Enzyme-free dissociation of adipose tissue based on hydrodynamic shearing

Abstract

The present application relates to the technical field of enzyme-free cell dissociation, and particularly relates to an enzyme-free dissociation adipose tissue method and device based on hydrodynamic shear, the method comprising: step 1, obtaining a dissociation index, and obtaining an optimal process parameter combination one meeting the dissociation index through a model based on the dissociation index; step 2, establishing a benchmark process based on the optimal process parameter combination one; step 3, judging whether there is a decision input, if yes, then entering step 4, otherwise using the benchmark process to perform a dissociation process; step 4, optimizing the benchmark process based on the decision input to obtain an optimal process parameter combination two, and performing a dissociation process based on the optimal process parameter combination two.

Description

Technical Field

[0001] This invention relates to the field of enzyme-free cell dissociation technology, and particularly to an enzyme-free method and apparatus for dissociating adipose tissue based on hydrodynamic shearing. Background Technology

[0002] Enzyme-free dissociation of adipose tissue is a method designed to avoid the use of enzymes such as collagenase, thereby reducing cell damage, lowering costs, or simplifying the process. Examples include physical forces (such as shearing or grinding) to break down adipose tissue, and the use of chemical reagents to degrade or disrupt intercellular connections. Commonly used reagents include chelated calcium ion solutions (EDTA), surfactants, and ammonia.

[0003] Enzyme-free hydrodynamic shearing is a method that utilizes the physical forces generated by fluids (mainly shear force and cavitation effect) to break down adipose tissue and release cells. It is more uniform and controllable than traditional manual shearing and can achieve a degree of automation. The principle is that high-speed flowing liquid (buffer solution) impacts, vortices, or passes through tiny pores, generating strong laminar shear forces and turbulence, thereby tearing soft adipose tissue, destroying adipocytes, and releasing lipids. Because no enzymes are used, the influence of batch-to-batch variations in enzyme activity on cell surface protein analysis is eliminated.

[0004] In the process of enzymatic dissociation of adipose tissue using hydrodynamic shearing, the key to standardizing the enzymatic dissociation process lies in transforming it from an empirical operation into a quantifiable and predictable one. To this end, we propose a method and apparatus for enzymatic dissociation of adipose tissue based on hydrodynamic shearing. Summary of the Invention

[0005] This invention, based on the dissociation index, uses a model to find the optimal combination of process parameters that meets the dissociation index, constructs a benchmark process based on this combination, and performs dissociation based on the benchmark process, thereby ensuring the consistency of the process and the stability of the dissociation quality.

[0006] The technical solution proposed in this invention is: an enzyme-free method for dissociating adipose tissue based on hydrodynamic shearing, the method comprising:

[0007] Step 1: Obtain the dissociation index, and based on the dissociation index, obtain the optimal combination of process parameters that satisfies the dissociation index through the model.

[0008] Step 2: Establish a baseline process based on the optimal combination of process parameters;

[0009] Step 3: Determine if there is a decision input. If yes, proceed to Step 4; otherwise, use the baseline process for the dissociation process.

[0010] Step 4: Optimize the baseline process based on the decision input to obtain the second optimal process parameter combination, and perform the dissociation process based on the second optimal process parameter combination.

[0011] Preferably, the step of obtaining the dissociation index, based on the dissociation index, involves obtaining the optimal combination of process parameters that satisfies the dissociation index through a model, including:

[0012] Historical process parameters are obtained from the database and preprocessed. These historical process parameters include shear force, chelating agent level, protective agent level, viscosity modifier apparent viscosity, and dissociation time.

[0013] Establish correlation models between process parameters and dissociation rate constant, and correlation models between process parameters and live cell yield;

[0014] Combining the two correlation models above, the optimal process parameter combination is obtained, which maximizes the yield of live cells.

[0015] Preferably, the establishment of the correlation model between process parameters and dissociation rate constant, and the correlation model between process parameters and live cell yield, includes:

[0016] The dissociation rate constant and process parameters are correlated through a power-law model, including:

[0017] Dissociation rate constant ;in, Indicates the chelating agent level. Protectant level, This indicates the apparent viscosity of the viscosity modifier; Shear force The index, Indicates the chelating agent level index. Indicates the level of protective agents. Indicates the apparent viscosity index; Represents positive terms. Indicates the scaling factor;

[0018] Linearization of power-law equations includes:

[0019] ;

[0020] set up ;

[0021] Obtain the equation ;

[0022] Establish a correlation model between dissociation index and process parameters: ;in, The predicted value representing the live cell yield. This represents the theoretical maximum cell yield. Indicates the dissociation process item. Indicates mechanical damage; cumulative shear dose ; Indicates the shearing duration; Indicates the chemical / time-dependent damage term. Indicates the chemical damage coefficient; Indicates the dose of chemical exposure.

[0023] Preferably, the optimal process parameter combination one, which combines the above two correlation models to obtain the process parameter combination that maximizes the yield of live cells, includes:

[0024] Using historical process parameters, a multiple linear regression algorithm was used to analyze the equations. Perform linear fitting to obtain the fitting coefficients. as well as , , , The value of; where, ;

[0025] because Inverse solution to obtain Substitute ;

[0026] Solve the association model using a global optimization algorithm. ,get , ;

[0027] Construct the objective function The objective function is solved using a genetic algorithm. The obtained optimal solution constitutes the optimal combination of process parameters. .

[0028] Preferably, the step of establishing a baseline process based on the optimal combination of process parameters includes:

[0029] The predicted value of live cell yield was repeatedly calculated under the optimal combination of process parameters, and the mean and standard deviation of live cell yield were calculated.

[0030] Calculate the coefficient of variation based on the mean and standard deviation. ;in, This represents the variance of the predicted live cell yield. The mean of the predicted values;

[0031] if Then, the optimal combination of process parameters is set as the baseline process, and the performance range of the baseline process parameters is... ;

[0032] if Adjust the optimal combination of process parameters.

[0033] Preferably, the step of obtaining the optimal process parameter combination two based on the decision input optimization benchmark process includes:

[0034] Obtain decision inputs, which include maximizing live cell yield and minimizing cost / buffer toxicity;

[0035] Constructing a multi-objective optimization function set ;in, Indicates the cost of chelating agents, Indicates the cost of protective agents, Indicates energy consumption cost, , , This represents the normalized chelating agent level, protective agent level, and shear time;

[0036] The constraints are ;in, , Indicates the minimum and maximum shear stresses;

[0037] The non-dominated sorting genetic algorithm NSGA-II is used to solve a set of multi-objective optimization functions, obtaining one or more solutions that satisfy the constraints, i.e., the optimal combination of process parameters. ; Indicates the number of solutions.

[0038] Preferably, a stability analysis is performed on the benchmark process, and based on the stability analysis results, an operation window for the benchmark process is set, including:

[0039] Calculate the sensitivity index of each process parameter within the baseline process: ;in, ; This represents the predicted yield of live cells under the optimal combination of process parameters. express The Middle Sensitivity index of each process parameter ;

[0040] For output The acceptable range of variation is ;

[0041] For each parameter within the baseline process, the allowable fluctuation range is: ;

[0042] The operation window for each parameter within the baseline process is: ;

[0043] if Then determine the corresponding process parameters. As a highly sensitive parameter with a small operating window, this parameter needs to be precisely controlled during the dissociation process. Indicates the sensitivity threshold;

[0044] Otherwise, determine the corresponding process parameters. As a low-sensitivity parameter, it has a wide operating window and can be combined with decision input to reduce costs by decreasing the corresponding amount of the relevant process parameters without affecting the dissociation results.

[0045] Preferably, the adjustment of the optimal process parameter combination one includes:

[0046] Random sampling is performed near the optimal process parameters in the optimal process parameter combination one, and the results are calculated for each sampling point. : The mean and variance of the predicted live cell yield;

[0047] Plot a scatter plot of the predicted mean and variance, and select sampling points on the Pareto front, that is, the points with the highest predicted mean for the same predicted variance, or the points with the smallest predicted variance for the corresponding predicted mean.

[0048] Perform variability analysis. If the variability of the sampling points is less than 15%, then update the optimal process parameter combination one using the sampling points to obtain the optimized optimal process parameter combination one. .

[0049] An enzyme-free adipose tissue dissociation device based on hydrodynamic shearing, the device being used to perform the aforementioned enzyme-free adipose tissue dissociation method based on hydrodynamic shearing.

[0050] A computer-readable storage medium storing a computer program that is executed by a processor to implement the described hydrodynamic shear-based enzyme-free adipose tissue dissociation method.

[0051] The beneficial effects of this invention are:

[0052] 1. In the present invention, when there is no strategy input, the dissociation process is carried out through the benchmark process; when there is strategy input, that is, when there are special production requirements (cost requirements), a multi-objective optimization function group is constructed to obtain the optimal combination of process parameters under the dissociation index and cost requirements, and the final process parameters are selected manually, thus avoiding the problem of black box operation of the model and improving the transparency and reliability of the dissociation process.

[0053] 2. This invention, through stability analysis, identifies sensitive indicators of different parameters when using a benchmark process, determines the process parameters that need to be finely controlled, and provides guidance for monitoring process parameters during the dissociation process. That is, during the dissociation process, process parameters with high sensitivity indices are monitored in detail, and the corresponding process parameters are adjusted with a smaller adjustment range, such as the control of shear stress. Attached Figure Description

[0054] Figure 1 This is a flowchart of the enzyme-free adipose tissue dissociation method based on hydrodynamic shearing of the present invention. Detailed Implementation

[0055] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0056] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0057] Example 1:

[0058] refer to Figure 1 The technical solution provided by this invention is: an enzyme-free method for dissociating adipose tissue based on hydrodynamic shearing, the method comprising:

[0059] Step 1: Obtain the dissociation index. Based on the dissociation index, obtain the optimal combination of process parameters that satisfies the dissociation index through a model. In the basic embodiment, the dissociation index is the live cell yield. Specifically, this includes the following steps:

[0060] Step 1.1: Obtain historical process parameters from the database and perform preprocessing. The historical process parameters include: shear force, chelating agent level, protective agent level, viscosity modifier apparent viscosity, and dissociation time.

[0061] Step 1.2: Establish correlation models between process parameters and dissociation rate constant, and correlation models between process parameters and viable cell yield, specifically as follows:

[0062] The dissociation rate constant and process parameters are correlated through a power-law model, including:

[0063] Dissociation rate constant ;in, Indicates the chelating agent level (unit: mM). Protectant level (in percentage concentration). The apparent viscosity of the viscosity modifier is expressed in mPa·s. Shear force The index, Indicates the chelating agent level index. Indicates the level of protective agents. Indicates the apparent viscosity index; This indicates a positive number (a very small positive number to avoid calculation problems when the concentration is zero). Indicates the scaling factor;

[0064] Linearization of power-law equations includes:

[0065] ;

[0066] set up ;

[0067] Obtain the equation .

[0068] The DNA / cell release curves indicate that the adipose tissue dissociation kinetic model is as follows: ;in, Indicates time The measured DNA concentration; This represents the fitting parameters (representing the theoretically maximum amount of DNA that can be released under the current conditions). This is the dissociation rate constant. The larger the value, the faster the dissociation process. It is a key indicator for measuring the ease or efficiency of dissociation.

[0069] Based on the above dynamic model, a correlation model between the dissociation index and process parameters is constructed. ;in, The predicted value representing the live cell yield. This represents the theoretical maximum cell yield (in cells / g), which can be approximated by the number of cells obtained by enzymatic digestion. Indicates the dissociation process item. Represents the mechanical damage term, and the parameters to be fitted. The larger the volume, the more sensitive the cell is to the cumulative shear dose; cumulative shear dose ; Indicates the shearing duration; Indicates the chemical / time-dependent damage term. This represents the chemical damage coefficient, indicating the toxicity of chelating agents to cells over a long period of time. Indicates the dose of chemical exposure.

[0070] Step 1.3: Combining the two correlation models above, obtain the process parameter combination that maximizes the yield of live cells, i.e., the optimal process parameter combination one, which is as follows:

[0071] Using historical process parameters, a multiple linear regression algorithm was used to analyze the equations. Perform a linear fit (e.g., using Python's statsmodels or MATELAB) to obtain the fit coefficients. as well as , , , The value of; where, ;

[0072] because Inverse solution to obtain Substitute ;

[0073] Solve the association model using a global optimization algorithm. ,get , ;

[0074] Construct the objective function The objective function is solved using a genetic algorithm. The obtained optimal solution constitutes the optimal combination of process parameters. .

[0075] Step 2: Establish a baseline process based on the optimal combination of process parameters, specifically as follows:

[0076] The predicted value of live cell yield was repeatedly calculated under the optimal combination of process parameters, and the mean and standard deviation of live cell yield were calculated.

[0077] Calculate the coefficient of variation based on the mean and standard deviation. ;in, This represents the variance of the predicted live cell yield. The mean of the predicted values;

[0078] if Then, the optimal combination of process parameters is set as the baseline process, and the performance range of the baseline process parameters is... ;

[0079] if The first step in adjusting the optimal combination of process parameters is as follows:

[0080] Random sampling is performed near the optimal process parameters in the optimal process parameter combination one, and the results are calculated for each sampling point. : The mean and variance of the predicted live cell yield;

[0081] Plot a scatter plot of the predicted mean and variance, and select sampling points on the Pareto front, that is, the points with the highest predicted mean for the same predicted variance, or the points with the smallest predicted variance for the corresponding predicted mean.

[0082] Perform variability analysis. If the variability of the sampling points is less than 15%, then update the optimal process parameter combination one using the sampling points to obtain the optimized optimal process parameter combination one. .

[0083] Step 3: Determine if there is a decision input. If yes, proceed to Step 4; otherwise, use the baseline process for the dissociation process.

[0084] Step 4: Optimize the baseline process based on the decision input to obtain the second optimal process parameter combination, and perform a dissociation process based on the second optimal process parameter combination, specifically including the following steps:

[0085] Obtain decision inputs, which include maximizing live cell yield and minimizing cost / buffer toxicity;

[0086] Constructing a multi-objective optimization function set ;in, Indicates the cost of chelating agents, Indicates the cost of protective agents, Indicates energy consumption cost, , , This represents the normalized chelating agent level, protective agent level, and shear time;

[0087] The constraints are ;in, , Indicates the minimum and maximum shear stresses;

[0088] The non-dominated sorting genetic algorithm NSGA-II is used to solve a set of multi-objective optimization functions, obtaining one or more solutions that satisfy the constraints, i.e., the optimal combination of process parameters. ; Indicates the number of solutions;

[0089] When there are multiple conflicting objectives, such as simultaneously increasing live cell yield, reducing costs, or reducing buffer toxicity, it is necessary to find a set of one or more process parameter combinations that satisfy multiple conditions, rather than a single optimal parameter combination, and the final decision is made manually.

[0090] Because if multiple solutions that satisfy the constraints are all on the approximate Pastor front, there is no absolute superiority or inferiority, so it is necessary to retain multiple sets of solutions.

[0091] For example, when At that time, the live cell yield was 1 million cells / g, and the cost was 10 yuan; when At that time, the live cell yield was 1.1 million cells / g, and the cost was 15 yuan; At that time, the yield was 900,000 cells / g and the cost was 7 yuan. The combination of process parameters could be determined manually according to actual needs, and the shear stress and buffer solution could be controlled through the corresponding combination of process parameters.

[0092] For example, for controllable shearing devices (such as cone-plate rheometers and custom shear chambers), in Next, the shear stress is converted into a shear rate parameter that the equipment can recognize. To control shear stress.

[0093] For syringe pushing, in Below this, the shear stress is converted into the pushing speed, i.e., the volumetric flow rate. ;in, Indicates the radius of the syringe's live connector. This indicates the apparent viscosity of the buffer solution.

[0094] Example 2:

[0095] In actual dissociation production, process parameters may change, requiring real-time monitoring and adjustment. Since the adjustment range and precision of each parameter differ, it is necessary to determine which process parameters require fine-tuning and which do not. Furthermore, when there is decision input (i.e., special production requirements), it is necessary to determine which process parameters can be adjusted to reduce costs while ensuring dissociation quality. Therefore, based on Example 1, we propose the following technical solution:

[0096] Perform stability analysis on the benchmark process, and based on the stability analysis results, set the operation window for the benchmark process, including:

[0097] Calculate the sensitivity index of each process parameter within the baseline process: ;in, ; This represents the predicted yield of live cells under the optimal combination of process parameters. express The Middle Sensitivity index of each process parameter ;

[0098] For output The acceptable range of variation is ;

[0099] For each parameter within the baseline process, the allowable fluctuation range is: ;

[0100] The operation window for each parameter within the baseline process is: ;

[0101] if Then determine the corresponding process parameters. As a highly sensitive parameter, it means that its operating window is small, and precise control of this parameter is required during the dissociation process; Indicates the sensitivity threshold;

[0102] Otherwise, determine the corresponding process parameters. The low sensitivity of the parameter means that it has a wide operating window and high operational flexibility, allowing for a reduction in the grade or amount of the material to save costs without affecting the results; it can also be combined with decision inputs to reduce costs by decreasing the amount of the corresponding reagent for the relevant process parameters without affecting the dissociation results.

[0103] The present invention also provides an enzyme-free adipose tissue dissociation device based on hydrodynamic shearing, the device being used to perform the aforementioned enzyme-free adipose tissue dissociation method based on hydrodynamic shearing.

[0104] The present invention also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the described method for the enzymatic dissociation of adipose tissue based on hydrodynamic shearing.

[0105] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation that may be implemented in systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0107] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the principles described, the implementation of the present invention may have any changes or modifications.

Claims

1. A hydrodynamic shear-based enzymatic method for dissociating adipose tissue, characterized in that, The method includes: Step 1: Obtain the dissociation index, and based on the dissociation index, obtain the optimal combination of process parameters that satisfies the dissociation index through a model; including: Historical process parameters are obtained from the database and preprocessed. These historical process parameters include shear force, chelating agent level, protective agent level, viscosity modifier apparent viscosity, and dissociation time. Establish correlation models between process parameters and dissociation rate constants, and between process parameters and viable cell yield; including: correlating the dissociation rate constant and process parameters using power-law models, including: Dissociation rate constant ;in, Indicates the chelating agent level. Protectant level, This indicates the apparent viscosity of the viscosity modifier; Shear force The index, Indicates the chelating agent level index. Indicates the level of protective agents. Indicates the apparent viscosity index; Represents positive terms. Indicates the scaling factor; Linearization of power-law equations includes: ； set up ; Obtain the equation ; Establish a correlation model between dissociation index and process parameters: ;in, The predicted value representing the live cell yield. This represents the theoretical maximum cell yield. Indicates the dissociation process item. Indicates mechanical damage; cumulative shear dose ; Indicates the shearing duration; Indicates the chemical / time-dependent damage term. Indicates the chemical damage coefficient; Indicates chemical exposure dose; Combining the two correlation models above, the optimal process parameter combination, which maximizes the yield of live cells, is obtained, and includes: Using historical process parameters, a multiple linear regression algorithm was used to analyze the equations. Perform linear fitting to obtain the fitting coefficients. as well as , , , The value of; where, ; because Inverse solution to obtain Substitute ; Solve the association model using a global optimization algorithm. ,get , ; Construct the objective function The objective function is solved using a genetic algorithm. The obtained optimal solution constitutes the optimal combination of process parameters. ; Step 2: Establish a baseline process based on the optimal combination of process parameters; Step 3: Determine if there is a decision input. If yes, proceed to Step 4; otherwise, use the baseline process for the dissociation process. Step 4: Optimize the baseline process based on the decision input to obtain the second optimal process parameter combination, and perform the dissociation process based on the second optimal process parameter combination.

2. The method for enzyme-free dissociation of adipose tissue based on hydrodynamic shearing according to claim 1, characterized in that, The establishment of a baseline process based on the optimal combination of process parameters includes: The predicted value of live cell yield was repeatedly calculated under the optimal combination of process parameters, and the mean and standard deviation of live cell yield were calculated. Calculate the coefficient of variation based on the mean and standard deviation. ;in, This represents the variance of the predicted live cell yield. The mean of the predicted values; if Then, the optimal combination of process parameters is set as the baseline process, and the performance range of the baseline process parameters is... ; if Adjust the optimal combination of process parameters.

3. The method for enzyme-free dissociation of adipose tissue based on hydrodynamic shearing according to claim 2, characterized in that, The second step of optimizing the baseline process based on decision input to obtain the optimal combination of process parameters includes: Obtain decision inputs, which include maximizing live cell yield and minimizing cost / buffer toxicity; Constructing a multi-objective optimization function set ;in, Indicates the cost of chelating agents, Indicates the cost of protective agents, Indicates energy consumption cost, , , This represents the normalized chelating agent level, protective agent level, and shear time; The constraints are ;in, , Indicates the minimum and maximum shear stresses; The non-dominated sorting genetic algorithm NSGA-II is used to solve a set of multi-objective optimization functions, obtaining one or more solutions that satisfy the constraints, i.e., the optimal combination of process parameters. ; Indicates the number of solutions.

4. The method for enzyme-free dissociation of adipose tissue based on hydrodynamic shearing according to claim 3, characterized in that, Perform stability analysis on the benchmark process, and based on the stability analysis results, set the operation window for the benchmark process, including: Calculate the sensitivity index of each process parameter within the baseline process: ;in, ; This represents the predicted yield of live cells under the optimal combination of process parameters. express The Middle Sensitivity index of each process parameter ; For output The acceptable range of variation is ; For each parameter within the baseline process, the allowable fluctuation range is: ; The operation window for each parameter within the baseline process is: ; if Then determine the corresponding process parameters. As a highly sensitive parameter with a small operating window, this parameter needs to be precisely controlled during the dissociation process. Indicates the sensitivity threshold; Otherwise, determine the corresponding process parameters. As a low-sensitivity parameter, it has a wide operating window and can be combined with decision input to reduce costs by decreasing the corresponding amount of the relevant process parameters without affecting the dissociation results.

5. The method for enzyme-free dissociation of adipose tissue based on hydrodynamic shearing according to claim 4, characterized in that, The first adjustment of the optimal process parameter combination includes: Random sampling is performed near the optimal process parameters in the optimal process parameter combination one, and the results are calculated for each sampling point. : The mean and variance of the predicted live cell yield; Plot a scatter plot of the predicted mean and variance, and select sampling points on the Pareto front, that is, the points with the highest predicted mean for the same predicted variance, or the points with the smallest predicted variance for the corresponding predicted mean. Perform variability analysis. If the variability of the sampling points is less than 15%, then update the optimal process parameter combination one using the sampling points to obtain the optimized optimal process parameter combination one. .

6. A device for the enzymatic dissociation of adipose tissue based on hydrodynamic shearing, characterized in that, The apparatus is used to perform the hydrodynamic shear-based enzyme-free adipose tissue dissociation method according to any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the hydrodynamic shear-based enzyme-free adipose tissue dissociation method according to any one of claims 1-5.

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