Storage management device for mesenchymal stem cells

Abstract

The invention relates to the technical field of stem cell storage, in particular to a storage management device for mesenchymal stem cells. Through cooperative work of an association construction module, an acquisition module, a label setting module and a state analysis module, an association relationship between a cell type and an observation dimension group is established based on the association construction module, and the acquisition module acquires images of cells in a monitoring area and extracts associated observation dimension features; the label setting module is used for calculating a cell stability characterization value according to the image processing characteristics of each observation dimension and setting a cell state label according to the cell stability characterization value; the state analysis module is used for dynamically analyzing the cell state based on the state label, including constructing a prediction curve to judge whether the activity is abnormal or not, and determining the release risk of the cell contents and defining an isolation range through cell membrane integrity analysis; the safety and effectiveness of stem cell storage are improved.

Description

Technical Field

[0001] This invention relates to the field of stem cell storage technology, and more particularly to a storage and management device for mesenchymal stem cells. Background Technology

[0002] With the rapid development of regenerative medicine and tissue engineering technology, mesenchymal stem cells have shown broad application prospects in cell therapy, tissue repair and immune regulation due to their advantages such as multi-directional differentiation potential, immunomodulatory capacity and low immunogenicity. In the construction and management of stem cell resource banks, high-quality stem cell storage has become a key link to ensure the safety and effectiveness of cell therapy products.

[0003] Chinese Patent Publication No. CN113879705A discloses a storage device for mesenchymal stem cell exosomes. The opening and closing device includes a vertical groove formed on the surface of a refrigerator. A spring is fixedly installed inside the vertical groove. A sliding plate is fixedly installed at the end of the spring away from the vertical groove. A horizontal plate is fixedly installed at the end of the sliding plate away from the vertical groove. Two hinge rods are hinged to the surface of the horizontal plate. A connecting rod is hinged to the end of the hinge rods away from the horizontal plate. A through groove is formed on the surface of the refrigerator, and two recesses are formed on the surface of the refrigerator. A displacement plate is slidably connected between the two recesses. This invention solves the problem that existing refrigerators have a single opening method, and most of the lids and refrigerators are connected by hinges, which prevent them from closing automatically after being opened by flipping.

[0004] However, the following problems still exist in the existing technology.

[0005] Different types of mesenchymal stem cells exhibit variations in visual observation dimensions when they undergo mutations. Therefore, using a global analysis approach to analyze cell image features can easily introduce interference, resulting in low efficiency and poor accuracy in identifying cell mutations. Summary of the Invention

[0006] To address this, the present invention provides a storage and management device for mesenchymal stem cells, which overcomes the problem in the prior art where, during actual cell storage and management, different types of mesenchymal stem cells exhibit variations in different visual observation dimensions. Therefore, the method of analyzing cell image features in a global image is prone to introducing interference, resulting in low efficiency and poor accuracy in identifying cell mutations.

[0007] To achieve the above objectives, the present invention provides a storage and management device for mesenchymal stem cells, comprising:

[0008] Storage module, which provides space for storing mesenchymal stem cells;

[0009] The association construction module is used to extract image processing features for different observation dimensions based on surface image samples of various types of mesenchymal stem cells, so as to construct the association relationship between various types of mesenchymal stem cells and observation dimension groups.

[0010] The acquisition module, which is connected to the association construction module, is used to acquire surface images of mesenchymal stem cells in each monitoring area, determine the types of mesenchymal stem cells, determine the observation dimension groups associated with the types of mesenchymal stem cells, and extract corresponding image processing features based on the observation dimension groups.

[0011] A label setting module, which is connected to the acquisition module, is used to determine the cell stability characterization value based on the image processing features corresponding to each observation dimension, and to set status labels for mesenchymal stem cells in the monitoring area.

[0012] A state analysis module, connected to the association construction module, the acquisition module, and the tag setting module, is used to analyze the mesenchymal stem cells based on the state tags, including:

[0013] Cell proliferation activity feature values ​​are determined by extracting cell proliferation activity features, and cell activity curves are constructed based on the cell proliferation activity feature values. Whether the activity is abnormal is determined based on the cell activity curve segments in each time domain segment and the corresponding sample cell activity curve segments.

[0014] Determine the characteristic values ​​of cell membrane integrity changes within the monitoring area, determine whether there is cell contents release in the local area of ​​the monitoring area, and determine the isolation range, including constructing an isolation range based on each damaged cell as the center, and determining the cells within the isolation range that need to be isolated.

[0015] Furthermore, the association construction module is used to extract image processing features for different observation dimensions based on surface image samples of various types of mesenchymal stem cells, including:

[0016] Used to extract normal surface image samples of single-species mesenchymal stem cells, and to extract image processing features for different observation dimensions;

[0017] This is used to compare the differences between image processing features of a single observation dimension, determine several first difference quantities, and determine whether the observation dimension meets the stability criteria.

[0018] This is used to extract abnormal surface image samples of a single type of mesenchymal stem cells, determine several second differences between image processing features of different observation dimensions and standard image processing features, and determine whether the single observation dimension meets the mutation criteria.

[0019] If a single observation dimension meets both the stability and mutation criteria, then the single observation dimension is stored in an observation dimension group, and the association between the observation dimension group and the type of mesenchymal stem cell is constructed.

[0020] Furthermore, the association construction module is also used to determine whether the stability criterion and the mutation criterion are met, including,

[0021] If the variance of several first variance quantities of a single observation dimension is less than a predetermined variance threshold, then the stability criterion is met.

[0022] If the mean of the second difference in a single observation dimension is greater than or equal to a predetermined change threshold, then the anomaly criterion is met.

[0023] Furthermore, the acquisition module is used to determine the observation dimension group associated with mesenchymal stem cell types, and extracts corresponding image processing features based on the observation dimension group, including...

[0024] Used to determine each observation dimension of the observation dimension group, and extract the image processing features corresponding to each observation dimension respectively;

[0025] Among them, the image processing features are local images within the contour, and the observation dimensions include the cell wall, cell nucleus, and cell membrane.

[0026] Furthermore, the label setting module is used to determine cell stability characterization values ​​based on the image processing features corresponding to each observation dimension, including:

[0027] Used to determine the individual observation similarity between the image processing features corresponding to each individual observation dimension in the observation dimension group and the standard image processing features;

[0028] The mean of the individual observation similarities was determined as the cell stability characterization value.

[0029] Furthermore, the tag setting module is used to set status tags for mesenchymal stem cells in the monitoring area, including:

[0030] If the cell stability characterization value is greater than or equal to the standard cell stability threshold, then a normal state label is set for mesenchymal stem cells in the monitoring area;

[0031] If the cell stability characterization value is less than the standard cell stability threshold, then an abnormal state label is set for mesenchymal stem cells in the monitoring area.

[0032] Furthermore, the state analysis module is used to analyze the mesenchymal stem cells based on the state tags, including,

[0033] If the mesenchymal stem cells in the monitoring area are labeled as being in a normal state, cell proliferation activity characteristic values ​​are determined based on extracted cell proliferation activity characteristics, cell activity curves are constructed based on the cell proliferation activity characteristic values, and whether the activity is abnormal is determined based on the cell activity curve segments in each time domain segment and the corresponding sample cell activity curve segments.

[0034] If mesenchymal stem cells in the monitoring area are labeled as abnormal, then the characteristic value of cell membrane integrity change in the monitoring area is determined, it is determined whether there is cell contents release phenomenon in the local area of ​​the monitoring area, and the isolation range is determined, including constructing an isolation range based on each damaged cell as the center, and determining the cells that need to be isolated within the isolation range.

[0035] Furthermore, the state analysis module is used to determine cell proliferation activity characteristic values ​​based on extracted cell proliferation activity characteristics, and to construct a cell activity curve based on the cell proliferation activity characteristic values, including:

[0036] Used to obtain the number of cells within the monitored area;

[0037] This is used to determine the rate of change in the number of cells as a characteristic value of cell proliferation activity;

[0038] A cell activity curve is constructed with time as the horizontal axis and the cell proliferation activity characteristic value as the vertical axis.

[0039] Furthermore, the state analysis module is used to determine whether cell activity is abnormal based on the cell activity curve segment within each time domain segment and the corresponding sample cell activity curve segment, including:

[0040] This is used to determine the cell activity curve segment within each time domain segment, and compare it with the sample cell activity curve segment within the corresponding time domain segment to obtain the goodness of fit;

[0041] If the fit is less than the predetermined fit threshold, the activity is determined to be abnormal.

[0042] Furthermore, the state analysis module is used to determine the characteristic values ​​of changes in cell membrane integrity within the monitoring area, and to determine whether there is a release of cell contents in the local area of ​​the monitoring area, including...

[0043] Used to identify several cells with incomplete cell membranes within the monitoring area, which are then labeled as damaged cells;

[0044] The ratio of damaged cells to the total number of cells in the monitored area is used to determine the characteristic value of cell membrane integrity change;

[0045] If the characteristic value of the cell membrane integrity change is greater than or equal to the preset cell membrane integrity change threshold, it is determined that there is a local release of cell contents in the monitoring area.

[0046] Compared with existing technologies, this invention improves the safety and effectiveness of stem cell storage by setting up a collaborative working module for association construction, acquisition, label setting, and state analysis. The association construction module establishes a correlation between cell types and observation dimension groups; the acquisition module collects images of cells within the monitoring area and extracts associated observation dimension features; the label setting module calculates cell stability characterization values ​​based on the image processing features of each observation dimension and sets cell state labels accordingly; and the state analysis module performs dynamic analysis of cell states based on the state labels, including constructing prediction curves to determine whether there are abnormal activity levels, and determining the risk of cell contents release and defining isolation areas through cell membrane integrity analysis. Through the synergistic effect of these modules, this invention improves the safety and effectiveness of stem cell storage.

[0047] In particular, this invention identifies observation dimension groups associated with different types of mesenchymal stem cells (MSCs) and extracts corresponding image processing features based on these observation dimension groups. In practice, different types of MSCs exhibit significant differences in morphological characteristics, surface structure, and biological properties, leading to varying patterns of stability and activity changes during storage. Using a uniform observation dimension to monitor all types of stem cells makes it difficult to accurately capture the unique image processing features of each cell type. Furthermore, using a global analysis approach for cell-related images introduces interference, potentially leading to misjudgments or omissions, thus reducing the accuracy and reliability of monitoring. Therefore, this invention selects observation dimensions associated with specific MSC types based on their characteristics, constructs observation dimension groups associated with each type, and extracts corresponding image processing features based on these observation dimension groups. This enables precise identification and dynamic monitoring of the abnormal characteristics of various types of MSCs, improving the safety and effectiveness of stem cell storage management.

[0048] In particular, this invention determines cell stability characterization values ​​based on image processing features corresponding to each observation dimension, setting status tags for mesenchymal stem cells within the monitoring area. In practice, because the activity change trends of mesenchymal stem cells in different states vary significantly during storage, adopting the same treatment strategy may lead to a mismatch between the treatment measures and the actual needs of the cells, failing to effectively guarantee cell quality and storage safety. For example, excessive intervention on cells in a normal state may accelerate their activity decay and shorten their effective usage period; while insufficient treatment of cells in an abnormal state may cause the release of cell contents, contaminating surrounding cells and disrupting the stability of the storage environment. Therefore, a differentiated management strategy is adopted based on cell status tags. For cells in a normal state, the focus should be on their activity decay trend; while for cells in an abnormal state, they need to be identified and isolated in a timely manner to prevent the release of cell contents from contaminating surrounding cells. By setting status tags, cells are clearly distinguished into different state categories, so that the subsequent state analysis module can adopt differentiated analysis strategies and treatment measures according to the tag type, thereby achieving accurate identification and dynamic monitoring of various types of mesenchymal stem cell mutation characteristics, improving the safety and effectiveness of stem cell storage management.

[0049] In particular, this invention determines the presence of cell contents release within the monitored area by identifying characteristic values ​​of changes in cell membrane integrity, thus defining the isolation range. During storage, cells may suffer cell membrane damage due to factors such as temperature fluctuations, nutrient deficiency, or prolonged storage time, leading to the leakage of intracellular components such as proteases, cytokines, and ions. These leaked substances can rapidly spread to surrounding areas, exerting toxic effects on neighboring cells, inducing inflammatory responses, and even inducing apoptosis or necrosis in surrounding healthy cells, forming a chain reaction and further expanding the scope of cell damage. For example, if a small number of cells with damaged cell membranes are not detected and isolated in time, their released contents may contaminate a large area in a short period, resulting in a decrease in the overall activity of the stored cells, or even rendering the entire batch of cells unusable. Therefore, it is necessary to define the isolation range based on the identification of cell membrane integrity changes, effectively isolating damaged cells from surrounding healthy cells, blocking the contamination diffusion path, preventing the expansion of the damage range, ensuring the overall activity of the stored cell population and the safety and stability of the storage system, thereby achieving accurate identification and dynamic monitoring of various types of mesenchymal stem cell mutation characteristics, and improving the safety and effectiveness of stem cell storage management. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the structure of a mesenchymal stem cell storage and management device according to an embodiment of the present invention;

[0051] Figure 2This is a logic decision diagram for setting status tags for mesenchymal stem cells in a monitoring area, according to an embodiment of the present invention.

[0052] Figure 3 This is a logic block diagram of the analysis of mesenchymal stem cells based on state tags according to an embodiment of the present invention;

[0053] Figure 4 This is a logic diagram for determining whether the activity of mesenchymal stem cells is abnormal, according to an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0055] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. 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 a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0057] Please see Figure 1 The diagram shown is a structural schematic of a mesenchymal stem cell storage and management device according to an embodiment of the present invention. The mesenchymal stem cell storage and management device according to an embodiment of the present invention includes:

[0058] Storage module, which provides space for storing mesenchymal stem cells;

[0059] The association construction module is used to extract image processing features for different observation dimensions based on surface image samples of various types of mesenchymal stem cells, so as to construct the association relationship between various types of mesenchymal stem cells and observation dimension groups.

[0060] The acquisition module, which is connected to the association construction module, is used to acquire surface images of mesenchymal stem cells in each monitoring area, determine the types of mesenchymal stem cells, determine the observation dimension groups associated with the types of mesenchymal stem cells, and extract corresponding image processing features based on the observation dimension groups.

[0061] A label setting module, which is connected to the acquisition module, is used to determine the cell stability characterization value based on the image processing features corresponding to each observation dimension, and to set status labels for mesenchymal stem cells in the monitoring area.

[0062] A state analysis module, connected to the association construction module, the acquisition module, and the tag setting module, is used to analyze the mesenchymal stem cells based on the state tags, including:

[0063] Cell proliferation activity feature values ​​are determined by extracting cell proliferation activity features, and cell activity curves are constructed based on the cell proliferation activity feature values. Whether the activity is abnormal is determined based on the cell activity curve segments in each time domain segment and the corresponding sample cell activity curve segments.

[0064] Determine the characteristic values ​​of cell membrane integrity changes within the monitoring area, determine whether there is cell contents release in the local area of ​​the monitoring area, and determine the isolation range, including constructing an isolation range based on each damaged cell as the center, and determining the cells within the isolation range that need to be isolated.

[0065] Specifically, there are no restrictions on the structure of the association construction module, the tag setting module, and the status analysis module. They can be composed of logical components or combinations of logical components, including field-programmable processors, computers, or microprocessors in computers.

[0066] Specifically, there are no restrictions on the structure of the acquisition module. Preferably, images can be acquired by a high-resolution electron microscopy imaging device placed in the space of the storage module. It is sufficient to obtain clear images of the surface of mesenchymal stem cells. For multiple monitoring areas, a mobile imaging device can be used, or multiple imaging devices can be deployed to observe multiple monitoring areas. This will not be elaborated further.

[0067] Specifically, there is no limitation on the specific structure of the storage module. It can be any storage device in the prior art that provides space corresponding to the storage conditions of mesenchymal stem cells. Those skilled in the art can choose for themselves.

[0068] Specifically, the association construction module is used to extract image processing features for different observation dimensions based on surface image samples of various types of mesenchymal stem cells, including:

[0069] Used to extract normal surface image samples of single-species mesenchymal stem cells, and to extract image processing features for different observation dimensions;

[0070] This is used to compare the differences between image processing features of a single observation dimension, determine several first difference quantities, and determine whether the observation dimension meets the stability criteria.

[0071] This is used to extract abnormal surface image samples of a single type of mesenchymal stem cells, determine several second differences between image processing features of different observation dimensions and standard image processing features, and determine whether the single observation dimension meets the mutation criteria.

[0072] If a single observation dimension meets both the stability and mutation criteria, then the single observation dimension is stored in an observation dimension group, and the association between the observation dimension group and the type of mesenchymal stem cell is constructed.

[0073] In practice, when comparing the differences between image processing features of a single observation dimension, the image similarity between the image processing features is compared, and the reciprocal of the image similarity is determined as the difference quantity. Then, the first difference quantity and the second difference quantity are calculated accordingly. The image similarity can be determined by calculating the cosine similarity between the image processing features, which will not be elaborated here.

[0074] In implementation, there is no limitation on the method of constructing the association between the observation dimension group and the type of mesenchymal stem cell. It can be constructed by creating an index table of "stem cell type - observation dimension group" in the database, or by other construction methods. As long as it is ensured that the corresponding observation dimension group can be retrieved according to the stem cell type when the module is called, this will not be elaborated further.

[0075] This invention identifies observation dimension sets associated with different types of mesenchymal stem cells (MSCs) and extracts corresponding image processing features based on these dimensions. In practice, different types of MSCs exhibit significant differences in morphology, surface structure, and biological characteristics, leading to varying patterns of stability and activity changes during storage. Using a uniform set of observation dimensions for monitoring all types of stem cells makes it difficult to accurately capture the unique aberrations of each cell type, potentially resulting in misidentification or missed detection, thus reducing the accuracy and reliability of monitoring. Therefore, this invention selects observation dimensions associated with specific MSC types based on their characteristics, constructs a set of observation dimension sets associated with each type, and extracts corresponding image processing features based on these sets. This enables precise identification and dynamic monitoring of aberrations in various types of MSCs, improving the safety and effectiveness of stem cell storage management.

[0076] Specifically, the association construction module is also used to determine whether the stability criterion and the mutation criterion are met, including,

[0077] If the variance of several first variance quantities of a single observation dimension is less than a predetermined variance threshold, then the stability criterion is met.

[0078] If the mean of the second difference in a single observation dimension is greater than or equal to a predetermined change threshold, then the anomaly criterion is met.

[0079] In practice, the purpose of setting a variance threshold is to characterize the situation where the image processing features of a single type of mesenchymal stem cell are highly discrete and the data representation is poor in a single observation dimension. The variance threshold is predetermined. Those skilled in the art can collect a large number of normal surface image samples of the same type of mesenchymal stem cells, extract the first difference between the image processing features corresponding to each observation dimension, solve the variance of the first difference, and solve the mean of the variance of the first difference. The variance threshold is set as a predetermined multiple of the mean of the variance of the first difference to indicate that the image processing features fluctuate greatly under normal conditions. The predetermined multiple is usually set to 0.85 times.

[0080] In practice, the change threshold is used to characterize the boundary by which the image processing feature corresponding to the observation dimension deviates from the normal state under abnormal conditions. The change threshold is predetermined. Those skilled in the art can collect a large number of abnormal surface image samples of the same type of mesenchymal stem cells, extract the image processing features of each observation dimension, and solve for the mean of the second difference to identify the difference between the image processing features of different observation dimensions under normal conditions and the standard image processing features. The change threshold is set as a predetermined multiple of the mean of the change to indicate that the image processing features change significantly under abnormal conditions. Typically, the predetermined multiple is set to 1.25 times.

[0081] Specifically, the acquisition module is used to determine the observation dimension group associated with mesenchymal stem cell types, and extracts corresponding image processing features based on the observation dimension group, including...

[0082] Used to determine each observation dimension of the observation dimension group, and extract the image processing features corresponding to each observation dimension respectively;

[0083] Among them, the image processing features are local images within the contour, and the observation dimensions include the cell wall, cell nucleus, and cell membrane.

[0084] Taking the cell wall as the observation dimension as an example, the cell wall has thickness. Identifying the outline of the cell wall and the local images within the outline makes it easier to compare the local images for image similarity later.

[0085] Specifically, the label setting module is used to determine cell stability characterization values ​​based on image processing features corresponding to each observation dimension, including:

[0086] Used to determine the individual observation similarity between the image processing features corresponding to each individual observation dimension in the observation dimension group and the standard image processing features;

[0087] The mean of the individual observation similarities was determined as the cell stability characterization value.

[0088] In practice, image processing features and standard image processing features are essentially images. Therefore, the similarity is determined by comparing the cosine similarity between the image processing features and the standard image processing features, and the cosine similarity is determined as the image similarity.

[0089] Specifically, the standard image processing features are predetermined, wherein normal surface image samples of mesenchymal stem cells are obtained, and the image processing features corresponding to the observation dimensions in the normal surface image samples are extracted as standard image processing features.

[0090] Please see Figure 2 As shown, this is a logic decision diagram for setting status tags for mesenchymal stem cells in a monitoring area according to an embodiment of the present invention. Specifically, the tag setting module is used to set status tags for mesenchymal stem cells in the monitoring area, including...

[0091] If the cell stability characterization value is greater than or equal to the standard cell stability threshold, then a normal state label is set for mesenchymal stem cells in the monitoring area;

[0092] If the cell stability characterization value is less than the standard cell stability threshold, then an abnormal state label is set for mesenchymal stem cells in the monitoring area.

[0093] In practice, the standard cell stability threshold is used to characterize the relationship between cell stability characterization values ​​and cell state. The standard cell stability threshold is predetermined. Those skilled in the art can collect a large number of surface image samples of the same type of mesenchymal stem cells without abnormalities, extract image processing features of the corresponding observation dimensions, and calculate the mean value of cell stability characterization values ​​to represent the stability level of the cell type under normal conditions. The standard cell stability threshold is set as a predetermined multiple of the mean value of cell stability characterization values, usually set to 1.2 times.

[0094] Please see Figure 3 The diagram shown is a logic block diagram of a state tag-based analysis of mesenchymal stem cells according to an embodiment of the present invention. Specifically, the state analysis module is used to analyze the mesenchymal stem cells based on the state tags, including:

[0095] If the mesenchymal stem cells in the monitoring area are labeled as being in a normal state, cell proliferation activity characteristic values ​​are determined based on extracted cell proliferation activity characteristics, cell activity curves are constructed based on the cell proliferation activity characteristic values, and whether the activity is abnormal is determined based on the cell activity curve segments in each time domain segment and the corresponding sample cell activity curve segments.

[0096] If mesenchymal stem cells in the monitoring area are labeled as abnormal, then the characteristic value of cell membrane integrity change in the monitoring area is determined, it is determined whether there is cell contents release phenomenon in the local area of ​​the monitoring area, and the isolation range is determined, including constructing an isolation range based on each damaged cell as the center, and determining the cells that need to be isolated within the isolation range.

[0097] In implementation, when constructing the isolation range based on the center, each damaged cell is used as the center, and a circle with a predetermined radius is drawn. The predetermined radius is pre-determined. Those skilled in the art can collect a large amount of historical data to determine the average maximum range of infection from damaged cells, thus characterizing the infection situation under normal conditions. The predetermined radius is set as a predetermined multiple to represent situations where the infection range may be large. Typically, the multiple is set to 1.3 times.

[0098] In practice, there are no restrictions on the method of isolating cells within the isolation range. After the isolation range is marked, those skilled in the art can use corresponding isolation methods to isolate cells outside the isolation range, which will not be elaborated further.

[0099] This invention determines cell stability characterization values ​​based on image processing features corresponding to each observation dimension, setting status tags for mesenchymal stem cells (MSCs) within the monitoring area. In practice, the activity trends of MSCs in different states exhibit significant differences during storage. Applying the same treatment strategy may lead to a mismatch between the treatment measures and the actual needs of the cells, failing to effectively guarantee cell quality and storage safety. For example, excessive intervention on cells in a normal state may accelerate their activity decay and shorten their effective lifespan; while insufficient treatment of cells in an abnormal state may cause the release of cell contents, contaminating surrounding cells and disrupting the stability of the storage environment. Therefore, differentiated management strategies are adopted based on cell status tags. For cells in a normal state, the focus should be on monitoring their activity decay trend to predict the optimal usage time in advance; while for cells in an abnormal state, timely identification and isolation are necessary to prevent the release of cell contents from contaminating surrounding cells. By setting status tags, cells are clearly distinguished into different status categories, allowing the subsequent status analysis module to adopt differentiated analysis strategies and treatment measures based on the tag type. This enables accurate identification and dynamic monitoring of various types of MSC mutation characteristics, improving the safety and effectiveness of stem cell storage management.

[0100] Specifically, the state analysis module is used to determine cell proliferation activity characteristic values ​​based on extracted cell proliferation activity features, and to construct a cell activity curve based on the cell proliferation activity characteristic values, including:

[0101] Used to obtain the number of cells within the monitored area;

[0102] This is used to determine the rate of change in the number of cells as a characteristic value of cell proliferation activity;

[0103] A cell activity curve is constructed with time on the horizontal axis and the cell proliferation activity characteristic value on the vertical axis. In practice, the method for obtaining the number of cells within the monitoring area is not limited; preferably, it can be obtained using an automated cell counter, as long as it can accurately and quickly obtain the number of cells within the monitoring area. Further details will not be elaborated here.

[0104] In practice, there are no restrictions on the method of constructing cell activity curves. For example, the corresponding curve can be constructed using MATLAB, as long as it can accurately reflect the trend of cell activity changes over time. This will not be elaborated further.

[0105] Please see Figure 4 As shown, this is a logic diagram for determining whether the activity of mesenchymal stem cells is abnormal according to an embodiment of the present invention. Specifically, the state analysis module is used to determine whether the activity is abnormal based on the cell activity curve segment and the corresponding sample cell activity curve segment in each time domain segment, including:

[0106] This is used to determine the cell activity curve segment within each time domain segment, and compare it with the sample cell activity curve segment within the corresponding time domain segment to obtain the goodness of fit;

[0107] If the fit is less than the predetermined fit threshold, the activity is determined to be abnormal.

[0108] The fit is the image similarity between two time-domain curves, which can be obtained by calculating the cosine similarity between the two time-domain curves;

[0109] The goodness-of-fit threshold is predetermined. Several cell activity curves are obtained during the culture process without abnormalities. The mean goodness-of-fit between each cell activity curve is calculated. The product of the mean goodness-of-fit and the error coefficient is determined as the goodness-of-fit threshold. The error coefficient is selected in the interval [0.85, 0.95].

[0110] Specifically, the state analysis module is used to determine the characteristic values ​​of changes in cell membrane integrity within the monitoring area, and to determine whether there is a release of cell contents in the local area of ​​the monitoring area, including...

[0111] Used to identify several cells with incomplete cell membranes within the monitoring area, which are then labeled as damaged cells;

[0112] The ratio of damaged cells to the total number of cells in the monitored area is used to determine the characteristic value of cell membrane integrity change;

[0113] If the characteristic value of the cell membrane integrity change is greater than or equal to the preset cell membrane integrity change threshold, it is determined that there is a local release of cell contents in the monitoring area.

[0114] Specifically, there are no restrictions on the method for determining whether the cell membrane is incomplete. The integrity of the cell membrane can be determined based on whether there are breaks in the cell membrane outline, which will not be elaborated further.

[0115] In practice, the threshold for changes in cell membrane integrity is used to characterize the presence of a large number of infections in a local area, and the threshold for changes in cell membrane integrity is selected within the range of [5%, 10%].

[0116] This invention determines the presence of cell contents release within a monitored area by identifying characteristic values ​​of changes in cell membrane integrity, thus defining the isolation range. During storage, cells may experience cell membrane damage due to factors such as temperature fluctuations, nutrient deficiencies, or prolonged storage, leading to the leakage of intracellular components such as proteases, cytokines, and ions. These leaked substances can rapidly spread to surrounding areas, exerting toxic effects on neighboring cells, inducing inflammatory responses, and even triggering apoptosis or necrosis in surrounding healthy cells, creating a chain reaction and further expanding the scope of cell damage. For example, if a small number of cells with damaged cell membranes are not detected and isolated in time, their released contents may contaminate a large area within a short period, resulting in a decrease in the overall activity of the stored cells and even rendering the entire batch of cells unusable. Therefore, it is necessary to define the isolation range based on the identification of cell membrane integrity changes, effectively isolating damaged cells from surrounding healthy cells, blocking the contamination diffusion path, preventing the expansion of the damage range, ensuring the overall activity of the stored cell population and the safety and stability of the storage system, thereby achieving accurate identification and dynamic monitoring of various types of mesenchymal stem cell mutation characteristics, and improving the safety and effectiveness of stem cell storage management.

[0117] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles 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 all fall within the scope of protection of the present invention.

Claims

1. A storage and management device for mesenchymal stem cells, characterized in that, include: Storage module, which provides space for storing mesenchymal stem cells; The association construction module is used to extract image processing features for different observation dimensions based on surface image samples of various types of mesenchymal stem cells, so as to construct the association relationship between various types of mesenchymal stem cells and observation dimension groups; The acquisition module, which is connected to the association construction module, is used to acquire surface images of mesenchymal stem cells in each monitoring area, determine the types of mesenchymal stem cells, determine the observation dimension groups associated with the types of mesenchymal stem cells, and extract corresponding image processing features based on the observation dimension groups. A label setting module, which is connected to the acquisition module, is used to determine the cell stability characterization value based on the image processing features corresponding to each observation dimension, and to set status labels for mesenchymal stem cells in the monitoring area. A state analysis module, connected to the association construction module, the acquisition module, and the tag setting module, is used to analyze the mesenchymal stem cells based on the state tags, including: Cell proliferation activity feature values ​​are determined by extracting cell proliferation activity features, and cell activity curves are constructed based on these cell proliferation activity feature values. Whether the activity is abnormal is determined by comparing the cell activity curve segments in each time domain segment with the corresponding sample cell activity curve segments. Determine the characteristic values ​​of cell membrane integrity changes within the monitoring area, determine whether there is cell contents release in the local area of ​​the monitoring area, and determine the isolation range, including constructing an isolation range based on each damaged cell as the center, and determining the cells within the isolation range that need to be isolated.

2. The mesenchymal stem cell storage and management device according to claim 1, characterized in that, The association construction module is used to extract image processing features for different observation dimensions based on surface image samples of various types of mesenchymal stem cells, including: Used to extract normal surface image samples of single-species mesenchymal stem cells, and to extract image processing features for different observation dimensions; This is used to compare the differences between image processing features of a single observation dimension, determine several first difference quantities, and determine whether the observation dimension meets the stability criteria. This is used to extract abnormal surface image samples of a single type of mesenchymal stem cells, determine several second differences between image processing features of different observation dimensions and standard image processing features, and determine whether the single observation dimension meets the mutation criteria. If a single observation dimension meets both the stability and mutation criteria, then the single observation dimension is stored in an observation dimension group, and the association between the observation dimension group and the type of mesenchymal stem cell is constructed.

3. The storage and management device for mesenchymal stem cells according to claim 2, characterized in that, The association construction module is also used to determine whether the stability criterion and the mutation criterion are met, including, If the variance of several first variance quantities of a single observation dimension is less than a predetermined variance threshold, then the stability criterion is met. If the mean of the second difference in a single observation dimension is greater than or equal to a predetermined change threshold, then the anomaly criterion is met.

4. The storage and management device for mesenchymal stem cells according to claim 1, characterized in that, The acquisition module is used to determine the observation dimension groups associated with mesenchymal stem cell types, and extracts corresponding image processing features based on the observation dimension groups, including... Used to determine each observation dimension of the observation dimension group, and extract the image processing features corresponding to each observation dimension respectively; Among them, the image processing features are local images within the contour, and the observation dimensions include the cell wall, cell nucleus, and cell membrane.

5. The mesenchymal stem cell storage and management device according to claim 4, characterized in that, The label setting module is used to determine cell stability characterization values ​​based on the image processing features corresponding to each observation dimension, including... Used to determine the individual observation similarity between the image processing features corresponding to each individual observation dimension in the observation dimension group and the standard image processing features; The mean of the individual observation similarities was determined as the cell stability characterization value.

6. The storage and management device for mesenchymal stem cells according to claim 5, characterized in that, The tag setting module is used to set status tags for mesenchymal stem cells in the monitoring area, including... If the cell stability characterization value is greater than or equal to the standard cell stability threshold, then a normal state label is set for mesenchymal stem cells in the monitoring area; If the cell stability characterization value is less than the standard cell stability threshold, then an abnormal state label is set for mesenchymal stem cells in the monitoring area.

7. The storage and management device for mesenchymal stem cells according to claim 1, characterized in that, The state analysis module is used to analyze the mesenchymal stem cells based on the state tags. include, If the mesenchymal stem cells in the monitoring area are labeled as being in a normal state, cell proliferation activity characteristic values ​​are determined based on extracted cell proliferation activity characteristics, cell activity curves are constructed based on the cell proliferation activity characteristic values, and whether the activity is abnormal is determined based on the cell activity curve segments in each time domain segment and the corresponding sample cell activity curve segments. If mesenchymal stem cells in the monitoring area are labeled as abnormal, then the characteristic value of cell membrane integrity change in the monitoring area is determined, it is determined whether there is cell contents release phenomenon in the local area of ​​the monitoring area, and the isolation range is determined, including constructing an isolation range based on each damaged cell as the center, and determining the cells that need to be isolated within the isolation range.

8. The storage and management device for mesenchymal stem cells according to claim 1, characterized in that, The state analysis module is used to determine cell proliferation activity characteristic values ​​based on extracted cell proliferation activity characteristics, and to construct a cell activity curve based on the cell proliferation activity characteristic values, including: Used to obtain the number of cells within the monitored area; This is used to determine the rate of change in the number of cells as a characteristic value of cell proliferation activity; A cell activity curve is constructed with time as the horizontal axis and the cell proliferation activity characteristic value as the vertical axis.

9. The storage and management device for mesenchymal stem cells according to claim 1, characterized in that, The state analysis module is used to determine whether cell activity is abnormal based on the cell activity curve segment within each time domain segment and the corresponding sample cell activity curve segment. This is used to determine the cell activity curve segment within each time domain segment, and compare it with the sample cell activity curve segment within the corresponding time domain segment to obtain the goodness of fit; If the fit is less than the predetermined fit threshold, the activity is determined to be abnormal.

10. The storage and management device for mesenchymal stem cells according to claim 1, characterized in that, The state analysis module is used to determine characteristic values ​​of changes in cell membrane integrity within the monitoring area, and to determine whether there is a release of cell contents from the local area within the monitoring area. Used to identify several cells with incomplete cell membranes within the monitoring area, which are then labeled as damaged cells; The ratio of damaged cells to the total number of cells in the monitored area is used to determine the characteristic value of cell membrane integrity change; If the characteristic value of the cell membrane integrity change is greater than or equal to the preset cell membrane integrity change threshold, it is determined that there is a local release of cell contents in the monitoring area.

Images