Probiotic preservation activity detection method and system

Abstract

The invention discloses a probiotic preservation activity detection method and system, and relates to the technical field of probiotic activity detection. According to the method, three-positive cell populations are screened by constructing three-dimensional feature vectors, and a cell bank with potential activity is delineated to eliminate interference of dead cells and impurities; the suspected dormant state probiotic population is accurately marked through a three-dimensional scoring system, and misjudgment of the suspected dormant state probiotic population in the initial stage is avoided; the resuscitation function activity rate is calculated through resuscitation culture in combination with a three-dimensional verification model, and dormant viable bacteria and dead bacteria can be clearly distinguished; finally, based on a clinical probiotic value weighting algorithm, data of initial complete functional viable bacteria and resuscitated complete functional viable bacteria are fused to calculate an actual activity level value, so that accurate detection of the resuscitation capacity of the probiotics in the dormant state in the simulated physiological environment is realized, and underestimation of the overall activity level caused by misjudgment of dead cells in the dormant state is avoided. Meanwhile, the relevance between the detection result and the clinical probiotic value is guaranteed, and the detection accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of probiotic activity detection technology, specifically to a method and system for detecting the activity of preserved probiotics. Background Technology

[0002] In real-world testing scenarios, probiotics face complex conditions such as weak dormant state signal characteristics, fluctuating simulated physiological environment recovery conditions, and significant differences in dormancy and recovery characteristics among different strains. Existing technologies reveal the following shortcomings: First, existing technologies mostly use a single signal or a simple combination of two-dimensional signals for cell screening, which can easily misidentify dormant probiotics as dead cells. It may also lead to distortion of the target in subsequent resuscitation tests due to the failure to exclude interference from other bacteria and dead cell fragments, thus creating a hidden danger of underestimating the activity level.

[0003] However, existing methods cannot truly stimulate the revival potential of dormant probiotics, and are prone to misjudging revivalable dormant live bacteria as unrevivalable dead cells. Assessing post-revival activity solely through cell counting directly leads to an overall activity level that is lower than the actual value. Furthermore, the lack of connection with clinical probiotic value results in poor practicality of the test results.

[0004] Therefore, there is an urgent need for a probiotic preservation activity detection technology that can construct a three-dimensional precise screening, simulate physiological environment for targeted resuscitation, and establish a correlation verification between resuscitation ability and functional activity, in order to solve the above-mentioned technical bottlenecks and improve the accuracy and practical value of probiotic preservation sample activity detection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for detecting the preservation activity of probiotics, solving the problem of how to detect the revival ability of suspected dormant probiotics in a simulated physiological environment, avoiding misjudging them as dead cells, and preventing the overall activity level detected from being lower than the actual activity level due to misjudgment.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting the preservation activity of probiotics, comprising the following steps: Fluorescence signals for cell membrane integrity, short-chain fatty acid metabolism, and cell scattering were collected from the initial probiotic suspension. A three-dimensional signal feature vector was constructed to determine the population of triple-positive cells and their number.

[0007] Based on a pre-defined probiotic activity discrimination model, the standardized signal set of the triple-positive cell population is processed to calculate metabolic activity score, membrane integrity score, and strain-specific matching score, and to label suspected dormant probiotic populations.

[0008] The suspected dormant probiotic population was placed in a probiotic resuscitation medium. The resuscitation function activity rate was calculated based on a three-dimensional verification model. If the resuscitation function activity rate was greater than the preset judgment threshold, it was marked as a dormant live bacterial population; otherwise, it was marked as a dead bacterial population.

[0009] The actual activity level was calculated by combining the number of live bacteria in the dormant probiotic population with the number of triple-positive cells in the initial probiotic suspension and using a weighted algorithm based on clinical probiotic value.

[0010] A probiotic preservation activity detection system, comprising: The positive cell identification module is used to collect fluorescence signals of cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light signals in the initial probiotic suspension, construct a three-dimensional signal feature vector, and determine the population of triple-positive cells and their number.

[0011] The suspected population identification module is used to import the three-dimensional signal feature vector into the preset probiotic activity discrimination model, calculate the metabolic activity score, membrane integrity score and strain specificity matching score, and mark suspected dormant probiotic populations.

[0012] The live bacteria population identification module is used to place suspected dormant probiotic populations in probiotic resuscitation culture medium, calculate the resuscitation functional activity rate based on a three-dimensional verification model, and mark them as live bacteria populations if the resuscitation functional activity rate is greater than a preset judgment threshold; otherwise, they are marked as dead cell populations.

[0013] The activity level assessment module combines the number of live bacteria in the dormant live bacteria population with the number of triple-positive cells in the initial probiotic suspension, and uses a weighted algorithm based on clinical probiotic value to calculate the actual activity level value.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention constructs a three-dimensional feature vector to screen for triple-positive cell populations, delineating a cell bank with potential activity to exclude dead cells and impurities. Then, a three-dimensional scoring system is used to accurately label suspected dormant probiotic populations, avoiding initial misclassification. Subsequent resuscitation culture combined with a three-dimensional validation model calculates the resuscitation functional activity rate, clearly distinguishing between dormant live bacteria and dead bacteria, ensuring that dormant live bacteria are not misclassified as dead cells. Finally, a clinical probiotic value weighted algorithm is used to fuse initial and resuscitation complete functional live bacteria data to calculate the actual activity level. This achieves accurate detection of the resuscitation ability of dormant probiotics under simulated physiological conditions, avoids underestimation of overall activity levels due to misclassification of dormant bacteria as dead cells, and ensures the correlation between test results and clinical probiotic value, thus improving the accuracy of the detection. Attached Figure Description

[0015] Figure 1This is a flowchart of the probiotic preservation activity detection method of the present invention.

[0016] Figure 2 This is a flowchart illustrating the triple-positive cell population and its quantity in the probiotic preservation activity detection method of the present invention.

[0017] Figure 3 This is a flowchart for calculating the resuscitation function activity rate in the probiotic preservation activity detection method of the present invention.

[0018] Figure 4 This is a schematic diagram of the module connections of the probiotic preservation activity detection system of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Please refer to the accompanying drawings. Figure 1 This invention provides a technical solution: a method for detecting the preservation activity of probiotics, comprising the following steps: S1. Collect fluorescence signals of cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light signals in the initial probiotic suspension, construct a three-dimensional signal feature vector, and determine the population of triple-positive cells and their number.

[0020] Specifically, such as Figure 2 As shown, the process of constructing a three-dimensional signal feature vector is as follows: Take the preserved probiotic sample to be tested, add sterile buffer solution, and gently shake under a set temperature to disperse it and obtain the initial probiotic suspension, such as by gently shaking at 28℃±2℃.

[0021] Fluorescent probes were added to the initial probiotic suspension to obtain a fluorescently labeled suspension. The fluorescent probes included cell membrane integrity probes (CFDA-SE) and probiotic species-specific short-chain fatty acid fluorescent probes. Specifically, the probiotic species-specific short-chain fatty acid fluorescent probes could be lactate-specific fluorescent probes or acetic acid-specific fluorescent probes, matching the metabolic characteristics of the target probiotics.

[0022] The fluorescently labeled suspension was incubated at a set constant temperature. After incubation, it was introduced into a flow cytometer to collect fluorescence signals of cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light signals for each cell in the fluorescently labeled suspension.

[0023] It should be noted that the cell membranes of dormant and revived probiotics remain intact, while dead cells, due to cell membrane damage, cannot prevent the leakage of hydrolysis products from the CFDA-SE probe, resulting in weak or no fluorescence signals. Furthermore, although dormant probiotics have lower metabolic activity than standard live bacteria, they still retain their basic metabolic enzyme systems, which can bind to specific fluorescent probes to produce weak fluorescence signals. Additionally, because dormant and revived probiotics have intact morphology and dense structure, their cell-scattered light signals are stronger. Therefore, by using fluorescence signals based on cell membrane integrity, short-chain fatty acid metabolism, and cell-scattered light signals, dormant probiotics, dead cells, and impurities can be comprehensively distinguished from other probiotics in terms of structure, metabolism, and morphology, ensuring that the triple-positive cell population represents the target probiotics with revival potential.

[0024] The fluorescence signals of cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light for each cell were quantified to obtain three quantified values ​​for each cell.

[0025] The specific process of signal strength quantization is as follows: After the CFDA-SE probe enters the cell, it is hydrolyzed into luciferin by intracellular esterase. Flow cytometry collects the photon count of this luciferin in the FITC channel. The digital value in the [0,1] interval obtained after Z-score normalization is used as the quantitative value of the fluorescence signal intensity of cell membrane integrity. The larger the value, the better the cell membrane integrity.

[0026] After the species-specific short-chain fatty acid fluorescent probe specifically binds to the short-chain fatty acids produced by the target probiotics, the flow cytometer collects the photon count of the binding product in the PE channel. The digital value in the [0,1] interval obtained after Z-score normalization is used as the quantitative value of the short-chain fatty acid metabolism fluorescence signal intensity. The larger the value, the stronger the short-chain fatty acid metabolism activity.

[0027] After a flow cytometer irradiates a cell with a laser, the forward scattered light reflects the cell size, while the side scattered light reflects the cell particle size. The digital values ​​in the [0,1] interval obtained after weighted fusion and Z-score normalization are used as the quantification values ​​of the cell scattered light signal intensity. The larger the value, the better the cell morphological integrity and structural compactness.

[0028] Using a single cell as the unit, the quantified values ​​of fluorescence signal intensity for cell membrane integrity, fluorescence signal intensity for short-chain fatty acid metabolism, and quantified values ​​of cell scattered light signal intensity are used as three-dimensional feature parameters to construct a three-dimensional signal feature vector for each cell.

[0029] The process of determining the population and number of triple-positive cells is as follows: The feature parameters in the three-dimensional signal feature vector of each cell are compared with the corresponding positive threshold.

[0030] Cells with fluorescence signal intensity values ​​of cell membrane integrity not less than the positive threshold of cell membrane integrity fluorescence signal, fluorescence signal intensity values ​​of short-chain fatty acid metabolism not less than the positive threshold of short-chain fatty acid metabolism fluorescence signal, and fluorescence signal intensity values ​​of cell scattered light not less than the positive threshold of cell scattered light signal were selected to form a triple-positive cell population.

[0031] The positive thresholds for each signal were determined based on the mean signal intensity of the blank control (sterile buffer and fluorescent probe) plus three times the standard deviation. For example, the positive threshold for cell membrane integrity fluorescence signal was 0.3, the positive threshold for short-chain fatty acid metabolism fluorescence signal was 0.25, and the positive threshold for cell scattering light signal was 0.4.

[0032] The number of cells in the triple-positive cell population is counted to obtain the number of triple-positive cells in the initial probiotic suspension.

[0033] By comparing the feature parameters in the three-dimensional signal feature vector with the corresponding positive thresholds, probiotics with intact cell membranes, metabolic capabilities, and morphological integrity can be identified. However, this includes not only revived live probiotics but also those that have not been successfully revived and remain dormant, or those that are nearing death. It is easy to understand that directly classifying dormant or near-death probiotics as live probiotics for activity testing will inevitably lead to biased results; therefore, further screening is necessary.

[0034] S2. Based on the preset probiotic activity discrimination model, the standardized signal set of the triple-positive cell population is processed to calculate the metabolic activity score, membrane integrity score and strain specificity matching score, and to label the suspected dormant probiotic population.

[0035] The process for calculating the metabolic activity score, membrane integrity score, and strain-specific matching score is as follows: Baseline correction, noise filtering, and signal standardization are performed on cell membrane integrity fluorescence signals, short-chain fatty acid metabolism fluorescence signals, cell scattering light signals, and strain-specific gene probe signals in the signal data to remove environmental interference signals, instrument background signals, and non-specific binding signals, and output a standardized signal set.

[0036] The signal standardization uses the Z-score normalization algorithm to convert each signal value to the [0,1] interval, ensuring that signals of different dimensions can be compared.

[0037] The integrity, metabolic, and specific characteristic parameters corresponding to the standardized cell membrane integrity fluorescence signal, short-chain fatty acid metabolism fluorescence signal, cell scattering light signal, and strain-specific gene probe signal were extracted from the standardized signal set.

[0038] For example, integrity characteristic parameters include the mean signal strength. and signal uniformity Metabolic characteristic parameters include signal peak values. and signal duration Specific characteristic parameters include probe binding efficiency. and signal-specific peaks The probe binding efficiency is the ratio of the specific binding signal intensity to the total signal intensity.

[0039] It should be noted that dormant probiotics are characterized by intact cell membranes. However, compared to standard live bacteria, the uniformity of their cell membrane integrity signal varies due to changes in membrane density during dormancy. Therefore, membrane integrity scores can be used to further distinguish between dormant and live bacteria. Additionally, since short-chain fatty acid metabolism fluorescence signals can only preliminarily confirm the presence of metabolic signals but cannot quantify the intensity of metabolic activity, the metabolic activity level of a single cell can be precisely quantified by signal peak value and duration. This allows for the identification of cells with metabolic activity lower than standard live bacteria but higher than dead cells, serving as a basis for distinguishing between dormant, live, and dead cells. Finally, probe binding efficiency and specificity peak values ​​quantify the matching degree between a single cell and the target strain, further eliminating interference from triple-positive cell populations and ensuring that all suspected dormant populations are the target strains.

[0040] The average peak value of short-chain fatty acid metabolism fluorescence signals in a triple-positive cell population was simultaneously retrieved. The average intensity of fluorescence signal indicating cell membrane integrity .

[0041] For metabolic characteristic parameters, the average peak value of the metabolic signal The corresponding metabolic standard signal features were normalized to obtain a metabolic activity score. The specific calculation formula is as follows: ,in, The standard peak value is a characteristic of metabolic standard signals. The standard duration in the metabolic standard signal characteristics, where All values ​​are taken from the range [0, 1]. Values ​​outside the range are counted as 1 or 0 respectively. The higher the metabolic activity score, the stronger the metabolic activity.

[0042] For integrity characteristic parameters, the average fluorescence intensity of integrity The membrane integrity score is obtained by performing deviation correction processing on the corresponding integrity standard signal characteristics. The specific calculation formula is as follows: ,in, The standard intensity mean in the integrity standard signal characteristics, For the standard uniformity in the integrity standard signal characteristics, similarly, and All values ​​are taken from the range [0, 1]. Values ​​outside the range are counted as 1 or 0 respectively. The higher the membrane integrity score, the better the cell membrane integrity.

[0043] The specific characteristic parameters and their corresponding specific standard signal features are matched and quantified to obtain the strain specificity matching score. The specific calculation formula is as follows: ,in, The standard binding rate is a characteristic of specific standard signals. Similarly, the standard specific peak value in the specific standard signal characteristics, All values ​​are taken from the range [0, 1]. Values ​​outside the range are counted as 1 or 0 respectively. The higher the strain specificity matching score, the stronger the strain matching.

[0044] Furthermore, considering that the cell membrane integrity fluorescence signal, short-chain fatty acid metabolism fluorescence signal, cell scattering light signal, and strain-specific gene probe signal of dormant probiotics differ from both standard live bacteria and standard dead bacteria, the degree of matching of this overall temporal trend can be quantified to identify cell populations matching standard dormant bacteria. Additionally, measuring the degree of fit between the three-dimensional score of a single cell and the standard dormant bacteria score benchmark range ensures that the screened cells meet the definition of dormancy in terms of metabolic potential, structural integrity, and strain-specific functional indicators. Therefore, the process of labeling suspected dormant probiotic populations is as follows: The cell membrane integrity fluorescence signal, short-chain fatty acid metabolism fluorescence signal, cell scattering light signal and strain-specific gene probe signal of each cell in the standardized signal set were aligned in time sequence, and a single-cell four-dimensional feature map containing four signal layers was constructed with time as the horizontal axis and standardized signal intensity as the vertical axis.

[0045] The time series of the single-cell four-dimensional feature map and the standard dormant bacteria feature map are length aligned to generate a time point distance matrix. The values ​​of the elements in the time point distance matrix are calculated using Euclidean distance. That is, the normalized signal intensity at a certain time point in the single-cell four-dimensional feature map is compared with the normalized signal intensity at the corresponding time point in the standard dormant bacteria feature map to calculate the Euclidean distance, thus obtaining the value of the element at the corresponding time point in the time point distance matrix.

[0046] The optimal matching path is searched using dynamic programming, with the path constraint that the matching deviation between adjacent time points is no greater than N time units, as detailed below: First, initialize the dynamic programming cumulative distance matrix. This cumulative distance matrix is ​​also a square matrix with the same dimensions as the time point distance matrix. The value of each position in the matrix represents the cumulative distance of the optimal path from the starting time point of the two time series to the current position. During initialization, the cumulative distance value of the starting position is equal to the element value of the corresponding starting position in the time point distance matrix. For other positions in the first row of the matrix, their cumulative distance value is equal to the cumulative distance value of the previous position plus the element value of the current position in the time point distance matrix. For other positions in the first column of the matrix, their cumulative distance value is equal to the cumulative distance value of the previous position plus the element value of the current position in the time point distance matrix.

[0047] Next, the cumulative distance matrix is ​​recursively calculated. For all positions in the matrix except the first row and the first column, the cumulative distance value is equal to the element value of the current position in the time point distance matrix, plus the minimum value of the cumulative distance value of the previous possible position. The previous possible position includes the position directly above the current position, the position directly to the left of the current position, and the position to the upper left of the current position. At the same time, a path constraint condition is applied, that is, the difference between the index of two adjacent matching time points does not exceed the preset number of time units (the preset time unit N is an integer, and three time units are preferred). Only when the difference between the index of two time points is not greater than the preset number, the position is included in the path search range.

[0048] If the difference in sequence number exceeds a preset number, the position is determined to be an invalid matching point, and its value in the cumulative distance matrix is ​​set to the maximum value, thereby excluding the position from participating in the selection of the optimal path, ensuring that the matching path follows the trend of the time axis, and avoiding mismatches at time points with too large a span.

[0049] Finally, the optimal matching path is determined by starting from the end position of the cumulative distance matrix and backtracking to the beginning position of the matrix. At each step, the previous possible position with the smallest cumulative distance value is selected. All the positions passed during the backtracking process are arranged in order, and the sequence formed is the optimal matching path.

[0050] The cumulative distance of the optimal path is obtained by summing the element values ​​of the time point distance matrix corresponding to all positions on the optimal matching path. Then, the maximum possible cumulative distance is calculated by iterating through all element values ​​in the time point distance matrix, selecting the maximum value of the column of element values ​​corresponding to each time point, and summing the maximum values ​​corresponding to all time points. This summation result is the maximum possible cumulative distance, which represents the theoretical maximum difference between the two time series graphs. The similarity is calculated by subtracting the cumulative distance from 1 and the ratio of the maximum possible cumulative distance.

[0051] The difference between 1 and the similarity score is used as the dissimilarity score.

[0052] The metabolic activity score, membrane integrity score, and strain-specific matching score of each cell were matched and validated against the benchmark range of the corresponding standard strain to obtain the deviation. It should be noted that this deviation refers to the mean deviation obtained from the matching validation of each cell in the population.

[0053] The deviation is calculated as: |Current score - Median of the baseline range| / Half width of the baseline range.

[0054] If the difference is no greater than a set difference threshold and the deviation is no greater than a set deviation threshold, the corresponding cells are labeled as suspected dormant probiotics. A suspected dormant probiotic population is then obtained based on the population to which the suspected dormant probiotics belong. For example, the difference threshold is set to 0.2, and the deviation threshold is set to 0.15.

[0055] It should be noted that both the degree of difference and the degree of deviation are calculated using standardized algorithms to ensure that the judgment criteria are objective and reproducible, and to avoid screening bias caused by subjective judgment.

[0056] S3. Place the suspected dormant probiotic population in a probiotic resuscitation medium. Calculate the resuscitation functional activity rate based on a three-dimensional validation model. If the resuscitation functional activity rate is greater than a preset threshold, it is marked as a dormant live bacterial population; otherwise, it is marked as a dead bacterial population. For example, the preset threshold is 0.3.

[0057] It should be noted that, as Figure 3 As shown, the process for calculating the resuscitation function activity rate is as follows: The metabolic activity score, membrane integrity score, and strain specificity matching score of each revived probiotic population in the probiotic resuscitation medium were calculated and compared with the standard viable bacterial characteristic map to determine the number of revived metabolically active cells in each revived probiotic population.

[0058] It should be noted that the calculation and comparison process in this part is the same as the calculation and comparison process for identifying suspected dormant probiotic populations mentioned above, and will not be repeated here.

[0059] For any revived probiotic population, a portion of the revived probiotics were combined with an intestinal epithelial model, and the adhesion rate of the probiotics to the intestinal epithelial model was observed and calculated using a laser confocal microscope.

[0060] The diameter of the inhibition zone of the remaining revived probiotics against intestinal pathogens was determined by the agar diffusion method.

[0061] The effective thresholds for population activity recovery, colonization, and probiotic function of each target probiotic standard strain were retrieved.

[0062] For a given revived probiotic population, if the proportion of its revived metabolically active cells in the revived probiotic population is not less than the effective threshold for population activity recovery of the corresponding target probiotic standard strain, then the revived probiotic population is labeled as a basic active subpopulation.

[0063] If the adhesion rate of the basic active subpopulation is not less than the corresponding effective colonization threshold and the diameter of its inhibition zone is not less than the corresponding effective probiotic function threshold, then the proportion of the number of revived metabolic active cells in the revived probiotic population is taken as the revived function activity rate of the revived probiotic population.

[0064] The resuscitation activity rate directly reflects the proportion of suspected dormant probiotics that successfully transform into cells with complete probiotic functions after resuscitation. This indicator can accurately distinguish between resuscitable live bacteria and unresuscitable dead cells. If the resuscitation activity rate is greater than the set judgment threshold, it indicates that there are resuscitable live bacteria in the suspected dormant population, and they should not be judged as dead cells.

[0065] S4. Combine the number of live bacteria in the dormant live bacteria population with the number of triple-positive cells in the initial probiotic suspension, and use a weighted algorithm based on clinical probiotic value to calculate the actual activity level value.

[0066] It should be noted that the specific process is as follows: Calculate the ratio of the number of live bacteria in the dormant probiotic population to the number of triple-positive cells. The number of live bacteria is the sum of the number of metabolically active cells in the entire revived probiotic population.

[0067] Calculate the ratio of the number of viable bacteria in the triple-positive cell population to the total number of triple-positive cells. It should be noted that the number of viable bacteria in the triple-positive cell population does not include live bacteria revived from dormant probiotics, but rather live bacteria from the initial probiotic suspension.

[0068] The actual activity level is obtained by weighting and summing the two ratios based on clinical prebiotic value. It should be noted that the weights of the two ratios are determined based on clinical prebiotic value, specifically by the normalized values ​​of the inhibition zone diameters of historically dormant live bacteria and live bacteria in triple-positive cell populations against intestinal pathogens.

[0069] By clearly distinguishing between the initial inherent live bacteria and the dormant revived live bacteria as the two sources of activity, and calculating the ratios of each separately and then weighting them, we can ensure that cells with initial activity are not missed, and that dormant live bacteria that can be revived are not misclassified as dead cells. This ensures that effective activity is not missed from the source, and makes the actual activity level value closer to the true state of the sample.

[0070] A probiotic preservation activity detection system, such as Figure 4 As shown, it includes: The positive cell identification module is used to collect fluorescence signals of cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light signals in the initial probiotic suspension, construct a three-dimensional signal feature vector, and determine the population of triple-positive cells and their number.

[0071] The suspected population identification module is used to import the three-dimensional signal feature vector into the preset probiotic activity discrimination model, calculate the metabolic activity score, membrane integrity score and strain specificity matching score, and mark suspected dormant probiotic populations.

[0072] The live bacteria population identification module is used to place suspected dormant probiotic populations in probiotic resuscitation culture medium, calculate the resuscitation functional activity rate based on a three-dimensional verification model, and mark them as live bacteria populations if the resuscitation functional activity rate is greater than a preset judgment threshold; otherwise, they are marked as dead cell populations.

[0073] The activity level assessment module combines the number of live bacteria in the dormant live bacteria population with the number of triple-positive cells in the initial probiotic suspension, and uses a weighted algorithm based on clinical probiotic value to calculate the actual activity level value.

[0074] The parameters involved in the above formula are all dimensionless and calculated numerically. The formula is a formula obtained from the most recent real situation by collecting a large amount of data and simulating it with software. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0075] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0076] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0077] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0078] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0079] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting the preservation activity of probiotics, characterized in that, Includes the following steps: Fluorescence signals for cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light were collected from the initial probiotic suspension. A three-dimensional signal feature vector was constructed to determine the population of triple-positive cells and their number. Based on the pre-defined probiotic activity discrimination model, the standardized signal set of the triple-positive cell population was processed to calculate the metabolic activity score, membrane integrity score and strain specificity matching score, and to label suspected dormant probiotic populations. The suspected dormant probiotic population was placed in a probiotic resuscitation medium. The resuscitation functional activity rate was calculated based on a three-dimensional verification model. If the resuscitation functional activity rate was greater than the preset judgment threshold, it was marked as a dormant live bacterial population; otherwise, it was marked as a dead bacterial population. The actual activity level was calculated by combining the number of live bacteria in the dormant probiotic population with the number of triple-positive cells in the initial probiotic suspension and using a weighted algorithm based on clinical probiotic value.

2. The method for detecting the preservation activity of probiotics according to claim 1, characterized in that, The process of acquiring fluorescence signals for cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light from the initial probiotic suspension, and constructing a three-dimensional signal feature vector, is as follows: A fluorescent probe was added to the initial probiotic suspension to obtain a fluorescently labeled suspension; The fluorescently labeled suspension was introduced into a flow cytometer, and the fluorescence signals of cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light were collected for each cell in the fluorescently labeled suspension. The fluorescence signals of cell membrane integrity, short-chain fatty acid metabolism, and cell scattered light signals of each cell were quantified to obtain three quantified values ​​for each cell. Using a single cell as the unit, the quantified values ​​of the fluorescence signal intensity of cell membrane integrity, the fluorescence signal intensity of short-chain fatty acid metabolism, and the quantified values ​​of cell scattered light signal intensity are used as three-dimensional feature parameters to construct a three-dimensional signal feature vector for each cell.

3. The method for detecting the preservation activity of probiotics according to claim 2, characterized in that, The process of determining the population and number of triple-positive cells is as follows: The feature parameters in the three-dimensional signal feature vector of each cell are compared with the corresponding positive threshold. Cells with a fluorescence signal intensity quantification value of cell membrane integrity not less than the positive threshold of cell membrane integrity fluorescence signal, a fluorescence signal intensity quantification value of short chain fatty acid metabolism not less than the positive threshold of short chain fatty acid metabolism fluorescence signal, and a cell scattered light signal intensity quantification value not less than the positive threshold of cell scattered light signal were selected to form a triple-positive cell population. The number of cells in the triple-positive cell population is counted to obtain the number of triple-positive cells in the initial probiotic suspension.

4. The method for detecting the preservation activity of probiotics according to claim 1, characterized in that, The process of processing the standardized signal set of the triple-positive cell population based on the preset probiotic activity discrimination model to calculate the metabolic activity score, membrane integrity score, and strain specificity matching score is as follows: The integrity, metabolic and specific characteristic parameters corresponding to the standardized cell membrane integrity fluorescence signal, short-chain fatty acid metabolism fluorescence signal, cell scattered light signal and strain-specific gene probe signal were extracted from the standardized signal set. The average peak value of short-chain fatty acid metabolism fluorescence signal, the average intensity of cell membrane integrity fluorescence signal, and the population-specific signal distribution characteristics corresponding to the number of triple-positive cells were simultaneously retrieved from the triple-positive cell population. Metabolic activity scores are obtained by normalizing the metabolic characteristic parameters, the average peak value of metabolic signals and the corresponding metabolic standard signal features. The integrity characteristic parameters, the average intensity of integrity fluorescence, and the corresponding integrity standard signal characteristics are subjected to deviation correction processing to obtain the membrane integrity score; The specific characteristic parameters and their corresponding specific standard signal features are matched and quantified to obtain the strain specificity matching score.

5. The method for detecting the preservation activity of probiotics according to claim 1, characterized in that, The process of labeling suspected dormant probiotic populations is as follows: The cell membrane integrity fluorescence signal, short-chain fatty acid metabolism fluorescence signal, cell scattered light signal and strain-specific gene probe signal of each cell in the standardized signal set were aligned according to time sequence, and a single-cell four-dimensional feature map was constructed with time as the horizontal axis and standardized signal intensity as the vertical axis. The similarity between the four-dimensional feature map of the single cell and the feature map of the standard dormant bacteria was calculated using the dynamic time warping algorithm. The difference between 1 and the similarity score is used as the dissimilarity score; The metabolic activity score, membrane integrity score, and strain-specific matching score of each cell were matched and verified with the corresponding standard strain's scoring benchmark range to obtain the deviation. If the difference is not greater than the set difference threshold and the deviation is not greater than the set deviation threshold, the corresponding cell is marked as a suspected dormant probiotic. The suspected dormant probiotic population is obtained based on the population in which the suspected dormant probiotic is located.

6. The method for detecting the preservation activity of probiotics according to claim 5, characterized in that, The process of calculating the similarity between the single-cell four-dimensional feature map and the standard dormant bacterial feature map using the dynamic time warping algorithm is as follows: The time series of single-cell four-dimensional feature maps and standard dormant bacterial feature maps are length-aligned to generate a time point distance matrix. The optimal matching path is searched by dynamic programming, with the path constraint that the matching deviation between adjacent time points is no greater than N time units. Calculate the cumulative distance of the optimal matching path, and use the ratio of 1 minus the cumulative distance to the maximum possible cumulative distance as the similarity.

7. The method for detecting the preservation activity of probiotics according to claim 1, characterized in that, The process of placing a suspected dormant probiotic population in a probiotic resuscitation medium and calculating the resuscitation functional activity rate based on a three-dimensional validation model is as follows: The metabolic activity score, membrane integrity score, and strain specificity matching score of each revived probiotic population in the probiotic resuscitation medium were calculated and compared with the standard viable bacteria characteristic map to determine the number of revived metabolically active cells in each revived probiotic population. For any revived probiotic population, a portion of the revived probiotics were combined with an intestinal epithelial model, and the adhesion rate of the probiotics to the intestinal epithelial model was observed and calculated using a laser confocal microscope. The diameter of the inhibition zone of the remaining revived probiotics against intestinal pathogens was determined by the agar diffusion method. After standardizing the number of revived metabolically active cells, adhesion rate, and inhibition zone diameter, the data were imported into a three-dimensional validation model to calculate the revived functional activity rate of each revived probiotic population.

8. The method for detecting the preservation activity of probiotics according to claim 7, characterized in that, The process of standardizing the number of revived metabolically active cells, adhesion rate, and inhibition zone diameter, and then importing them into the three-dimensional validation model to calculate the revived functional activity rate of each revived probiotic population is as follows: The effective thresholds for population activity recovery, colonization, and probiotic function of each target probiotic standard strain were retrieved. For a certain revived probiotic population, if the proportion of its revived metabolically active cells in the revived probiotic population is not less than the effective threshold for population activity recovery of the corresponding target probiotic standard strain, then the revived probiotic population is marked as a basic active subpopulation. If the adhesion rate of the basic active subpopulation is not less than the corresponding effective colonization threshold and the diameter of its inhibition zone is not less than the corresponding effective probiotic function threshold, then the proportion of the number of revived metabolic active cells in the revived probiotic population is taken as the revived function activity rate of the revived probiotic population.

9. The method for detecting the preservation activity of probiotics according to claim 1, characterized in that, The process of combining the number of live bacteria in the dormant probiotic population with the number of triple-positive cells in the initial probiotic suspension, and then using a weighted algorithm based on clinical probiotic value to calculate the actual activity level is as follows: Calculate the ratio of the number of viable bacteria in the dormant bacterial population to the number of triple-positive cells; Calculate the ratio of the number of viable bacteria in the triple-positive cell population to the total number of triple-positive cells; The actual activity level value is obtained by weighting and summing the two ratios based on the clinical probiotic value.

10. A probiotic preservation activity detection system, characterized in that, include: The positive cell identification module is used to collect fluorescence signals of cell membrane integrity, short-chain fatty acid metabolism, and cell scattering light signals in the initial probiotic suspension, construct a three-dimensional signal feature vector, and determine the population of triple-positive cells and their number. The suspected population identification module is used to import the three-dimensional signal feature vector into the preset probiotic activity discrimination model, calculate the metabolic activity score, membrane integrity score and strain specificity matching score, and mark the suspected dormant probiotic population. The live bacteria population identification module is used to place suspected dormant probiotic populations in probiotic resuscitation culture medium, calculate the resuscitation functional activity rate based on a three-dimensional verification model, and mark them as live bacteria populations if the resuscitation functional activity rate is greater than a preset judgment threshold; otherwise, they are marked as dead cell populations. The activity level assessment module combines the number of live bacteria in the dormant live bacteria population with the number of triple-positive cells in the initial probiotic suspension, and uses a weighted algorithm based on clinical probiotic value to calculate the actual activity level value.

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