Method and system for detecting activity of umbilical cord blood stem cells

Abstract

The invention relates to the technical field of cell activity detection, in particular to an umbilical cord blood stem cell activity detection method and system.The method comprises the following steps that cells are separated from a fresh umbilical cord blood sample in a sterile environment, cell activity preliminary identification is conducted on the separated stem cells, and a cell activity evaluation result is generated; screening cells with activity meeting a preset standard, and performing function evaluation to obtain a screened cell set. According to the method, through accurate preliminary cell viability identification and function evaluation, the scheme optimizes the selection standard of umbilical cord blood stem cells, effectively screens out cells with high regeneration capacity and biological activity, enhances the success rate of cell therapy and reduces the risk caused by improper selection, and by analyzing the gene regulatory network of the screened cells, the cell therapy effect is improved. The functionality is accurately evaluated, it is ensured that only the most potential cells are used for subsequent culture and clinical application, the curative effect and the product quality are remarkably improved, and the culture efficiency and the success rate are further improved through an optimized culture scheme and a monitoring system.

Description

Technical Field

[0001] The present invention relates to the technical field of cell activity detection, and in particular to a method and system for detecting the activity of umbilical cord blood stem cells. Background Art

[0002] The field of cell activity detection technology is a field that involves a wide range of biotechnology and experimental methods, focusing on evaluating and analyzing the functional status of cells at the physiological, biochemical and molecular levels. The core of the technology field is to determine whether the cells are healthy, active and able to perform their biological functions. Common detection techniques include MTT assay, ATP assay, flow cytometer analysis and a variety of live / dead cell staining techniques. This field also uses various biomarkers and biochemical indicators to evaluate the metabolic activity and physiological response of cells. The technology is widely used in drug development, cell therapy, toxicity testing and basic biological research, and is an indispensable part of modern biomedicine and drug research.

[0003] Among them, the activity detection method of umbilical cord blood stem cells is a technology specifically for evaluating the functional status and physiological activity of stem cells extracted from the umbilical cord blood of newborns. Stem cells have important clinical application potential because they can develop into different types of cells, especially in regenerative medicine and cell therapy. The purpose of activity detection is to ensure that the cells maintain their biological activity during the extraction and storage process and can play the expected therapeutic effect in future treatments. Through this test, stem cells with high activity and excellent function can be screened to provide patients with safer and more effective cell therapy solutions.

[0004] Existing methods for detecting the activity of umbilical cord blood stem cells rely on standardized biological tests, such as MTT assay or ATP assay, for cell activity detection. Although these methods provide a basic assessment of cell activity, they lack in-depth analysis of cell functionality and therapeutic potential. The limitation of this method is that it cannot fully evaluate the performance of stem cells in actual clinical applications, especially in terms of long-term biological activity and therapeutic effects. Traditional cell activity detection technology cannot provide sufficient information to guide cell selection and optimization of culture conditions, resulting in cells used in cell therapy not being the best choice. This not only limits the potential effects of treatment, but also increases uncertainty and risk during the treatment process. The lack of highly personalized and targeted cell evaluation methods makes existing technologies insufficient in providing high-quality and efficient cell therapy solutions, which leads to poor treatment effects and low patient satisfaction in clinical applications. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for detecting the activity of umbilical cord blood stem cells.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a method for detecting the activity of umbilical cord blood stem cells, comprising the following steps:

[0007] S1: isolating cells from a fresh umbilical cord blood sample in a sterile environment, performing preliminary identification of cell activity on the isolated stem cells, generating a cell activity evaluation result, and based on the cell activity evaluation result, screening cells whose activity meets a preset standard, performing functional evaluation, and obtaining a screening cell set;

[0008] S2: performing gene analysis of functional genes on the screened cell set, analyzing the gene regulatory network, obtaining gene regulatory network analysis results, and using the gene regulatory network analysis results to evaluate the regeneration and repair capabilities of the cells, determine the application potential of the cells, and generate cell potential evaluation results;

[0009] S3: According to the cell potential assessment results, cells with regenerative ability are selected for preparation before cell culture, the culture medium and growth conditions are adjusted to obtain an optimized culture plan, the optimized culture plan is implemented, the growth of cells under the improved conditions is monitored, and the culture effect monitoring results are obtained;

[0010] S4: Using the culture effect monitoring results, perform functional evaluation on the differentiation and maturation of cells, identify cells that meet the treatment criteria, perform cell phenotypic characteristic analysis, determine the cell population of target markers, classify and summarize the data of qualified cells, and generate umbilical cord blood stem cell activity identification results.

[0011] As a further embodiment of the present invention, the step of obtaining the screening cell set is specifically as follows:

[0012] S111: Isolate cells from fresh umbilical cord blood samples in a sterile environment and measure the concentration of biomarkers extracted from stem cell samples, combining extraction efficiency and bioactivity response coefficient using the formula:

[0013]

[0014] Calculate the activity score of each sample and generate an activity score array, where c a represents the concentration of the biomarker, η a represents the ratio of biomarkers extracted from samples, k a is the conversion factor, r a is the nonlinear response index, E a Represents an array of activity scores;

[0015] S112: Calculate the mean value and standard deviation from the activity score array using the threshold setting formula:

[0016]

[0017] Get the preset activity threshold, where: is the mean of the activity scores, σ a represents the standard deviation of the rating, λ a is the weighted adjustment coefficient, exp represents the natural exponential function, T a Indicates the preset activity threshold;

[0018] S113: Perform a biological function test on the preset activity threshold to obtain a function score, using the formula:

[0019]

[0020] Calculate and obtain the cell activity evaluation results, where F a represents the functional score, ψ a is the result conversion coefficient, ω a is the adjustment coefficient of the functional score, γ a is the baseline offset, δ a is the score adjustment index, R a represents the results of cell viability assessment;

[0021] S114: Based on the cell activity evaluation result, a threshold is set by comparing the activity evaluation result of each cell with the threshold, using the formula:

[0022] S a = {x∈R a |x≥T a}

[0023] Select the condition x≥T a cells to generate a screening cell set, where R a Represents the results of cell viability assessment, T a is the preset activity threshold, S a is the screening cell set, and x represents the activity evaluation result of a single cell.

[0024] As a further solution of the present invention, the step of obtaining the gene regulatory network analysis results is specifically:

[0025] S211: Extract RNA sequence data from the screened cell set, quantify the activity level of each gene using quantitative PCR technology, and apply the normalization processing formula:

[0026]

[0027] Generate a normalized gene activity dataset, where E b is the original gene activity matrix obtained by quantitative PCR, μ b and σ b E bThe column mean and standard deviation of D b is the normalized gene activity data;

[0028] S212: Analyze the cross-correlation between genes in the standardized gene activity dataset using the formula:

[0029]

[0030] and

[0031]

[0032] Calculate and generate the gene transcription correlation matrix, where D b is the normalized gene activity data, Yes D b The transpose of M b is the gene interaction strength matrix, C b represents the gene correlation matrix;

[0033] S213: constructing a gene regulatory network using the gene interaction intensity matrix, and applying a threshold processing formula:

[0034] N b =threshold(M b ,θ b )

[0035] Screen gene regulatory relationships and obtain gene regulatory network analysis results, among which M b is the gene interaction strength matrix, θ b is the threshold for screening gene regulatory relationships, N b Represents a gene regulatory network.

[0036] As a further embodiment of the present invention, the step of obtaining the cell potential assessment result is specifically as follows:

[0037] S221: Extracting cell activity data from the gene regulatory network analysis results, and standardizing the data using the formula:

[0038]

[0039] Calculate the activity deviation of each cell to generate a cell baseline calibration dataset, where E c represents the real-time activity data of the cell, E0 represents the baseline level data, and E std represents the standard deviation, D c represents the cell baseline calibration dataset;

[0040] S222: Utilizing the cell baseline calibration data set, applying the cell physiological response model, and calculating the cell response analysis results, using the formula:

[0041]

[0042] Adjust the sensitivity of cell response to obtain preliminary evaluation results of cell physiological response ability, among which D c is the cell baseline calibration data set, e is the base of the natural logarithm, D norm is the normalization factor, R c It indicates the preliminary evaluation results of cell physiological response capacity;

[0043] S223: The preliminary evaluation results of cell physiological response ability are used to evaluate the potential of cells through a multi-factor analysis model, using the formula:

[0044]

[0045] Generate cell potential assessment results, where n is the number of factors involved in the assessment, R c,i represents the physiological response ability of the ith cell, w i Represents the weight of the corresponding factor, P c Represents the results of cell potential assessment.

[0046] As a further solution of the present invention, the steps for obtaining the optimized culture scheme are specifically:

[0047] S311: Based on the cell potential evaluation results, cells showing high regenerative ability are screened, and the high regenerative ability of each cell is calculated according to the gene activity data of the cells and the key gene markers associated with the regenerative ability, using the formula:

[0048]

[0049] Generate a high regenerative capacity cell fraction, where b i represents the weight based on gene criticality, G di represents the gene activity value of the i-th cell, n represents the number of cells, S d represents the standard deviation of gene activity, H d Indicates the fraction of cells with high regenerative capacity;

[0050] S312: According to the high regenerative capacity cell fraction, the culture medium composition is adjusted to calculate the optimal growth efficiency using the formula:

[0051]

[0052] The growth efficiency after the medium adjustment is generated, where C di represents the compound concentration of the ith cell, E d The index representing the environmental regulation factor, L drepresents the growth efficiency after medium adjustment, H d Indicates the fraction of cells with high regenerative capacity;

[0053] S313: Develop a cell culture protocol with reference to laboratory resources and the growth efficiency of the culture medium after adjustment, using the formula:

[0054]

[0055] Generate an optimized culture plan, where R d Represents the amount of laboratory resources, P d Indicates the optimized training program, L d Indicates the growth efficiency after medium adjustment.

[0056] As a further solution of the present invention, the steps for obtaining the culture effect monitoring results are specifically as follows:

[0057] S321: Based on the optimized culture scheme, monitor the cell growth environment parameters under the improved conditions, collect the culture medium temperature, pH value, and nutrient concentration, and use the formula:

[0058]

[0059] Get environmental parameter data, where T h Represents the culture medium temperature, pH h Represents pH value, C h represents the nutrient concentration, a, b, c are adjustment coefficients, E h Represents environmental parameter data;

[0060] S322: Based on the environmental parameter data, the cell density and survival rate are monitored, and the cell growth effect is evaluated using the formula:

[0061]

[0062] Get cell growth effect data, where S h Represents the number of viable cells, N h represents the total number of cells, D h is the cell growth effect data, E h Represents environmental parameter data;

[0063] S323: Using the preliminary growth effect data, compared with the preset growth standard, using the formula:

[0064]

[0065] Calculate the percentage difference between the real-time growth effect and the expected one, and establish the culture effect monitoring results, where L h Represents the preset growth effect standard, Rh is the percentage deviation between real time and expected value, D h Cell growth effect data.

[0066] As a further embodiment of the present invention, the steps for obtaining the result of the identification of the activity of the umbilical cord blood stem cells are specifically as follows:

[0067] S411: Screen the cell population that meets the basic treatment standard from the culture effect monitoring results, use the standard threshold as a benchmark, calculate the ratio of the cell activity index to the threshold, and use the formula:

[0068]

[0069] The average activity ratio of the screened cells was obtained, where A g,i represents the activity index of the ith cell, T g is the activity threshold, N g is the total number of cells, α is the adjustment coefficient, F g is the average cell activity ratio;

[0070] S412: Using the average activity ratio of the screened cells, phenotypic characteristics of the cells are analyzed using the formula:

[0071]

[0072] The weighted average index of key markers is obtained, where B g,j is the marker activity of the jth cell, M g is the total number of cells analyzed, F g is the average cell activity ratio, β is the coefficient for adjusting the activity, S g It is the weighted average index of key markers;

[0073] S413: According to the weighted average index of key markers, the qualified cell data are classified and summarized using the formula:

[0074] R g =log(max(S g ))

[0075] Calculate the logarithm of the activity index to determine the representative activity of umbilical cord blood stem cells and generate umbilical cord blood stem cell activity identification results, where S g is the weighted average index of key markers, R g This is the result of identifying the activity of umbilical cord blood stem cells.

[0076] An activity detection system for umbilical cord blood stem cells, the activity detection system for umbilical cord blood stem cells is used to perform the activity detection method for umbilical cord blood stem cells, the system comprising:

[0077] The cell sampling module separates cells from fresh umbilical cord blood samples in a sterile environment, uses automated flow cytometry separation equipment to classify cells according to surface markers, records the number of live cells, records cell activity responses through real-time monitoring equipment, and obtains cell sampling records;

[0078] The activity identification module uses a fluorescent staining method to stain the cells in the cell sampling record, uses a fluorescent microscope to detect the fluorescence intensity of the target protein, records and analyzes the data to evaluate the cell activity level, and obtains activity level data;

[0079] The gene network analysis module extracts RNA from the highly active cells selected from the activity level data, analyzes gene transcription through high-throughput sequencing, and constructs a gene regulation map using bioinformatics software;

[0080] The culture optimization module adjusts the culture medium composition and pH value according to the gene regulation map, sets the optimal temperature and humidity conditions, and establishes a culture condition optimization plan;

[0081] The cell function evaluation module cultures cells according to the culture condition optimization scheme, regularly uses labeling and immunostaining techniques to detect cell surface markers, evaluates cell differentiation and maturity through image analysis, and obtains the activity identification results of umbilical cord blood stem cells.

[0082] Compared with the prior art, the advantages and positive effects of the present invention are:

[0083] In the present invention, by refining the selection and evaluation process of umbilical cord blood stem cells, the potential effect and safety of cell therapy are significantly improved, and the selection criteria of cells are optimized by preliminary identification of cell activity and further functional evaluation based on the results, so that cells with high regenerative ability and biological activity can be screened out. This method not only improves the success rate of cell therapy, but also reduces the risk of side effects and treatment failure caused by improper cell selection. By analyzing the gene regulatory network of the screened cells and combining the data to evaluate the regeneration and repair ability of the cells, the functional evaluation of stem cells is further refined. This evaluation mechanism ensures that only the most potential cells are used for subsequent culture and clinical application, greatly improving the quality and efficacy of cell products, implementing optimized culture schemes and monitoring cell growth, making the culture process more controllable, and improving the efficiency and success rate of cell culture. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0085] Figure 2 A flow chart for screening cell sets in the present invention;

[0086] Figure 3A flow chart of the gene regulatory network analysis results in the present invention;

[0087] Figure 4 A flow chart showing the results of cell potential assessment in the present invention;

[0088] Figure 5 A flow chart of optimizing the culture scheme in the present invention;

[0089] Figure 6 It is a flow chart of the culture effect monitoring results in the present invention;

[0090] Figure 7 The flowchart of the activity identification results of umbilical cord blood stem cells in the present invention. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0092] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0093] Embodiment 1

[0094] See also Figure 1 The present invention provides a technical solution: a method for detecting the activity of umbilical cord blood stem cells, comprising the following steps:

[0095] S1: Isolate cells from fresh umbilical cord blood samples in a sterile environment, perform preliminary identification of the cell activity of the isolated stem cells, generate cell activity evaluation results, screen cells whose activity meets the preset standards based on the cell activity evaluation results, perform functional evaluation, and obtain a screened cell set;

[0096] S2: Perform gene analysis of functional genes on the screened cell set, analyze the gene regulatory network, obtain the gene regulatory network analysis results, use the gene regulatory network analysis results to evaluate the regeneration and repair ability of the cells, determine the application potential of the cells, and generate cell potential evaluation results;

[0097] S3: Based on the results of cell potential assessment, select cells with regenerative capacity for preparation before cell culture, adjust the culture medium and growth conditions, obtain an optimized culture plan, implement the optimized culture plan, monitor the growth of cells under the improved conditions, and obtain culture effect monitoring results;

[0098] S4: Using the culture effect monitoring results, conduct functional evaluation on the differentiation and maturation of cells, identify cells that meet the treatment criteria, analyze cell phenotypic characteristics, determine the cell population of target markers, classify and summarize the data of qualified cells, and generate the activity identification results of umbilical cord blood stem cells.

[0099] The results of cell activity assessment include cell activity grading, metabolic rate, and activity markers. The screening cell sets include cells that meet the screening criteria, surviving cells after screening, and cells with high activity ratings. The results of gene regulatory network analysis include key gene sets, regulatory factors, and expression regulation networks. The results of cell potential assessment include potential therapeutic applications, regenerative capacity grading, and repair function index. The optimized culture program includes culture medium optimization formula, growth environment setting, and nutrient supply adjustment. The culture effect monitoring results include growth rate indicators, morphological normality assessment, and cell survival rate. The functional evaluation results include cell differentiation indicators, maturity grade, and cell functionality verification. The results of umbilical cord blood stem cell activity identification include treatment suitability assessment, cell marker matching, and phenotypic characteristic analysis.

[0100] See also Figure 2 , the steps for obtaining the screening cell set are as follows:

[0101] S111: Isolate cells from fresh umbilical cord blood samples in a sterile environment and measure the concentration of biomarkers extracted from stem cell samples, combining extraction efficiency and bioactivity response coefficient using the formula:

[0102]

[0103] Calculate the activity score of each sample and generate an activity score array, where c a represents the concentration of the biomarker, η a represents the ratio of biomarkers extracted from samples, k a is the conversion factor, r a is the nonlinear response index, E a Represents an array of activity scores;

[0104] S112: Calculate the mean and standard deviation from the activity score array using the threshold setting formula:

[0105]

[0106] Get the preset activity threshold, where: is the mean of the activity scores, σ a represents the standard deviation of the rating, λ a is the weighted adjustment coefficient, exp represents the natural exponential function, T a Indicates the preset activity threshold;

[0107] S113: Perform biological function test on the preset activity threshold to obtain a function score, using the formula:

[0108]

[0109] Calculate and obtain the cell activity evaluation results, where F a represents the functional score, ψ a is the result conversion coefficient, ω a is the adjustment coefficient of the functional score, γ a is the baseline offset, δ a is the score adjustment index, R a represents the results of cell viability assessment;

[0110] S114: Based on the cell activity evaluation result, a threshold is set by comparing the activity evaluation result of each cell with the threshold, using the formula:

[0111] S a = {x∈R a |x≥T a}

[0112] Select the condition x≥T a cells to generate a screening cell set, where R a Represents the results of cell viability assessment, T a is the preset activity threshold, S a is the screening cell set, and x represents the activity evaluation result of a single cell.

[0113] Activity score array formula

[0114]

[0115] c a : Concentration of biomarkers

[0116] η a : Extraction efficiency

[0117] k a : Response coefficient

[0118] r a : nonlinear response index;

[0119] Suppose that biomarkers are extracted from a cell sample and the following data are obtained: a=2.0mg / mL (concentration), η a =0.8 (80% extraction efficiency), k a =1.5 (response coefficient), r a =1.5 (used to increase the response to high concentrations);

[0120] Calculate E a :

[0121] E a =1.5×(2.0×0.8) 1.5 =1.5×(1.6) 1.5 =1.5×2.026=3.039

[0122] The calculated result of 3.039 represents the activity score of the sample, with higher scores indicating higher biological activity.

[0123] The formula for the initial screening of cell sets is:

[0124]

[0125] Mean value of activity score;

[0126] σ a : standard deviation;

[0127] λ a : weighted adjustment coefficient;

[0128] Assume that the statistical parameters of a set of activity score data are as follows: σ a =0.5,λ a =1.0;

[0129] Calculate T a :

[0130] T a =3.0×exp(1.0×0.5 / 3.0)=3.0×exp(0.1667)=3.0×1.181=3.543

[0131] This threshold of 3.543 is used to filter cells with activity scores above this value, representing the baseline of highly active cells.

[0132] Cell activity evaluation result formula:

[0133]

[0134] F a : Functional score;

[0135] ψ a : Result conversion factor;

[0136] ω a : Functional score adjustment coefficient;

[0137] γ a : baseline offset;

[0138] δ a : score adjustment index;

[0139] Assume that a cell's functional score is: F a =1.0,ψ a =2.0,ω a =1.0,γ a =0.1,δ a =2.0;

[0140] Calculate R a :

[0141] R a =2.0×(1.0×1.0+0.1) 2.0 =2.0×1.1 2 =2.0×1.21=2.42

[0142] The final cell activity evaluation result of 2.42 indicated that the cells had relatively high biological functions.

[0143] Formula for screening cell sets:

[0144] S a = {x∈R a |x≥T a}

[0145] Used to filter out activity evaluation results that meet the preset activity threshold T a Cell assembly S a , here are the sources and calculation methods of the parameters:

[0146] R a : This is the array of cell activity evaluation results obtained from the previous step. Assume that R a Contains a set of cell activity evaluation values, such as [1.2, 3.5, 2.0, 0.5, 4.0];

[0147] T a :This is the preset activity threshold used to screen cells, assuming that T a =2.5; Assume that there is a set of cell activity evaluation results R a =[1.2, 3.5, 2.0, 0.5, 4.0] and preset threshold T a =2.5

[0148] Filter cells that meet the threshold: Check whether each element x satisfies x ≥ T a , for R a For each element in , compare whether it is greater than or equal to 2.5. The elements that meet the condition are 3.5 and 4.0;

[0149] Generate screening cell sets:

[0150] Filter results: S a =[3.5,4.0]

[0151] In this example, the cell activity evaluation result set R a The two cells in (with activity values ​​of 3.5 and 4.0) meet the activity threshold T a =2.5, therefore, these two cells were selected into the screening cell set S a , indicating that the activity of these two cells is higher than the preset threshold and is suitable for subsequent functional evaluation.

[0152] See also Figure 3 , the specific steps for obtaining the results of gene regulatory network analysis are:

[0153] S211: Extract RNA sequence data from the screened cell set, quantify the activity level of each gene using quantitative PCR, and apply the normalization formula:

[0154]

[0155] Generate a normalized gene activity dataset, where E b is the original gene activity matrix obtained by quantitative PCR, μ b and σ b E b The column mean and standard deviation of D b is the normalized gene activity data;

[0156] S212: Analyze the cross-correlation between genes in the normalized gene activity dataset using the formula:

[0157]

[0158] and

[0159]

[0160] Calculate and generate the gene transcription correlation matrix, where D b is the normalized gene activity data, Yes D b The transpose of M b is the gene interaction strength matrix, C b represents the gene correlation matrix;

[0161] S213: Use the gene interaction intensity matrix to construct a gene regulatory network and apply the threshold processing formula:

[0162] N b =threshold(M b ,θ b )

[0163] Screen gene regulatory relationships and obtain gene regulatory network analysis results, among which M b is the gene interaction strength matrix, θ b is the threshold for screening gene regulatory relationships, N b Represents a gene regulatory network.

[0164] The formula for normalized gene activity data is:

[0165]

[0166] Assume that from the cell assembly S a Extracted gene activity data R b As follows (randomly generated sample data):

[0167] Gene 1: [5, 15, 25];

[0168] Gene 2: [10, 20, 30];

[0169] This will be represented as the expression matrix E b :

[0170] Calculate mean and standard deviation

[0171] Mean (μ b ):

[0172] Gene 1:

[0173] Gene 2:

[0174] Standard deviation (σ b ):

[0175] Gene 1:

[0176] Gene 2:

[0177] Normalized result D b :

[0178]

[0179] Gene interaction intensity matrix formula:

[0180]

[0181] and

[0182]

[0183] Correlation matrix calculation:

[0184]

[0185] Convert to interaction strength matrix:

[0186]

[0187] Gene regulatory network formula

[0188] N b =threshold(M b ,θ b )

[0189] Set the threshold θ b =0.85, so all connections with values ​​greater than 0.85 were considered significant.

[0190] Network Modeling

[0191] Only correlations exceeding a threshold are considered:

[0192] Final result N b It indicates that there is a significant regulatory relationship between genes, which means that under a given threshold condition, all gene pairs are significantly correlated, indicating that the genes are jointly involved in certain biological processes or functions. This analysis is helpful for further biological research and potential clinical applications.

[0193] See also Figure 4 , the specific steps for obtaining the cell potential assessment results are:

[0194] S221: Extract cell activity data from gene regulatory network analysis results and standardize the data using the formula:

[0195]

[0196] Calculate the activity deviation of each cell to generate a cell baseline calibration dataset, where E c represents the real-time activity data of the cell, E0 represents the baseline level data, and E std represents the standard deviation, D c represents the cell baseline calibration dataset;

[0197] S222: Using the cell baseline calibration data set, apply the cell physiological response model and calculate the cell response analysis results using the formula:

[0198]

[0199] Adjust the sensitivity of cell response to obtain preliminary evaluation results of cell physiological response ability, among which D c is the cell baseline calibration data set, e is the base of the natural logarithm, D norm is the normalization factor, R c It indicates the preliminary evaluation results of cell physiological response capacity;

[0200] S223: The preliminary results of the cell physiological response ability assessment are used to evaluate the potential of the cells through a multi-factor analysis model using the formula:

[0201]

[0202] Generate cell potential assessment results, where n is the number of factors involved in the assessment, R c,i represents the physiological response ability of the ith cell, w i Represents the weight of the corresponding factor, P c Represents the results of cell potential assessment.

[0203] Cell baseline calibration data set formula:

[0204]

[0205] E c : Actual transcriptional activity data of cells

[0206] E0: Baseline level data

[0207] E std : Standard deviation

[0208] Data collection: Collecting transcriptional activity data of cells under different conditions c ;

[0209] Baseline data determination: Determine the transcriptional activity data E0 of a control group or basic experiment, which is the average value of data under untreated or normal conditions;

[0210] Standard Deviation Calculation: Calculate the standard deviation E of transcriptional activity data from a large number of samples std , reflecting the degree of variation in transcriptional activity data;

[0211] Assumption: E c =150 (the transcriptional activity of a cell under experimental conditions), E0 =100 (the baseline transcriptional activity), E std= 25 (standard deviation of transcriptional activity data);

[0212] Calculate D c :

[0213]

[0214] This result D c =2 means that the cell transcription activity deviates by 2 standard deviation units relative to the baseline transcription activity after standard deviation adjustment.

[0215] Preliminary evaluation formula of cell physiological response ability:

[0216]

[0217] D c : The cell baseline calibration dataset obtained from the previous step;

[0218] e: the base of natural logarithm, approximately 2.71828;

[0219] D norm : Normalization factor used to adjust the sensitivity of cell response

[0220] Cell response model construction: Use logistic models to describe the response of cells to changes in transcriptional activity data under experimental conditions;

[0221] Normalization factor setting: Set a normalization factor D norm , making the model moderately sensitive to the data;

[0222] Assumption D norm =1.5, continue to use D c =2;

[0223] Calculate R c :

[0224]

[0225] This result R c = 0.881 represents the activity level of the cells under the given conditions, close to 88.1% of the maximum response.

[0226] Cell potential assessment result formula:

[0227]

[0228] R c,i : The physiological response ability of the ith cell;

[0229] w i : The weight of the corresponding factor;

[0230] n: the number of factors involved in the evaluation;

[0231] Weight assignment: Assign a weight w to each cell based on its biological importance or experimental design i ;

[0232] Comprehensive evaluation model construction: A weighted sum of squares model is used to comprehensively consider the response data of all cells, giving more importance to cells that perform more actively;

[0233] Suppose there are two cells with weights w1=0.6, w2=0.4, R c,1 =0.881, R c,2 =0.75;

[0234] Calculate P c :

[0235] P c =0.6 0.881 2 +0.4 0.75 2 ≈0.533+0.225=0.758

[0236] This result P c =0.758 indicates the average activity level of the entire cell population under current conditions, reflecting the overall potential of the cells.

[0237] See also Figure 5 , the specific steps for obtaining the optimized training program are:

[0238] S311: Based on the results of cell potential assessment, cells with high regenerative capacity are screened, and the high regenerative capacity of each cell is calculated based on the gene activity data of the cells and the key gene markers associated with regenerative capacity, using the formula:

[0239]

[0240] Generate a high regenerative capacity cell fraction, where b i represents the weight based on gene criticality, G di represents the gene activity value of the i-th cell, n represents the number of cells, S d represents the standard deviation of gene activity, H d Indicates the fraction of cells with high regenerative capacity;

[0241] S312: According to the fraction of cells with high regenerative capacity, adjust the culture medium composition and calculate the optimal growth efficiency using the formula:

[0242]

[0243] The growth efficiency after the medium adjustment is generated, where C direpresents the compound concentration of the ith cell, E d The index representing the environmental regulation factor, L d represents the growth efficiency after medium adjustment, H d Indicates the fraction of cells with high regenerative capacity;

[0244] S313: Develop a cell culture protocol based on laboratory resources and the growth efficiency of the culture medium after adjustment, using the formula:

[0245]

[0246] Generate an optimized culture plan, where R d Represents the amount of laboratory resources, P d Indicates the optimized training program, L d Indicates the growth efficiency after medium adjustment.

[0247] High regenerative capacity cell fraction formula:

[0248]

[0249] G di : Gene activity value of the ith cell;

[0250] b i : Predefined weights based on gene importance;

[0251] n: total number of cells analyzed;

[0252] S d : standard deviation of gene expression;

[0253] Gene activity value collection: Gene expression analysis is performed on n cells to obtain the gene activity value G of each cell di ;

[0254] Weight assignment: Assign weights to each gene based on its importance in cell regeneration. i ;

[0255] Standard deviation calculation: Calculate the standard deviation S of the gene activity values ​​of n cells d ;

[0256] Suppose there are 3 cells, with gene activity values ​​of 100, 150, and 200, weights of 0.2, 0.3, and 0.5, and standard deviation S d is 40;

[0257] Calculate H d :

[0258]

[0259] This Hd The values ​​represent the fraction of cells in the collection that have high regenerative capacity given the weights and gene activity data.

[0260] Growth efficiency formula after medium adjustment:

[0261]

[0262] H d : The fraction of cells with high regenerative capacity obtained from the previous step;

[0263] C di : The concentration of the i-th compound;

[0264] E d : Index of environmental regulation factors;

[0265] Compound concentration adjustment: According to H d The concentration of different compounds was adjusted by the value of C di ;

[0266] Environmental factor setting: Set the index E of the environmental adjustment factor according to the experimental conditions d ;

[0267] Assume H d is 414.87, the compound concentrations are 2, 3, and 4 (k = 3), and the environmental adjustment factor E d is 2;

[0268] Calculate L d :

[0269]

[0270] This L d Values ​​represent the growth efficiency of cells under given compound concentrations and environmental conditions.

[0271] Optimized training program formula:

[0272]

[0273] L d : The optimal growth efficiency obtained from the above steps;

[0274] R d : The amount of resources available in the laboratory;

[0275] log(R d ): logarithmic adjustment of resource quantity;

[0276] Resource Considerations: Evaluate the amount of resources available in the laboratory d ;

[0277] Logarithmic adjustment: Logarithmic adjustment of resource quantities to balance resource efficiency;

[0278] Assume that L d is 1.204, and the resource volume R d is 100;

[0279] Calculate P d :

[0280]

[0281] This P d Values ​​represent the efficiency of the final cell culture protocol, taking into account the balance of resource usage.

[0282] See also Figure 6 , the specific steps for obtaining the cultivation effect monitoring results are as follows:

[0283] S321: Based on the optimized culture scheme, monitor the cell growth environment parameters under the improved conditions, collect the culture medium temperature, pH value, and nutrient concentration, and use the formula:

[0284]

[0285] Get environmental parameter data, where T h Represents the culture medium temperature, pH h Represents pH value, C h represents the nutrient concentration, a, b, c are adjustment coefficients, E h Represents environmental parameter data;

[0286] S322: Based on the environmental parameter data, monitor the cell density and viability, and evaluate the cell growth effect using the formula:

[0287]

[0288] Get cell growth effect data, where S h Represents the number of viable cells, N h represents the total number of cells, D h is the cell growth effect data, E h Represents environmental parameter data;

[0289] S323: Using the preliminary growth effect data, compared with the preset growth standard, the formula is used:

[0290]

[0291] Calculate the percentage difference between the real-time growth effect and the expected one, and establish the culture effect monitoring results, where L h Represents the preset growth effect standard, R his the percentage deviation between real time and expected value, D h Cell growth effect data.

[0292] Environmental parameter data formula:

[0293]

[0294] T h : medium temperature;

[0295] pH h : pH value of culture medium;

[0296] C h : Nutrient concentration;

[0297] a, b, c: adjustment coefficients, determined based on experiments;

[0298] Suppose in a particular experiment we have the following data: T h =37°C (common human cell culture temperature), pH h =7.4 (ideal pH for cell culture), C h =5mg / L (hypothetical nutrient concentration), the coefficients are assumed to be a=1, b=

[0299] 1, c = 1 (simplified calculation);

[0300] Calculate P h :

[0301] E h =37 1 7.4 1 / 5 1 =37·7.4 / 5=54.68

[0302] The obtained P h =54.68 represents the comprehensive environmental parameter index, and a higher value indicates a more optimal environmental state for cell growth.

[0303] Cell growth effect data formula:

[0304]

[0305] S h : Number of viable cells;

[0306] N h : total cell number;

[0307] E h : Comprehensive index of environmental parameters obtained from the previous step;

[0308] Assume that during the monitoring period: S h=800 (number of surviving cells), N h = 1000 (total number of cells), E obtained from the previous step h =54.68;

[0309] Calculate D h :

[0310]

[0311] Get D h =121 represents the cell growth effect index. A high value of this index means that the cell survival rate is high and the cell growth is good under given environmental conditions.

[0312] The formula for the percentage deviation between real time and expected is:

[0313]

[0314] D h : The cell growth effect index obtained from the previous step;

[0315] L h : Preset growth effect standard

[0316] Assumption: G h =121 (calculated from the previous step), L h =100 (default standard);

[0317] Calculate R h :

[0318]

[0319] The obtained R h =0.21 represents the percentage deviation between the actual growth effect and the expected one, which indicates that the actual effect is 21% higher than expected, indicating that the environmental conditions are better than expected.

[0320] See also Figure 7 The specific steps for obtaining the results of umbilical cord blood stem cell activity identification are as follows:

[0321] S411: Screen the cell population that meets the basic treatment standards from the culture effect monitoring results, use the standard threshold as the benchmark, calculate the ratio of the cell activity index to the threshold, and use the formula:

[0322]

[0323] The average activity ratio of the screened cells was obtained, where A g,i represents the activity index of the ith cell, T g is the activity threshold, N g is the total number of cells, α is the adjustment coefficient, Fg is the average cell activity ratio;

[0324] S412: Use the average activity ratio of the screened cells to analyze the phenotypic characteristics of the cells using the formula:

[0325]

[0326] The weighted average index of key markers is obtained, where B g,j is the marker activity of the jth cell, M g is the total number of cells analyzed, F g is the average cell activity ratio, β is the coefficient for adjusting the activity, S g It is the weighted average index of key markers;

[0327] S413: According to the weighted average index of key markers, the qualified cell data are classified and summarized using the formula:

[0328] R g =log(max(S g ))

[0329] Calculate the logarithm of the activity index to determine the representative activity of umbilical cord blood stem cells and generate umbilical cord blood stem cell activity identification results, where S g is the weighted average index of key markers, R g This is the result of identifying the activity of umbilical cord blood stem cells.

[0330] The formula for the average cell activity ratio is:

[0331]

[0332] A g,i : Activity index of the ith cell;

[0333] T g : Activity threshold;

[0334] N g : total cell number;

[0335] α: nonlinear adjustment coefficient;

[0336] Assumption example: total number of cells N g =100, activity threshold T g =50, nonlinear adjustment coefficient α = 2, assuming that the activity index of all cells is A g,i Randomly distributed between 40 and 60;

[0337] Suppose there are three cells with activity indices of 40, 50, and 600;

[0338] For Ag,1 =40:

[0339] For A g,2 =50:

[0340] For A g,3 =60:

[0341] Average value calculation F g :

[0342]

[0343] The average value F g =1.0267 indicates that the average activity index of each cell is proportional to the square of the threshold, and a value higher than 1 indicates that the cell activity is generally higher than the threshold.

[0344] The weighted average index formula of key markers is:

[0345]

[0346] B g,j : The marker activity of the jth cell;

[0347] M g : Total number of cells analyzed;

[0348] F g : The screening cell data obtained from the previous step;

[0349] β: coefficient to adjust the effect of activity level;

[0350] Hypothetical example: The total number of cells analyzed is M g =3, F g =1.0267 (obtained from the previous step), B g,j Assume 10, 20 and 30, β = 1.5;

[0351] Calculation process:

[0352] For B g,1 =10:(10·1.0267) 1.5 =10.267 1.5 =32.703;

[0353] For B g,2 =20: (20 1.0267) 1.5 =20.534 1.5 =93.153;

[0354] For B g,3=30: (30 1.0267) 1.5 =30.801 1.5 =172.682;

[0355] Average calculation:

[0356]

[0357] The obtained S g =99.179 represents the weighted average index of specific marker expression, and a high value indicates a cell population with high transcriptional activity.

[0358] Umbilical cord blood stem cell activity identification result formula:

[0359] R g =log(max(S g ))

[0360] S g : The expression index calculated from the previous step;

[0361] Assumption example: S g =99.179 (obtained from the previous step);

[0362] Calculate R g :

[0363] R g =log(99.179)=1.996

[0364] The obtained R g =1.996 represents the logarithmic value of the highest activity index, which can be used to distinguish highly active cell populations.

[0365] An activity detection system for umbilical cord blood stem cells, the activity detection system for umbilical cord blood stem cells is used to perform the above-mentioned activity detection method for umbilical cord blood stem cells, the system comprises:

[0366] The cell sampling module separates cells from fresh umbilical cord blood samples in a sterile environment, uses automated flow cytometry separation equipment to classify cells according to cell type markers, collects data on the number of live cells, records the activity response of cells through real-time cell activity monitoring, and obtains cell sampling records;

[0367] The activity identification module uses fluorescent staining to stain the cells in the cell sampling record, and uses a fluorescent microscope to detect the fluorescence intensity of the target protein in the cell, records and analyzes the data to evaluate the activity level of the cell and obtain activity level data;

[0368] The gene network analysis module extracts RNA from highly active cells selected from the activity level data, analyzes the gene expression of cells through high-throughput sequencing, uses bioinformatics software to identify key signal transduction pathways, and constructs a gene regulation map;

[0369] The culture optimization module adjusts the nutrient composition and pH value of the culture medium according to the information provided by the gene regulation map to match the needs of the target cell type, sets the optimal temperature and humidity conditions to promote cell growth, and establishes a culture condition optimization plan;

[0370] The cell function evaluation module cultures cells according to the culture condition optimization plan, regularly uses cell labeling and immunostaining techniques to detect cell surface markers, and uses image analysis software to evaluate the differentiation state and maturity of cells to obtain the results of umbilical cord blood stem cell activity identification.

[0371] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for detecting the activity of umbilical cord blood stem cells, It is characterized in that The following steps are involved: Separating cells from fresh umbilical cord blood samples in a sterile environment, performing preliminary identification of cell activity on the separated stem cells, generating cell activity evaluation results, screening cells whose activity meets the preset standards, performing functional evaluation, and obtaining a screening cell set; Performing gene analysis of functional genes on the screened cell set, analyzing the gene regulatory network, obtaining gene regulatory network analysis results, and using the gene regulatory network analysis results to evaluate the regeneration and repair capabilities of the cells, determine the application potential of the cells, and generate cell potential evaluation results; According to the cell potential assessment results, cells with regenerative ability are selected for preparation before cell culture, the culture medium and growth conditions are adjusted to obtain an optimized culture plan, the optimized culture plan is implemented, the growth of cells under the improved conditions is monitored, and the culture effect monitoring results are obtained; The culture effect monitoring results are used to perform functional evaluation on the differentiation and maturation of cells, identify cells that meet the treatment criteria, perform cell phenotypic characteristic analysis, determine the cell population of the target marker, classify and summarize the data of the qualified cells, and generate the umbilical cord blood stem cell activity identification results.

2. The method for detecting the activity of umbilical cord blood stem cells according to claim 1, It is characterized in that The steps of obtaining the screening cell set are specifically as follows: Cells were isolated from fresh umbilical cord blood samples in a sterile environment, and the concentration of biomarkers extracted from stem cell samples was determined by combining the extraction efficiency and the bioactivity response coefficient using the formula: Calculate the activity score of each sample and generate an activity score array, where c a represents the concentration of the biomarker, η a represents the ratio of biomarkers extracted from samples, k a is the conversion factor, r a is the nonlinear response index, E a Represents an array of activity scores; The mean and standard deviation are calculated from the activity score array using the thresholding formula: Get the preset activity threshold, where: is the mean of the activity scores, σ a represents the standard deviation of the rating, λ a is the weighted adjustment coefficient, exp represents the natural exponential function, T a Indicates the preset activity threshold; The preset activity threshold is subjected to a biological function test to obtain a function score, and the formula is applied: Calculate and obtain the cell activity evaluation results, where F a represents the functional score, ψ a is the result conversion coefficient, ω a is the adjustment coefficient of the functional score, γ a is the baseline offset, δ a is the score adjustment index, R a represents the results of cell viability assessment; Based on the cell activity evaluation results, a threshold is set by comparing the activity evaluation result of each cell with the threshold, using the formula: S a ={x∈R a |x≥T a } Select the condition x≥T a cells to generate a screening cell set, where R a Represents the results of cell viability assessment, T a is the preset activity threshold, S a is the screening cell set, and x represents the activity evaluation result of a single cell.

3. The method for detecting the activity of umbilical cord blood stem cells according to claim 2, It is characterized in that The steps for obtaining the gene regulatory network analysis results are specifically as follows: RNA sequence data were extracted from the screened cell set, and the activity level of each gene was quantified using quantitative PCR technology, and the normalization processing formula was applied: Generate a normalized gene activity dataset, where E b is the original gene activity matrix obtained by quantitative PCR, μ b and σ b E b The column mean and standard deviation of D b is the normalized gene activity data; To analyze the cross-correlation between genes in the standardized gene activity dataset, the formula was used: and Calculate and generate the gene transcription correlation matrix, where D b is the normalized gene activity data, Yes D b The transpose of M b is the gene interaction strength matrix, C b represents the gene correlation matrix; The gene regulatory network was constructed using the gene interaction intensity matrix, and the threshold processing formula was applied: N b =threshold(M b ,θ b ) Screen gene regulatory relationships and obtain gene regulatory network analysis results, among which M b is the gene interaction strength matrix, θ b is the threshold for screening gene regulatory relationships, N b Represents a gene regulatory network.

4. The method for detecting the activity of umbilical cord blood stem cells according to claim 3, It is characterized in that The steps for obtaining the cell potential assessment results are specifically as follows: Cell activity data were extracted from the gene regulatory network analysis results and the data were standardized using the formula: Calculate the activity deviation of each cell to generate a cell baseline calibration dataset, where E c Represents the real-time activity data of cells, E 0 represents the baseline data, E std represents the standard deviation, D c represents the cell baseline calibration dataset; Using the cell baseline calibration data set, the cell physiological response model is applied to calculate the cell response analysis results using the formula: Adjust the sensitivity of cell response to obtain preliminary evaluation results of cell physiological response ability, among which D c is the cell baseline calibration data set, e is the base of the natural logarithm, D norm is the normalization factor, R c It indicates the preliminary evaluation results of cell physiological response capacity; The preliminary evaluation results of cell physiological response ability were used to evaluate the potential of cells through a multi-factor analysis model using the formula: Generate cell potential assessment results, where n is the number of factors involved in the assessment, R c,i represents the physiological response ability of the ith cell, w i Represents the weight of the corresponding factor, P c Represents the results of cell potential assessment.

5. The method for detecting the activity of umbilical cord blood stem cells according to claim 4, It is characterized in that The steps for obtaining the optimized culture scheme are specifically as follows: Based on the cell potential assessment results, cells showing high regenerative ability were screened, and the high regenerative ability of each cell was calculated based on the gene activity data of the cells and the key gene markers associated with regenerative ability, using the formula: Generate a high regenerative capacity cell fraction, where b i represents the weight based on gene criticality, G di represents the gene activity value of the i-th cell, n represents the number of cells, S d represents the standard deviation of gene activity, H d Indicates the fraction of cells with high regenerative capacity; According to the high regenerative capacity cell fraction, the medium composition was adjusted and the optimal growth efficiency was calculated using the formula: The growth efficiency after the medium adjustment is generated, where C di represents the compound concentration of the ith cell, E d The index representing the environmental regulation factor, L d represents the growth efficiency after medium adjustment, H d Indicates the fraction of cells with high regenerative capacity; Based on laboratory resources and the growth efficiency of the medium adjusted as described, a cell culture protocol was developed using the formula: Generate an optimized culture plan, where R d Represents the amount of laboratory resources, P d Indicates the optimized training program, L d Indicates the growth efficiency after medium adjustment.

6. The method for detecting the activity of umbilical cord blood stem cells according to claim 5, It is characterized in that The steps for obtaining the culture effect monitoring results are specifically as follows: Based on the optimized culture scheme, the cell growth environment parameters under the improved conditions were monitored, the culture medium temperature, pH value, and nutrient concentration were collected, and the formula was used: Get environmental parameter data, where T h Represents the culture medium temperature, pH h Represents pH value, C h represents the nutrient concentration, a, b, c are adjustment coefficients, E h Represents environmental parameter data; Based on the environmental parameter data, the cell density and viability were monitored and evaluated in combination with the cell growth effect, using the formula: Get cell growth effect data, where S h Represents the number of viable cells, N h represents the total number of cells, D h is the cell growth effect data, E h Represents environmental parameter data; Using the preliminary growth effect data, compared with the preset growth standard, the formula is used: Calculate the percentage difference between the real-time growth effect and the expected one, and establish the culture effect monitoring results, where L h Represents the preset growth effect standard, R h is the percentage deviation between real time and expected value, D h Cell growth effect data.

7. The method for detecting the activity of umbilical cord blood stem cells according to claim 6, It is characterized in that The steps for obtaining the result of the activity identification of umbilical cord blood stem cells are specifically as follows: The cell population that meets the basic treatment standard is selected from the culture effect monitoring results, the standard threshold is used as a benchmark, and the ratio of the cell activity index to the threshold is calculated using the formula: The average activity ratio of the screened cells was obtained, where A g,i represents the activity index of the ith cell, T g is the activity threshold, N g is the total number of cells, α is the adjustment coefficient, F g is the average cell activity ratio; The average activity ratio of the cells after the screening was used to analyze the phenotypic characteristics of the cells using the formula: The weighted average index of key markers is obtained, where B g,j is the marker activity of the jth cell, M g is the total number of cells analyzed, F g is the average cell activity ratio, β is the coefficient for adjusting the activity, S g It is the weighted average index of key markers; According to the weighted average index of key markers, the data of qualified cells were classified and summarized using the formula: R g =log(max(S g )) Calculate the logarithm of the activity index to determine the representative activity of umbilical cord blood stem cells and generate umbilical cord blood stem cell activity identification results, where S g is the weighted average index of key markers, R g This is the result of identifying the activity of umbilical cord blood stem cells.

8. An activity detection system for umbilical cord blood stem cells, It is characterized in that The method for detecting the activity of umbilical cord blood stem cells according to any one of claims 1 to 7, wherein the system comprises: The cell sampling module separates cells from fresh umbilical cord blood samples in a sterile environment, uses automated flow cytometry separation equipment to classify cells according to surface markers, records the number of live cells, records cell activity responses through real-time monitoring equipment, and obtains cell sampling records; The activity identification module uses a fluorescent staining method to stain the cells in the cell sampling record, uses a fluorescent microscope to detect the fluorescence intensity of the target protein, records and analyzes the data to evaluate the cell activity level, and obtains activity level data; The gene network analysis module extracts RNA from the highly active cells selected from the activity level data, analyzes gene transcription through high-throughput sequencing, and constructs a gene regulation map using bioinformatics software; The culture optimization module adjusts the culture medium composition and pH value according to the gene regulation map, sets the optimal temperature and humidity conditions, and establishes a culture condition optimization plan; The cell function evaluation module cultures cells according to the culture condition optimization scheme, regularly uses labeling and immunostaining techniques to detect cell surface markers, evaluates cell differentiation and maturity through image analysis, and obtains the activity identification results of umbilical cord blood stem cells.

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