Automatic coprophilous fungus sample processing method and system

Abstract

The invention discloses an automatic coprophilous fungus sample treatment method and system, and belongs to the technical field of coprophilous fungus treatment. The method comprises the following steps: acquiring initial bacterial colony parameters of a coprophilous fungus sample, and matching a bacterial liquid receptor according to the initial bacterial colony parameters; acquiring planned transplantation time and target bacterial colony parameters of a bacterial liquid receptor; performing activity attenuation analysis on the coprophilous fungi sample according to the initial bacterial colony parameter, the standard bacterial liquid storage parameter and the planned transplantation time, and outputting a first bacterial colony parameter prediction result; judging whether the first bacterial colony parameter prediction result is qualified or not according to the target bacterial colony parameter; and if not, determining the optimal storage temperature and the optimal exogenous bacterial liquid addition amount according to the initial bacterial colony parameter, the target bacterial colony parameter and the planned transplantation time. Through combination of prediction and adjustment mechanisms, the stability of the sample in the storage and treatment process is ensured, and adverse effects caused by activity attenuation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of fecal bacteria treatment, and specifically to an automatic fecal bacteria sample processing method and system. Background Art

[0002] With the continuous development of modern medical technology, fecal microbiota transplantation, as an effective treatment method, has shown good effects in treating intestinal diseases. However, traditional fecal bacteria sample processing methods have problems such as cumbersome manual operations and difficulty in maintaining the colony activity, resulting in unstable transplantation effects and affecting the wide application of fecal bacteria treatment. In addition, during the sample storage and processing, it is difficult to achieve appropriate parameter adjustment, increasing the complexity of the experiment.

[0003] In response to the above problems, in recent years, technicians have adopted methods such as reducing human intervention and standardizing operation processes to improve the efficiency and consistency of fecal bacteria sample processing. For example, means such as extending the sample storage time through cold chain technology and introducing automated equipment to reduce manual operations have all played a positive role in improving fecal bacteria sample processing.

[0004] Although these methods have improved the processing efficiency of fecal bacteria samples to a certain extent, the existing technology still has deficiencies in accurately matching the bacterial liquid receptor and effectively maintaining the activity and quality of the bacterial liquid. Summary of the Invention

[0005] To solve the problems existing in the above background art, the purpose of the present invention is to provide an automatic fecal bacteria sample processing method and system, which combines prediction and adjustment mechanisms to ensure the stability of the sample during storage and processing and reduce the adverse effects caused by activity attenuation.

[0006] The present invention specifically adopts the following technical solutions: In the first aspect, the present invention provides an automatic fecal bacteria sample processing method, including the steps of: Obtaining the initial colony parameters of the fecal bacteria sample and matching the bacterial liquid receptor according to the initial colony parameters; Obtaining the planned transplantation time and target colony parameters of the bacterial liquid receptor; Performing activity attenuation analysis on the fecal bacteria sample according to the initial colony parameters, standard bacterial liquid storage parameters, and planned transplantation time, and outputting the first colony parameter prediction result; Judging whether the first colony parameter prediction result is qualified according to the target colony parameters; if not, determining the optimal storage temperature and optimal exogenous bacterial liquid addition amount according to the initial colony parameters, target colony parameters, and planned transplantation time.

[0007] As a preferred solution of the present invention, the matching of the bacterial liquid receptor according to the initial colony parameters includes the steps of: Obtain the receptor flora requirement database; the receptor flora requirement database includes several receptor information; the receptor information includes the planned transplantation time, receptor identification, indication, target strain distribution, contraindicated strains, and target viable bacteria concentration; the target strain distribution includes the standard key strain ratio and the standard common strain ratio; Generate a sample strain abundance vector according to the initial colony parameters; Initialize the required strain abundance vector according to the sample strain abundance vector, and map the standard key strain ratio and the standard common strain ratio to the required strain abundance vector; Obtain the receptor matching degree according to the sample strain abundance vector and the required strain abundance vector, and determine the bacterial liquid receptor according to the receptor matching degree and the contraindicated strains.

[0008] As a preferred embodiment of the present invention, the receptor matching degree is expressed as: , , , Wherein, represents the receptor matching degree, represents the standard key strain matching degree, represents the standard common strain matching degree; is the balance coefficient; represents the relative abundance of the i-th strain in the sample strain abundance vector, i = 1, 2,..., n, and n represents the number of strains; represents the relative abundance of the i-th strain in the required strain abundance vector; Q represents the set of key strains; is the strengthening weight.

[0009] As a preferred embodiment of the present invention, the prediction result of the first colony parameter includes the predicted target strain distribution and the predicted viable bacteria concentration; The prediction result of the first colony parameter is expressed as: , , , Wherein, represents the predicted viable bacteria concentration of the bacterial liquid at time t; represents the initial viable bacteria concentration of the bacterial liquid; represents the viable bacteria concentration of the i-th strain at time t; t represents the storage time; represents the initial viable bacteria concentration of the i-th strain; is the reaction rate constant, and A is the pre-exponential factor; is the decay constant of the i-th strain obtained by fitting.

[0010] As a preferred embodiment of the present invention, the attenuation constant is obtained through the following steps: Set the experimental temperature parameter group, and conduct the activity attenuation experiment according to the experimental temperature parameter group; Fit the attenuation constant of each strain according to the experimental results of the activity attenuation experiment.

[0011] As a preferred embodiment of the present invention, determining the optimal storage temperature and the optimal exogenous bacterial liquid addition amount according to the initial colony parameters, the target colony parameters, and the planned transplantation time includes the steps of: Input the initial colony parameters and the target colony parameters into the dynamic joint optimization model, and output the optimal storage temperature and the optimal exogenous bacterial liquid addition amount; Inject the exogenous bacterial liquid according to the optimal exogenous bacterial liquid addition amount to obtain a bacterial liquid product; Transfer the bacterial liquid product to a temperature-controlled storage box, and configure the temperature-controlled storage box according to the optimal storage temperature.

[0012] As a preferred embodiment of the present invention, the optimal storage temperature and the optimal exogenous bacterial liquid addition amount are obtained through the following steps: Generate a number of particles according to the initial colony parameters, initialize the particles, and set variable range constraints; Obtain the predicted target strain distribution and the predicted viable bacteria concentration corresponding to each particle, and calculate the fitness value according to the target colony parameters, the predicted target strain distribution, and the predicted viable bacteria concentration; Update the individual optimal position and the population optimal position according to the fitness values of each particle; Update the position and velocity of each particle according to the individual optimal position and the population optimal position; Iterate until the maximum number of iterations is reached or the convergence condition is satisfied, and output the optimal storage temperature and the optimal exogenous bacterial liquid according to the population optimal position.

[0013] As a preferred embodiment of the present invention, the variable range constraints are obtained through the following steps: Calculate the viable bacteria concentration deviation and the strain abundance deviation of each key strain according to the prediction result of the first colony parameter and the target colony parameter; Select the key strains with negative strain abundance deviation and calculate their corresponding reference addition amounts, and set the addition amount constraints for each key strain according to the reference addition amounts of each key strain; Set the temperature range constraints according to the reference storage temperatures of each key strain.

[0014] As a preferred embodiment of the present invention, injecting the exogenous bacterial liquid according to the optimal exogenous bacterial liquid addition amount specifically means controlling the mixing and filtration of the tissue sample and the exogenous bacterial liquid through a microfluidic chip.

[0015] As a preferred embodiment of the present invention, the fitness value is expressed as: ; Wherein, represents the deviation of viable bacteria concentration, represents the deviation of the species abundance of the i-th key species, represents the species weight, represents the weight parameter; N is the number of key species with abundances lower than the set threshold in the predicted target species distribution.

[0016] In a second aspect, the present invention provides an automatic fecal bacteria sample processing system, including: An initial colony analysis module for obtaining the initial colony parameters of the fecal bacteria sample; A bacterial liquid receptor matching module for matching the bacterial liquid receptor according to the initial colony parameters; An activity decay analysis module for performing activity decay analysis on the fecal bacteria sample according to the initial colony parameters, the standard bacterial liquid storage parameters, and the planned transplantation time to obtain a first colony parameter prediction result; An inspection and optimization module for determining the optimal storage temperature and the optimal amount of exogenous bacterial liquid to be added according to the initial colony parameters, the target colony parameters, and the planned transplantation time when the first colony parameter prediction result does not meet the preset conditions.

[0017] Compared with the prior art, the present invention has the following beneficial effects: By obtaining and analyzing the initial colony parameters, the present invention realizes an effective match between the sample and the bacterial liquid receptor, and performs activity decay analysis according to the initial colony parameters, the standard bacterial liquid storage parameters, and the planned transplantation time to accurately predict the change of the colony parameters; when the prediction result does not meet the preset conditions, the storage temperature and the amount of exogenous bacterial liquid to be added are adjusted according to the specific situation, so as to ensure the colony activity and quality; through the combination of the prediction and adjustment mechanisms, the present invention ensures the stability of the sample during storage and processing, and reduces the adverse effects caused by activity decay. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 is a schematic flow chart of an automatic fecal bacteria sample processing method according to an embodiment of the present invention; Figure 2 is a schematic flow chart of matching the bacterial liquid receptor according to the initial colony parameters according to an embodiment of the present invention; Figure 3Schematic flowchart for determining the optimal storage temperature and the optimal exogenous bacterial liquid addition amount according to the initial colony parameters, target colony parameters, and planned transplantation time in an embodiment of the present invention; Figure 4 Schematic flowchart for obtaining the optimal storage temperature and the optimal exogenous bacterial liquid addition amount in an embodiment of the present invention. Detailed implementation manners

[0020] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0021] With the continuous development of modern medical technology, fecal microbiota transplantation, as an effective treatment method, has shown good effects in the treatment of intestinal diseases. However, traditional fecal sample processing methods have problems such as cumbersome manual operations and difficulty in maintaining colony activity, resulting in unstable transplantation effects and affecting the wide application of fecal microbiota treatment. In addition, during the sample storage and processing, it is difficult to achieve appropriate parameter adjustment, increasing the complexity of the experiment.

[0022] In response to the above problems, in recent years, technicians have adopted methods such as reducing human intervention and standardizing operation processes to improve the efficiency and consistency of fecal sample processing. For example, extending the sample preservation time through cold chain technology and introducing automated equipment to reduce manual operations have all played a positive role in improving fecal sample processing.

[0023] Although these methods have improved the processing efficiency of fecal samples to a certain extent, the existing technologies still have deficiencies in accurately matching the bacterial liquid receptor and effectively maintaining the activity and quality of the bacterial liquid.

[0024] In view of the above problems, it is urgent to propose an automatic fecal sample processing method and system to ensure the stability of the sample during storage and processing, and it is particularly important to reduce the adverse effects caused by activity attenuation.

[0025] The following will elaborate on the specific embodiments of the present invention in detail: Embodiment 1 Please refer to Figure 1 , the present invention provides an automatic fecal sample processing method, including the steps: S1. Obtain the initial colony parameters of the fecal microbiota sample, and match the bacterial liquid receptor according to the initial colony parameters; The initial colony parameters include the initial species distribution and the initial viable bacteria concentration.

[0026] Further, please refer to Figure 2 , and the matching of the bacterial liquid receptor according to the initial colony parameters includes the steps of: S11. Obtain the receptor microbiota requirement database; the receptor microbiota requirement database includes a number of receptor information; the receptor information includes the planned transplantation time, the receptor identifier, the indication, the target species distribution, the taboo species, and the target viable bacteria concentration.

[0027] In one embodiment, the receptor information is stored in the json format. The target species distribution includes the standard key species ratio and the standard common species ratio, such as "{ "receptor identifier": "P001", "indication": "Crohn's disease", "standard key species ratio": ["Bacteroidetes": "50%", "Firmicutes": "20%",...], "standard common species ratio": ["Verrucomicrobia": "3%", "Actinobacteria": "3%",...], "taboo species": ["Clostridium difficile", "Escherichia coli O157:H7"], "target viable bacteria concentration": "≥1×10^8 CFU / mL"}".

[0028] S12. Generate a sample species abundance vector according to the initial colony parameters; Among them, the sample species abundance vector is expressed as: V 样本 =[v1, v2,..., v n ; where v i represents the relative abundance of the i-th species, i = 1, 2,..., n, and n represents the number of species.

[0029] S13. Initialize the required species abundance vector according to the sample species abundance vector, and map the standard key species ratio and the standard common species ratio to the required species abundance vector; In this embodiment, the required species abundance vector is initialized according to the sample species abundance vector, that is, the required species abundance vector is set to include all dimensions of the sample species abundance vector, and the values of the corresponding dimensions are set according to the value of the "standard target species ratio" for comparison and matching with the sample species abundance vector.

[0030] S14. Obtain the receptor matching degree according to the sample species abundance vector and the required species abundance vector, and determine the bacterial liquid receptor according to the receptor matching degree and the taboo species.

[0031] By comparing the sample strain abundance vector with the required strain abundance vector, the matching degree with each recipient is obtained, and then the recipient with the highest matching degree is selected as the most suitable target. If a taboo strain is detected in the sample, the matching degree is directly set to zero.

[0032] In one embodiment, the recipient matching degree is expressed as: ; ; ; Wherein, represents the recipient matching degree, represents the standard key strain matching degree, represents the standard ordinary strain matching degree; is the balance coefficient, and its value ranges from 0.6 to 0.8; represents the relative abundance of the i-th strain in the sample strain abundance vector, i = 1, 2,..., n, and n represents the number of strains; represents the relative abundance of the i-th strain in the required strain abundance vector; Q represents the set of key strains; is the strengthening weight, and its value ranges from 1.5 to 2.

[0033] Before implementing this step, it is necessary to clarify which strains have an important impact (key strains) in a specific application scenario, and which strains have a relatively small impact (non-key strains). This distinction helps to assign a higher weight to key strains in the matching algorithm, thereby improving the accuracy and reliability of the matching. In this embodiment, the set of key strains is consistent with the strains in the standard key strain ratio, and strains with significant positive effects on immune regulation, metabolic function, inflammatory response, etc. are selected, such as Bacteroidetes and Firmicutes. Strains such as Clostridium that are widely present in the intestine but not significantly associated with certain health conditions, or Enterococcus faecalis for which current research data is insufficient to determine its key role, are classified into the non-key strain set and the specific value of the "standard ordinary strain ratio" is set according to clinical trial data and large-scale microbial research project data.

[0034] This embodiment can more accurately consider the importance of key strains in strain matching based on the improved Jaccard index, improving the reliability and effectiveness of the matching. This method is particularly suitable for application scenarios that require special attention to specific strains.

[0035] S2. Obtain the planned transplantation time and target colony parameters of the recipient of the bacterial liquid.

[0036] The purpose of this step is to obtain the receptor information of the bacterial liquid receptor, so as to prepare for the subsequent fecal microbiota sample processing steps. Specifically, the subsequent processing steps focus more on the planned transplantation time of the receptor information and the target colony parameters. The planned transplantation time refers to the specific time point when the target fecal microbiota sample is applied to the receptor. Understanding the planned transplantation time helps to adjust and optimize the sample storage and processing strategies to ensure the activity and desired biological characteristics of the bacterial liquid at the time of transplantation. The target colony parameters include the target bacterial species distribution and the target viable bacteria concentration, and the target colony parameters serve as a benchmark for evaluating and adjusting the sample processing. The target bacterial species distribution determines the ideal relative percentage that different bacterial species should reach in the sample, while the target viable bacteria concentration indicates the minimum activity requirement that the bacterial liquid must achieve to ensure its effectiveness and safety in actual applications.

[0037] S3. Perform an activity decay analysis on the fecal microbiota sample according to the initial colony parameters, standard bacterial liquid storage parameters, and planned transplantation time, and output the first colony parameter prediction result; Among them, the first colony parameter prediction result is a two-dimensional vector calculated based on the initial colony parameters, standard bacterial liquid storage parameters, and activity decay model, including the predicted target bacterial species distribution and the predicted viable bacteria concentration. Among them, the predicted target bacterial species distribution includes the viable bacteria concentration of each bacterial species at the planned transplantation time. In one embodiment, the activity decay model is based on the Arrhenius equation, and the Arrhenius equation is a classic model that describes the relationship between the chemical reaction rate and temperature. In this embodiment, the Arrhenius equation is expressed as: ; Among them, is the reaction rate constant, A is the pre-exponential factor (a constant related to the molecular collision frequency and orientation); is the decay constant of the i-th bacterial species obtained by fitting, , represents the activation energy, R is the ideal gas constant, and T is the environmental temperature.

[0038] The first colony parameter prediction result is expressed as: ; ; Among them, represents the predicted viable bacteria concentration of the bacterial liquid at time t; represents the initial viable bacteria concentration of the bacterial liquid; represents the viable bacteria concentration of the i-th bacterial species at time t; t represents the storage time; represents the initial viable bacteria concentration of the i-th bacterial species.

[0039] Furthermore, the decay constant is obtained through the following steps: S31. Set the experimental temperature parameter group and conduct the activity decay experiment according to the experimental temperature parameter group; In this embodiment, different experimental temperature parameter groups are set to simulate different storage conditions respectively to conduct the activity decay experiment. By monitoring the changes in the activity and species ratio of the strains at different temperatures, the experimental results are obtained.

[0040] S32. Fit the decay constants of each strain according to the experimental results of the activity decay experiment; The decay constants of each strain are expressed as: ; The experimental results of the activity decay experiment include several groups of and . Based on the aforementioned Arrhenius equation, in this embodiment, a linear fit is performed on the decay constant based on the experimental results. By fitting the decay constants of each strain through the experimental results, the activity decay model can more accurately predict the activity changes of the sample under different storage conditions.

[0041] S4. Judge whether the prediction result of the first colony parameter is qualified according to the target colony parameter; if not, determine the optimal storage temperature and the optimal addition amount of exogenous bacterial liquid according to the initial colony parameter, the target colony parameter and the planned transplantation time.

[0042] Further, please refer to Figure 3 , the determining the optimal storage temperature and the optimal addition amount of exogenous bacterial liquid according to the initial colony parameter, the target colony parameter and the planned transplantation time includes the steps of: S41. Input the initial colony parameter and the target colony parameter into the dynamic joint optimization model, and output the optimal storage temperature and the optimal addition amount of exogenous bacterial liquid; S42. Inject the exogenous bacterial liquid according to the optimal addition amount of exogenous bacterial liquid to prepare a bacterial liquid product; S43. Transfer the bacterial liquid product to a temperature-controlled storage box and configure the temperature-controlled storage box according to the optimal storage temperature.

[0043] In one embodiment, please refer to Figure 4 , the dynamic joint optimization model is based on the particle swarm optimization algorithm, and the optimal storage temperature and the optimal addition amount of exogenous bacterial liquid are specifically obtained through the following steps: S411. Generate several particles according to the initial colony parameter, initialize the particles and set the variable range constraints; The position of each particle is the vector X j = [T j , m 1,j , m 2,j ,..., m N,j . Among them, T j represents the storage temperature of the jth particle, m1,j to m N,j represents the addition amounts of the 1st to N key bacterial species of the j-th particle; N is the number of key bacterial species with abundances lower than the set threshold in the predicted target bacterial species distribution. Generally, according to the specific implementation situation, the value of N is 1 or 2.

[0044] Among them, the initialization of the particle and subsequent position updates both need to satisfy the set variable range constraints, including temperature range constraints, sample volume constraints, and addition amount constraints of each key bacterial species.

[0045] In one embodiment, to achieve only allocating external bacterial liquid or diluent parameters to the bacterial species that need to be adjusted to reduce the ineffective search dimension, and dynamically setting the initialization range according to the deviation amplitude to improve the effectiveness of the initial particle, the variable range constraints are obtained through the following steps: S4111. Calculate the viable bacteria concentration deviation and the bacterial species abundance deviation of each key bacterial species according to the first colony parameter prediction result and the target colony parameter; The viable bacteria concentration deviation is defined as the percentage difference between the predicted viable bacteria concentration and the target viable bacteria concentration , where the storage time can be set as the difference between the planned transplantation time and the production time.

[0046] The bacterial species abundance deviation of each bacterial species is defined as the absolute difference between the predicted relative abundance and the target relative abundance corresponding to each bacterial species.

[0047] S4112. Select the key bacterial species with negative bacterial species abundance deviation and calculate its corresponding reference addition amount, and set the addition amount constraints of each key bacterial species according to the reference addition amounts of each key bacterial species; The addition amount constraint is expressed as , where represents the reference addition amount of the i-th bacterial species, , .

[0048] S4113. Set the temperature range constraint according to the reference storage temperature of each key bacterial species; The temperature range constraint is expressed as , where is the minimum value of the reference storage temperature of each key bacterial species, is the maximum value of the reference storage temperature of each key bacterial species, , .

[0049] This embodiment significantly improves the rationality of particle initialization and the algorithm efficiency by combining bacterial species deviation classification and theoretical calculation, providing a reliable basis for the real-time optimization of fecal bacteria samples.

[0050] S412. Obtain the predicted target strain distribution and predicted viable cell concentration corresponding to each particle, and calculate the fitness value according to the target colony parameters, predicted target strain distribution, and predicted viable cell concentration; Among them, the predicted target strain distribution and predicted viable cell concentration are calculated in the same way as in step S3 of this embodiment, and will not be elaborated here.

[0051] The fitness value is expressed as: ; Among them, represents the viable cell concentration deviation, represents the strain abundance deviation of the i-th key strain, represents the strain weight (key strain = 2, ordinary strain = 1), represents the weight parameter; N is the number of key strains with abundances lower than the set threshold in the predicted target strain distribution. Based on the foregoing, the target colony parameters include the target strain distribution and target viable cell concentration. The viable cell concentration deviation is calculated from the target strain distribution and the predicted target strain distribution, and the target viable cell concentration is calculated from the target viable cell concentration and the predicted viable cell concentration.

[0052] S413. Update the individual optimal position and the population optimal position according to the fitness values of each particle; If the fitness value of the current particle is better than (less than) its historical individual optimal position, then update the individual optimal position to the current position; and record the position corresponding to the best fitness value among all particles as the population optimal position.

[0053] S414. Update the position and velocity of each particle according to the individual optimal position and the population optimal position; During the update process, the velocity of the particle is expressed as: , The position of the particle is expressed as: = + , Among them, is the inertia weight; and are the first learning factor and the second learning factor respectively. The first learning factor is used to control the amplitude of the particle moving towards the personal best position, and the second learning factor is used to control the amplitude of the particle moving towards the population best position; is the personal best position of the particle; g is the population best position of the group to which the particle belongs; and are both random numbers in the interval [0, 1].

[0054] S415. Iterate until the maximum number of iterations is reached or the convergence condition is satisfied, and output the optimal storage temperature and the optimal exogenous bacterial liquid according to the global optimal position.

[0055] Among them, iteration means repeating steps S412 - S415 until the maximum number of iterations is reached or the convergence condition is satisfied.

[0056] In this embodiment, through storage temperature optimization and strain compensation, it is ensured that the bacterial liquid reaches the target activity and flora structure at the planned transplantation time, realizing the collaborative optimization of temperature and exogenous bacterial liquid, and providing high-precision and automated decision support for fecal microbiota sample processing.

[0057] Furthermore, injecting the exogenous bacterial liquid according to the optimal exogenous bacterial liquid addition amount requires controlling the mixing and filtration of the tissue sample and the exogenous bacterial liquid through a microfluidic chip. This embodiment adopts an intelligent dilution and filtration technology to adjust the viable bacteria concentration of the fecal microbiota sample. It mainly faces the complexity and high impurity characteristics in the tissue sample. Through automated and precise dilution and filtration, the accuracy and efficiency of subsequent analysis can be improved.

[0058] Among them, the microfluidic chip includes a first cavity, a filtration component, and a second cavity; the method of controlling the mixing and filtration of the tissue sample and the diluent through the microfluidic chip to obtain the fecal microbiota sample includes the steps of: Setting the mixing ratio of the fecal microbiota sample and the exogenous bacterial liquid according to the optimal exogenous bacterial liquid addition amount; Injecting the tissue sample and the diluent into the first cavity according to the mixing ratio, and driving the mixed liquid of the fecal microbiota sample and the exogenous bacterial liquid in the first cavity to be transmitted to the second cavity through the filtration component to obtain the fecal microbiota sample.

[0059] Embodiment 2 An automatic fecal microbiota sample processing system includes: An initial colony analysis module for obtaining the initial colony parameters of the fecal microbiota sample; A bacterial liquid receptor matching module for matching the bacterial liquid receptor according to the initial colony parameters; An activity decay analysis module for performing activity decay analysis on the fecal microbiota sample according to the initial colony parameters, the standard bacterial liquid storage parameters, and the planned transplantation time to obtain the first colony parameter prediction result; An inspection and optimization module for determining the optimal storage temperature and the optimal exogenous bacterial liquid addition amount according to the initial colony parameters, the target colony parameters, and the planned transplantation time when the first colony parameter prediction result does not meet the preset conditions.

[0060] The present invention realizes an effective matching between a sample and a bacterial liquid receptor by acquiring and analyzing initial colony parameters, and performs an activity decay analysis based on the initial colony parameters, standard bacterial liquid storage parameters, and planned transplantation time to accurately predict changes in colony parameters; when the prediction result does not meet the preset conditions, the storage temperature and the amount of exogenous bacterial liquid added are adjusted according to the specific situation, thereby ensuring the colony activity and quality; the present invention combines prediction with an adjustment mechanism to ensure the stability of the sample during storage and processing, and reduce the adverse effects caused by activity decay.

[0061] In several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the modules can be in electrical, mechanical or other forms.

[0062] The modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules, that is, they can be located in one place, or they can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0063] In addition, in each embodiment of the present application, the various functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0064] When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, read-only memory), random access memories (RAM, random access memory), magnetic disks, or optical discs.

Claims

1. A method for automatically processing fecal bacteria samples, characterized in that: Includes steps: Obtain the initial colony parameters of the fecal bacteria sample, and match the bacterial solution receptor according to the initial colony parameters; Obtaining the planned transplantation time and target colony parameters of the bacterial solution recipient; Conduct activity decay analysis on fecal bacteria samples based on initial colony parameters, standard bacterial solution storage parameters, and planned transplantation time, and output the first colony parameter prediction results; Whether the first colony parameter prediction result is qualified is determined according to the target colony parameters; if unqualified, the optimal storage temperature and the optimal amount of exogenous bacterial solution added are determined according to the initial colony parameters, the target colony parameters and the planned transplantation time.

2. The method for automatically processing fecal bacteria samples according to claim 1, characterized in that: The method of matching the bacterial solution receptor according to the initial bacterial colony parameters comprises the following steps: Obtaining a recipient microbiome demand database; the recipient microbiome demand database includes a number of recipient information; the recipient information includes a planned transplant time, recipient identification, indications, target bacterial species distribution, contraindicated bacterial species, and target live bacterial concentration; the target bacterial species distribution includes a standard key bacterial species ratio and a standard common bacterial species ratio; Generate a sample bacterial species abundance vector according to the initial colony parameters; Initializing the required bacterial species abundance vector according to the sample bacterial species abundance vector, and mapping the standard key bacterial species ratio and the standard common bacterial species ratio to the required bacterial species abundance vector; The receptor matching degree is obtained according to the sample bacterial species abundance vector and the required bacterial species abundance vector, and the bacterial liquid receptor is determined according to the receptor matching degree and the taboo bacterial species.

3. The method for automatically processing fecal bacteria samples according to claim 2, characterized in that: The receptor matching degree is expressed as: ， ， ， in, Represents the receptor matching degree, Indicates the matching degree of standard key strains, Indicates the matching degree of standard common strains; is the balance coefficient; Represents the relative abundance of the i-th bacterial species in the sample bacterial species abundance vector, i=1,2,...,n, n represents the number of bacterial species; represents the relative abundance of the i-th bacterial species in the required bacterial species abundance vector; Q represents the key bacterial species set; To strengthen the weight.

4. The method for automatically processing fecal bacteria samples according to claim 1, characterized in that: The first colony parameter prediction result includes the predicted target bacterial species distribution and the predicted live bacterial concentration; The first colony parameter prediction result is expressed as: ， ， ， in, It represents the predicted live bacterial concentration of the bacterial solution at time t; Indicates the initial live bacterial concentration of the bacterial solution; represents the live bacterial concentration of the i-th bacterial species at time t; t represents the storage time; represents the initial viable bacterial concentration of the i-th bacterial species; is the reaction rate constant, A is the pre-exponential factor; is the decay constant of the i-th bacterial species obtained by fitting.

5. The method for automatically processing fecal bacteria samples according to claim 4, characterized in that: The attenuation constant is obtained by the following steps: Setting an experimental temperature parameter group, and performing an activity decay experiment according to the experimental temperature parameter group; The decay constants of each bacterial species were fitted according to the experimental results of the activity decay experiment.

6. The method for automatically processing fecal bacteria samples according to claim 1, characterized in that: The method of determining the optimal storage temperature and the optimal amount of exogenous bacterial solution to be added according to the initial bacterial colony parameters, the target bacterial colony parameters and the planned transplantation time comprises the following steps: The initial colony parameters and target colony parameters are input into the dynamic joint optimization model to output the optimal storage temperature and the optimal amount of exogenous bacterial solution added; Injecting exogenous bacterial liquid according to the optimal exogenous bacterial liquid addition amount to obtain a bacterial liquid product; Transfer the bacterial liquid product to a temperature-controlled storage box and configure the temperature-controlled storage box according to the optimal storage temperature.

7. The method for automatically processing fecal bacteria samples according to claim 6, characterized in that: The optimal storage temperature and the optimal amount of exogenous bacterial liquid added are obtained by the following steps: Generate a number of particles according to the initial colony parameters, initialize the particles and set variable range constraints; Obtain the predicted target bacterial species distribution and predicted live bacterial concentration corresponding to each particle, and calculate the fitness value according to the target colony parameters, the predicted target bacterial species distribution and the predicted live bacterial concentration; Update the individual optimal position and the group optimal position according to the fitness value of each particle; Update the position and velocity of each particle according to the individual optimal position and the group optimal position; Iterate until the maximum number of iterations is reached or the convergence condition is met, and output the optimal storage temperature and the optimal exogenous bacterial solution according to the optimal position of the group.

8. The method for automatically processing fecal bacteria samples according to claim 7, characterized in that: The variable range constraints are obtained by following the steps below: Calculate the live bacteria concentration deviation and the abundance deviation of each key bacterial species according to the first colony parameter prediction result and the target colony parameter; Select key bacterial species with negative bacterial species abundance deviation to calculate their corresponding reference addition amounts, and set addition amount constraints for each key bacterial species according to the reference addition amount of each key bacterial species; Set temperature range constraints based on the reference storage temperature of each key bacterial species.

9. The method for automatically processing fecal bacteria samples according to claim 8, characterized in that: The injecting of the exogenous bacterial liquid according to the optimal exogenous bacterial liquid addition amount is specifically to control the mixing and filtering of the tissue sample and the exogenous bacterial liquid through a microfluidic chip.

10. The method for automatically processing fecal bacteria samples according to claim 7, characterized in that: The fitness value is expressed as: ； in, Indicates the concentration deviation of live bacteria. represents the abundance deviation of the i-th key bacterial species, represents the weight of bacterial species, represents the weight parameter; N is the number of key bacterial species whose abundance in the predicted target bacterial species distribution is lower than the set threshold.

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