtained in the multi-dimensional observation and analysis step of step S2 and the learning rate parameter α of the biological network integration risk assessment model BNRIM in the risk assessment step of step S3, and a new mathematical model is further optimized: α(t) = a0 × exp(-λ × Var(D(t))). Its technical effect is that the degree of multi-parameter data fluctuation Var(D(t)) obtained in step S2 is extracted as the information reflecting the stability of the experimental system and the complexity of the biological response. This information is transmitted to the BNRIM model in step S3. The learning rate parameter α of the BNRIM model is no longer a fixed value, but is controlled by Var(D(t)) and is adaptively adjusted according to the size of Var(D(t)). When Var(D(t)) increases, α(t) decreases; when Var(D(t)) decreases, α(t) increases. This control relationship enables the BNRIM model of Embodiment 1 to dynamically adjust its own learning behavior according to the quality and characteristics of the experimental data. In the traditional BNRIM model, the learning rate parameter α is usually set to a fixed empirical value. The adaptive learning rate BNRIM model proposed in this embodiment can change this fixed mode and dynamically associate the learning rate parameter α with the quality of the experimental data, which is a technical improvement. When the experimental data fluctuates greatly, the adaptive learning rate BNRIM model can automatically decrease the learning rate, slow down the information propagation speed, avoid overfitting of the model in the noisy data, thereby improving the accuracy of risk assessment and reducing the misjudgment rate. In addition, the adaptive learning rate BNRIM model can dynamically adjust its learning behavior according to the data quality, has stronger adaptability to experimental data of different qualities, and can provide relatively reliable risk assessment results even under unstable experimental conditions or low data quality, enhancing the robustness of risk assessment. When the experimental data fluctuates slightly, the adaptive learning rate BNRIM model can appropriately increase the learning rate, accelerate information propagation and model convergence, improve the efficiency of risk assessment, and shorten the assessment time.

[0281] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes, characterized in that, It includes the following steps: S1. Co-culture system construction step: Co-culture primary chondrocytes, primary immune cells and apoptotic vesicles from cell line culture. During this process, set the cell ratio, apoptotic vesicle concentration gradient, control group and osteoarthritis microenvironment simulation factors; S2. Multi-dimensional observation and analysis step: Conduct multi-dimensional observation and analysis on the effects of apoptotic vesicles on cells. That is, collect samples at different time points, and perform cell viability detection, chondrocyte function index detection, immune cell function index detection, cell migration experiment and cell uptake experiment, and provide multi-parameter data through dynamic monitoring at different time points; S3. Surgical risk assessment step: Construct a multi-dimensional risk scoring system, apply a biological network model, and conduct risk level classification. Establish a quantitative scoring standard based on the multi-parameter data in step S2 to form a closed-loop feedback system and realize the process evaluation from in vitro simulation to clinical decision support.

2. The medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes according to claim 1, wherein, In step S1: Set the ratio of primary chondrocytes to primary immune cells to 1:0.5 to 1:5 to simulate different degrees of inflammatory infiltration states; When setting the concentration gradient of apoptotic vesicles, set the control group, low-dose group, medium-dose group and high-dose group respectively within the concentration range of 0-150 μg / mL; When setting the control group, set the cell control group, blank vesicle control group, irrelevant vesicle control group and culture medium control group respectively. Among them, in the cell control group, only primary chondrocytes and primary immune cells are cultured without adding apoptotic vesicles; in the blank vesicle control group, blank vesicles prepared from the cell line culture supernatant without apoptosis induction are added; in the irrelevant vesicle control group, apoptotic vesicles prepared from a cell line different from or functionally irrelevant to the experimental cell line are added; in the culture medium control group, only the culture medium is used without adding any cells and vesicles; When simulating the osteoarthritis microenvironment, add the inflammatory factor IL-1β, set the final concentration to 8-12 ng / mL, apply periodic compressive stress through a mechanical loading device, perform hypoxic culture in a hypoxic incubator, add an oxidative stress inducer, and use a low-glucose and low-serum culture medium to simulate the nutrient deprivation state.

3. The medical simulation experiment method based on culturing apoptotic vesicles from a cell line to act on primary chondrocytes according to claim 1, wherein In step S2, the steps for performing cell viability detection include: Conduct a CCK8 experiment to evaluate cell proliferation activity; Conduct flow cytometry to detect the cell apoptosis rate, and distinguish early apoptosis, late apoptosis and necrotic cells by Annexin V / PI double staining; Conduct an LDH release experiment to evaluate cell toxicity; Conduct an MTT experiment to detect cell metabolic activity.

4. The medical simulation experiment method based on culturing apoptotic vesicles from a cell line to act on primary chondrocytes according to claim 1, wherein, In step S2, the steps for performing chondrocyte function index detection include: Detect the expression levels of chondromatrix anabolism marker genes COL2A1, ACAN, SOX9 and catabolism marker genes ADAMTS5, MMP13, MMP3 by qPCR, and calculate the synthesis / catabolism balance index (SDBI); detect the corresponding protein expression levels by Western Blotting; observe the distribution of related proteins by cell immunofluorescence; evaluate the chondromatrix components by histochemical staining.

5. The medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes according to claim 1, wherein, In step S2, the steps for performing immune cell function index detection include: Flow cytometry was used to detect immune cell surface markers, including macrophage M1 marker CD86 and M2 marker CD206, and the M1 / M2 phenotype ratio was calculated; The concentrations of proinflammatory cytokines TNF-α, IL-6, and IL-1β and anti-inflammatory cytokines IL-10, IL-4, and TGF-β in the culture supernatant were detected by ELISA or Cytokine Array detection technology, and the proinflammatory / anti-inflammatory cytokine ratio was calculated to evaluate the degree of inflammatory response.

6. The medical simulation experiment method based on culturing apoptotic vesicles from a cell line to act on primary chondrocytes according to claim 1, wherein In step S2, the steps of performing a cell migration experiment include: Perform a scratch healing experiment. Create a scratch on the cell monolayer, add serum-free culture medium containing different concentrations of apoptotic vesicles, and observe and calculate the healing rate at different time points. Perform a Transwell assay: Add chondrocytes or immune cells to the upper chamber of the Transwell and complete culture medium containing varying concentrations of apoptotic vesicles as a chemoattractant to the lower chamber. After 12-24 hours of culture, fix and stain the migrated cells, count them, and calculate the migration index.

7. The medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes according to claim 1, characterized in that, In step S2, the steps for performing a cellular uptake experiment include: By fluorescent labeling of apoptotic vesicles, apoptotic vesicles were mixed with the lipid-soluble fluorescent dye PKH26 or PKH67 to label them; Fluorescently labeled apoptotic vesicles were added to the co-culture system, and the cells were fixed at different time points. Different cell types were distinguished by immunofluorescence, and the distribution of apoptotic vesicles in the cells was observed. Flow cytometry was used to quantitatively analyze the proportion of PKH26 fluorescence-positive cells and the fluorescence intensity in different cell types, and the uptake rate and uptake intensity were calculated; The precise location and transport pathway of apoptotic vesicles in cells were analyzed by confocal microscopy combined with organelle-specific fluorescent probes.

8. The medical simulation experiment method based on culturing apoptotic vesicles from cell lines to act on primary chondrocytes according to claim 1, wherein In step S3, the steps of constructing a multidimensional risk scoring system include: Key indicator selection and weight allocation: assign weights to each indicator in the dimensions of cell activity, cartilage matrix metabolism, inflammatory immune response, and cell behavior; Dimensional risk score calculation: Calculate the dimension risk score based on the changes in indicators of each dimension; Comprehensive risk score calculation: The risk scores of the four dimensions are integrated to obtain the comprehensive risk score (CRS); Introduction of risk modification factors: Considering the impact of factors such as cell line source, apoptotic vesicle preparation method, and clinical application dose on risk, the final risk score (FRS) is calculated.

9. The medical simulation experiment method based on culturing apoptotic vesicles from a cell line to act on primary chondrocytes according to claim 1, wherein, In step S3: The application of biological network model adopts the biological network integrated risk assessment model. By constructing a dynamic network of apoptotic vesicle-chondrocyte-immune cell interactions, it realizes the overall assessment of the risk of complex biological systems. Its risk assessment algorithm is as follows: R i (t+1)=R i (t)+α×∑[R j (t)×w j→i -R i (t)×w i→j ] where R i (t) represents the risk value of node i at time t, and R j (t) represents the risk value of node j at time t; w j→i represents the influence weight of node j on node i; w i→j represents the influence weight of node i on node j, and α is the learning rate parameter; In addition, the risk level classification adopts the following threshold relationship formula: CRV=β1×FRS+β2×BNR Among them, FRS is the final risk score, BNR is the network risk value output by the biological network integrated risk assessment model, β1 and β2 are weight coefficients and β1+β2=1; The risk levels are classified into low risk (CRV < X1), medium risk (X1 ≤ CRV < X2), and high risk (CRV ≥ X2) based on the CRV value, where the range of X1 is 0.25 - 0.35 and the range of X2 is 0.55 - 0.

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10. An experimental device for implementing the medical simulation experiment method according to any one of claims 1 to 9, characterized in that, Including: A co - culture module configured to construct an in - vitro co - culture system. Through the co - culture module, primary chondrocytes, primary immune cells, and apoptotic vesicles from cell lines are co - cultured, and it supports setting different cell ratios, apoptotic vesicle concentration gradients, control groups, and osteoarthritis microenvironment simulation factors; A detection module configured to perform multi - dimensional observation and analysis on samples in the co - culture system. Through the detection module, cell viability detection, chondrocyte function index detection, immune cell function index detection, cell migration experiments, and cell uptake experiments are carried out to generate multi - parameter data; A data processing module configured to receive and process the multi - parameter data generated by the detection module. Through the data processing module, the multi - parameter data is analyzed and an analysis result for risk assessment is generated; A risk assessment module configured to perform surgical risk assessment based on the analysis result generated by the data processing module. Through the risk assessment module, a multi - dimensional risk scoring system is constructed, a biological network model is applied, and risk levels are classified; A control module configured to coordinate the operations of the co - culture module, detection module, data processing module, and risk assessment module. Through the control module, the experimental process, data collection, and data transfer between modules are controlled to achieve a closed - loop process from in - vitro simulation to risk assessment.

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