Radix astragali polysaccharide / oligosaccharide intestinal micro-ecological potency grading evaluation method and system

Abstract

The present application discloses a method and system for grading evaluation of Astragalus polysaccharide / oligosaccharide intestinal microecological potency, belonging to the technical field of functional evaluation and quality control of homology of medicine and food raw materials. The present application aims to solve the problem that the existing physical and chemical indicators cannot truly reflect the comprehensive biological potency of Astragalus polysaccharide / oligosaccharide after being subjected to gastrointestinal digestion and intestinal flora action, and lack systematic grading standards. The technical solution is characterized in that: the sample after simulated gastrointestinal digestion is placed in an in vitro intestinal flora anaerobic fermentation system to obtain fermentation supernatant; the supernatant is used on intestinal epithelial barrier damage, inflammatory damage and oxidative stress models to detect flora metabolism, intestinal barrier protection, anti-inflammatory, antioxidant and safety indicators; a biological potency fingerprint is constructed based on the above indicators, the microecological potency index is calculated, and the potency is graded in combination with the veto conditions. The main use of the present application is for high potency screening, quality grading and batch quality control of Astragalus polysaccharide / oligosaccharide raw materials, extracts or functional food raw materials.

Description

Technical Field

[0001] This invention relates to the field of quality evaluation technology for medicinal and edible raw materials and functional foods. More specifically, this invention relates to a method and system for grading and evaluating the intestinal microecological potency of Astragalus polysaccharides / oligosaccharides. Background Technology

[0002] Astragalus polysaccharides are one of the main active ingredients in Astragalus membranaceus. They have various biological activities such as immunomodulation, anti-inflammation, anti-oxidation and regulation of intestinal flora, and are widely used in functional foods and medicines.

[0003] In the existing technology, the quality evaluation of Astragalus polysaccharides mainly relies on physicochemical indicators, such as total sugar content, molecular weight distribution, and monosaccharide composition. Although these methods can reflect the chemical composition of the sample, they cannot directly and comprehensively evaluate the comprehensive biological effects on the host's intestinal function (such as barrier protection, immune regulation, and antioxidation) after human digestion and utilization by intestinal flora.

[0004] In addition, some samples performed well at the level of microbial metabolism, but may be toxic to host cells or disrupt the intestinal barrier.

[0005] Therefore, existing evaluation methods are one-sided and may misjudge potentially risky samples as high-potency raw materials. There is an urgent need for a comprehensive evaluation method that can simulate the entire process of human intestinal function and integrate multi-dimensional biological function and safety indicators. Summary of the Invention

[0006] The present invention aims to solve the technical problem that the existing methods for evaluating the quality of Astragalus polysaccharides / oligosaccharides are singular and one-sided, and cannot effectively identify functional differences and potential risks between batches.

[0007] One object of the present invention is to provide a method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharides / oligosaccharides, comprising:

[0008] S1. The Astragalus polysaccharide / oligosaccharide samples to be evaluated are subjected to simulated gastrointestinal digestion to obtain digested samples, and the digestion stability index of the digested samples is detected.

[0009] S2. The digested sample is added to an in vitro anaerobic fermentation system of intestinal flora for culture, and a fermentation supernatant containing bacterial metabolites is obtained. The bacterial metabolic response index of the fermentation supernatant is then detected.

[0010] S3. Apply the fermentation supernatant to intestinal epithelial barrier damage models, inflammation damage models, and / or oxidative stress models, and detect biological response indicators including at least intestinal barrier protection, anti-inflammation, anti-oxidation, and safety indicators.

[0011] S4. Integrate digestive stability indicators, microbial metabolic response indicators, and the aforementioned biological response indicators to construct a biological efficacy fingerprint; wherein, the digestive stability indicators serve as pre-processing evaluation indicators or auxiliary input indicators for the biological efficacy fingerprint, and the biological efficacy fingerprint includes at least five functional evaluation dimensions: microbial metabolic response, intestinal barrier protection, anti-inflammation, anti-oxidation, and safety.

[0012] S5. Based on the bio-potency fingerprint and combined with rejection criteria, the samples are classified into three levels: high, medium, and low, according to the bio-potency index. The rejection criteria include:

[0013] If the test results show at least one of the following: cell viability reduced to below 70%, increased lactate dehydrogenase release, increased intestinal barrier permeability, increased inflammatory factor levels, or increased oxidative stress levels, then the sample is deemed not to qualify for high microecological potency.

[0014] Preferably, the five functional evaluation dimensions of the biopotency fingerprint constructed in step S4 are characterized by at least one of the following core indicators:

[0015] The core indicators of microbial metabolism are selected from at least one of the total amount of short-chain fatty acids and butyrate production.

[0016] The core indicators for intestinal barrier protection are selected from at least one of transepithelial resistance recovery rate and fluorescently labeled dextran permeability inhibition rate;

[0017] The core anti-inflammatory indicator is selected from at least one of interleukin-6 inhibition rate and tumor necrosis factor-α inhibition rate;

[0018] The core antioxidant indicator is selected from at least one of the reactive oxygen species reduction rate and the malondialdehyde reduction rate.

[0019] The core safety indicators are selected from at least one of cell viability and lactate dehydrogenase release.

[0020] Preferably, in step S5, the microecological efficacy grading is achieved by calculating the microecological efficacy index, which is calculated according to the following formula: MEI=0.25×GMS+0.25×BPS+0.20×AIS+0.20×AOS+0.10×SS, where GMS is the microbial metabolic score, BPS is the intestinal barrier protection score, AIS is the anti-inflammatory score, AOS is the antioxidant score, and SS is the safety score;

[0021] The rules for the microecological efficacy grading are as follows:

[0022] When the microecological efficacy index is ≥80 and the aforementioned rejection condition is not triggered, it is judged as high microecological efficacy;

[0023] When the microbial ecosystem valence index is ≥60 and <80, and the aforementioned rejection condition is not triggered, it is determined to be of medium microbial ecosystem valence.

[0024] When the microbial ecosystem valence index is less than 60, or when the aforementioned veto condition is triggered, it is judged as having low microbial ecosystem valence.

[0025] Preferably, it also includes:

[0026] Based on the complete biopotency fingerprint data obtained from multiple batches of samples through steps S1-S5, a machine learning model is constructed. The machine learning model has at least hierarchical decision logic, which includes:

[0027] The safety assessment layer determines whether the sample to be evaluated triggers the rejection conditions based on at least some of the physicochemical characteristics or microbial metabolic indicators of the sample to be evaluated.

[0028] The valence level judgment layer outputs the microecological valence level of the sample to be evaluated based on the calculation result of the microecological valence index for samples that have not triggered the veto conditions.

[0029] The key feature interpretation layer outputs one or more core indicators that contribute most to the determination of the valence level.

[0030] Furthermore, machine learning models are used to rapidly predict the microecological potency, provide risk warnings, or determine batch consistency of new batches of Astragalus polysaccharide / oligosaccharide samples.

[0031] Preferably, the simulated gastrointestinal digestion process includes the following sub-steps:

[0032] S11. Prepare an aqueous solution or buffer solution with a concentration of 5~20 mg / mL for the Astragalus polysaccharide / oligosaccharide sample to be evaluated, and add it to the simulated digestion system to make the final concentration in the system 1~10 mg / mL.

[0033] S12. Perform a simulated gastric digestion stage, adjust the pH of the system to 2.0~3.0, add pepsin, and perform shaking digestion at 37℃ for 1~2 hours;

[0034] S13. Perform simulated small intestinal digestion stage. Adjust the pH of the product obtained in step S12 to 6.8-7.2, add pancreatic enzymes and bile salts, and continue to digest by shaking at 37°C for 2 hours.

[0035] S14. Adjust the digestion product obtained in step S13 to neutral and remove insoluble residues to obtain the digested sample.

[0036] Preferably, it also includes a batch consistency evaluation step:

[0037] S6. Use the biopotency fingerprint obtained by the reference batch of Astragalus polysaccharide / oligosaccharide samples through steps S1~S5 as the baseline fingerprint;

[0038] S7. Obtain the biotiter fingerprints of the Astragalus polysaccharide / oligosaccharide samples to be evaluated through steps S1~S5, and use them as the fingerprints to be evaluated.

[0039] S8. Calculate the similarity between the fingerprint to be evaluated and the reference fingerprint. The similarity is calculated by Pearson correlation coefficient, cosine similarity, or by normalizing the Euclidean distance to the similarity in the range of 0 to 1.

[0040] S9. Based on the similarity calculation results, determine the consistency of the microecological potency between the batch to be evaluated and the reference batch.

[0041] Preferably, in step S9, the rule for determining the consistency of the microecological potency between the batch to be evaluated and the reference batch is as follows:

[0042] When the similarity is ≥0.90, it is determined that the microecological potency of the batch to be evaluated and the reference batch are of good consistency.

[0043] When the similarity is ≥0.80 and <0.90, the consistency of the microecological potency between the batch to be evaluated and the reference batch is considered acceptable.

[0044] When the similarity is less than 0.80, it is determined that the consistency of the microecological potency between the batch to be evaluated and the reference batch is insufficient or the batch is abnormal.

[0045] Preferably, in step S3, the intestinal epithelial barrier damage model is a Caco-2 monolayer cell model or a Caco-2 / HT29-MTX co-culture model, and is constructed by stimulation with TNF-α, IFN-γ or H2O2, and the intestinal barrier protection index is the transepithelial resistance recovery rate or the fluorescently labeled dextran permeability inhibition rate.

[0046] Preferably, in step S3, the oxidative stress model is constructed by H2O2 stimulation, and the antioxidant index is the rate of reduction of intracellular reactive oxygen species level or the activity of antioxidant enzymes, wherein the rate of reduction of reactive oxygen species level is detected by DCFH-DA fluorescent probe method.

[0047] The inflammatory injury model is a Caco-2 / HT29-MTX / THP-1 three-cell co-culture model, constructed by LPS stimulation, and the anti-inflammatory indicators are interleukin-6 inhibition rate or tumor necrosis factor-α inhibition rate.

[0048] A system for grading and evaluating the intestinal microecological potency of Astragalus polysaccharides / oligosaccharides is provided, comprising a module for performing the method, the system comprising:

[0049] The simulated digestion module is configured to perform simulated gastrointestinal digestion on the Astragalus polysaccharide / oligosaccharide sample to be evaluated, obtain the digested sample, and detect the digestive stability index of the digested sample.

[0050] The microbial fermentation module is connected to the simulated digestion module and is configured to add the digested sample into an in vitro intestinal microbial anaerobic fermentation system for culture, obtain a fermentation supernatant containing microbial metabolites, and detect the microbial metabolic response index of the fermentation supernatant.

[0051] A biological response evaluation module, connected to the microbial fermentation module, is configured to apply the fermentation supernatant to an intestinal epithelial barrier damage model, an inflammation damage model, and / or an oxidative stress model, and to detect biological response indicators including at least intestinal barrier protection, anti-inflammation, anti-oxidation, and safety indicators.

[0052] The bioactivity fingerprint construction module is connected to the simulated digestion module, the microbial fermentation module, and the biological response evaluation module, respectively, and is configured to integrate the digestion stability index, the microbial metabolic response index, and the biological response index to construct the bioactivity fingerprint; wherein, the digestion stability index is used as a pretreatment evaluation index or auxiliary input index, and the bioactivity fingerprint includes at least five functional evaluation dimensions: microbial metabolic response, intestinal barrier protection, anti-inflammation, anti-oxidation, and safety.

[0053] The potency grading module is connected to the biological potency fingerprint construction module and is configured to grade the microecological potency of the sample based on the biological potency fingerprint and in combination with rejection conditions.

[0054] The present invention has at least the following beneficial effects:

[0055] First, this invention uses digestive stability as a pretreatment evaluation index or auxiliary input index, and constructs a biopotency fingerprint that includes five functional evaluation dimensions: microbial metabolic response, intestinal barrier protection, anti-inflammation, anti-oxidation, and safety. It calculates the microecological potency index (MEI) through a weighted formula, which comprehensively reflects the true biopotency of Astragalus polysaccharides / oligosaccharides in the entire intestinal process (from digestion to microbial metabolism to host cell response). Compared with traditional methods that rely solely on total sugar content, molecular weight, or the amount of single short-chain fatty acid produced for evaluation, this invention can effectively distinguish samples with similar physicochemical compositions but significantly different actual microecological potency (such as APS-H and APS-L in Example 1), avoiding misjudgments caused by evaluation with a single index.

[0056] Secondly, this invention introduces explicit rejection conditions. When indicators such as cell viability decreasing to below 70%, intestinal barrier permeability increasing, or inflammatory factor levels rising are detected, the sample is determined not to have high microecological potency. The safety threshold directly defined by the technical solution can effectively identify those samples that are mistakenly judged as "high potency" in traditional evaluations due to high SCFA production, but actually have the risk of cytotoxicity or barrier damage, thereby ensuring the safety of the screened raw materials.

[0057] Third, based on the MEI index, this invention clearly classifies samples into three microecological potency levels: "high", "medium", and "low". This classification standard enables quantitative comparison of the functional advantages and disadvantages of Astragalus polysaccharide / oligosaccharide raw materials from different batches and sources, providing a direct and objective basis for raw material procurement and screening, product formulation design, and batch stability control during the production process, overcoming the vague evaluation mode of "whether it has an effect" in the existing technology.

[0058] Fourth, after accumulating at least 30 batches of complete biological potency fingerprint data, this invention uses a machine learning model to build a rapid prediction tool. This model allows for the output of possible potency levels and risk warnings by only detecting a few easily measurable indicators (such as total sugar content, molecular weight distribution, and total short-chain fatty acid content) when screening new batches. The prediction tool derived from the technical solution significantly reduces the number of sample batches that need to be fully carried out in all biological evaluation experiments, improves the efficiency of raw material screening, and reduces the overall quality evaluation cost.

[0059] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0060] Figure 1 This is a flowchart of the evaluation method of the present invention;

[0061] Figure 2 This is a graph showing the dynamic changes in pH of Astragalus polysaccharide / oligosaccharide samples with different molecular weights during in vitro microbial fermentation in Example 1 of the present invention.

[0062] Figure 3 This is a comparative chart showing the effects of different molecular weight Astragalus polysaccharide / oligosaccharide samples on the permeability of the intestinal barrier injury model FITC-dextran in Example 1 of the present invention. In this chart, A is a bar chart of the relative TEER values ​​of different treatment groups, and B is a bar chart of the FITC-dextran permeability inhibition rate (%) of different treatment groups.

[0063] Figure 4This is a comparative graph showing the effects of different molecular weight Astragalus polysaccharide / oligosaccharide samples on the levels of IL-6 and TNF-α in an inflammatory injury model in Example 1 of the present invention. In this graph, A is a bar chart of IL-6 content (pg / mL) in different treatment groups, and B is a bar chart of TNF-α content (pg / mL) in different treatment groups.

[0064] Figure 5 This is a comparison chart of the effects of different molecular weight Astragalus polysaccharide / oligosaccharide samples on ROS and MDA levels in an oxidative stress model in Example 1 of the present invention. In this chart, A is a bar chart of ROS levels (fluorescence intensity) in different treatment groups, and B is a bar chart of MDA content (nmol / mg) in different treatment groups.

[0065] Figure 6 This is a graph showing the cell safety evaluation of different Astragalus polysaccharide / oligosaccharide fermentation supernatants in Example 1 of the present invention. In this graph, A is a bar chart of cell viability (%) in different treatment groups, and B is a bar chart of LDH release (%) in different treatment groups.

[0066] Figure 7 This is a comparison chart of the high-titer sample APS-H-DF and the risk sample R-DF in terms of intestinal barrier protection, anti-inflammation and safety in Example 3 of the present invention. In this chart, A is a bar chart of the relative TEER values ​​of different treatment groups, B is a bar chart of the FITC-dextran permeability of different treatment groups, C is a bar chart of the IL-6 level of different treatment groups, and D is a bar chart of the cell viability (%) of different treatment groups.

[0067] Figure 8 This is a comparison chart of the effects of simulated gastrointestinal digestion treatment (APS-H-DF) and no simulated digestion treatment (APS-H-UF) on the intestinal barrier protection, anti-inflammation and safety of R-DF and R-UF in Example 3 of the present invention. In this chart, A is a bar chart of the relative TEER values ​​of different treatment groups, B is a bar chart of the IL-6 level of different treatment groups, and C is a bar chart of the cell viability (%) of different treatment groups.

[0068] As attached Figures 2-7 As shown, NC represents the normal control group, IM represents the damage model group, BF represents the fermentation blank supernatant group, and PC represents the positive control group (inulin). Detailed Implementation

[0069] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0070] It should be noted that, unless otherwise specified, the experimental methods described in the following implementation plan are all conventional methods, and the reagents and materials described are all commercially available unless otherwise specified.

[0071] This invention provides a method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharides / oligosaccharides. The core of this method lies in sequentially subjecting the samples to be evaluated through a complete intestinal evaluation chain: simulated gastrointestinal digestion, in vitro anaerobic fermentation by intestinal flora, response model of intestinal epithelial barrier / inflammation / oxidative stress, construction of multi-dimensional biopotency fingerprints, and calculation and grading of potency index. A machine learning model is introduced as an auxiliary tool. This method can realistically, comprehensively, and efficiently reflect the comprehensive regulatory function of Astragalus polysaccharides / oligosaccharides in the human gut.

[0072] <Example 1>

[0073] A method for evaluating the whole-process microecological potency of Astragalus polysaccharide samples with different molecular weights, including the following steps:

[0074] 1. Sample preparation and physicochemical characteristic detection

[0075] Sample preparation: Astragalus polysaccharides extracted and precipitated by water and alcohol from the same batch were separated by membrane to obtain three different molecular weight fractions, which were denoted as APS-H (high molecular weight fraction), APS-M (medium molecular weight fraction), and APS-L (low molecular weight fraction or oligosaccharified fraction). Each sample was freeze-dried for later use.

[0076] Physicochemical testing:

[0077] The total sugar content of all three substances was approximately 85%, as determined by the phenol-sulfuric acid method.

[0078] Molecular weight distribution was determined using high-performance gel permeation chromatography (HPGPC or SEC-MALLS).

[0079] Each sample was prepared into an aqueous solution with a total sugar concentration of 10 mg / mL, and sterilized by filtration through a 0.22 μm microporous membrane before use.

[0080] 2. Simulates gastrointestinal digestion.

[0081] Procedure: Add 10 mL of the above sample solution (equivalent to 100 mg of total sugar) to 10 mL of simulated digestion solution, mix thoroughly, and ensure a final concentration of 5 mg / mL in the digestion system. The digestion process includes:

[0082] Simulated gastric digestion stage: Add simulated gastric juice and pepsin (enzyme to substrate ratio of approximately 1:100), adjust pH to 2.5, and digest by shaking at 37°C for 2 hours;

[0083] Simulated small intestinal digestion stage: Adjust the pH of the gastric digestion products to 7.0, add pancreatic enzymes and bile salts, and continue to digest by shaking at 37°C for 2 hours;

[0084] Post-processing: After digestion, adjust the digestion solution to neutral and inactivate the enzyme by heating at 80°C for 5 minutes, or remove insoluble residue by centrifugation and filtration to obtain the "digested sample", which is denoted as APS-H-DF, APS-M-DF, and APS-L-DF, respectively.

[0085] Digestive stability test: The total sugar retention rate before and after digestion was measured. The results showed that the retention rate of APS-H-DF was about 85%, that of APS-M-DF was 82%, and that of APS-L-DF was 78%.

[0086] 3. In vitro anaerobic fermentation of gut microbiota

[0087] Preparation of bacterial suspension: Fresh feces from healthy adults (the provider had not used antibiotics within 3 months prior to sampling) were used to prepare a 10% (m / v) fecal bacterial suspension under anaerobic conditions;

[0088] Experimental Groups:

[0089] Blank fermentation group: 9 mL basal culture medium + 1 mL fecal bacterial suspension (no sample added);

[0090] Positive control group: 9 mL basal culture medium + 1 mL fecal bacterial suspension + equal amount of inulin (a known prebiotic) with total sugar.

[0091] Experimental groups: Three experimental groups were added with APS-H-DF, APS-M-DF and APS-L-DF samples respectively. Each group consisted of 9 mL of basal culture medium + 1 mL of fecal bacterial suspension + 1 mL of digested sample (equivalent to 100 mg total sugar equivalent).

[0092] Cultivation and Detection: Each group was cultured anaerobically at 37℃ for 48 hours. After cultivation, the mixture was centrifuged, and the fermentation supernatant was collected. Gas chromatography was used to detect the content of short-chain fatty acids (SCFAs), including total SCFAs, acetic acid, propionic acid, and butyric acid. Exemplary results are shown in Table 1 below:

[0093] Table 1. Microbial metabolic response data of different Astragalus polysaccharide fractions

[0094]

[0095] The data in Table 1 initially show that the high molecular weight fraction APS-H is closest to the positive control inulin in promoting the production of SCFA (especially butyric acid). However, microbial metabolism is only one link in the entire evaluation chain, and the efficacy in other dimensions needs to be revealed by subsequent experiments.

[0096] Figure 2The results showed that the pH dynamics of different samples during fermentation were as follows: the pH of the APS-H-DF group decreased significantly, approaching that of the positive control inulin group, indicating that high molecular weight astragalus polysaccharide can be effectively utilized by intestinal flora and produce acidic metabolites. The pH decrease of the APS-M-DF and APS-L-DF groups decreased progressively, indicating that there are differences in the fermentation capacity of the flora of different molecular weight fractions.

[0097] Figures 3-6 Further research revealed differences among different samples in terms of intestinal barrier protection, anti-inflammation, anti-oxidation, and safety. Specifically, the APS-H-DF group showed the best performance in increasing the relative TEER value, reducing FITC-dextran permeability, reducing IL-6 and TNF-α levels, and reducing ROS and MDA levels. It also exhibited high cell viability and normal LDH release, consistent with the characteristics of high microecological potency. The APS-M-DF group showed moderate performance, while the APS-L-DF group showed weaker improvement.

[0098] The above results collectively demonstrate that the multidimensional biopotency fingerprint constructed in this invention can effectively distinguish samples with similar physicochemical compositions but significantly different actual biopotency.

[0099] 4. Evaluation of intestinal barrier protection, anti-inflammatory and antioxidant efficacy

[0100] Cell model construction: Caco-2 monolayer cell model or Caco-2 / HT29-MTX co-culture model were used. Cells were seeded in 0.4 μm pore size polycarbonate Transwell inserters and cultured until the transepithelial electrical resistance (TEER) value stabilized. A TEER value was defined as a change of less than 10% over several consecutive days (e.g., 3 days) and reaching a pre-set threshold (e.g., not less than 300 Ω·cm). 2 The barrier model is considered successfully constructed when it reaches a stable plateau value in the same batch of normal control groups.

[0101] Injury model establishment: In this embodiment, a model of intestinal barrier injury was established by combined stimulation with TNF-α (e.g., 20 ng / mL) and IFN-γ (e.g., 10 ng / mL).

[0102] Processing: The fermentation supernatants obtained in step 3 were sterilized by filtration through a 0.22 μm filter membrane and diluted to an appropriate concentration with cell culture medium. To eliminate the influence of cytotoxicity on the results, the dilution ratio needed to be determined based on preliminary cell tolerance experiments. For example, the fermentation supernatants were added to the culture medium at different volume ratios (v / v) such as 1%, 2%, and 5%, respectively. After treatment for 24 hours, cell viability was measured, and the highest dilution factor that had no significant effect on Caco-2 cell viability (e.g., viability ≥90%) was selected as the working concentration for subsequent experiments. In this embodiment, the 1% (v / v) dilution ratio was proven to be safe for all samples used. After adjusting the pH to 7.0-7.4, the solution was added to cells with established damage models and treated for 12-24 hours.

[0103] Indicator Testing:

[0104] Intestinal barrier protection: TEER values ​​were measured and recovery rates were calculated. The formula for calculating the TEER recovery rate is: (TEER value of the treated group - TEER value of the damaged model group) / (TEER value of the normal control group - TEER value of the damaged model group) × 100%. Simultaneously, FITC-dextran permeability experiments can be performed to calculate the permeability inhibition rate.

[0105] Anti-inflammatory: Basal side culture medium was collected, and the levels of interleukin-6 (IL-6) and / or tumor necrosis factor-α were detected using an ELISA kit. The inhibition rate of inflammatory factors was calculated.

[0106] Antioxidant: Intracellular reactive oxygen species (ROS) levels were detected using the DCFH-DA fluorescent probe method, and the ROS reduction rate was calculated;

[0107] Safety: Cell viability was measured using a CCK-8 or MTT assay kit, and the release of lactate dehydrogenase (LDH) in the culture medium was also measured. In this example, cell viability in all sample treatment groups was higher than 90% of that in the normal control group, and the LDH release was not significantly different from that in the normal control group, thus not triggering the following rejection criteria.

[0108] 5. Construction of biological valence fingerprint and classification of microecological valence

[0109] Constructing a biogenic valence fingerprint: Integrating all the above indicators, a biogenic valence fingerprint is constructed. In this embodiment, the biogenic valence fingerprint includes the following five core indicators:

[0110] Microbial metabolism: total SCFA production and butyrate production;

[0111] Intestinal barrier protection: TEER recovery rate;

[0112] Anti-inflammatory agents: IL-6 inhibition rate;

[0113] Antioxidants: ROS reduction rate;

[0114] Safety-related: Cell viability;

[0115] Calculating the Microbial Ecology Valuation Index (MEI): First, standardize the scores of each indicator, representing them from 0 to 100. The damage model group is assigned 0 points, and the normal or positive control group is assigned 100 points. Scores above 100 are counted as 100, and scores below 0 are counted as 0. Then, calculate the MEI using the following weighted formula:

[0116] MEI = 0.25 × Gastrointestinal Metabolic Score (GMS) + 0.25 × Intestinal Barrier Protection Score (BPS) + 0.20 × Anti-inflammatory Score (AIS) + 0.20 × Antioxidant Score (AOS) + 0.10 × Safety Score (SS);

[0117] Table 2 below shows examples of scores for each dimension and MEI calculation results:

[0118] Table 2. Calculation and grading of the microecological efficacy index of different Astragalus polysaccharide fractions.

[0119]

[0120] Microecological efficacy grading refers to the final grading based on a set of "veto conditions";

[0121] The rejection criteria are as follows: if the test results show that the cell viability of the sample treatment group is less than 70% of that of the normal control group, or the release of lactate dehydrogenase is significantly increased, or the intestinal barrier permeability (such as FITC-dextran permeability) is increased, or the levels of inflammatory factors (such as IL-6, TNF-α) are increased, or the levels of oxidative stress (such as ROS, MDA) are increased, the sample will be judged as not having high microecological potency.

[0122] The grading rules are as follows:

[0123] High microbial ecological efficacy: MEI≥80, and no veto conditions were triggered.

[0124] The microbial ecological potency was 60 ≤ MEI < 80, and no rejection conditions were triggered.

[0125] Low microbial ecovalence: MEI < 60, or any veto condition has been triggered.

[0126] The three samples in this embodiment are graded according to this rule:

[0127] APS-H: MEI=85.1≥80, and all safety indicators are normal, without triggering the rejection conditions, therefore it is judged to be of high microbial efficacy.

[0128] APS-M: MEI=73.1, which is between 60 and 80, and the rejection condition was not triggered, so it is judged to be a microbial ecological value.

[0129] APS-L: MEI=57.2<60, therefore it is judged to be of low microbial ecological value.

[0130] The results showed that three Astragalus polysaccharide samples with similar physicochemical compositions (total sugar content) exhibited significant differences in their microecological potency through the comprehensive evaluation process of this invention. APS-H was accurately identified as a high-potency sample, APS-M as a medium-potency sample, and APS-L as a low-potency sample. This demonstrates the significant advantages and inventiveness of this invention compared to traditional single physicochemical indicator evaluation methods.

[0131] In summary, the results of this embodiment demonstrate that the method of the present invention can distinguish Astragalus polysaccharide / oligosaccharide samples of different molecular weights into different levels of microecological potency. APS-H-DF exhibited strong microbial fermentation response, high TEER level, low FITC-dextran permeability, low levels of inflammatory factors and oxidative stress, and good cell safety, consistent with high microecological potency characteristics; APS-M-DF showed moderate potency; and APS-L-DF had a weak improving effect, consistent with low microecological potency characteristics. These results prove that the present invention can effectively distinguish samples with similar physicochemical compositions but significantly different actual biological potencies, avoiding misjudgments caused by simply relying on total sugar content or molecular weight for quality evaluation.

[0132] <Example 2>

[0133] Comparison of comprehensive evaluation methods and single short-chain fatty acid evaluation methods

[0134] 1. Experimental Grouping

[0135] Five different Astragalus polysaccharide / oligosaccharide samples were selected and designated APS-1 to APS-5, respectively. Each sample was evaluated according to the complete method described in Example 1.

[0136] 2. Comparison of Evaluation Methods

[0137] Traditional single SCFA evaluation: This method judges solely based on the total SCFA and butyric acid production after fermentation. If the sample shows a significant increase compared to the control group, it is considered to have "potentially high SCFA levels."

[0138] The comprehensive evaluation of this invention is based on the calculated MEI value and the rejection criteria for final classification.

[0139] 3. Exemplary evaluation results

[0140] The evaluation results are compared in Table 3 below:

[0141] Table 3 Comparison between the single SCFA evaluation method and the comprehensive evaluation method of this invention

[0142]

[0143] APS-3 is a typical example. Traditional methods would misjudge it as "high potency" due to its high butyrate production. However, the comprehensive evaluation system of this invention reveals its deficiency in intestinal barrier protection, and therefore it is judged as "low microbial potency". This strongly proves that the biopotency fingerprint adopted by this invention, which includes multiple dimensions such as barrier protection, anti-inflammation, and anti-oxidation, can avoid the one-sidedness and potential risks caused by single indicator evaluation, and the evaluation results are more accurate and reliable.

[0144] <Example 3>

[0145] Risk Sample Identification Based on Veto Conditions

[0146] 1. Samples and Processing

[0147] Two representative samples were selected:

[0148] APS-H: The high molecular weight Astragalus polysaccharide fraction in Example 1, used as a high-efficiency reference sample;

[0149] R: Astragalus oligosaccharide samples prepared by enzymatic hydrolysis have extremely low molecular weight and high reducing sugar content, making them potential risk samples.

[0150] Each sample was processed in the following two ways:

[0151] Simulated digestion treatment group: Simulated gastrointestinal digestion was performed according to the complete process of step 2 in Example 1, and the digested samples were obtained and denoted as APS-H-DF and R-DF, respectively.

[0152] Control group without simulated digestion: An equal amount of total sugar sample was added directly to the fermentation system, skipping the simulated digestion step, and was denoted as APS-H-UF and R-UF, respectively.

[0153] 2. Evaluation of gut microbiota metabolism

[0154] The four groups of samples (APS-H-DF, R-DF, APS-H-UF, and R-UF) were subjected to in vitro anaerobic fermentation of gut microbiota according to step 3 of Example 1. After 24 hours of fermentation, the R-DF group showed a significant increase in total SCFA and butyrate content (butyrate up to 20 mM) compared to the blank fermentation group, demonstrating extremely strong butyrate production potential. However, from the perspective of single-microbiota metabolism, this sample showed excellent "prebiotic" potential.

[0155] 3. Intestinal barrier and safety evaluation

[0156] The fermentation supernatants from each group were used in the intestinal barrier injury model (same as in Example 1), and the results are shown in Table 4 below:

[0157] Table 4 Evaluation Results of Rejection Conditions for Risk Samples' R-DF

[0158]

[0159] 4. Rejection Judgment and Final Classification

[0160] According to the rejection criteria set by this invention, cells treated with AOS-R exhibited a series of safety issues, including "cell viability lower than 70% of the normal control group," "increased intestinal barrier permeability," and "elevated levels of inflammatory factors," triggering the rejection criteria. Therefore, despite its impressive SCFA (Social Microbiota Metabolism Aspect) indicators, according to the method of this invention, it could not ultimately be classified as a high-microbiota potency sample, but rather as a low-microbiota potency or risk sample.

[0161] Figure 7 This further validates the necessity of the rejection condition. Compared with the high-titer sample APS-H-DF, the risky sample R-DF, although exhibiting stronger acid production capacity during fermentation, had a lower relative TEER value than the damage model group, higher FITC-dextran permeability, and a further increase in IL-6 levels. Simultaneously, cell viability decreased to approximately 65%, triggering the rejection condition set in this invention. This result directly proves that the rejection condition in this invention is not an additional description, but a key technical feature to prevent "misjudgment caused by a single favorable indicator."

[0162] Figure 8 This reveals the importance of pretreatment before simulating gastrointestinal digestion. A comparison of the APS-H-DF and APS-H-UF groups shows that the APS-H-UF group, which fermented directly without simulated digestion, had a lower TEER recovery rate and higher IL-6 level than the APS-H-DF group, which underwent digestion before fermentation. Similarly, for the risk sample R, the R-DF group exhibited a lower TEER recovery rate, higher IL-6 level, and lower cell viability than the R-UF group. This demonstrates the necessity and unexpected technical benefits of the continuous evaluation process of "simulated gastrointestinal digestion—in vitro microbial fermentation—host cell response," which can more realistically reflect the intestinal state after oral administration and more fully expose potential risks.

[0163] This embodiment fully demonstrates the core value of the "veto condition" in this invention. It ensures that the evaluation system not only focuses on "beneficial" metabolites, but also prioritizes "host safety" and "barrier integrity," effectively avoiding the misjudgment of potentially harmful raw materials as high-potency ones.

[0164] In summary, the results of this embodiment indicate that although the risk sample R-DF exhibits strong fermentation and acid production capacity, it simultaneously causes a decrease in TEER, an increase in FITC-dextran permeability, an increase in IL-6, and a decrease in cell viability, triggering the rejection criteria set by this invention. Therefore, it cannot be classified as a high-microbial-ecological-potency sample. This result demonstrates that this invention can identify potentially risky samples that may be missed by traditional single-community metabolic assessments.

[0165] Meanwhile, compared with the method of direct fermentation without simulated gastrointestinal digestion, the present invention uses simulated gastrointestinal digestion followed by in vitro microbial fermentation, which can more fully distinguish between high-titer samples and risky samples, demonstrating the necessity and technical effectiveness of the continuous evaluation process of "simulated gastrointestinal digestion - in vitro microbial fermentation - host cell response".

[0166] <Example 4>

[0167] Rapid prediction of micro-ecological valence based on machine learning

[0168] 1. Dataset Construction

[0169] Following the method in Example 1, a complete evaluation was performed on more than 50 batches of Astragalus polysaccharide / oligosaccharide samples to obtain all detection index data and final potency grades (high, medium, and low) for each batch of samples, forming a database containing a complete biopotency fingerprint.

[0170] 2. Machine learning model construction and hierarchical determination logic

[0171] Input features: Select some easily measurable indicators from the database as model inputs, such as total sugar content, molecular weight distribution, total sugar retention rate after simulated digestion, and total amount of short-chain fatty acids after fermentation.

[0172] Output objectives: The final potency level of the sample (high, medium, low), and a risk warning indicating whether the rejection conditions have been triggered.

[0173] Layer determination logic:

[0174] First layer: Safety assessment: The model first predicts the probability that the sample will trigger the aforementioned rejection conditions based on the input features. If the probability exceeds the threshold, a "risk sample" warning is directly output, and no further level assessment is performed.

[0175] Second layer: Valence level judgment: For samples predicted to be safe, the model enters the second layer to predict whether they belong to the high, medium or low valence level.

[0176] The third layer: Explanation of key features: The model outputs one or more core indicators that contribute the most to this level determination. For example, "butyric acid production" is the most critical feature for this determination.

[0177] 3. Exemplary prediction results

[0178] The constructed model was used to predict four new batches of samples, and the results are shown in Table 5 below.

[0179] Table 5. Prediction results of machine learning model for new batch of samples

[0180]

[0181] This embodiment demonstrates that the machine learning model built on a complete biomarker fingerprint database can achieve high-throughput, low-cost, and rapid initial screening of new batches of samples. The model's stratification logic can simultaneously perform safety warnings and potency grading, providing intuitive output results and guiding subsequent process optimization.

[0182] <Example 5>

[0183] Batch Consistency Evaluation of Astragalus Polysaccharides Based on Biopotency Fingerprint

[0184] 1. Sample Source

[0185] Five batches of Astragalus polysaccharide samples prepared using the same production process were selected and designated as Batch-A to Batch-E, with Batch-A, which had a stable production history and high potency, serving as the reference batch.

[0186] 2. Construction of biogenic fingerprints

[0187] Following the method in Example 1, each batch of samples was fully evaluated. The standardized scores of its core metrics (such as GMS, BPS, AIS, AOS, and SS) were extracted to construct a fingerprint vector representing the functional characteristics of that batch.

[0188] 3. Batch Consistency Index (BCI) Calculation

[0189] The similarity between the fingerprints of the batch being evaluated and the reference batch is quantitatively calculated using methods such as cosine similarity or Pearson correlation coefficient. This embodiment uses cosine similarity; the closer the value is to 1, the more similar the biopotency fingerprints of the two batches are. The calculation formula is: , where X is the fingerprint vector of the reference batch, Y is the fingerprint vector of the batch to be evaluated, X·Y is the vector dot product, and |X| and |Y| are the magnitudes of vectors X and Y, respectively.

[0190] 4. Consistency Determination and Exemplary Results

[0191] Based on the BCI value, a consistency judgment standard was set. For example, BCI ≥ 0.90 is "good consistency", 0.80 ≤ BCI < 0.90 is "acceptable consistency", and BCI < 0.80 is "insufficient consistency or batch abnormality". The results are shown in Table 6 below:

[0192] Table 6. Consistency evaluation of biopotency of different batches of Astragalus polysaccharides

[0193]

[0194] This embodiment shows that the biopotency fingerprints of Batch-B and Batch-C are highly similar to those of the reference batch Batch-A, indicating stable production processes and good functional consistency between batches. However, the BCIs of Batch-D and Batch-E are significantly lower than the threshold, and their potency levels also decrease accordingly, suggesting that changes may have occurred in the raw materials or production process, requiring further investigation.

[0195] This invention elevates quality control from the traditional "consistency of chemical composition" to "consistency of biological efficacy," which is more in line with the actual application needs of functional food ingredients.

[0196] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharides / oligosaccharides, characterized in that, include: S1. The Astragalus polysaccharide / oligosaccharide samples to be evaluated are subjected to simulated gastrointestinal digestion to obtain digested samples, and the digestion stability index of the digested samples is detected. S2. The digested sample is added to an in vitro anaerobic fermentation system of intestinal flora for culture, and a fermentation supernatant containing bacterial metabolites is obtained. The bacterial metabolic response index of the fermentation supernatant is then detected. S3. Apply the fermentation supernatant to intestinal epithelial barrier damage models, inflammation damage models, and / or oxidative stress models, and detect biological response indicators including at least intestinal barrier protection, anti-inflammation, anti-oxidation, and safety indicators. S4. Integrate digestive stability indicators, microbial metabolic response indicators, and the aforementioned biological response indicators to construct a biological efficacy fingerprint; wherein, the digestive stability indicators serve as pre-processing evaluation indicators or auxiliary input indicators for the biological efficacy fingerprint, and the biological efficacy fingerprint includes at least five functional evaluation dimensions: microbial metabolic response, intestinal barrier protection, anti-inflammation, anti-oxidation, and safety. S5. Based on the bio-potency fingerprint and combined with rejection criteria, the samples are classified into three levels: high, medium, and low, according to the bio-potency index. The rejection criteria include: If the detection results of the biological response indicators show that at least one of the following conditions is present: cell viability is reduced to below 70% in the safety indicators or lactate dehydrogenase release is increased; intestinal barrier permeability is increased in the intestinal barrier protection indicators; inflammatory factor levels are increased in the anti-inflammatory indicators or oxidative stress levels are increased in the antioxidant indicators, then the sample is determined not to have high microecological potency.

2. The method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharide / oligosaccharide as described in claim 1, characterized in that, The five functional evaluation dimensions of the biopotency fingerprint constructed in step S4 are characterized by at least one of the following core indicators: The core indicators of microbial metabolism are selected from at least one of the total amount of short-chain fatty acids and butyrate production. The core indicators for intestinal barrier protection are selected from at least one of transepithelial resistance recovery rate and fluorescently labeled dextran permeability inhibition rate; The core anti-inflammatory indicator is selected from at least one of interleukin-6 inhibition rate and tumor necrosis factor-α inhibition rate; The core antioxidant indicator is selected from at least one of the reactive oxygen species reduction rate and the malondialdehyde reduction rate. The core safety indicators are selected from at least one of cell viability and lactate dehydrogenase release.

3. The method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharides / oligosaccharides as described in claim 2, characterized in that, In step S5, the microecological efficacy grading is achieved by calculating the microecological efficacy index, which is calculated according to the following formula: MEI=0.25×GMS+0.25×BPS+0.20×AIS+0.20×AOS+0.10×SS, where GMS is the microbial metabolic score, BPS is the intestinal barrier protection score, AIS is the anti-inflammatory score, AOS is the antioxidant score, and SS is the safety score; The rules for the microecological efficacy grading are as follows: When the microecological efficacy index is ≥80 and the aforementioned rejection condition is not triggered, it is judged as high microecological efficacy; When the microbial ecosystem valence index is ≥60 and <80, and the aforementioned rejection condition is not triggered, it is determined to be of medium microbial ecosystem valence. When the microbial ecosystem valence index is less than 60, or when the aforementioned veto condition is triggered, it is judged as having low microbial ecosystem valence.

4. The method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharide / oligosaccharide as described in claim 3, characterized in that, Also includes: Based on the complete biopotency fingerprint data obtained from multiple batches of samples through steps S1-S5, a machine learning model is constructed. The machine learning model has at least hierarchical decision logic, which includes: The safety assessment layer determines whether the sample to be evaluated triggers the rejection conditions based on at least some of the physicochemical characteristics or microbial metabolic indicators of the sample to be evaluated. The valence level judgment layer outputs the microecological valence level of the sample to be evaluated based on the calculation result of the microecological valence index for samples that have not triggered the veto conditions. The key feature interpretation layer outputs one or more core indicators that contribute most to the determination of the valence level. Furthermore, machine learning models are used to rapidly predict the microecological potency, provide risk warnings, or determine batch consistency of new batches of Astragalus polysaccharide / oligosaccharide samples.

5. The method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharide / oligosaccharide as described in claim 1, characterized in that, The simulated gastrointestinal digestion process includes the following sub-steps: S11. Prepare an aqueous solution or buffer solution with a concentration of 5~20 mg / mL for the Astragalus polysaccharide / oligosaccharide sample to be evaluated, and add it to the simulated digestion system to make the final concentration in the system 1~10 mg / mL. S12. Perform a simulated gastric digestion stage, adjust the pH of the system to 2.0~3.0, add pepsin, and perform shaking digestion at 37℃ for 1~2 hours; S13. Perform simulated small intestinal digestion stage. Adjust the pH of the product obtained in step S12 to 6.8-7.2, add pancreatic enzymes and bile salts, and continue to digest by shaking at 37°C for 2 hours. S14. Adjust the digestion product obtained in step S13 to neutral and remove insoluble residues to obtain the digested sample.

6. The method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharide / oligosaccharide as described in claim 1, characterized in that, It also includes batch consistency evaluation steps: S6. Use the biopotency fingerprint obtained by the reference batch of Astragalus polysaccharide / oligosaccharide samples through steps S1~S5 as the baseline fingerprint; S7. Obtain the biotiter fingerprints of the Astragalus polysaccharide / oligosaccharide samples to be evaluated through steps S1~S5, and use them as the fingerprints to be evaluated. S8. Calculate the similarity between the fingerprint to be evaluated and the reference fingerprint. The similarity is calculated by Pearson correlation coefficient, cosine similarity, or by normalizing the Euclidean distance to the similarity in the range of 0 to 1. S9. Based on the similarity calculation results, determine the consistency of the microecological potency between the batch to be evaluated and the reference batch.

7. The method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharides / oligosaccharides as described in claim 6, characterized in that, In step S9, the rule for determining the consistency of the microecological potency between the batch to be evaluated and the reference batch is as follows: When the similarity is ≥0.90, it is determined that the microecological potency of the batch to be evaluated and the reference batch are of good consistency. When the similarity is ≥0.80 and <0.90, the consistency of the microecological potency between the batch to be evaluated and the reference batch is considered acceptable. When the similarity is less than 0.80, it is determined that the consistency of the microecological potency between the batch to be evaluated and the reference batch is insufficient or the batch is abnormal.

8. The method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharide / oligosaccharide as described in claim 1, characterized in that, In step S3, the intestinal epithelial barrier damage model is a Caco-2 monolayer cell model or a Caco-2 / HT29-MTX co-culture model, and is constructed by stimulation with TNF-α, IFN-γ or H2O2. The intestinal barrier protection index is the transepithelial resistance recovery rate or the fluorescently labeled dextran permeability inhibition rate.

9. The method for grading and evaluating the intestinal microecological potency of Astragalus polysaccharide / oligosaccharide as described in claim 1, characterized in that, In step S3, the oxidative stress model is constructed by H2O2 stimulation, and the antioxidant index is the rate of reduction of intracellular reactive oxygen species level or the activity of antioxidant enzymes. The rate of reduction of reactive oxygen species level is detected by DCFH-DA fluorescent probe method. The inflammatory injury model is a Caco-2 / HT29-MTX / THP-1 three-cell co-culture model, constructed by LPS stimulation, and the anti-inflammatory indicators are interleukin-6 inhibition rate or tumor necrosis factor-α inhibition rate.

10. A grading and evaluation system for the intestinal microecological potency of Astragalus polysaccharides / oligosaccharides, characterized in that... The system includes modules for performing the method as described in any one of claims 1 to 9, and the system comprises: The simulated digestion module is configured to perform simulated gastrointestinal digestion on the Astragalus polysaccharide / oligosaccharide sample to be evaluated, obtain the digested sample, and detect the digestive stability index of the digested sample. The microbial fermentation module is connected to the simulated digestion module and is configured to add the digested sample into an in vitro intestinal microbial anaerobic fermentation system for culture, obtain a fermentation supernatant containing microbial metabolites, and detect the microbial metabolic response index of the fermentation supernatant. A biological response evaluation module, connected to the microbial fermentation module, is configured to apply the fermentation supernatant to an intestinal epithelial barrier damage model, an inflammation damage model, and / or an oxidative stress model, and to detect biological response indicators including at least intestinal barrier protection, anti-inflammation, anti-oxidation, and safety indicators. The bioactivity fingerprint construction module is connected to the simulated digestion module, the microbial fermentation module, and the biological response evaluation module, respectively, and is configured to integrate the digestion stability index, the microbial metabolic response index, and the biological response index to construct the bioactivity fingerprint; wherein, the digestion stability index is used as a pretreatment evaluation index or auxiliary input index, and the bioactivity fingerprint includes at least five functional evaluation dimensions: microbial metabolic response, intestinal barrier protection, anti-inflammation, anti-oxidation, and safety. The potency grading module is connected to the biological potency fingerprint construction module and is configured to grade the microecological potency of the sample based on the biological potency fingerprint and in combination with rejection conditions.

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