CN117210403A - Preparation method and application of stem cell and immune cell composition for resisting aging - Google Patents
Preparation method and application of stem cell and immune cell composition for resisting aging Download PDFInfo
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Abstract
The application discloses a preparation method and application of a stem cell and immune cell composition for resisting aging. It firstly obtains stem cells from fetal tissue, umbilical cord blood, adipose tissue or bone marrow, then puts the stem cells into a culture medium to perform culture and expansion treatment to obtain a predetermined number of stem cells, then obtains immune cells from peripheral blood or bone marrow, then performs separation and purification treatment of immune cells by using density gradient centrifugation or magnetic bead separation equipment to obtain a pure immune cell population, then mixes the predetermined number of stem cells with the pure immune cell population to obtain a composition, then performs washing and freezing treatment of the composition, and adds a protective agent or additive to obtain a treated composition, and finally performs quality control and verification of the treated composition. In this way, objectivity, efficiency and accuracy can be improved.
Description
Technical Field
The application relates to the field of cell composition preparation, and more particularly relates to a preparation method and application of a stem cell and immune cell composition for resisting aging.
Background
Anti-aging is an important research field, and methods and treatment means capable of delaying the aging process are always sought. Stem cells and immune cells are believed to have potential anti-aging effects and may produce synergistic effects when used in combination.
Stem cells are a class of cells with self-renewing and differentiating potential that can differentiate into a variety of cell types, including muscle cells, nerve cells, and the like. They have important regeneration and repair capabilities that can help restore function to damaged tissues and organs.
Immune cells are part of the body's defensive system and include various types of leukocytes such as lymphocytes, monocytes and macrophages. They are involved in regulating immune responses, combating infections and diseases, and have anti-inflammatory and antioxidant properties.
Quality control and verification is important in the preparation of stem cell and immune cell compositions, and detection of these aspects can ensure that the prepared compositions have good cell viability, purity and cell phenotype, thereby ensuring their effectiveness and safety in anti-aging treatment.
However, conventional cell phenotype detection typically relies on immunocytochemistry staining and microscopic observation, which requires manual manipulation and subjective judgment, and is susceptible to experience and subjective opinion of the operator. Different operators may have different interpretation and assessment results, resulting in inconsistent and unreliable results. Moreover, conventional cell phenotype detection schemes typically require significant time and human resources. The steps of staining, microscopic observation, and data analysis all require a significant amount of time and labor, limiting their efficiency and feasibility in large-scale applications.
Thus, an optimized preparation scheme for anti-aging stem cell and immune cell compositions is desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides a preparation method and application of a stem cell and immune cell composition for resisting aging. The method can realize automatic evaluation and control of the quality of the prepared stem cell and immune cell composition so as to avoid low efficiency and high error caused by manual detection, thereby improving the detection and evaluation of cell phenotypes, obtaining more comprehensive and accurate cell phenotype evaluation results and improving the objectivity, efficiency and accuracy of the cell phenotype evaluation results.
According to one aspect of the present application, there is provided a method for preparing a stem cell and immune cell composition for anti-aging, comprising:
obtaining stem cells from fetal tissue, cord blood, adipose tissue, or bone marrow;
placing the stem cells into a culture medium for culture and expansion treatment to obtain a predetermined number of stem cells;
obtaining immune cells from peripheral blood or bone marrow;
performing immune cell separation and purification treatment by using density gradient centrifugation or magnetic bead separation equipment to obtain pure immune cell populations;
mixing the predetermined number of stem cells with the purified population of immune cells to obtain a composition;
subjecting the composition to washing and cryopreservation treatments and adding a protective agent or additives to obtain a treated composition; and
the quality control and verification of the treated composition was performed.
According to another aspect of the present application there is provided the use of a stem cell and immune cell composition for anti-aging comprising:
a stem cell obtaining module for obtaining stem cells from fetal tissue, umbilical cord blood, adipose tissue or bone marrow;
a culture and amplification module for placing the stem cells into a culture medium for culture and amplification treatment to obtain a predetermined number of stem cells;
an immune cell acquisition module for acquiring immune cells from peripheral blood or bone marrow;
the separation and purification module is used for separating and purifying immune cells by using density gradient centrifugation or magnetic bead separation equipment so as to obtain pure immune cell populations;
a mixing module for mixing the predetermined number of stem cells with the purified population of immune cells to obtain a composition;
the post-treatment composition acquisition module is used for washing and freezing the composition and adding a protective agent or an additive to obtain the post-treatment composition;
and a quality verification module for quality control and verification of the treated composition.
Compared with the prior art, the preparation method and the application of the stem cell and immune cell composition for aging resistance provided by the application are characterized in that firstly, stem cells are obtained from fetal tissues, umbilical cord blood, adipose tissues or bone marrow, then, the stem cells are put into a culture medium for culture and expansion treatment to obtain a preset number of stem cells, then, immune cells are obtained from peripheral blood or bone marrow, then, the immune cells are separated and purified by using a density gradient centrifugation or magnetic bead separation device to obtain a pure immune cell population, then, the preset number of stem cells and the pure immune cell population are mixed to obtain a composition, then, the composition is washed and frozen, a protective agent or an additive is added to obtain a treated composition, and finally, the treated composition is subjected to quality control and verification. In this way, objectivity, efficiency and accuracy can be improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below, the following drawings not being drawn to scale with respect to actual dimensions, emphasis instead being placed upon illustrating the gist of the present application.
Fig. 1 is a flowchart of a method of preparing a stem cell and immune cell composition for anti-aging according to an embodiment of the present application.
Fig. 2 is a flowchart of substep S170 of the preparation method of stem cells and immune cell compositions for anti-aging according to an embodiment of the present application.
Fig. 3 is a schematic diagram of the architecture of substep S170 of the preparation method of stem cells and immune cell compositions for anti-aging according to an embodiment of the present application.
Fig. 4 is a flowchart of sub-step S172 of the preparation method of stem cells and immune cell compositions for anti-aging according to an embodiment of the present application.
Fig. 5 is a flowchart of substep S173 of the preparation method of the stem cell and immune cell composition for anti-aging according to an embodiment of the present application.
Fig. 6 is a block diagram of an application of stem cell and immune cell compositions for anti-aging according to an embodiment of the present application.
Fig. 7 is an application scenario diagram of a preparation method of stem cell and immune cell compositions for anti-aging according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are also within the scope of the application.
As used in the specification and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Although the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server. The modules are merely illustrative, and different aspects of the systems and methods may use different modules.
A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Fig. 1 is a flowchart of a method of preparing a stem cell and immune cell composition for anti-aging according to an embodiment of the present application. As shown in fig. 1, a method for preparing a stem cell and immune cell composition for aging resistance according to an embodiment of the present application includes the steps of: s110, obtaining stem cells from fetal tissue, umbilical cord blood, adipose tissue or bone marrow; s120, placing the stem cells into a culture medium for culture and expansion treatment to obtain a predetermined number of stem cells; s130, obtaining immune cells from peripheral blood or bone marrow; s140, performing immune cell separation and purification treatment by using density gradient centrifugation or magnetic bead separation equipment to obtain a pure immune cell population; s150, mixing the predetermined number of stem cells with the purified immune cell population to obtain a composition; s160, washing and freezing the composition, and adding a protective agent or an additive to obtain a treated composition; and S170, performing quality control and verification on the treated composition.
In view of the above technical problems, the technical idea of the present application is to collect microscopic images of stem cells and immune cell compositions by microscopic equipment, and introduce image processing and analysis techniques at the rear end to realize analysis of the microscopic images, so as to evaluate the cell phenotype of the stem cells and immune cell compositions, in this way, the quality of the prepared stem cells and immune cell compositions can be automatically evaluated and controlled, so as to avoid low efficiency and high error caused by manual detection, thereby improving the detection and evaluation of cell phenotypes, obtaining more comprehensive and accurate cell phenotype evaluation results, and improving the objectivity, efficiency and accuracy thereof.
Fig. 2 is a flowchart of substep S170 of the preparation method of stem cells and immune cell compositions for anti-aging according to an embodiment of the present application. Fig. 3 is a schematic diagram of the architecture of substep S170 of the preparation method of stem cells and immune cell compositions for anti-aging according to an embodiment of the present application. As shown in fig. 2 and 3, the method for preparing stem cells and immune cell compositions for anti-aging according to the embodiment of the present application, performing quality control and verification on the treated composition, comprises the steps of: s171, acquiring microscopic images of the stem cells and the immune cell composition by microscopic equipment; s172, performing image characteristic analysis on microscopic images of the stem cells and the immune cell composition to obtain cell composition characteristics; and S173 determining whether the cellular phenotype of the stem cells and the immune cell composition meets a predetermined quality criterion based on the cellular composition characteristics.
Specifically, in the technical scheme of the present application, first, microscopic images of stem cells and immune cell compositions collected by a microscopic apparatus are acquired. It will be appreciated that microscopic images of cell compositions typically contain rich information such as cell morphology, cell arrangement, and cell internal structure. In the actual detection process of cell phenotype, the shallow features such as edges, textures, shapes and the like of cells are more focused, and interference feature information irrelevant to phenotype detection is filtered. Thus, in the technical scheme of the application, microscopic images of the stem cells and the immune cell composition are passed through a convolution layer-based image shallow feature extractor to obtain a cell composition shallow feature map. By means of the convolution-layer-based image shallow feature extractor, local feature information about the composition, such as edges, textures, and shapes, in the image can be captured, which can help to distinguish between different cell types and evaluate the phenotypic characteristics of the cells.
Then, it is considered that in the shallow feature map of the cell composition, there is spatial structure information since it is a three-dimensional tensor. However, in actually performing phenotypic detection of the composition, the spatial structure of individual local features in the profile is of greater concern than the entire profile. Thus, for subsequent feature analysis and processing, it is necessary to feature flatten the individual feature matrices along the channel dimension of the cell composition shallow feature map to obtain a plurality of composition shallow local feature vectors. Each feature matrix can be converted to a one-dimensional vector by feature flattening the shallow feature map of the cellular composition along the respective feature matrix in the channel dimension. Therefore, the local features in the image can be converted from a two-dimensional space to a one-dimensional vector space, so that the features have better comparability and handleability, and each local feature information in the image can be subjected to associated coding, and the accuracy of quality detection of the cell phenotype of the composition is improved.
Further, in order to capture correlation information between local characteristics of cellular phenotype related to the composition in the microscopic image, so as to perform cellular phenotype quality detection of the composition by using global information, in the technical scheme of the application, the shallow local characteristic vectors of the plurality of compositions are further encoded in a shallow inter-characteristic context encoder based on a converter module, so as to extract global context correlation characteristic information between local characteristics of cellular phenotype related to the composition in the microscopic image, thereby obtaining the context correlation characteristic vectors of the shallow characteristics of the cellular composition.
Accordingly, as shown in fig. 4, image characterization of microscopic images of the stem cells and immune cell composition to obtain cell composition characteristics includes: s1721, passing the microscopic images of the stem cells and the immune cell composition through a convolution layer-based image shallow feature extractor to obtain a cell composition shallow feature map; s1722, carrying out feature flattening on each feature matrix of the cell composition shallow feature map along the channel dimension to obtain a plurality of composition shallow local feature vectors; and S1723, performing global association coding on the plurality of composition shallow local feature vectors to obtain a cell composition shallow feature context association feature vector as the cell composition feature. It should be appreciated that in step S1721, microscopic images of stem cells and immune cell compositions are processed using a convolution layer based image shallow feature extractor. The convolution layer is a neural network layer commonly used in deep learning, and can effectively extract features in an image, and through the step, a shallow feature map of the cell composition can be obtained, wherein the shallow feature map contains local feature information in the image. In step S1722, each feature matrix along the channel dimension in the shallow feature map of the cellular composition is flattened to obtain a plurality of shallow local feature vectors of the cellular composition, and each feature matrix is converted into a feature vector for subsequent processing and analysis by the flattening operation. In step S1723, a global associative encoding is performed on the plurality of shallow local feature vectors of the composition. Global associative coding is a method of correlating local feature vectors with surrounding feature vectors to capture the contextual relationship between them, and by global associative coding, shallow feature contextual associative feature vectors of a cellular composition can be obtained, where these feature vectors include global feature information and contextual associative information of the cellular composition, and can be used as a feature representation of the cellular composition. In other words, step S1721 is used to extract a shallow feature map of the cell composition, step S1722 is used to convert the shallow feature map into a local feature vector, and step S1723 is used to globally correlate the local feature vector to obtain a context-correlated feature vector of the cell composition, thereby obtaining a feature representation of the cell composition that can be used for subsequent analysis and processing tasks of the cell composition.
More specifically, in step S1723, globally correlating the plurality of composition shallow local feature vectors to obtain a cell composition shallow feature context correlation feature vector as the cell composition feature, comprising: the plurality of composition shallow local feature vectors are passed through a shallow inter-feature context encoder based on a transducer module to obtain the cell composition shallow feature context-dependent feature vector. It is worth mentioning that the converter module is a neural network module for feature conversion and context coding. In image feature analysis, the converter module may be configured to globally correlate the plurality of composition shallow feature vectors to obtain a context-correlated feature vector of the cell composition shallow features. The converter module converts the input shallow local feature vectors of the compositions into feature representations with more characterization capability by learning a mapping relation, and the conversion can be realized through a series of linear transformation, nonlinear activation functions and normalization operations so as to extract the correlation and importance among different features. The transducer module captures the contextual relationships between the shallow local feature vectors of the plurality of compositions by globally correlating them, which helps integrate the information of the local features to better describe the overall features and structure of the cell composition. By application of the transducer module, context-dependent feature vectors of shallow features of the cellular composition can be obtained, which feature vectors contain global information and context-dependent features of the cellular composition, which can be used as a representation of features of the cellular composition for subsequent classification, identification or other tasks.
Further, the cell composition shallow feature context-associated feature vectors are passed through a classifier to obtain classification results indicative of whether the cell phenotypes of the stem cells and immune cell composition meet predetermined quality criteria. That is, the quality of the stem cells and the immune cell composition is evaluated by performing the classification processing using the global relevant characteristic information of the cell phenotype of the composition, and in this way, the quality of the prepared stem cells and immune cell composition can be automatically evaluated and controlled, so that the inefficiency and high error caused by the manual detection are avoided, the detection and evaluation of the cell phenotype are improved, and the more comprehensive and accurate cell phenotype evaluation result is obtained.
Accordingly, as shown in fig. 5, determining whether the cellular phenotype of the stem cells and the immune cell composition meets a predetermined quality criterion based on the cellular composition characteristics comprises: s1731, performing feature distribution optimization on the cell composition shallow feature context correlation feature vector to obtain an optimized cell composition shallow feature context correlation feature vector; and S1732, passing the optimized cell composition shallow feature context-associated feature vectors through a classifier to obtain a classification result, the classification result being indicative of whether the cell phenotypes of the stem cells and the immune cell composition meet a predetermined quality standard.
More specifically, in step S1731, the feature distribution optimization is performed on the cell composition shallow feature context-associated feature vector to obtain an optimized cell composition shallow feature context-associated feature vector, including: cascading the plurality of composition shallow local feature vectors to obtain composition shallow local feature cascading feature vectors; and performing Hilbert spatial heuristic sequence tracking equalization fusion on the composition shallow local feature cascade feature vector and the cell composition shallow feature context correlation feature vector to obtain the optimized cell composition shallow feature context correlation feature vector. It should be understood that the purpose of cascading a plurality of composition shallow local feature vectors to obtain a composition shallow local feature cascading feature vector is to merge a plurality of local feature vectors into a feature vector with a higher dimension to capture more abundant feature information, and through cascading operation, the plurality of local feature vectors can be connected in a certain order to form a longer feature vector. The purpose of the hilbert space heuristic sequence tracking equalization fusion of the composition shallow local feature cascade feature vector and the cell composition shallow feature context correlation feature vector is to optimize the feature distribution, so that the hilbert space heuristic sequence tracking equalization is more suitable for subsequent processing and analysis, is a signal processing technology, can adjust the spectrum distribution of signals, and can enhance or inhibit the signal energy in a specific frequency range, and is applied to the distribution optimization of the feature vectors. Through the Hilbert space heuristic sequence tracking equalization fusion, the spectrum adjustment can be carried out on the composition shallow local feature cascading feature vector and the cell composition shallow feature context correlation feature vector so as to strengthen or inhibit feature energy at specific frequencies, and the optimization can help to highlight important feature information and inhibit noise or redundant features, so that the optimized cell composition shallow feature context correlation feature vector with more differentiation and characterization capability is obtained. In other words, by cascading a plurality of composition shallow local feature vectors and performing hilbert space heuristic sequence tracking equalization fusion, optimized cell composition shallow feature context correlation feature vectors can be obtained, and the feature vectors have better distribution and characterization capability and are suitable for subsequent cell composition analysis and processing tasks.
In particular, in the technical solution of the present application, when the plurality of composition shallow local feature vectors are obtained by passing the composition shallow feature context-related feature vectors through a shallow inter-feature context encoder based on a converter module, the composition shallow feature context-related feature vectors may express a composition local image semantic context-related feature of each composition shallow local feature vector, but, considering a distribution difference of each composition shallow local feature vector within an image feature domain, there may be an imbalance in distribution of the composition shallow feature context-related feature vectors obtained by context-related encoding with respect to the plurality of composition shallow local feature vectors.
Based on this, in the technical solution of the present application, considering that the cell composition shallow feature context-related feature vector is substantially obtained by concatenating a plurality of composition shallow local feature vectors obtained by a context encoder based on a transducer, the cell composition shallow feature context-related feature vector also conforms to a serialized arrangement of the local-related image semantic representations corresponding to the plurality of composition shallow local feature vectors, and thus, the applicant of the present application cascades the composition shallow local feature cascade feature vectors obtained by concatenating the plurality of composition shallow local feature vectors, for example, denoted as V 1 And the cell composition shallow feature context-associated feature vector, e.g., denoted as V 2 Hilbert spatial heuristic sequence-tracking equalization fusion is performed to optimize the cell composition shallow feature context-dependent feature vector, e.g., denoted as V 2 ′。
Accordingly, in one specific example, performing hilbert spatial heuristic sequence tracking equalization fusion on the composition shallow local feature cascade feature vector and the cell composition shallow feature context correlation feature vector to obtain the optimized cell composition shallow feature context correlation feature vector, comprising: performing Hilbert space heuristic sequence tracking equalization fusion on the composition shallow local feature cascade feature vector and the cell composition shallow feature context correlation feature vector by using the following optimized fusion formula to obtain the optimized cell composition shallow feature context correlation feature vector; wherein, the optimized fusion formula is:
wherein V is 1 Is the cascade characteristic vector of the composition shallow local characteristic, V 2 Is the characteristic vector, V, of the cell composition shallow characteristic context 2 T A transpose vector representing the cell composition shallow feature context associated feature vector, and a feature vector V 1 And V 2 Are all row vectors, | (V) 1 ；V 2 )‖ 2 Representing feature vector V 1 And V 2 Is used to determine the two norms of the cascade of vectors,representing feature vector V 1 And V 2 Mean value of union set of all eigenvalues of (a), +.>A set of eigenvalues representing all positions in the composition shallow local feature cascade eigenvector,/->Indicating the set of feature values at all positions in the feature vector associated with the shallow feature context of the cell composition, +.>Representing vector addition, V 2 ' is the optimized cell composition shallow feature context-associated feature vector.
Here, the completeness of the hilbert space with inner product is utilizedInner product space characteristics by cascading feature vectors V of shallow local features of the composition 1 And the cell composition shallow feature context associated feature vector V 2 Is a collective mean (collective average) of sequence aggregation of the composition, exploring the cascade of feature vectors V of the shallow local features of the composition 1 And the cell composition shallow feature context associated feature vector V 2 Sequence-based spatial distribution heuristics (heuristics) within a feature fusion space encoded via context correlation, thereby correlating the shallow feature context-dependent feature vector V of the cellular composition 2 The local feature distribution of the sequence is converted into a sequence tracking instance (tracking instance) in a fusion space so as to realize tracking small-fragment cognition (tracking let-aware) distribution equalization of the feature space distribution of the sequence, thus improving the image semantic feature expression of the feature context-associated feature vector of the shallow feature of the cell composition. Thus, the quality of the prepared stem cells and immune cell compositions can be automatically evaluated and controlled, so that more comprehensive and accurate cell phenotype evaluation results can be obtained, the preparation effect and quality of the stem cells and immune cell compositions are improved, and powerful support is provided for anti-aging treatment.
Further, in step S1732, the optimized cell composition shallow feature context-dependent feature vectors are passed through a classifier to obtain a classification result, which is used to indicate whether the cell phenotypes of the stem cells and immune cell compositions meet a predetermined quality standard, including: performing full-connection coding on the feature context associated feature vector of the shallow layer feature of the optimized cell composition by using a full-connection layer of the classifier to obtain a coded classification feature vector; and inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.
That is, in the technical solution of the present disclosure, the signature of the classifier includes that the cell phenotype of the stem cells and the immune cell composition meets a predetermined quality criterion (first signature), and that the cell phenotype of the stem cells and the immune cell composition does not meet a predetermined quality criterion (second signature), wherein the classifier determines to which classification signature the optimized cell composition shallow feature context-associated feature vector belongs by a soft maximum function. It is noted that the first and second tags p1 and p2 do not contain the concept of artificial settings, and in fact, during the training process, the computer model does not have the concept of whether the cell phenotype of the stem cells and immune cell composition meets the predetermined quality criterion, which is simply the probability that there are two class tags and the output characteristics are under these two class tags, i.e. the sum of p1 and p2 is one. Thus, the classification of whether the cell phenotype of the stem cells and immune cell composition meets the predetermined quality criteria is actually converted by classifying the tags into a classified probability distribution conforming to the natural law, essentially using the physical meaning of the natural probability distribution of the tags, rather than the linguistic-textual meaning of whether the cell phenotype of the stem cells and immune cell composition meets the predetermined quality criteria.
It should be appreciated that the role of the classifier is to learn the classification rules and classifier using a given class, known training data, and then classify (or predict) the unknown data. Logistic regression (logistics), SVM, etc. are commonly used to solve the classification problem, and for multi-classification problems (multi-class classification), logistic regression or SVM can be used as well, but multiple bi-classifications are required to compose multiple classifications, but this is error-prone and inefficient, and the commonly used multi-classification method is the Softmax classification function.
In summary, the preparation method of stem cells and immune cell compositions for anti-aging according to the embodiments of the present application is elucidated, which can improve objectivity, efficiency and accuracy.
Fig. 6 is a block diagram of an application 100 of a stem cell and immune cell composition for anti-aging according to an embodiment of the present application. As shown in fig. 6, an application 100 of a stem cell and immune cell composition for anti-aging according to an embodiment of the present application includes: a stem cell obtaining module 110 for obtaining stem cells from fetal tissue, umbilical cord blood, adipose tissue or bone marrow; a culture expansion module 120 for placing the stem cells into a culture medium to perform culture and expansion processes to obtain a predetermined number of stem cells; an immune cell acquisition module 130 for acquiring immune cells from peripheral blood or bone marrow; a separation and purification module 140 for performing separation and purification treatment of immune cells using density gradient centrifugation or magnetic bead separation equipment to obtain a pure immune cell population; a mixing module 150 for mixing the predetermined number of stem cells with the purified population of immune cells to obtain a composition; a post-treatment composition acquisition module 160 for washing and freezing the composition and adding a protective agent or additive to obtain a post-treatment composition; and a quality verification module 170 for quality control and verification of the treated composition.
In one example, in the application 100 of the stem cell and immune cell composition for aging described above, the quality verification module 170 includes: a microscopic image acquisition unit for acquiring microscopic images of the stem cells and the immune cell composition by microscopic equipment; an image feature analysis unit for performing image feature analysis on microscopic images of the stem cells and the immune cell composition to obtain cell composition features; and a quality judgment unit for determining whether the cell phenotype of the stem cells and the immune cell composition meets a predetermined quality criterion based on the cell composition characteristics.
Here, it will be understood by those skilled in the art that the specific functions and operations of the respective modules in the application 100 for the anti-aging stem cell and immune cell composition described above have been described in detail in the above description of the preparation method for the anti-aging stem cell and immune cell composition with reference to fig. 1 to 5, and thus, repetitive descriptions thereof will be omitted.
As described above, the application 100 for the anti-aging stem cell and immune cell composition according to the embodiment of the present application may be implemented in various wireless terminals, for example, a server or the like having a preparation algorithm for the anti-aging stem cell and immune cell composition. In one example, the application 100 for anti-aging stem cell and immune cell compositions according to embodiments of the present application may be integrated into a wireless terminal as one software module and/or hardware module. For example, the application 100 for the anti-aging stem cell and immune cell composition may be a software module in the operating system of the wireless terminal, or may be an application developed for the wireless terminal; of course, the application 100 for the anti-aging stem cell and immune cell composition can equally be one of the many hardware modules of the wireless terminal.
Alternatively, in another example, the application 100 for the anti-aging stem cell and immune cell composition and the wireless terminal may also be separate devices, and the application 100 for the anti-aging stem cell and immune cell composition may be connected to the wireless terminal through a wired and/or wireless network and transmit interactive information according to a agreed data format.
Fig. 7 is an application scenario diagram of a preparation method of stem cell and immune cell compositions for anti-aging according to an embodiment of the present application. As shown in fig. 7, in this application scenario, first, microscopic images of stem cells and immune cell compositions (for example, D illustrated in fig. 7) are acquired by a microscopic apparatus, and then, the microscopic images of stem cells and immune cell compositions are input into a server (for example, S illustrated in fig. 7) in which a preparation algorithm for anti-aging stem cells and immune cell compositions is deployed, wherein the server is capable of processing the microscopic images of stem cells and immune cell compositions using the preparation algorithm for anti-aging stem cells and immune cell compositions to obtain classification results for indicating whether cell phenotypes of stem cells and immune cell compositions meet predetermined quality standards.
Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present application and is not to be construed as limiting thereof. Although a few exemplary embodiments of this application have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this application. Accordingly, all such modifications are intended to be included within the scope of this application as defined in the following claims. It is to be understood that the foregoing is illustrative of the present application and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The application is defined by the claims and their equivalents.
Claims (10)
1. A method of preparing a stem cell and immune cell composition for use in anti-aging comprising:
obtaining stem cells from fetal tissue, cord blood, adipose tissue, or bone marrow;
placing the stem cells into a culture medium for culture and expansion treatment to obtain a predetermined number of stem cells;
obtaining immune cells from peripheral blood or bone marrow;
performing immune cell separation and purification treatment by using density gradient centrifugation or magnetic bead separation equipment to obtain pure immune cell populations;
mixing the predetermined number of stem cells with the purified population of immune cells to obtain a composition;
subjecting the composition to washing and cryopreservation treatments and adding a protective agent or additives to obtain a treated composition; and
the quality control and verification of the treated composition was performed.
2. The method of claim 1, wherein the quality control and validation of the post-treatment composition comprises:
acquiring microscopic images of the stem cells and the immune cell composition by microscopic means;
performing image characterization of microscopic images of the stem cells and immune cell composition to obtain cell composition characteristics;
and determining whether the cellular phenotype of the stem cells and the immune cell composition meets a predetermined quality criterion based on the cellular composition characteristics.
3. The method of claim 2, wherein performing image characterization of microscopic images of the stem cells and immune cell composition to obtain cell composition characteristics comprises:
passing the microscopic images of the stem cells and immune cell composition through a convolution layer-based image shallow feature extractor to obtain a cell composition shallow feature map;
performing feature flattening on each feature matrix of the cell composition shallow feature map along the channel dimension to obtain a plurality of composition shallow local feature vectors;
and globally correlating the plurality of composition shallow local feature vectors to obtain a cell composition shallow feature context correlated feature vector as the cell composition feature.
4. The method of claim 3, wherein globally correlating the plurality of composition shallow feature vectors to obtain a cell composition shallow feature context correlation feature vector as the cell composition feature comprises:
the plurality of composition shallow local feature vectors are passed through a shallow inter-feature context encoder based on a transducer module to obtain the cell composition shallow feature context-dependent feature vector.
5. The method of claim 4, wherein determining whether the cell phenotype of the stem cells and the immune cell composition meets a predetermined quality criterion based on the characteristics of the cell composition comprises:
performing feature distribution optimization on the cell composition shallow feature context correlation feature vector to obtain an optimized cell composition shallow feature context correlation feature vector;
and passing the optimized cell composition shallow feature context-associated feature vector through a classifier to obtain a classification result, the classification result being indicative of whether the cell phenotype of the stem cells and the immune cell composition meets a predetermined quality criterion.
6. The method of claim 5, wherein optimizing the feature distribution of the cell composition shallow feature context-related feature vectors to obtain optimized cell composition shallow feature context-related feature vectors comprises:
cascading the plurality of composition shallow local feature vectors to obtain composition shallow local feature cascading feature vectors;
and performing Hilbert spatial heuristic sequence tracking equalization fusion on the composition shallow local feature cascade feature vector and the cell composition shallow feature context correlation feature vector to obtain the optimized cell composition shallow feature context correlation feature vector.
7. The method of claim 6, wherein performing hilbert spatial heuristic sequence-tracking equalization fusion on the composition shallow local feature cascade feature vector and the cell composition shallow feature context correlation feature vector to obtain the optimized cell composition shallow feature context correlation feature vector comprises:
performing Hilbert space heuristic sequence tracking equalization fusion on the composition shallow local feature cascade feature vector and the cell composition shallow feature context correlation feature vector by using the following optimized fusion formula to obtain the optimized cell composition shallow feature context correlation feature vector;
wherein, the optimized fusion formula is:
wherein V is 1 Is the cascade characteristic vector of the composition shallow local characteristic, V 2 Is the characteristic vector, V, of the cell composition shallow characteristic context 2 T A transpose vector representing the cell composition shallow feature context associated feature vector, and a feature vector V 1 And V 2 Are all row vectors, | (V) 1 ；V 2 )‖ 2 Representing feature vector V 1 And V 2 Is used to determine the two norms of the cascade of vectors,representing feature vector V 1 And V 2 A mean value of a union set of all feature values of (a),/>a set of eigenvalues representing all positions in the composition shallow local feature cascade eigenvector,/->Indicating the set of feature values at all positions in the feature vector associated with the shallow feature context of the cell composition, +.>Representing vector addition, V 2 ' is the optimized cell composition shallow feature context-associated feature vector.
8. The method of claim 7, wherein the step of passing the optimized cell composition shallow feature context-dependent feature vector through a classifier to obtain a classification result indicating whether the cell phenotype of the stem cell and immune cell composition meets a predetermined quality criterion comprises:
performing full-connection coding on the feature context associated feature vector of the shallow layer feature of the optimized cell composition by using a full-connection layer of the classifier to obtain a coded classification feature vector; and
and inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.
9. Use of a stem cell and immune cell composition for anti-aging comprising:
a stem cell obtaining module for obtaining stem cells from fetal tissue, umbilical cord blood, adipose tissue or bone marrow;
a culture and amplification module for placing the stem cells into a culture medium for culture and amplification treatment to obtain a predetermined number of stem cells;
an immune cell acquisition module for acquiring immune cells from peripheral blood or bone marrow;
the separation and purification module is used for separating and purifying immune cells by using density gradient centrifugation or magnetic bead separation equipment so as to obtain pure immune cell populations;
a mixing module for mixing the predetermined number of stem cells with the purified population of immune cells to obtain a composition;
the post-treatment composition acquisition module is used for washing and freezing the composition and adding a protective agent or an additive to obtain the post-treatment composition;
and a quality verification module for quality control and verification of the treated composition.
10. The use of stem cells and immune cell compositions for anti-aging according to claim 9, characterized in that said quality verification module comprises:
a microscopic image acquisition unit for acquiring microscopic images of the stem cells and the immune cell composition by microscopic equipment;
an image feature analysis unit for performing image feature analysis on microscopic images of the stem cells and the immune cell composition to obtain cell composition features;
and a quality judgment unit for determining whether the cell phenotype of the stem cells and the immune cell composition meets a predetermined quality criterion based on the cell composition characteristics.
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