Human Stem Cell-Based Detection Method

Abstract

The present invention relates to the technical field of stem cell detection, and particularly to a detection method based on human stem cells. The method includes preprocessing the acquired image data of the stem cell culture process to obtain analyzable image data; determining a prediction method for the number of stem cells based on the fluctuation degree of the proliferation rate of mesenchymal stem cells in a plurality of analyzable image data; determining to replace the culture medium or divide the mesenchymal stem cells into a new culture dish based on the proportion of the increased volume of mesenchymal stem cells and whether the mesenchymal stem cells show a multi-layer growth phenomenon under the condition that the deviation value is greater than a preset deviation value; determining to adjust the preset fluctuation degree or adjust the preset uniform distribution degree based on the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells being greater than the preset deviation value under each prediction method after corresponding adjustment within a preset time period, and whether the image data corresponding to the occurrence time point has been preprocessed.

Description

Technical Field

[0001] The present invention relates to the technical field of stem cell detection, and particularly to a detection method based on human stem cells. Background Art

[0002] Due to their unique self-renewal and multi-directional differentiation abilities, human stem cells have shown great application potential in many fields such as regenerative medicine, disease research, and drug development, bringing new hope for the treatment of various intractable diseases such as Parkinson's disease, diabetes, and cardiovascular diseases. Therefore, in-depth research and precise control of them are of great significance. Traditional stem cell detection methods mostly rely on manual observation and limited biochemical index detection, such as cell counting using a cell counting plate, immunoassay of specific protein markers, etc. These methods are not only time-consuming and laborious, but also difficult to monitor the changes of stem cells in real time and dynamically during the culture process, unable to obtain comprehensive information of cells, easily leading to information lag and incomplete data, and affecting the accurate judgment of the state of stem cells.

[0003] Chinese Patent Application Publication No.: CN118374570A discloses a detection method based on human stem cells. The method includes obtaining human stem cells and culturing the stem cells in a culture dish, cryopreserving the stem cells; thawing the stem cells at a preset period for stem cell viability detection; transferring the stem cells to a pre-prepared culture medium or buffer for thawing; obtaining the culture data of the thawed stem cells in the culture dish; determining whether the stem cell processing process is qualified according to the cell viability evaluation value of the thawed stem cells; periodically obtaining the number of adjustments during the stem cell viability detection process of human stem cells, calculating the parameter volatility evaluation value according to the fluctuations of the corresponding parameters in each adjustment, and judging whether the corresponding stem cell processing process parameters are adjusted based on the parameter volatility evaluation value. The invention improves the accuracy of the stem cell detection process by improving the accuracy of the analysis of the stem cell processing process.

[0004] It can be seen that the existing technology has the problem that the analysis of the proliferation rate of human stem cells during the human stem cell culture process is not accurate enough, resulting in the inability to timely monitor the abnormalities in the human stem cell culture process, resulting in low efficiency and low quality of human stem cell culture. Summary of the Invention

[0005] To this end, the present invention provides a detection method based on human stem cells to overcome the problem in the prior art that the analysis of the proliferation rate of human stem cells during the human stem cell culture process is not accurate enough, resulting in the inability to timely monitor the abnormalities in the human stem cell culture process, resulting in low efficiency and low quality of human stem cell culture.

[0006] To achieve the above object, the present invention provides a detection method based on human stem cells, including:

[0007] Monitor the mesenchymal stem cell culture process in real time, periodically obtain the image data and the environmental data during the mesenchymal stem cell culture process, and when there is salt-and-pepper blur in the obtained image data, based on the distribution uniformity of isolated pixel points and whether the position of the isolated pixel points is within the mesenchymal stem cell position interval, determine to use filtering method, histogram equalization method, or cutting method to preprocess the image data to obtain analyzable image data;

[0008] Based on the fluctuation degree of the proliferation rate of mesenchymal stem cells in several pieces of analyzable image data, determine to predict the number of mesenchymal stem cells at the next division time by the regular prediction method, or predict the number of mesenchymal stem cells at the next division time by the model prediction method;

[0009] Obtain the deviation value between the actual number and the predicted number of mesenchymal stem cells under the corresponding prediction method;

[0010] Under the condition that the deviation value is greater than the preset deviation value, based on the proportion of the increased volume of mesenchymal stem cells and whether the mesenchymal stem cells show a multi-layer growth phenomenon, determine to replace the culture medium or divide the mesenchymal stem cells into a new culture dish;

[0011] Based on the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells being greater than the preset deviation value under each prediction method after corresponding adjustment within the preset duration, and whether the image data corresponding to the time point when the deviation value between the actual number and the predicted number of mesenchymal stem cells is greater than the preset deviation value under each prediction method after corresponding adjustment within the preset duration has been preprocessed, determine to adjust the preset fluctuation degree or the preset distribution uniformity.

[0012] Further, when there is salt-and-pepper blur in the obtained image data, preprocessing the image data to obtain analyzable image data includes:

[0013] If the distribution uniformity of the isolated pixel points is greater than the preset uniformity and the position of the isolated pixel points is within the mesenchymal stem cell position interval, determine to preprocess the image data by the filtering method to obtain analyzable image data;

[0014] If the distribution uniformity of the isolated pixel points is less than or equal to the preset uniformity and the position of the isolated pixel points is within the mesenchymal stem cell position interval, determine to preprocess the image data by the histogram equalization method to obtain analyzable image data;

[0015] If the position of the isolated pixel points is not within the mesenchymal stem cell position interval, determine to preprocess the image data by the cutting method to obtain analyzable image data.

[0016] Further, determining that there is salt-and-pepper blur in the acquired image data includes that there are isolated pixel points in the image with gray values within a preset gray value range, and the preset gray value range is 0 - 10 and 245 - 255.

[0017] Further, determining the distribution uniformity of the isolated pixel points includes:

[0018] Dividing the image data into several regions with equal areas;

[0019] Obtaining the number of isolated pixel points in several regions;

[0020] Calculating the variance of the isolated pixel points in several regions is the distribution uniformity of the isolated pixel points.

[0021] Further, predicting the number of mesenchymal stem cells at the next division time includes:

[0022] When the fluctuation degree of the proliferation rate of mesenchymal stem cells in the several analyzable image data is less than the preset fluctuation degree, determining to predict the number of mesenchymal stem cells at the next division time by a regular prediction method;

[0023] When the fluctuation degree of the proliferation rate of mesenchymal stem cells in the several analyzable image data is greater than or equal to the preset fluctuation degree, determining to predict the number of mesenchymal stem cells at the next division time by a model prediction method.

[0024] Further, the fluctuation degree of the proliferation rate of mesenchymal stem cells is determined according to the variance of the proliferation rate of mesenchymal stem cells in several analyzable image data.

[0025] Further, determining to replace the culture medium or divide the mesenchymal stem cells into a new culture dish under the condition that the deviation value is greater than the preset deviation value includes:

[0026] If the proportion of the increased volume of mesenchymal stem cells is greater than the preset proportion, determining to replace the culture medium;

[0027] If there is a multi-layer growth phenomenon of mesenchymal stem cells, determining to divide the mesenchymal stem cells into a new culture dish.

[0028] Further, determining that there is a multi-layer growth phenomenon of mesenchymal stem cells includes that the average intercellular distance of mesenchymal stem cells is greater than the preset distance.

[0029] Further, determining to adjust the preset fluctuation degree or the preset distribution uniformity includes:

[0030] If the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after corresponding adjustment within the preset duration being greater than the preset deviation value is greater than the preset occurrence frequency and the image data corresponding to the time points when the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after corresponding adjustment within the preset duration is greater than the preset deviation value has not been preprocessed, determine to adjust the preset fluctuation degree;

[0031] If the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after corresponding adjustment within the preset duration being greater than the preset deviation value is greater than the preset occurrence frequency and the image data corresponding to the time points when the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after corresponding adjustment within the preset duration is greater than the preset deviation value has been preprocessed, determine to adjust the preset distribution uniformity degree.

[0032] Furthermore, the adjustment amount of the preset fluctuation degree is negatively correlated with the occurrence frequency of the deviation value being greater than the preset deviation value, and the adjustment amount of the preset distribution uniformity degree is negatively correlated with the occurrence frequency of the deviation value being greater than the preset deviation value.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows. In the process of culturing mesenchymal stem cells, the image data obtained often shows salt-and-pepper blur phenomenon due to various factors. By selecting appropriate preprocessing methods according to the distribution uniformity degree and position of isolated pixel points, it can accurately cope with the noise interference generated in different situations. For example, at the image acquisition moment 1, when the distribution uniformity degree of isolated pixel points is greater than the preset uniformity degree and is located in the cell position interval, the median filtering method is used, effectively removing those isolated pixel points similar to salt-and-pepper noise randomly distributed in the cell area, making the cell image clearer and reducing the interference caused by these noise pixels to the observation of cell morphology, quantity and other characteristics, providing a high-quality image basis for the subsequent accurate analysis of the growth, proliferation and morphological changes of mesenchymal stem cells. In the case of the image acquisition moment 2, when the distribution uniformity degree of isolated pixel points is less than or equal to the preset uniformity degree and is in the cell position interval, the histogram equalization method is used for preprocessing. This operation can redistribute the gray levels of the image, significantly enhancing the contrast between the cells and the background, clearly showing details such as the contours, internal structures and boundaries between cells, which helps to more finely study the physiological characteristics of cells and the intercellular relationships, such as judging whether the cells show abnormal morphological changes or aggregation conditions.

[0034] Furthermore, in the embodiments of the present invention, when the fluctuation degree of the proliferation rate of mesenchymal stem cells is less than the preset fluctuation degree, it means that the proliferation of stem cells presents a relatively stable and regular state. At this time, using a regular prediction method, such as a linear growth model, prediction based on the cell cycle stage, etc., can accurately estimate according to this stable proliferation pattern. Because it calculates the number of stem cells at the next division based on the observed stable law, it can more accurately reflect the change trend of the number of stem cells in the short term, and reduce the prediction deviation caused by introducing too many uncertain factors by complex models. When the proliferation rate of stem cells fluctuates greatly (greater than or equal to the preset fluctuation degree), its proliferation process is often affected by the interaction of multiple complex factors, showing non-linear and irregular change characteristics. Using a model prediction method can capture the complex time series characteristics, non-linear relationships between multiple factors, and the dynamic changes of the system. They can comprehensively consider the influence of many factors on the proliferation of stem cells, such as cell-cell interaction, environmental factor changes, changes in the cell's own internal regulation mechanism, etc., so as to better fit the complex proliferation data and more realistically predict the number of stem cells at the next division, and can provide a relatively reasonable prediction reference even in complex situations with large fluctuations. Through the above methods, the accuracy of the analysis of the proliferation rate of mesenchymal stem cells during the culture process of mesenchymal stem cells is improved, and thus the quality and efficiency of the culture of mesenchymal stem cells are improved.

[0035] Furthermore, during the culture process of mesenchymal stem cells in the present invention, the proportion of the increased cell volume is an important indicator reflecting the cell growth state. When this proportion is greater than the preset proportion, it means that the cells may have overgrown due to factors such as excessive nutrients and abnormal growth environment. By setting such a judgment criterion and determining to replace the culture medium, the nutritional environment of the cells can be adjusted in time, for example, reducing the content of excessive nutrients (such as glucose, amino acids, growth factors, etc.) in the culture medium, avoiding continuous abnormal growth of the cells, and enabling their growth state to return to normal, which helps to maintain a stable and healthy proliferation rhythm of the cells and ensure that the functions and activities of the cells are not adversely affected. Under normal circumstances, mesenchymal stem cells mostly grow in a monolayer adherent manner. When the phenomenon of multi-layer growth appears, it indicates that the growth space of the cells in the planar direction is insufficient, which may cause a series of problems such as intensified competition for nutrients between cells, difficulty in discharging metabolic wastes, and abnormal signal transduction, thereby affecting the normal proliferation and function maintenance of the cells. At this time, determining to divide the mesenchymal stem cells into a new culture dish can provide sufficient growth space for the cells, restore the suitable growth environment for the cells, and enable the cells to continue to grow, proliferate, and maintain normal cell-cell interaction according to the normal law, preventing the cell state from deteriorating or the culture from failing due to limited space. Through the above methods, abnormal proliferation of human stem cells during the culture process of human stem cells can be detected in time, and thus the quality and efficiency of the culture of human stem cells are improved.

[0036] Furthermore, in the process of culturing mesenchymal stem cells, the present invention comprehensively considers multiple factors such as the occurrence frequency of the deviation value between the actual quantity and the predicted quantity being greater than the preset deviation value within a preset time period and whether the corresponding image data has been preprocessed to determine whether to adjust the preset fluctuation degree or the preset distribution uniformity degree. This refined judgment mechanism can more accurately identify potential problems in the cell culture process. For example, by comparing the occurrence frequency with the preset occurrence frequency and combining the preprocessing situation of the image data, it is possible to distinguish whether it is a fluctuation problem of the cell proliferation law itself or a deviation problem reflected under the influence of the image data quality, so as to accurately locate the key link that needs to be adjusted, and achieve precise control through the above method, thereby improving the quality and efficiency of human stem cell culture. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flowchart of the working process of the detection method based on human stem cells according to an embodiment of the present invention;

[0038] Figure 2 is a flowchart of the working process of the method for determining the prediction method of the number of mesenchymal stem cells in the detection method based on human stem cells according to an embodiment of the present invention;

[0039] Figure 3 is a flowchart of the working process of determining the distribution uniformity degree of isolated pixel points in the detection method based on human stem cells according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0041] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0042] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0043] In addition, it should be noted that in the description of the present invention, unless otherwise clearly defined and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] Please refer to Figures 1 - 3 as shown Figure 1 which is the flowchart of the detection method based on human stem cells according to the embodiment of the present invention; Figure 2 which is the flowchart of the method for determining the prediction method of the number of mesenchymal stem cells according to the detection method based on human stem cells in the embodiment of the present invention; Figure 3 which is the flowchart of determining the degree of uniformity of the distribution of isolated pixel points in the detection method based on human stem cells according to the embodiment of the present invention.

[0045] The detection method based on human stem cells according to the embodiment of the present invention includes:

[0046] Step S1, monitor the culture process of mesenchymal stem cells in real time, periodically obtain the image data and the environmental data during the culture process of mesenchymal stem cells, and when there is salt-and-pepper blur in the obtained image data, based on the degree of uniformity of the distribution of isolated pixel points and whether the position of the isolated pixel points is in the position interval of mesenchymal stem cells, determine to use the filtering method, histogram equalization method, or cutting method to preprocess the image data to obtain analyzable image data;

[0047] Step S2, based on the degree of fluctuation of the proliferation rate of mesenchymal stem cells in several analyzable image data, determine to predict the number of mesenchymal stem cells at the next division time by the regular prediction method or predict the number of mesenchymal stem cells at the next division time by the model prediction method;

[0048] Step S3, obtain the deviation value between the actual number and the predicted number of mesenchymal stem cells under the corresponding prediction method;

[0049] Step S4, under the condition that the deviation value is greater than the preset deviation value, based on the proportion of the increased volume of mesenchymal stem cells and whether the mesenchymal stem cells show a multi-layer growth phenomenon, determine to replace the culture medium or divide the mesenchymal stem cells into a new culture dish;

[0050] Step S5: Based on the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells being greater than the preset deviation value under each prediction method after corresponding adjustment within the preset time period, and whether the image data corresponding to the time point when the deviation value between the actual number and the predicted number of mesenchymal stem cells is greater than the preset deviation value under each prediction method after corresponding adjustment within the preset time period has been preprocessed, determine whether to adjust the preset fluctuation degree or the preset distribution uniformity degree.

[0051] In the embodiments of the present invention, the image data includes but is not limited to "cell morphology data, cell quantity data, and cell aggregation state data", and the environmental data includes but is not limited to "nutrient concentration data, metabolic waste concentration data, and temperature".

[0052] Specifically, in step S1, when it is determined to preprocess the image data to obtain analyzable image data under the condition that there is a salt-and-pepper blur phenomenon in the acquired image data, determine to preprocess the image data to obtain analyzable image data by using a filtering method, a histogram equalization method, or a cutting method according to the distribution uniformity degree of isolated pixel points and whether the position of the isolated pixel points is within the mesenchymal stem cell position interval;

[0053] When the distribution uniformity degree of the isolated pixel points is greater than the preset uniformity degree and the position of the isolated pixel points is within the mesenchymal stem cell position interval, determine to preprocess the image data by using a filtering method to obtain analyzable image data;

[0054] When the distribution uniformity degree of the isolated pixel points is less than or equal to the preset uniformity degree and the position of the isolated pixel points is within the mesenchymal stem cell position interval, determine to preprocess the image data by using a histogram equalization method to obtain analyzable image data;

[0055] When the position of the isolated pixel points is not within the mesenchymal stem cell position interval, determine to preprocess the image data by using a cutting method to obtain analyzable image data.

[0056] In the embodiments of the present invention, when there is no salt-and-pepper blur phenomenon in the acquired image data, some conventional image enhancement methods, such as global or local contrast adjustment, brightness adjustment, etc., are used to improve the visual effect of the image for better subsequent analysis. The determination that there is a salt-and-pepper blur phenomenon in the acquired image data includes that there are isolated pixel points in the image whose gray values are within the preset gray value range, and the preset gray value range is 0 - 10 and 245 - 255.

[0057] In an embodiment of the present invention, at image acquisition time 1, the obtained image data shows that there are some isolated pixel points with gray values of 5 and 250 in the image. It is determined that there is salt-and-pepper blur. The distribution uniformity of the isolated pixel points is calculated: the image is divided into multiple small regions, the number of isolated pixel points in each small region is counted, and the calculated variance is 60, which is greater than the preset uniformity degree (50). At the same time, through image analysis, it is determined that the positions of these isolated pixel points are in the position interval of mesenchymal stem cells. According to the above conditions, it is determined to preprocess the image data by a filtering method. Here, median filtering is used, and the size of the filtering window is selected as 3×3. For each pixel point in the image, a 3×3 neighborhood pixel is taken with it as the center, these pixel values are sorted from small to large, and the middle value is taken to replace the center pixel value. After processing, the isolated noise pixel points are removed, and the cell image is clearer. At image acquisition time 2, there is again salt-and-pepper blur in the image data, with isolated pixel points having gray values of 8 and 248. The distribution uniformity of the isolated pixel points is calculated: the calculated variance is 40, which is less than or equal to the preset uniformity degree (50), and it is determined that the positions of these isolated pixel points are in the position interval of mesenchymal stem cells. It is determined to preprocess the image data by the histogram equalization method. After performing histogram equalization processing on this image, the contrast between the cells and the background is significantly enhanced, and the contours, internal structures of the cells, and the boundaries between cells are all more clearly visible. At image acquisition time 3, there are isolated pixel points with gray values of 3 and 252 in the image. It is determined that there is salt-and-pepper blur. After analysis, it is determined that the positions of these isolated pixel points are not in the position interval of mesenchymal stem cells. It is determined to preprocess the image data by the cutting method. That is, the region containing the isolated pixel points but not containing mesenchymal stem cells is cut from the image, and only the part containing mesenchymal stem cells is retained to obtain analyzable image data.

[0058] Specifically, in step S1, the steps of determining the distribution uniformity of the isolated pixel points include:

[0059] Step S1101, equally divide the image data into several regions;

[0060] Step S1102, obtain the number of isolated pixel points in several regions;

[0061] Step S1103, calculate the variance of the isolated pixel points in several regions, which is the distribution uniformity of the isolated pixel points.

[0062] In the embodiment of the present invention, the preset distribution uniformity degree is the historical distribution uniformity degree of the isolated pixel points after the image data in the process of culturing mesenchymal stem cells is obtained. However, the above values are not limited to this, and those skilled in the art can also adjust this value according to actual needs.

[0063] During the culturing process of mesenchymal stem cells, the image data obtained often exhibits salt-and-pepper blur due to various factors. By selecting an appropriate preprocessing method based on the distribution uniformity and position of isolated pixel points, it is possible to accurately handle the noise interference caused by different situations. For example, at image acquisition time 1, when the distribution uniformity of isolated pixel points is greater than the preset uniformity and they are located within the cell position interval, the median filtering method is used, effectively removing those isolated pixel points similar to salt-and-pepper noise randomly distributed within the cell region, making the cell image clearer and reducing the interference caused by these noise pixels to the observation of cell characteristics such as morphology and quantity. This provides a high-quality image basis for the subsequent accurate analysis of the growth, proliferation, and morphological changes of mesenchymal stem cells. In the case of image acquisition time 2, when the distribution uniformity of isolated pixel points is less than or equal to the preset uniformity and within the cell position interval, the histogram equalization method is used for preprocessing. This operation can redistribute the gray levels of the image, significantly enhancing the contrast between the cells and the background, and clearly showing details such as the cell contours, internal structures, and boundaries between cells. This helps to more precisely study the physiological characteristics of cells and the intercellular relationships, such as determining whether there are abnormal morphological changes or aggregation situations in the cells.

[0064] Specifically, in step S2, when predicting the number of mesenchymal stem cells at the next division time, determine whether to predict the number of mesenchymal stem cells at the next division time using a regular prediction method or a model prediction method based on the degree of fluctuation in the proliferation rate of mesenchymal stem cells in a number of analyzable image data;

[0065] When the degree of fluctuation in the proliferation rate of mesenchymal stem cells in a number of analyzable image data is less than the preset degree of fluctuation, determine to predict the number of mesenchymal stem cells at the next division time using a regular prediction method;

[0066] When the degree of fluctuation in the proliferation rate of mesenchymal stem cells in a number of analyzable image data is greater than or equal to the preset degree of fluctuation, determine to predict the number of mesenchymal stem cells at the next division time using a model prediction method.

[0067] In the embodiments of the present invention, the degree of fluctuation of the proliferation rate of mesenchymal stem cells is the variance of the proliferation rates of mesenchymal stem cells in a number of analyzable image data. The preset degree of fluctuation is the average degree of fluctuation of the proliferation rates of the same type of mesenchymal stem cells under the same culture conditions throughout the entire culture process. The method for predicting the number of mesenchymal stem cells at the next division by the rule prediction method includes that if the proliferation rate of stem cells is observed to be relatively stable in the early stage, that is, the variance is small, it can be assumed that the number of stem cells increases linearly. For example, by analyzing the analyzable image data at multiple time points, it is found that the number of stem cells increases by a fixed value each time in the past few time cycles (such as every 2 hours as a cycle). The method for predicting the number of mesenchymal stem cells at the next division by the model prediction method includes extracting features related to stem cell proliferation from the analyzable image data, such as the current number of stem cells, cell density, environmental nutrient concentration (if relevant image features can be obtained), etc. as input feature vectors. Using these feature vectors and the corresponding actual number of stem cells at different past time points as training data, selecting a suitable kernel function (such as the radial basis function RBF), and determining the parameters of the SVR model, such as the penalty parameter and the width parameter of the kernel function, through methods such as cross-validation, training the SVR model to enable the model to learn the non-linear relationship between the input features and the number of stem cells. For the prediction of the number of stem cells at the next division, input the feature vector at the current time point into the trained SVR model, and the model will predict the number of stem cells according to the learned relationship. For example, the model may learn that when the cell density reaches a certain threshold and the nutrient concentration is low, the growth of the number of stem cells will slow down, so these factors will be considered during prediction.

[0068] In the embodiments of the present invention, when the fluctuation degree of the proliferation rate of mesenchymal stem cells is less than the preset fluctuation degree, it means that the proliferation of stem cells presents a relatively stable and regular state. At this time, using a regular prediction method, such as a linear growth model, a prediction based on the cell cycle stage, etc., can accurately estimate according to this stable proliferation pattern. Since it calculates the number of stem cells at the next division based on the observed stable pattern, it can more accurately reflect the change trend of the number of stem cells in the short term and reduce the prediction deviation caused by introducing too many uncertain factors by complex models. When the proliferation rate of stem cells fluctuates greatly (greater than or equal to the preset fluctuation degree), its proliferation process is often affected by the interaction of multiple complex factors and shows non-linear and irregular change characteristics. Using a model prediction method can capture the complex time series characteristics, the non-linear relationship between multiple factors, and the dynamic changes of the system. They can comprehensively consider the influence of many factors on the proliferation of stem cells, such as cell-cell interaction, environmental factor changes, and changes in the cell's own internal regulation mechanism, so as to better fit the complex proliferation data and more realistically predict the number of stem cells at the next division, and can provide a relatively reasonable prediction reference even in complex situations with large fluctuations. Through the above methods, the accuracy of the analysis of the proliferation rate of mesenchymal stem cells during the culture process of mesenchymal stem cells is improved, and thus the quality and efficiency of the culture of mesenchymal stem cells are improved.

[0069] Specifically, in step S3, obtaining the deviation value between the actual number and the predicted number of mesenchymal stem cells under the corresponding prediction method includes calculating the absolute value of the difference between the actual number and the predicted number of mesenchymal stem cells under the corresponding prediction method.

[0070] In the embodiments of the present invention, within the first 6 hours of culture, through the analysis of the analyzable image data, it is found that the proliferation rate of stem cells is relatively stable (its variance is less than the preset fluctuation degree), so the linear growth model in the regular prediction method is used to predict the number of stem cells. After counting the number of stem cells at the previous several time points (0 hour, 2 hours, 4 hours, 6 hours), it is found that the number of stem cells increases by 500,000 every 2 hours. Given that the initial (0 hour) number of stem cells is 2 million, according to the linear growth model prediction, the number of stem cells at the 6-hour time point should be 4 million, which is the predicted number. At the 6-hour time point, by accurately counting the stem cells in the image data collected and preprocessed at this moment (it can be automatically counted by image analysis software or accurately counted with the assistance of manual work, etc.), the actual number of stem cells is obtained as 3.6 million. Then, the deviation value is determined by calculating the absolute value of the difference between the actual number and the predicted number of mesenchymal stem cells under the corresponding prediction method, and the deviation value is 400,000.

[0071] Specifically, in step S4, when it is determined to replace the culture medium or split the mesenchymal stem cells into a new culture dish, under the condition that the deviation value is greater than the preset deviation value, determine whether to replace the culture medium or split the mesenchymal stem cells into a new culture dish according to the proportion of the increased volume of the mesenchymal stem cells and whether the mesenchymal stem cells show a multi-layer growth phenomenon;

[0072] When the proportion of the increased volume of the mesenchymal stem cells is greater than the preset proportion, determine to replace the culture medium;

[0073] When the mesenchymal stem cells show a multi-layer growth phenomenon, determine to split the mesenchymal stem cells into a new culture dish.

[0074] In the embodiment of the present invention, the value range of the preset proportion is set to 0.3 - 0.8, and the value of the preset proportion is preferably 0.4. Determining that the mesenchymal stem cells show a multi-layer growth phenomenon includes that the average intercellular distance of the mesenchymal stem cells is greater than the preset distance. The value range of the preset distance is set to 30 - 60 microns, and the value of the preset distance is preferably 40 microns. However, the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0075] In the embodiment of the present invention, the replaced culture medium is a culture medium with a nutrient content lower than that of the original culture medium. The nutrient content is reduced to three-fifths of the nutrient content of the original culture medium. The nutrient content includes but is not limited to "sugar, vitamins, and amino acids". For example, assume that the glucose content in the original culture medium is 5 g / L, the concentration of vitamin B1 (thiamine) is 0.1 mg / L, the concentration of vitamin B12 is 0.01 mg / L, the concentration of vitamin C is 0.05 mg / L, the lysine content is 0.3 g / L, the leucine content is 0.25 g / L, and the valine content is 0.2 g / L. After replacement, the nutrient content of the culture medium is that the glucose content is reduced to 3 g / L, the concentration of vitamin B1 (thiamine) is adjusted to 0.06 mg / L, the concentration of vitamin B12 is reduced to 0.006 mg / L, the concentration of vitamin C is 0.03 mg / L, the lysine content is adjusted to 0.18 g / L, the leucine content is 0.15 g / L, and the valine content is 0.12 g / L. However, the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0076] In the embodiments of the present invention, the value of the preset deviation is one tenth of the actual number of mesenchymal stem cells, the preset aggregation degree is the average aggregation degree of mesenchymal stem cells of the same type under several identical culture conditions, and the preset metabolic waste concentration is the average metabolic waste concentration of mesenchymal stem cells of the same type under several identical culture conditions. However, the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0077] In the embodiments of the present invention, the average intercellular distance of the mesenchymal stem cells is calculated according to the following method: the position of each stem cell in the image is determined through image recognition technology. For clear cell images, the morphological characteristics of the cells (such as the round or oval contour) can be used to determine the center position of the cells. For example, for round cells, the center coordinates can be determined by finding the geometric center of the cell contour. If the cells are irregularly shaped, some complex algorithms can be used, such as first segmenting the cells based on the watershed algorithm and then calculating the centroid position of the segmented cell region as the cell center. After determining the center positions of all cells, the distances between the centers of pairwise cells are calculated, the distances between all cells are statistically analyzed, and the average intercellular distance is calculated. In the embodiments of the present invention, the value of the first adjustment coefficient is preferably 0.84 - 0.96, and the value of the first adjustment coefficient is preferably 0.91. However, the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0078] During the culture process of mesenchymal stem cells, the proportion of the increased cell volume is an important indicator reflecting the cell growth state. When this proportion is greater than the preset proportion, it means that the cells may have overgrown due to factors such as excess nutrients and abnormal growth environment. By setting such a judgment criterion and determining to replace the culture medium, the nutritional environment of the cells can be adjusted in a timely manner. For example, reducing the content of excess nutrients (such as glucose, amino acids, growth factors, etc.) in the culture medium can avoid continuous abnormal growth of the cells, enabling their growth state to return to normal, helping to maintain a stable and healthy proliferation rhythm of the cells, and ensuring that the functions and activities of the cells are not adversely affected. Under normal circumstances, mesenchymal stem cells mostly grow in a monolayer adherent manner. When the phenomenon of multi-layer growth occurs, it indicates that the growth space of the cells in the planar direction is insufficient, which may lead to a series of problems such as intensified competition for nutrients between cells, difficulty in discharging metabolic wastes, and abnormal signal transduction, thereby affecting the normal proliferation and function maintenance of the cells. At this time, determining to divide the mesenchymal stem cells into a new culture dish can provide sufficient growth space for the cells, restore the appropriate growth environment for the cells, enable the cells to continue to grow, proliferate, and maintain normal cell-cell interactions according to normal rules, and prevent the cell state from deteriorating or the culture from failing due to limited space. Through the above methods, abnormal proliferation of human stem cells during the culture process of human stem cells can be detected in a timely manner, thereby improving the quality and efficiency of human stem cell culture.

[0079] Specifically, when determining the adjustment of the preset fluctuation degree or the adjustment of the preset distribution uniformity degree, based on the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after the corresponding adjustment within the preset time period being greater than the preset deviation value, and whether the image data corresponding to the time point when the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after the corresponding adjustment within the preset time period is greater than the preset deviation value has been preprocessed, determine the adjustment of the preset fluctuation degree or the adjustment of the preset distribution uniformity degree;

[0080] When the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after the corresponding adjustment within the preset time period is greater than the preset occurrence frequency and the image data corresponding to the time point when the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after the corresponding adjustment within the preset time period is greater than the preset deviation value has not been preprocessed, determine the adjustment of the preset fluctuation degree;

[0081] When the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after corresponding adjustment within the preset time duration being greater than the preset deviation value is greater than the preset occurrence frequency and the image data corresponding to the time points when the deviation value between the actual number and the predicted number of mesenchymal stem cells under each prediction method after corresponding adjustment within the preset time duration is greater than the preset deviation value has been preprocessed, determine to adjust the preset distribution uniformity.

[0082] In the embodiments of the present invention, the value range of the preset occurrence frequency is set to 0.3 - 0.55, and the preferred value of the preset occurrence frequency is 0.35. However, the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0083] Specifically, in step S5, when it is determined to adjust the preset fluctuation degree, determine to adjust the preset fluctuation degree with a first adjustment coefficient; when it is determined to adjust the preset distribution uniformity, determine to adjust the preset deviation value with a second adjustment coefficient.

[0084] In the embodiments of the present invention, the preset time duration can be set to the time duration for obtaining the image data of mesenchymal stem cells and the environmental data 5 times. The value range of the time duration for obtaining the image data of mesenchymal stem cells and the environmental data each time can be set to 3 minutes - 15 minutes. The time duration for obtaining the image data of mesenchymal stem cells and the environmental data each time can be set to 5 minutes. The value range of the first adjustment coefficient is set to 0.82 - 0.96, the preferred value of the second adjustment coefficient is 0.88, the value range of the second adjustment coefficient is set to 0.81 - 0.94. The adjustment amount of the preset fluctuation degree is negatively correlated with the occurrence frequency of the deviation value being greater than the preset deviation value, and the adjustment amount of the preset distribution uniformity is negatively correlated with the occurrence frequency of the deviation value being greater than the preset deviation value. However, the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0085] The present invention comprehensively considers multiple factors such as the occurrence frequency of the deviation value between the actual number and the predicted number being greater than the preset deviation value within the preset time duration and whether the corresponding image data has been preprocessed during the culture process of mesenchymal stem cells to determine whether to adjust the preset fluctuation degree or the preset distribution uniformity. This refined judgment mechanism can more accurately identify potential problems in the cell culture process. For example, by comparing the occurrence frequency with the preset occurrence frequency and combining the preprocessing situation of the image data, it is possible to distinguish whether it is a problem of the fluctuation of the cell proliferation law itself or a deviation problem reflected under the influence of the image data quality, so as to accurately locate the key link that needs to be adjusted, and achieve precise control through the above method, thereby improving the quality and efficiency of human stem cell culture.

[0086] Example: Compare the effects of the mesenchymal stem cell detection method of the present invention and the general detection method during the cultivation of mesenchymal stem cells, including differences in aspects such as the culture cycle, cell viability, cell morphology, and proliferation rate.

[0087] Experimental materials and preparations:

[0088] Cell source: Select mesenchymal stem cells of the same type and from a healthy source. Conduct strict detection on them before the experiment to ensure that the initial state of the cells is basically the same in terms of viability, purity, etc.

[0089] Experimental grouping: Divide the mesenchymal stem cells into two equal groups. One group is the experimental group using the detection method of the present invention (further subdivided into experimental groups 1 - 8), and the other group is the control group using the general detection method.

[0090] Experimental equipment and reagents: Prepare the same basic culture medium, culture containers, and a cell viability detection kit (such as the trypan blue staining method), and set up a suitable and identical culture environment, including temperature, humidity, carbon dioxide concentration, etc.

[0091] Experimental methods and procedures:

[0092] Control of culture conditions: Both groups of cells are cultured using the same basic culture medium, culture containers, and placed in the same culture environment to ensure that, except for the different detection methods, the influence of other external conditions on the experimental results is as similar as possible.

[0093] Operation of the control group: The control group uses the conventional stem cell detection method. At regular intervals (such as every 2 hours), cell counting is performed by manual microscopy, and the growth state of the cells is judged based on experience, such as observing whether the cell morphology is normal, whether there are obvious signs of death or differentiation, etc. Cultivation is carried out according to a fixed culture protocol, for example, changing the culture medium at fixed intervals and culturing at the initially set cell seeding density. The entire cultivation process does not involve a mechanism for dynamic adjustment according to fluctuations and deviations in the cell proliferation rate.

[0094] Experimental group operation: For experimental groups 1 - 8, the detection method of the present invention is adopted. During the cultivation process, closely monitor various indicators of the cells, including the ratio of the deviation value between the predicted cell number and the actual cell number to the actual cell number, the proportion of the increased cell volume, whether there is a multi-layer growth phenomenon, etc. When the preset deviation value (set to one-tenth of the actual cell number) is exceeded, and the proportion of the increased cell volume is greater than the preset proportion, a multi-layer growth phenomenon occurs, etc., trigger corresponding adjustment operations, such as replacing the culture medium, splitting mesenchymal stem cells into new culture dishes, etc. At the same time, according to the occurrence frequency of the deviation value being greater than the preset deviation value (the preset value range is 0.3 - 0.55, and the preferred value is 0.35), determine whether it is necessary to adjust the preset fluctuation degree or the preset distribution uniformity to optimize the cell culture process. During the cultivation process, record the relevant data in Table 1, and the initial number of mesenchymal stem cells is set to 10 million.

[0095] Experimental detection and data recording: After the end of the cultivation period (this period is within the logarithmic growth phase of the stem cells), use a cell viability detection kit (such as trypan blue staining method) to detect the two groups of cells respectively, and obtain data such as the logarithmic growth phase, the proportion of live cells, the proportion of the number of cells with normal morphology, and the average cell proliferation rate of the two groups of stem cells, and record them in Table 2. At the same time, record the time taken for the two groups of stem cells to reach the pre-set cultivation goals respectively, and also record them in Table 2.

[0096] Table 1: Statistical table of cultivation data

[0097]

[0098]

[0099] Table 2: Comparison table of test data

[0100]

[0101] As can be seen from the data in Table 1 above, taking Experimental Group 1 as an example, the predicted cell number is 120, the actual cell number is 105, and the ratio of the deviation value to the actual cell number reaches 0.14, exceeding the preset deviation value (about one-tenth of the actual number 105 is 10.5, the deviation value is 120 - 105 = 15, 15÷105≈0.14). In this case, according to the detection method mechanism of the present invention, the proportion of the increased cell volume is 0.62 (the situation of being greater than the preset proportion exists), and the phenomenon of multi-layer growth appears, thereby triggering adjustment operations such as changing the culture medium and splitting mesenchymal stem cells into new culture dishes. This shows that the preset deviation value can sensitively capture the significant deviation between the actual cell growth situation and the predicted situation, enabling the subsequent countermeasures for abnormal cell growth states to be initiated, ensuring that the cell culture process can be corrected in a timely manner and develop in a more healthy and stable direction. Similarly, in Experimental Groups 5, 7, and 8, the ratios of the deviation value to the actual cell number are 0.11, 0.13, and 0.23 respectively, all exceeding the preset deviation value, and correspondingly, situations such as the proportion of the increased cell volume exceeding the standard and the phenomenon of multi-layer growth occur, and correspondingly trigger adjustment behaviors such as changing the culture medium and splitting cells. Therefore, it is concluded that the preset deviation value is one-tenth of the actual cell number.

[0102] The setting of the value range of the preset occurrence frequency is also crucial for accurately judging abnormal situations in the cell culture process and conducting effective regulation. In this experiment, the value range of the preset occurrence frequency was set to 0.3 - 0.55, and the preferred value was 0.35. Its acquisition process was based on various considerations and combined with actual experimental data. From the occurrence frequencies of the deviation values in each experimental group in Table 1 being greater than the preset deviation value, different experimental groups showed different frequency values, which reflected that during the cell culture process, the cells were affected by various complex factors, and there were differences in the occurrence frequencies of large deviations between the actual quantity and the predicted quantity. For example, the occurrence frequency of Experimental Group 1 was 0.55, and that of Experimental Group 2 was 0.21. These different frequency value distributions demonstrated the volatility and uncertainty in the cell culture process. Based on multiple repeated experiments (here, 8 experimental groups were selected as representatives) and data accumulation, we statistically analyzed the occurrence frequencies of deviation values greater than the preset deviation value under different culture conditions and found that the occurrence frequencies of most experimental groups were concentrated within a certain range. For example, in this experiment, the occurrence frequencies of multiple experimental groups fell within the relatively broad range of 0.2 - 0.6. After further screening and comprehensively considering factors such as the overall stability of cell culture and the sensitivity to abnormal situations, the relatively more reasonable and practically guiding value range of 0.3 - 0.55 was finally determined. At the same time, we also observed that when the occurrence frequency was higher than the preset occurrence frequency, it often meant that there were some common problems in the cell culture process, such as the cell growth being greatly interfered by environmental factors or there being an obvious mismatch between the prediction method and the actual cell proliferation law. It was necessary to adjust the preset fluctuation degree or the preset distribution uniformity to optimize the subsequent cell culture process. From the data of the occurrence frequencies of deviation values greater than the preset deviation value after adjustment (for example, the occurrence frequency of Experimental Group 1 decreased from 0.55 to 0.31 after adjustment), it could also be seen that the setting of the value range of the preset occurrence frequency helped us dynamically adjust the culture strategy according to the actual situation, making it more in line with the real growth needs of the cells and ensuring the quality of cell culture. Therefore, the value range of the preset occurrence frequency was obtained as 0.3 - 0.55.

[0103] It can be seen from the data in Table 2 above that the logarithmic growth phase of culturing stem cells by using the method of the present invention, the proportion of living cells in the experimental group, the proportion of the number of cells with normal morphology in the experimental group, and the average cell proliferation rate are all higher than those in the control group. Moreover, the time taken for the experimental group to culture stem cells to the preset quantity is less than that of the control group. In summary, compared with using a general mesenchymal stem cell detection method to detect the state of mesenchymal stem cells during the culture process, using the mesenchymal stem cell detection method of the present invention to detect the state of mesenchymal stem cells during the culture process has the advantages of a short culture cycle and high mesenchymal stem cell activity.

[0104] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, those skilled in the art can easily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0105] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A detection method based on human stem cells, characterized in that, Including: Real-time monitoring of the mesenchymal stem cell culture process, periodically obtaining image data and environmental data during the mesenchymal stem cell culture process, and when there is salt-and-pepper blur in the obtained image data, based on the degree of uniform distribution of isolated pixel points and whether the position of the isolated pixel points is within the mesenchymal stem cell position interval, determining to use a filtering method, histogram equalization method, or cutting method to preprocess the image data to obtain analyzable image data; Based on the degree of fluctuation of the proliferation rate of mesenchymal stem cells in a number of analyzable image data, determining to predict the number of mesenchymal stem cells at the next division time using a regular prediction method or predicting the number of mesenchymal stem cells at the next division time using a model prediction method; Obtaining the deviation value between the actual number and the predicted number of mesenchymal stem cells under the corresponding prediction method; Under the condition that the deviation value is greater than the preset deviation value, based on the proportion of the increased volume of mesenchymal stem cells and whether the mesenchymal stem cells show a multi-layer growth phenomenon, determining to replace the culture medium or divide the mesenchymal stem cells into a new culture dish; Based on the occurrence frequency of the deviation value between the actual number and the predicted number of mesenchymal stem cells being greater than the preset deviation value under each prediction method after corresponding adjustment within a preset time period, and whether the image data corresponding to the time point when the deviation value between the actual number and the predicted number of mesenchymal stem cells is greater than the preset deviation value under each prediction method after corresponding adjustment within the preset time period has been preprocessed, determining to adjust the preset fluctuation degree or adjust the preset uniform distribution degree.

2. The detection method based on human stem cells according to claim 1, characterized in that When there is salt-and-pepper blur in the obtained image data, preprocessing the image data to obtain analyzable image data includes: If the degree of uniform distribution of isolated pixel points is greater than the preset uniform degree and the position of the isolated pixel points is within the mesenchymal stem cell position interval, determining to preprocess the image data using a filtering method to obtain analyzable image data; If the degree of uniform distribution of isolated pixel points is less than or equal to the preset uniform degree and the position of the isolated pixel points is within the mesenchymal stem cell position interval, determining to preprocess the image data using a histogram equalization method to obtain analyzable image data; If the position of the isolated pixel points is not within the mesenchymal stem cell position interval, determining to preprocess the image data using a cutting method to obtain analyzable image data.

3. The detection method based on human stem cells according to claim 2, characterized in that, Judging that there is salt-and-pepper blur in the obtained image data includes that there are isolated pixel points in the image with gray values within the preset gray value range, and the preset gray value range is 0 - 10 and 245 - 255.

4. The detection method based on human stem cells according to claim 3, wherein Determining the degree of uniform distribution of isolated pixel points includes: Dividing the image data into several regions with equal area; Obtaining the number of isolated pixel points in several regions; Calculating the variance of the isolated pixel points in several regions, which is the degree of uniform distribution of the isolated pixel points.

5. The detection method based on human stem cells according to claim 4, wherein Predicting the number of mesenchymal stem cells at the next division time includes: If the degree of fluctuation of the proliferation rate of mesenchymal stem cells in the number of analyzable image data is less than the preset fluctuation degree, determining to predict the number of mesenchymal stem cells at the next division time using a regular prediction method; The fluctuation degree of the proliferation rate of the mesenchymal stem cells that can analyze the image data is greater than or equal to a preset fluctuation degree, and it is determined to predict the number of mesenchymal stem cells at the next division time by the model prediction method.

6. The detection method based on human stem cells according to claim 5, wherein The fluctuation degree of the proliferation rate of the mesenchymal stem cells is determined according to the variance of the proliferation rate of the mesenchymal stem cells in a plurality of analyzable image data.

7. The detection method based on human stem cells according to claim 6, wherein The determination of replacing the culture medium or dividing the mesenchymal stem cells into a new culture dish under the condition that the deviation value is greater than the preset deviation value includes: If the proportion of the increased volume of the mesenchymal stem cells is greater than the preset proportion, it is determined to replace the culture medium; If there is a phenomenon of multi-layer growth of the mesenchymal stem cells, it is determined to divide the mesenchymal stem cells into a new culture dish.

8. The detection method based on human stem cells according to claim 7, characterized in that, The judgment of the phenomenon of multi-layer growth of the mesenchymal stem cells includes that the average intercellular distance of the mesenchymal stem cells is greater than the preset distance.

9. The detection method based on human stem cells according to claim 8, wherein, The determination of adjusting the preset fluctuation degree or the preset distribution uniformity degree includes: If the occurrence frequency of the deviation value between the actual number and the predicted number of the mesenchymal stem cells under each prediction method after the corresponding adjustment within the preset time period is greater than the preset occurrence frequency and the image data corresponding to the time point when the deviation value between the actual number and the predicted number of the mesenchymal stem cells under each prediction method after the corresponding adjustment within the preset time period is greater than the preset deviation value has not been preprocessed, it is determined to adjust the preset fluctuation degree; If the occurrence frequency of the deviation value between the actual number and the predicted number of the mesenchymal stem cells under each prediction method after the corresponding adjustment within the preset time period is greater than the preset occurrence frequency and the image data corresponding to the time point when the deviation value between the actual number and the predicted number of the mesenchymal stem cells under each prediction method after the corresponding adjustment within the preset time period is greater than the preset deviation value has been preprocessed, it is determined to adjust the preset distribution uniformity degree.

10. The detection method based on human stem cells according to claim 9, characterized in that, The adjustment amount of the preset fluctuation degree is negatively correlated with the occurrence frequency of the deviation value greater than the preset deviation value, and the adjustment amount of the preset distribution uniformity degree is negatively correlated with the occurrence frequency of the deviation value greater than the preset deviation value.

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