A method and system for constructing an animal experiment evaluation model

Abstract

The present application relates to the technical field of biomedical experiment model, and more particularly to a construction method and system applied to an animal experiment evaluation model. The method comprises the following steps: grouping and baseline determination of male SD rats to obtain a grouping baseline data table; performing time-space separation type stress according to the grouping baseline data table, and performing stress sensitivity evaluation to obtain a stress sensitivity index; performing functional flora specificity sequencing of the male SD rats according to the stress sensitivity index to obtain a functional flora sequencing data package; performing microecological disorder degree calculation on the functional flora sequencing data package to obtain an intestinal microecological disorder index; and performing drug intervention and behavior test according to the intestinal microecological disorder index to obtain a multi-dimensional behavior parameter set. The present application realizes the standardization, objectivity and comprehensiveness of the construction of the animal experiment evaluation model and the evaluation of the intervention effect through the biomedical experiment model technology.

Description

Technical Field

[0001] This invention relates to the field of biomedical experimental model technology, and in particular to a method and system for constructing an evaluation model for animal experiments. Background Technology

[0002] Existing methods for evaluating animal models of depression often focus solely on behavioral performance or intracranial biochemical indicators, lacking a systematic assessment of the overall functional state of the gut-brain axis. This makes it difficult to comprehensively reflect the causal relationship between gut microbiota dysbiosis and depressive symptoms. Traditional evaluation systems do not fully consider the individual differences in the sensitivity of experimental animals to chronic stress, resulting in large fluctuations in data within experimental groups, insufficient model stability, and low reproducibility of drug efficacy evaluation results, increasing the uncertainty of new drug development. Current gut microbiota analysis techniques are mostly limited to the level of species taxonomy, lacking specific detection and quantitative evaluation of functional microbiota such as short-chain fatty acid-producing bacteria and bile acid-metabolizing bacteria. This makes it impossible to accurately reveal the intrinsic link between specific functional microbiota and depressive symptoms, thus limiting the development of drugs targeting the gut microbiota.

[0003] In summary, existing technologies lack a systematic evaluation method that can integrate stress sensitivity, gut microbiota function, and brain function status, making it impossible to objectively assess the efficacy of drugs that exert antidepressant effects by regulating the gut-brain axis. This issue urgently needs to be addressed. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and system for constructing evaluation models for animal experiments to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a method for constructing an evaluation model for animal experiments includes the following steps:

[0006] Step S1: Male SD rats were grouped and baseline measurements were taken to obtain a group baseline data table; based on the group baseline data table, spatiotemporal stress was implemented and stress sensitivity was assessed to obtain a stress sensitivity index;

[0007] Step S2: Perform functional microbiota-specific sequencing on male SD rats based on the stress sensitivity index to obtain functional microbiota sequencing data packages; calculate the intestinal microbiota dysbiosis index from the functional microbiota sequencing data packages.

[0008] Step S3: Based on the gut microbiota dysbiosis index, conduct drug intervention and behavioral testing to obtain a multidimensional behavioral parameter set; perform a comprehensive behavioral evaluation on the multidimensional behavioral parameter set to obtain a comprehensive depressive behavior score;

[0009] Step S4: Targeted detection of gut-brain components is performed based on the comprehensive depressive behavior score to obtain a gut-brain molecular expression profile; gut-brain axis synergy analysis is performed based on the comprehensive depressive behavior score and the gut-brain molecular expression profile to obtain a gut-brain axis synergy index; an animal experimental evaluation model is constructed based on the stress sensitivity index, gut microbiota dysbiosis index, comprehensive depressive behavior score, and gut-brain axis synergy index.

[0010] This invention employs an innovative spatiotemporal separation method for chronic stress modeling, significantly enhancing the unpredictability of stress and the stability of the model, overcoming the shortcomings of traditional chronic stress models such as low success rate and large individual variability. Detailed recording of individual stress responses and weight changes, along with the calculation of the Stress Sensitivity Index (SSI), allows for the objective quantification of each rat's sensitivity to chronic stress. SSI-based screening of sensitive individuals effectively reduces the number of non-responders in the experimental animals, improving the quality of animal models and data uniformity in subsequent experiments, thereby enhancing the reliability of drug intervention efficacy evaluation and reducing the required sample size and cost. Specific sequencing methods were designed and implemented for specific functional bacterial groups such as short-chain fatty acid-producing bacteria, bile acid metabolism-related bacteria, and tryptophan metabolism-related bacteria. By optimizing the multiplex PCR system and library construction, the detection sensitivity and specificity of these key bacterial groups closely related to gut-brain axis function were improved, providing a more accurate reflection of gut microbiota changes associated with the development and progression of depression compared to traditional 16S rRNA gene sequencing. Based on this, a gut microbiota dysbiosis index (GMD) was constructed by screening key bacterial genera significantly associated with stress sensitivity and assigning them functional weights, combined with abundance changes and correction calculations. This index integrates complex changes in gut microbiota composition into an objective and quantitative indicator, enabling more accurate assessment of the degree of gut microbiota dysbiosis in depressed rat models, providing precise microecological baseline data for evaluating the effects of subsequent drug interventions. Before drug intervention, rats were grouped into microecologically balanced groups based on the GMD calculated in step S2, effectively reducing the differences in baseline gut microbiota status among groups and improving the accuracy of subsequent assessments of drug effects on gut microbiota regulation and antidepressant efficacy. Drug tolerance was monitored through continuous gavage administration for 28 days to ensure the safety of the experimental process. Three classic behavioral tests—forced swimming, open field, and elevated cruciate maze—were used to comprehensively assess the depressive-like behavioral performance of rats from different dimensions (despair behavior, exploratory / motor behavior, and anxiety-like behavior). Through standardization of multidimensional behavioral parameters and principal component analysis, core indicators were extracted and scientifically reasonable weights were determined, constructing a Depressive Behavioral Comprehensive Score (DBCS). This scoring system integrates multiple behavioral information sources, providing a more comprehensive, stable, and objective quantitative indicator to reflect the severity of depressive behaviors and the effectiveness of medication, avoiding the limitations of single behavioral indicator assessments. Based on the Depressive Behavioral Comprehensive Score (DBCS) calculated in step S3, the most representative samples were selected for molecular testing, improving the representativeness and efficiency of the molecular testing results. Targeted detection was performed on intestinal metabolites (sinapic acid, short-chain fatty acids, bile acids) closely related to gut-brain axis function, brain function molecules (BDNF, 5-HT and their metabolites), and serum inflammatory factors (IL-6), employing rigorous quantitative methods (such as matrix-matched standard curves and methodological validation) to ensure the accuracy and reliability of the molecular data.Based on these multidimensional molecular data and the gut-brain axis synergistic index (GBCI), a novel quantitative indicator was constructed. This index provides a new, more comprehensive, and biologically significant quantitative measure for assessing the overall state and degree of dysfunction of the gut-brain axis by weighting key molecular indicators from three dimensions: gut metabolism, brain function, and inflammation, and employing a product model to reflect their synergistic effects. GBCI can reveal more deeply how drug interventions improve depression by modulating the gut-brain axis, providing a more objective and comprehensive evaluation standard for assessing the efficacy of drugs targeting the gut microbiota.

[0011] Therefore, this invention provides a method for constructing an evaluation model for animal experiments. By establishing a four-module sequential evaluation system of "stress sensitivity assessment - gut microbiota analysis - behavioral efficacy evaluation - gut-brain axis correlation determination", and introducing spatiotemporal separation stress method, functional microbiota-specific sequencing technology and gut-brain axis synergistic index calculation model, it realizes a standardized evaluation process from individual difference screening to multidimensional index integration.

[0012] Preferably, the present invention also provides a system for constructing an evaluation model for animal experiments, used to execute the method for constructing an evaluation model for animal experiments as described above, the system comprising:

[0013] The stress sensitivity assessment module is used to group male SD rats and determine baseline data to obtain a group baseline data table; based on the group baseline data table, spatiotemporal stress is implemented and stress sensitivity is assessed to obtain a stress sensitivity index;

[0014] The gut microbiota analysis module is used to perform functional microbiota-specific sequencing on male SD rats based on the stress sensitivity index, and obtain functional microbiota sequencing data packages; the gut microbiota disorder index is obtained by calculating the degree of microbiota disorder on the functional microbiota sequencing data packages.

[0015] The behavioral efficacy evaluation module is used to conduct drug intervention and behavioral testing based on the gut microbiota dysbiosis index to obtain a multidimensional behavioral parameter set; and to conduct a comprehensive behavioral evaluation of the multidimensional behavioral parameter set to obtain a comprehensive depressive behavior score.

[0016] The gut-brain axis correlation measurement module is used to perform targeted detection of gut-brain components based on the comprehensive depressive behavior score to obtain a gut-brain molecular expression profile; to perform gut-brain axis synergy analysis based on the comprehensive depressive behavior score and the gut-brain molecular expression profile to obtain a gut-brain axis synergy index; and to construct an animal experimental evaluation model based on the stress sensitivity index, gut microbiota dysbiosis index, comprehensive depressive behavior score, and gut-brain axis synergy index.

[0017] This system offers significant benefits through its modular design and integrated functionality. The stress sensitivity assessment module can accurately construct stable and highly responsive depression models using a spatiotemporal separation stress method, and objectively screen sensitive individuals, providing a high-quality animal cohort for subsequent research and reducing experimental variability. The gut microbiota analysis module utilizes functional microbiota-specific sequencing and disorder index calculation to deeply and accurately analyze changes in gut microbiota structure and function, particularly key microbiota related to gut-brain axis function, providing quantitative evidence for understanding the gut mechanisms of depression. The behavioral pharmacodynamic evaluation module, through balanced microbiota grouping, multidimensional behavioral testing, and comprehensive scoring, can comprehensively and objectively evaluate the effects of drugs on depressive-like behaviors and effectively differentiate the effects of different interventions. The gut-brain axis correlation determination module further integrates multi-level molecular data from behavioral, gut, and brain perspectives, innovatively constructing a gut-brain axis synergy index, providing a holistic quantitative indicator reflecting the functional state of the gut-brain axis, powerfully revealing the gut-brain axis synergistic mechanism by which drugs improve depression by regulating the gut microbiota. Therefore, by integrating multi-dimensional data and introducing innovative evaluation indicators, this system has achieved standardization, objectification, and comprehensiveness in the construction of animal experiment evaluation models and the assessment of intervention effects, providing a more reliable and in-depth technical platform for drug development and mechanism research targeting the gut microbiota. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps involved in constructing an evaluation model for animal experiments.

[0019] Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1 in this invention.

[0020] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] In this embodiment of the invention, reference Figure 1 The diagram shown is a flowchart illustrating the steps of a method for constructing an evaluation model for animal experiments according to the present invention. In this example, the method for constructing an evaluation model for animal experiments includes the following steps:

[0025] Step S1: Male SD rats were grouped and baseline measurements were taken to obtain a group baseline data table; based on the group baseline data table, spatiotemporal stress was implemented and stress sensitivity was assessed to obtain a stress sensitivity index;

[0026] In this embodiment of the invention, male SD rats (150-180g) were acclimatized for 14 days at 22℃ and 60% humidity. Based on body weight and acclimatization scores, they were randomly divided into a control group, a model group, a low-dose group of Citrus aurantium and Magnolia officinalis, a medium-dose group, a high-dose group, and a fluoxetine group, with 12 rats in each group. Baseline sucrose preference was measured at 1%, and body weight was recorded. Subsequently, all individuals except the control group underwent 28 days of spatiotemporally separated chronic stress: a four-time period × nine-stressor matrix was randomly assigned, ensuring 2-3 stressors per day and a total intensity of 5-7 points, increasing weekly. Behavioral responses (0-3 points) and body weight were recorded immediately after each stressor event. Sucrose preference was measured again on day 28, and the stress sensitivity index (SSI) was calculated as (1 - after preference / before preference) × 100%. An SSI ≥ 50% was considered highly sensitive, and a stress sensitivity index table was generated.

[0027] Step S2: Perform functional microbiota-specific sequencing on male SD rats based on the stress sensitivity index to obtain functional microbiota sequencing data packages; calculate the intestinal microbiota dysbiosis index from the functional microbiota sequencing data packages.

[0028] In this embodiment of the invention, rats with SSI ≥ 50% were screened, with 10 rats retained in each group (total sample size: 60 rats). Fresh feces were collected within 4 hours, and DNA was extracted using a modified CTAB method to establish a fecal DNA sample library. V3-V4 specific triple primer pairs were designed for three functional bacterial groups: short-chain fatty acid-producing bacteria, bile acid-metabolizing bacteria, and tryptophan-metabolizing bacteria. A multiplex PCR system was constructed (three-stage gradient annealing, final primer concentration 0.3-0.4 μM). After product purification, the products were sequenced using an Illumina MiSeq PE300. Raw reads underwent dual-threshold filtering and chimera rejection using Cutadapt-FLASH-UCHIME, and 97% similarity clustering generated a genus-level OTU table. Spearman and random forest dual screening, combined with SSI, identified 15 key genera and assigned weights (positive weight 1.1-1.5 for beneficial bacteria, negative weight -1.2 for conditionally pathogenic bacteria). The corrected change values ​​were calculated and the original scores were obtained by weighting. The gut microbiota disorder index (GMD) and its classification data table were obtained by linear normalization from blank 0 to model 100.

[0029] Step S3: Based on the gut microbiota dysbiosis index, conduct drug intervention and behavioral testing to obtain a multidimensional behavioral parameter set; perform a comprehensive behavioral evaluation on the multidimensional behavioral parameter set to obtain a comprehensive depressive behavior score;

[0030] In this embodiment of the invention, the groups are regrouped using a serpentine method based on GMD ranking to ensure no difference in GMD between groups; a gavage regimen is formulated: Citrus aurantium-Magnolia officinalis 10g / kg. -1 Medium dose 15g·kg -1 Or 20g·kg -1 Fluoxetine 10 mg / kg -1 A control group with an equal volume of physiological saline was used for 28 consecutive days. From D0 to D28, body weight was recorded every 7 days, and the drug tolerance index (TI) was calculated as: (weight growth rate / blank growth rate) × 100%. From D21 to D25, forced swimming (4 minutes of immobility after immersion), open field (center rest), and elevated cross maze (open arm rest) tests were performed sequentially. Raw data were corrected for outliers and standardized using Z-scores; the open field and maze indices were negative.

[0031] Principal component regression was used to determine the weights as 0.45, -0.30, and -0.25. The initial scores were calculated and converted to standardized DBCS = 50 + 10·(Score_raw - μ_blank) / σ_blank based on the blank group, forming a comprehensive depressive behavior score.

[0032] Step S4: Targeted detection of gut-brain components is performed based on the comprehensive depressive behavior score to obtain a gut-brain molecular expression profile; gut-brain axis synergy analysis is performed based on the comprehensive depressive behavior score and the gut-brain molecular expression profile to obtain a gut-brain axis synergy index; an animal experimental evaluation model is constructed based on the stress sensitivity index, gut microbiota dysbiosis index, comprehensive depressive behavior score, and gut-brain axis synergy index.

[0033] In this embodiment of the invention, six representative samples were selected based on the principle that the DBCS values ​​of each group were closest to the mean. Serum, colonic contents, hippocampus, and prefrontal cortex were collected after anesthesia and stored at -80℃. Sinapic acid, butyric acid, and deoxycholic acid in the fecal extract were quantified by LC-MS / MS in negative ion MRM mode. A matrix standard curve (R²) was plotted using the internal standard method. 2 ≥0.995), LOD / LOQ, recovery rate, and precision validation were completed; BDNF was measured by ELISA in brain tissue homogenate, and 5-HT and 5-HIAA were measured by HPLC-ECD; IL-6 was measured in serum using the CBA microbead method, generating a gut-brain molecular expression profile. The correlation between the seven indicators and DBCS was calculated after Z-normalization (threshold |r|≥0.30), and all were included. Standardized coefficients β1-β7 were obtained from PLSR and weighted accordingly. The gut metabolic index GM, brain function index BN, and inflammatory index INF were calculated stratified, and the product of the three indices S was determined. k Form a collaborative model. For S k Skewness / kurtosis correction and z-score mapping from 0 to 100 were performed, and interpretation criteria were established (0-20 normal, 20-40 mild, 40-60 moderate, 60-100 severe). After passing Cronbach's α, Boot-CI, and ±10% sensitivity tests, the gut-brain axis synergistic index (GBCI) was output and written into the total model dataset along with SSI, GMD, and DBCS, completing the construction of a multi-level animal experimental evaluation model.

[0034] As an example of the present invention, reference is made to Figure 2 As shown, in this example, step S1 includes:

[0035] Step S11: Male SD rats were placed in a standard animal room with a temperature set at 20℃ to 24℃ and a humidity set at 56% to 64% to conduct an experimental adaptability assessment and obtain an animal adaptability score sheet.

[0036] In this embodiment of the invention, male SD rats (weighing 150-180g) were housed for 14 days in a barrier-level animal room with a temperature of 22±1℃, humidity of 60±2%, and a light-dark cycle of 12h-12h. Individual weight, food intake (g), water intake (ml), and general activity level were recorded daily at 08:00 and 20:00. The mean and standard deviation of the same indicator over 14 days were compared with a preset normal range, and a single-item score was calculated using the following formula: S i= [(X i -μ n ) / (σ n )] 2 , wherein X i is the actually measured mean value of the i-th item, μ n and σ n are respectively the central value and standard deviation of the normal interval of the indicator; the total adaptability score AS is obtained by summing the scores of the four indicators, AS=∑S i . When AS≤4, it is determined as "adaptation"; when 4<AS≤8, it is determined as "mild deviation"; when AS>8, it is determined as "excluded". Individuals determined as "adaptation" and "mild deviation" are retained, an animal adaptability scoring table is established, and the number, body weight, four individual scores and AS are listed.

[0037] Step S12: performing grouping and baseline measurement according to the animal adaptability scoring table to obtain a grouped baseline data table;

[0038] In the embodiment of the present invention, the retained individuals are divided into a blank group, a model group, a low-dose Aurantii Fructus-Magnoliae Officinalis Cortex group, a medium-dose Aurantii Fructus-Magnoliae Officinalis Cortex group, a high-dose Aurantii Fructus-Magnoliae Officinalis Cortex group, and a fluoxetine control group by a stratified random method, with 12 individuals in each group; the stratification factors are body weight (150-165g, >165-180g) and AS (≤4, >4). After grouping, single-cage feeding is implemented, and conventional feed and pure water are supplied continuously for 48h. Then a baseline sucrose preference test is performed: one bottle of 1% sucrose solution and one bottle of pure water are provided for free intake for 1h, and the consumption of sucrose solution V s and pure water consumption Vw are measured; the sucrose preference _pre is calculated as V s / (V s +Vw)·100%. Body weight is weighed on the same day and recorded as W_pre. All data are aggregated to form the grouped baseline data table.

[0039] Step S13: implementing time-space separated stress by using a pre-established stress source library according to the grouped baseline data table to obtain a stress implementation record table;

[0040] In the embodiment of the present invention:

[0041] (1) Stress source classification and intensity assignment: 3 items of physiological stress, 4 items of environmental stress, and 2 items of physical stress; the corresponding intensity coefficients are 1, 2 and 3 points respectively, which have been listed in the stress source intensity classification table.

[0042] (2) Space-time matrix construction: 24h is divided into four time periods: morning (08:00-12:00), noon (12:00-16:00), evening (16:00-20:00) and night (20:00-08:00), and a 4×9 matrix M ts (t=1-4, s=1-9) is established.

[0043] (3) Stress allocation: Based on the principle of modified Latin square, first in M ts Enter 9 stressors, and then call the intensity constraint algorithm to ensure the total daily intensity ∑ j S j ·δ j =5-7 points, where S j Let δ be the intensity coefficient of the j-th stress source. j The flag (1 or 0) was used to indicate whether the stress response was implemented. After generating a 28-day stress schedule, participants were assigned to the model group and the four-drug group.

[0044] (4) Individualized Implementation and Monitoring: Operators strictly follow the schedule to complete fasting, water abstinence, and cold water swimming during designated time periods; immediately after each operation, a score is given based on a pre-set behavioral response scale (0-3 points), and recorded in the individual stress response record sheet; weight is measured at the same time each week. k Weight change rate ΔW in week k k =(W k -W_pre) / W_pre·100%.

[0045] (5) Cumulative load calculation: Daily load L_d = ∑ i (S i ·T i ·R i ), where S i T is the strength coefficient. i R is the duration normalization coefficient (24h stress T = 1, 3min stress T = 0.0021, etc.). i Behavioral response scores; 28-day cumulative stress load CL = ∑_{d=1}^{28}L_d; average daily load in the fourth week. Compared with the average daily load of the first week The ratio yields the fitness index. All data are aggregated to generate a stress response record table.

[0046] Step S14: Based on the stress implementation record table and the group baseline data table, conduct a stress sensitivity assessment to obtain the stress sensitivity index.

[0047] In this embodiment of the invention, after completing 28 days of stress, a sucrose preference test was performed again, and the sucrose solution consumption V was recorded. s 'Based on pure water consumption Vw', calculate sugar water preference _post=V s ' / (V sThe stress sensitivity index (SSI) is calculated as follows: SSI = (1 - sucrose preference_post / sucrose preference_pre)·100%, where sucrose preference_pre and sucrose preference_post represent sucrose preference before and after stress, respectively. Individuals with an SSI ≥ 50% are considered highly sensitive. The SSI, CL, and AI of all individuals are listed in the stress sensitivity index table to provide a basis for animal selection and data correction in subsequent experimental stages.

[0048] Preferably, step S13 includes:

[0049] Using a pre-established stressor database, stressors are classified and their intensity is assessed based on the grouped baseline data table, resulting in a stressor intensity classification table.

[0050] The 24 hours are divided into four time periods, and a 4×9 two-dimensional spatiotemporal matrix is ​​constructed based on the stressor intensity classification table. The time periods of the two-dimensional spatiotemporal matrix are listed as stressors.

[0051] Stress allocation is performed based on a two-dimensional spatiotemporal matrix to obtain a spatiotemporal stress allocation scheme.

[0052] Individualized stress implementation and monitoring were conducted based on a spatiotemporal stress allocation scheme to obtain individual stress response records;

[0053] Cumulative stress load is calculated based on individual stress response records to obtain a stress implementation record table.

[0054] In this embodiment of the invention, a pre-established stressor database includes nine chronic unpredictable stress operations: 24-hour fasting, 24-hour water deprivation, 5-minute cold water swimming (4°C), 24-hour cage tilting (45°C), 24-hour wet cage (bedding moisture content ≈70%), 24-hour empty cage (no bedding), 24-hour crowded feeding (6-8 animals / cage), 2-hour restraint (transparent polycarbonate restraint tube), and 3-minute tail clamping (rubber tail clamp 1cm from the tail root). Based on the reported neuroendocrine response amplitudes in the literature, intensity coefficients are assigned sequentially: 1 point for empty cage, cage tilting, and crowded feeding; 2 points for wet cage, fasting, and restraint; and 3 points for water deprivation, tail clamping, and cold water swimming. The stressor, category, effective time, intensity coefficient, and key operational points are listed in a stressor intensity classification table, which is stored in a standardized laboratory template for direct retrieval later.

[0055] The 24-hour period is divided into four time slots according to circadian rhythms: early period (08:00-12:00), middle period (12:00-16:00), late period (16:00-20:00), and night period (20:00-08:00). A 4×9 matrix M is constructed with time slots as rows and nine stressors as columns. ts Where t∈{1,2,3,4} represents the time period, and s∈{1…9} represents the stressor number. The matrix element value is denoted as m. tsIt is initialized to 0.

[0056] For matrix M ts The Latin square permutation was implemented to ensure that each of the nine stressors appeared once and without repetition within the first four days; then the following constraints were used to recursively fill the 28-day stress schedule: (1) The same stressor does not reappear in the same time period within 7 consecutive days, constraint expression: m ts (d)·m ts (d+1)=0;(2) No stress of the same type should occur in the same time period on two adjacent days. If the category code is C s Then C must be satisfied. s (d,t)≠C s (d+1,t); (3) Select 2-3 stresses daily to meet a total intensity of 5-7 points, i.e., ∑ s S s ·δ s (d)∈[5,7], where S s δ is the strength coefficient. s (d) indicates whether the indicator (1 or 0) is selected on day d; (4) Weekly average intensity increasing pattern: the weekly average intensity is 5 points in the first week, 5.5 points in the second week, 6 points in the third week, and 6.5 points in the fourth week. For dates that cannot directly meet the constraints, the backtracking insertion method is used for adjustment. The final output is a spatiotemporal stress allocation scheme for a 28-day × 4-period period, which includes the date, time period, stress source, and intensity.

[0057] Rats in the model group and drug group underwent stress according to the prescribed regimen. Fasting and water restriction were achieved by periodically removing feed or water bottles; cold water swimming was conducted in a 30cm diameter, 30cm deep tank, with the water temperature monitored in real-time to maintain 4±0.5℃; cage tilting was performed using an adjustable-angle metal bracket to fix the cage at 45°; wet cages were uniformly sprayed with deionized water on the bottom, and the moisture content was measured in real-time; empty cages had their bedding removed and the bottom cleaned; crowd feeding involved placing 6-8 rats from the same group into a single standard IVC cage; restraint was performed using a 6cm diameter polycarbonate tube to restrict movement; tail clamps were used to fix the rats in place for 3 minutes using a 5mm wide rubber clamp; all procedures were cross-checked by two technicians. Immediately after the stress period ended, startle reflexes, shivering, struggling, and withdrawal were recorded using a 0-3 point behavioral rating scale: 0 points for no obvious response, and 3 points for a severe response. Data were recorded in an individual stress response record sheet; body weight was measured daily after 20:00 using a 0.1g precision electronic scale.

[0058] Daily load L_d=∑ i (S i ·T i ·R i ), S i Let T be the stress intensity coefficient for the i-th stress. iR is the duration normalization factor (example: 24h operation T=1, 2h operation T=2 / 24, 5min operation T=5 / 1440). i Scoring of behavioral responses. Calculating the 28-day cumulative load CL = ∑_{d=1}^{28}L_d; the average daily loads for the first and fourth weeks are respectively... Stress Adaptability Index Date, time period, stressor, S i T i R i L_d, CL, and AI are integrated into a stress implementation record form, which is archived in CSV format for subsequent stress sensitivity assessment and data traceability.

[0059] Preferably, the functional microbial community-specific sequencing in step S2 includes:

[0060] Sensitive individuals are screened using the stress sensitivity index to obtain a sensitive individual screening table;

[0061] Fecal samples were collected and processed according to a sensitive individual screening table to obtain a gut microbiota DNA sample library;

[0062] Identify conserved regions of functional flora based on gut microbiota DNA sample banks;

[0063] Extracting regional sequence features from conserved regions of functional bacterial communities;

[0064] Specific primer pairs were designed based on regional sequence characteristics to obtain primer combinations specific to functional bacterial communities.

[0065] The multiplex PCR system was optimized based on primer combinations specific to functional bacterial communities, resulting in an optimized multiplex PCR system.

[0066] Based on the optimized multiplex PCR system, primer amplification efficiency was balanced to obtain a set of PCR amplification products of functional bacterial groups.

[0067] Specific libraries were constructed from the PCR amplification product set of functional bacterial groups, and library quality control was performed to obtain functional bacterial group sequencing libraries.

[0068] High-throughput sequencing was performed on the functional microbial community sequencing library, and preliminary data processing was carried out to obtain the functional microbial community sequencing data package.

[0069] In this embodiment of the invention, the Stress Sensitivity Index (SSI) is used as a threshold of 50% for recruitment. All rats are sorted by SSI in descending order, and individuals with an SSI ≥ 50% are selected, with 10 rats retained from each group. A screening table for sensitive individuals is established, with fields including rat number, weight, SSI value, and experimental group.

[0070] Individuals selected for screening were placed in individual metabolic cages, and fresh fecal samples (≥200 mg) excreted within 4 hours were collected. The entire procedure was performed on an ice bath at 4°C. The samples were aliquoted into three tubes: a DNA extraction tube, a metabolite analysis tube, and a backup tube. DNA extraction was performed using a modified CTAB method: 50 mg of fecal sample was weighed and added to 800 μl of preheated CTAB lysis buffer (2% CTAB·w / v, 1.4 M NaCl, 20 mM EDTA, 100 mM Tris-HCl, pH 8.0) at 65°C, along with 20 μl of proteinase K (20 mg·ml). -1 The DNA was incubated in a water bath at 65℃ for 60 min; after cooling to room temperature, an equal volume of chloroform-isoamyl alcohol (24:1) was added, and the mixture was inverted and mixed for 10 min. The mixture was then centrifuged at 12000×g at 4℃ for 15 min. The supernatant was transferred to a new tube, and 0.7 volume of isopropanol was added to precipitate the DNA. The tube was incubated at -20℃ for 30 min; centrifuged at 12000×g at 4℃ for 10 min, and the supernatant was discarded. The tube was washed twice with 70% ethanol, vacuum dried for 5 min, and dissolved in 50 μl of TE buffer (pH 8.0). A was measured using NanoDrop. 260 / A 280 With DNA concentration, A is required 260 / A 280 Between 1.8 and 2.0 and with a concentration ≥ 20 ng·μl -1 All samples were compiled to establish a gut microbiota DNA sample bank.

[0071] The 16S rRNA V3-V4 sequences of three major functional bacterial groups—short-chain fatty acid-producing bacteria, bile acid-metabolizing bacteria, and tryptophan-metabolizing bacteria—were obtained from the NCBI database and imported into the MEGA platform for ClustalW multiple sequence alignment. The alignment results identified fragments that were ≥95% conserved within each functional bacterial group and ≤80% homologous between groups. The start and end base positions and sequence information were recorded as templates for subsequent primer design.

[0072] Conserved fragments were input using the Primer-BLAST online tool, with parameters set as follows: GC content 45-55%, annealing temperature 55-60℃, product length 420-480bp, and avoidance of ≥4 consecutive identical bases; three sets of candidate primer pairs were obtained. Further in vitro specificity searches were performed using the Mothur-SILVA database to exclude cross-amplification; finally, three primer pairs were determined: F-SCFA / R-SCFA, F-BA / R-BA, and F-TRP / R-TRP. An 8bp functional tag (Index1-Index3) and Illumina adapter sequence were added to the 5' end of each primer. Sequence, Tm, GC% and other parameters were recorded and compiled into functional microbial community-specific primer combinations.

[0073] The reaction volume was 50 μl: 2 μl template DNA (20-50 ng), 5 μl 10× buffer, 1 μl dNTP (10 mM), 0.4 μM each of F-SCFA and R-SCFA, 0.3 μM each of F-BA and R-BA, 0.3 μM each of F-TRP and R-TRP, 0.5 μl (2.5 U) high-fidelity polymerase, and nucleic acid-free water to 50 μl. The PCR program was: 95℃ for 5 min; (95℃ 30 s, 55℃ 30 s, 72℃ 45 s) × 10; (95℃ 30 s, 53℃ 30 s, 72℃ 45 s) × 10; (95℃ 30 s, 51℃ 30 s, 72℃ 45 s) × 10; 72℃ for 10 min. Positive and negative controls were included for each batch of reactions. The product was confirmed by 1.5% agarose gel electrophoresis (120V, 25min) to be a single band of approximately 450-500bp. Repeatability was judged by a Ct deviation of <0.5.

[0074] The amplification efficiency E of the three primer pairs was detected by qPCR: E = (10^(-1 / slope)-1)·100%; the efficiencies of F-SCFA, F-BA, and F-TRP were 97.2%, 94.5%, and 95.8%, respectively. For F-BA / R-BA with an efficiency <95%, the final primer concentration in the system was increased by 0.05 μM, and the E was detected again to reach over 96%, thus achieving amplification efficiency equilibrium.

[0075] PCR products were purified using 1.8 volumes of AMPure XP magnetic beads, and the concentration was determined using the Qubit dsDNA HS kit to ensure ≥10 ng / μl. -1 After equimolar pooling of the technical repeat products, end repair (A-tailing), adapter ligation, and eight PCR amplifications with adapter addition were performed; followed by magnetic bead purification. The library fragments were 500-550 bp in length, and distribution peaks and primer-free dimer detection were performed using an Agilent 2100 bioanalyzer. Absolute quantification of the library was performed using qPCR, requiring a library concentration of 2-4 nM per sample. The final mixed library was 4 nM, equilibrated with 10% PhiX, and then ready for assay.

[0076] Illumina MiSeq paired-end 300bp sequencing, with a target of 100,000 effective reads per sample. Raw data was assessed using FastQC, with a Q30 ≥ 80%. Cutadapt was used to remove adapter sequences and filter low-quality reads (Qscore < 25), reads containing N > 2, or reads < 200bp in length. Paired-end sequences were assembled using FLASH, with a minimum overlap of 20bp and a maximum mismatch rate of 10%. UCHIME was used to detect and remove chimeras. Based on primer tags, reads were first categorized into three groups: short-chain fatty acid bacteria, bile acid metabolism bacteria, and tryptophan metabolism bacteria. Then, they were further split according to sample barcodes, and FASTQ files and quality statistics tables were output. A functional microbial sequencing data package was generated, including high-quality sequences, total reads, effective reads, Q30 ratio, and assembly success rate for each sample's three functional microbial groups, providing input for subsequent calculations of microbial dysbiosis.

[0077] Preferably, the calculation of microecological disorder in step S2 includes:

[0078] The functional microbial community sequencing data packets were subjected to dual-threshold quality filtering and sequence chimerism processing to obtain chimeric sequences;

[0079] Microbial taxonomic units were constructed from chimeric sequences to obtain a microbial taxonomic phylogenetic table.

[0080] Based on the microbial community classification phylogenetic table, key bacterial genera were screened and weighted according to the stress sensitivity index, resulting in a key bacterial genera weight table.

[0081] The relative abundance data of key bacterial genera were extracted from the blank group rats; the abundance changes were calculated and corrected based on the relative abundance data of key bacterial genera, the bacterial community classification phylogenetic table and the key bacterial genera weight table, and the bacterial genera change correction table was obtained.

[0082] The gut microbiota disorder index is calculated based on the genus variation correction table and the key genus weight table.

[0083] In this embodiment of the invention, the original FASTQ files of functional microbial community sequencing data are first evaluated for base quality using FastQC, retaining data with an average quality score ≥25 and single sequences containing ≤2 N bases; then, Cutadapt is used to remove adapter sequences and filter read sequences <200bp in length. Quality-compliant paired-end sequences are then assembled using FLASH, with a minimum overlap of 20bp and a maximum mismatch rate of 10%. Successfully assembled sequences are input into the UCHIME algorithm for chimera detection; chimeras are identified and removed at a 97% similarity threshold, and the number of such sequences is denoted as N_chimera. After removal, a high-quality clean sequence set is obtained, and the total number of sequences is denoted as N_clean.

[0084] Clean sequences were imported into the QIIME2 platform. Operational taxonomic units (OTUs) were generated using the VSEARCH clustering module with a 97% similarity threshold, and a representative sequence for each OTU was recorded. Representative sequences were annotated at the genus level using a self-built functional microbial reference database; sequences that did not match were categorized as "Unclassified". An OTU abundance table was generated, containing a two-dimensional matrix of sample × genus, where rows represent samples, columns represent genus, and elements represent the number of corresponding OTU sequences. The relative abundance A was calculated. ij =reads ij / ∑ k reads ik reads ij This represents the sequence number of the genus j in the i-th sample. The output of this matrix is ​​a phylogenetic table of the bacterial community.

[0085] First, Spearman rank correlation analysis was performed on the relative abundance of each genus and its SSI (Sum of Saturations). Genuses with |ρ| ≥ 0.3 and P < 0.05 were selected for the candidate set. Then, a random forest algorithm was used to build a model with SSI as the dependent variable and relative abundance as the independent variable, extracting the Mean Precision Degradation (MAD) and Gini index (GI). The candidate genera were sorted in descending order of MAD + GI, and the top 15 genera were selected as key genera. Weighting coefficients w were set based on the functional positioning and relevance of the literature. j Butyrate-producing bacteria (1.5), acetate-producing bacteria (1.2), bile acid-metabolizing bacteria (1.3), tryptophan-metabolizing bacteria (1.4), probiotics (1.1), and conditionally pathogenic bacteria (-1.2). All results are imported into a key genus weight table, with fields including genus name, functional category, ρ, MAD, GI, and w. j .

[0086] A_control: Mean relative abundance of key bacterial genera extracted from blank control samples j and standard deviation σ_control j For any experimental sample k, calculate the relative abundance change rate ΔA_k of the j-th genus. j =(A_k) j -A_control j ) / A_control j Subsequently, an abundance correction factor CF was introduced. j =log10(A_control j ×10000+1), to obtain the correction change value C_k j =ΔA_k j ·CF j The above calculations generate a genus variation correction table, with the dimension being the number of samples × 15.

[0087] For each sample k, the original disorder score RS_k is obtained according to the following formula: RS_k=∑_{j=1}^{15}(C_k) j ·w j ), where w j C_k represents the weighting coefficient for key bacterial genera. j To correct for variations, the raw scores of the blank group and the model group are denoted as RS_blank. - With RS_model - .

[0088] Finally, linear standardization is performed on all samples: If GMD_k < 0, set it to 0; if GMD_k > 100, set it to 100. After standardization, GMD values ​​range from 0 to 100, and are categorized into four levels: normal, mild, moderate, and severe, based on 0-20, 20-40, 40-60, and 60-100. The contribution rate of bacterial genus in all samples is calculated according to |C_k|. j ·w j | / ∑ l |C_k l ·w l • 100% calculation, visualizing the impact of core bacterial genera on GMD. The output file is a gut microbiota dysbiosis index data table, including sample number, RS_k, GMD_k, and contribution rate columns for each bacterial genera.

[0089] Preferably, the drug intervention and behavioral testing in step S3 include:

[0090] Based on the gut microbiota dysbiosis index, gut microbiota balance groups were formed to obtain a gut microbiota balance grouping table;

[0091] The drug tolerance index was calculated based on the microecological balance grouping table;

[0092] A drug intervention tracking table is generated based on the drug tolerance index;

[0093] Multidimensional behavioral parameters were obtained by conducting multidimensional behavioral tests on male SD rats based on the drug intervention tracking table.

[0094] In this embodiment of the invention, all sensitive rats with obtained GMD values ​​were sorted in ascending order of value and sequentially assigned to the blank group, model group, low-dose Citrus aurantium-Magnolia officinalis group, medium-dose Citrus aurantium-Magnolia officinalis group, high-dose Citrus aurantium-Magnolia officinalis group, and fluoxetine group using a serpentine method, ensuring that the sample size in each group was the same (10 rats per group). After allocation, one-way ANOVA was used to test the differences in GMD between groups. If P ≥ 0.05, the balance was considered successful; if P < 0.05, the nearest neighbor swap algorithm was immediately used to swap extreme individuals between groups, and the test was repeated until P ≥ 0.05. All information was summarized into a microecological balance grouping table, with fields including rat number, weight, GMD, and group.

[0095] For each rat, body weights W0, W7, and W were recorded on D0, D7, D14, D21, and D28 after drug administration. 14 W 21 W 28 Weight was measured using an electronic scale with an accuracy of 0.1g. Weight gain rate (GR) at each time point was recorded. t =(W t -W0) / W0·100%. The mean weight gain rate of the blank group is labeled as GR_blank. t Drug Tolerance Index (TI) t =GR_drug t / GR_blank t 100%, TI t ≥80% is considered good tolerance, 50%-80% is considered moderate tolerance, and <50% is considered poor tolerance. All TI values ​​and corresponding body weight data are recorded in the drug intervention tracking table.

[0096] Solution preparation: The concentration of the aqueous extract of Citrus aurantium and Magnolia officinalis compound is 1 g / ml. -1 (low dose) with 2g·ml -1 (High dose); Fluoxetine solution 1 mg / ml -1 The control group and the model group were given the same volume of physiological saline. Daily dose setting: low dose 10g / kg -1 Medium dose 15g·kg -1 High dose 20g·kg -1 Fluoxetine 10 mg / kg --1 The dosage volume is uniformly 10 ml / kg. --1 Administer orally via gavage daily from 09:00 to 10:00. Record general indicators such as mental state, coat, and stool characteristics before and after the procedure. The tracking form should list the date, time of administration, type of drug, dosage, weight, GR (growth rate), TI (total irritation), and immediate observation items. Both staff members must sign for confirmation.

[0097] Multidimensional behavioral test:

[0098] 1. Forced swimming test conducted on D21. The cylindrical apparatus was 20cm in diameter and 50cm high, with a water depth of 30cm and a water temperature of 24±1℃. Pre-test training was conducted for 15 minutes on D20, followed by a 6-minute formal test. The camera was fixed at the top. An automatic video analysis system was set to monitor the detection area, and the stationary time (IMM4) was recorded for the last 4 minutes.

[0099] 2. Open field test D23. A 100cm×100cm×40cm PVC black square box was used, with 25 squares on the bottom and 9 squares in the center. Each test lasted 5 minutes, with the camera viewing the test from a vertical overhead angle. The system outputs the total movement distance (DIST) and the center dwell time (CENT).

[0100] 3. The elevated cross maze test was conducted on D25. The device arm was 50cm long, 10cm wide, and 50cm high. The open arm had no guardrails, while the closed arm had three 40cm barriers. The test lasted 5 minutes, starting from the central platform. The system output the number of times the open arm entered (OPEN_Entry) and the dwell time of the open arm (OPEN_Time).

[0101] 4. The raw values ​​of behavioral parameters were compiled into a multidimensional behavioral parameter set. Standardization was then performed: Z_k = (X_k - μ) / σ, where X_k is the raw value, and μ and σ are the mean and standard deviation of the entire sample, respectively. The time spent in the open field center and the time spent in the open arm were multiplied by -1 before standardization to align the direction with the degree of depression. The multidimensional behavioral parameter set ultimately retained five columns: IMM4_Z, CENT_Z, OPEN_Time_Z, DIST_Z, and OPEN_Entry_Z, for the next step of comprehensive scoring.

[0102] Preferably, the behavioral comprehensive evaluation in step S3 includes:

[0103] The multidimensional behavioral parameter set is subjected to behavioral standardization processing to obtain a behavioral standard score data table;

[0104] Determine the core indicator weight coefficient table based on the behavioral standard score data table;

[0105] The core indicator standard scores are extracted from the behavioral standard score data table based on the core indicator weight coefficient table. The core indicator standard scores include the standard score for the forced swimming immobility time, the standard score for the dwell time in the open field center area, and the standard score for the dwell time in the elevated cross maze open arm.

[0106] Calculate the initial depressive behavior comprehensive score based on the standard scores of the core indicators;

[0107] Obtain initial rating data from the blank group and generate a standardized comprehensive score for depressive behaviors;

[0108] The depressive behavior composite score for each rat was calculated based on the initial depressive behavior composite score and the standardized depressive behavior composite score.

[0109] In this embodiment of the invention, a multidimensional behavioral parameter set is imported into SPSS 26.0, and box plot outlier checks are performed on IMM4 (forced swimming immobility time), CENT (open field center dwell time), OPEN_Time (elevated cross maze open arm dwell time), DIST (total open field movement distance), and OPEN_Entry (number of open arm entries). For each indicator, the upper quartile Q3, lower quartile Q1, and interquartile range IQR = Q3 - Q1 are calculated. Values ​​below Q1 - 1.5·IQR or above Q3 + 1.5·IQR are replaced with the corresponding boundary values. Normality is verified by performing a ln(X+1) transformation on IMM4 and CENT with skewness |Skew|>1 or kurtosis |Kurt|>3. Subsequently, the mean μ and standard deviation σ are calculated, and the standard score Z = (X - μ) / σ. To maintain consistent indicator direction, CENT_Z and OPEN_Time_Z are multiplied by -1. The five Z-values ​​and sample numbers are summarized to generate a behavioral standard score data table.

[0110] The 5×5 Pearson correlation matrix R was calculated based on the behavioral standard score data table. The results showed strong correlations between IMM4_Z and CENT_Z (r = 0.72) and between IMM4_Z and OPEN_Time_Z (r = 0.66), while the correlation between DIST_Z and OPEN_Entry_Z was relatively weak. Principal component analysis was performed on all indicators, with a KMO value of 0.71 and a Bartlett's test of sphericity (P < 0.001). The first two principal components with eigenvalues ​​> 1 were extracted, with a cumulative contribution rate of 82.4%. The loadings of each indicator on the first principal component were, in descending order: IMM4_Z (0.91), CENT_Z (-0.78), OPEN_Time_Z (-0.65), DIST_Z (-0.31), and OPEN_Entry_Z (-0.29). Based on the loading magnitude, IMM4_Z, CENT_Z, and OPEN_Time_Z were selected as the core indicators. Through resampling verification using 80% of the samples and 100 iterations, the selection frequency of all three indicators was >92%. Using the overall behavior's first principal component score (PC1) as the dependent variable and the three core indicators as independent variables, a multiple linear regression was established, yielding standardized regression coefficients β1 = 0.45, β2 = -0.30, and β3 = -0.25, corresponding to IMM4_Z, CENT_Z, and OPEN_Time_Z, forming the core indicator weight coefficient table.

[0111] For each rat, extract IMM4_Z, CENT_Z, and OPEN_Time_Z and substitute them into the following formula:

[0112] Score_raw = 0.45·IMM4_Z - 0.30·CENT_Z - 0.25·OPEN_Time_Z, where Score_raw represents the initial comprehensive score of depressive behavior, and IMM4_Z, CENT_Z, and OPEN_Time_Z are the standard scores of the three core indicators, respectively. All Score_raw values ​​are imported into the initial depression score table.

[0113] The mean μ_blank and standard deviation σ_blank of the blank group (Score_raw) were calculated. A linear transformation was performed on all samples: DBCS = 50 + 10·(Score_raw - μ_blank) / σ_blank, where DBCS is the Standardized Depressive Behavior Score, and a value greater than the mean of the blank group indicates an increased degree of depression. Reference intervals were set based on the distribution of the blank and model groups: 40-60 (normal), 60-70 (mild), 70-80 (moderate), and >80 (severe). The sample numbers, Score_raw, and DBCS were then entered into the Depressive Behavior Score data table.

[0114] One-way ANOVA was performed on the Depressive Behavior Scale (DBCS) data. The results showed that the mean of the model group (78.6±5.3) was significantly higher than that of the blank group (52.1±4.1) (P<0.001). The DBCS values ​​of the low-dose, medium-dose, and high-dose groups of Citrus aurantium-Magnolia officinalis and the fluoxetine group were 66.4±4.7, 63.5±4.5, 60.8±4.3, and 58.2±4.6, respectively. The effect rates of each drug group were calculated as follows: low-dose = (78.6-66.4) / (78.6-52.1)·100% = 47.8%; medium-dose = (78.6-63.5) / (78.6-52.1)·100% = 56.9%; high-dose = 68.2%; fluoxetine = 75.1%. The final DBCS, group, and effect rate will be integrated and output to provide a behavioral quantitative basis for the subsequent construction of the gut-brain axis synergy index.

[0115] Preferably, the gut-brain component targeting detection in step S4 includes:

[0116] A representative sample screening table was obtained by screening the comprehensive depressive behavior score.

[0117] Tissue samples were collected and processed according to a representative sample screening table to obtain a multi-component biobank.

[0118] Samples of colon contents, serum, and hippocampal and prefrontal cortex tissue were extracted from a multi-component biobank.

[0119] The colon contents sample was pretreated to obtain an intestinal metabolite extract;

[0120] The intestinal metabolite extract was subjected to chromatographic-mass spectrometry analysis to obtain raw spectral data.

[0121] Based on the raw spectral data, intestinal metabolites were quantitatively processed to obtain a table of intestinal metabolite content.

[0122] Neuromolecular analysis of brain tissue samples from the hippocampus and prefrontal cortex was performed to obtain a table of brain functional molecular data.

[0123] Serum samples were subjected to multiple detection of serum inflammatory factors to obtain a serum inflammatory factor data table;

[0124] Gut-brain molecular expression profiles were generated based on intestinal metabolite content tables, brain functional molecular data tables, and serum inflammatory factor data tables.

[0125] In this embodiment of the invention, the mean μ_g and standard deviation σ_g of the Depressive Behavior Comprehensive Score (DBCS) were calculated for each group. A distance coefficient D was calculated for each rat within each group. i =|DBCS i -μ_g| / σ_g. The rats in each group were divided according to D... i Sort in ascending order, select D i For individuals with a value ≤0.5 and ranked in the top 6, a representative sample screening table is created, with fields including ID, group, weight, DBCS, and D. i Six birds were retained in each group, for a total of 36 birds, which then proceeded to the next stage of testing.

[0126] Representative samples were administered 3.5 ml / kg of 10% chloral hydrate solution after a 12-hour fast. -1 Intraperitoneal anesthesia was administered. 5 ml of blood was collected from the abdominal aorta, and serum was separated at 3000×g for 10 min at 4°C, aliquoted into 0.5 ml tubes, and stored at -80°C. Approximately 300 mg of contents from the distal 3 cm of the colon was obtained via laparotomy, aliquoted into 100 mg tubes, and flash-frozen in liquid nitrogen. The skull was rapidly opened, and the hippocampus and prefrontal cortex were separated, with approximately 50 mg tissue blocks from each section immediately frozen in liquid nitrogen. All samples were assigned the same serial number and entered into a multi-component biobank.

[0127] Accurately weigh 50 mg of frozen colon contents and add 1.0 ml of methanol-water (4:1, v / v) solution and 10 μl of internal standard mixture (isooctanoic acid 100 μg·ml⁻¹). Vortex for 1 min, sonicate on ice for 10 min, and centrifuge at 12000×g for 15 min at 4℃. Transfer 800 μl of the supernatant to a new tube, add an equal volume of n-hexane to extract the bile acid fraction, centrifuge at 12000×g for 10 min, and collect the lower methanol phase. Purge to 200 μl with nitrogen and filter through a 0.22 μm membrane to obtain the intestinal metabolite extract.

[0128] The liquid chromatography system was configured with a C18 column (2.1 mm × 100 mm, 1.7 μm). Mobile phase A was 0.1% formic acid aqueous solution, and mobile phase B was acetonitrile. The mobile phase gradient was: 0–2 min 5% B, 2–8 min 5–50% B, 8–10 min 50–95% B, 10–12 min 95% B, 12.1–15 min 5% B; the flow rate was 0.3 mL / min. -1 The column temperature was 40℃. Mass spectrometry was performed in ESI negative ion mode with multiple reaction monitoring (MRM). Ion source parameters were: spray voltage 3.0 kV, nebulizer gas 10 L / min. -1 The instrument was dried at 350℃. The acquisition channel covered ion transfer of sinapic acid (m / z 179→135), butyric acid (m / z 87→43), and deoxycholic acid (m / z 391→345). The instrument acquired raw spectral data and stored them according to sample number.

[0129] Quantification was performed using the internal standard method. Single metabolite content C i =(A i / A_IS)·(C_IS·V_ext / W_sam), where A i Here, A_IS represents the peak area of ​​the metabolite, C_IS represents the internal standard peak area, V_ext represents the extraction solvent volume, and W_sam represents the sample mass. The contents of sinapic acid, butyric acid, and deoxycholic acid are summarized in a table of intestinal metabolite contents (unit: μg·g). -1 (Dried manure).

[0130] 50 mg each of hippocampal and prefrontal cortex tissue were added to 500 μl of lysis buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1% NP-40, 1 mM PMSF). After homogenization, the supernatant was collected at 4℃ and 12000×g for 15 min. BDNF content was determined using an ELISA kit, with readings taken at 450 nm using a microplate reader. The concentration (ng·g) was calculated using a standard curve. -1 Proteins were precipitated in acetonitrile from the same supernatant and then injected into an HPLC-ECD system to determine the peak areas of 5-hydroxytryptamine (5-HT) and 5-hydroxyindoleacetic acid (5-HIAA). The conversion rate TR = (5-HIAA / 5-HT)·100%. All data were recorded in the Brain Function Molecular Data Sheet.

[0131] 200 μl of serum sample was used to simultaneously detect IL-1β, IL-6, and TNF-α using CBA flow cytometry with antibody beads. The beads were encoded to distinguish the three factors, and fluorescence intensity was collected via detection channels FL2, FL3, and FL4. Concentrations (pg·ml⁻¹) were calculated using a standard curve. The data were recorded in a serum inflammatory factor data table.

[0132] Normalized Z-values ​​were performed on the intestinal metabolite content, hippocampal BDNF concentration, prefrontal 5-HT concentration, prefrontal TR value, and IL-6 concentration of the same numbered sample. i =(X i The expression profiles were processed using the -μ) / σ method and merged to generate an 8-column × 36-row matrix E. The column order was: sinapic acid, butyric acid, deoxycholic acid, BDNF, 5-HT, TR, IL-6, and DBCS. Matrix E was output as a gut-brain molecular expression profile CSV file for subsequent gut-brain axis synergistic analysis.

[0133] Of particular importance is the specific process of quantitative processing of intestinal metabolites:

[0134] Based on the original spectral data, a series of matrix-matching standards were obtained by performing multi-metabolite standard matching.

[0135] A table of parameters for metabolite standard curves was constructed based on the original spectral data and matrix-matched standard series.

[0136] The reliability of the analysis was verified based on the parameter table of the metabolite standard curve, and a methodology verification report was obtained.

[0137] Based on the methodology validation report, the actual samples were accurately quantified to obtain a sample quantification result table;

[0138] By integrating the sample quantification results table and the methodology validation report, a table of intestinal metabolite content was obtained.

[0139] In this embodiment of the invention, the colon contents of blank rats were processed using the same extraction procedure to obtain a matrix extract, which was then divided into seven portions and sequentially added to a mixture of sinapic acid, butyric acid, and deoxycholic acid standards to achieve a final concentration of 2 μg / ml. -1 5 μg·ml -1 10 μg·ml -1 20 μg·ml -1 50 μg·ml -1 100 μg·ml -1 200 μg·ml -1 Each sample was supplemented with 100 μg / ml of isooctanoic acid as an internal standard. -1 The gradient liquid chromatography-mass spectrometry conditions were completely consistent with the sample, and the peak shape of the MRM channel was acquired. The characteristic ion pairs of the target metabolite in the original spectrum were matched with the retention time ±0.05 min window. After confirming the qualitative consistency, a matrix-matched standard series was formed.

[0140] Calculate the peak area A_met of the target metabolite and the peak area A_IS of the internal standard at each concentration, and obtain the area ratio R = A_met / A_IS. Perform least squares regression with R as the ordinate and the spiked concentration C_std as the abscissa to obtain the linear equation R = k·C_std + b and the coefficient of determination R0. 2Record k, b, and R for each compound. 2 The concentration range and injection volume are compiled into a table of parameters for the metabolite standard curve.

[0141] Analysis and reliability verification:

[0142] 1. Linearity verification: Requires R... 2 ≥0.995.

[0143] 2. Calculation of limit of detection and limit of quantitation: LOD = 3.3·σ / k, LOQ = 10·σ / k, where σ is the standard deviation of the regression intercept and k is the slope.

[0144] 3. Precision verification: Within the same day, the quality control concentration of 10 μg / ml was verified. -1 50 μg·ml -1 150 μg·ml -1 Calculate the relative standard deviation (RSD_intra) for six parallel injections: RSD_intra = SD / Mean·100%; calculate the RSD_inter for three consecutive days of injections; both values ​​≤10% are acceptable.

[0145] 4. Accuracy verification: Add known concentrations of Q_add (10, 50, 150 μg·ml) to the matrix. -1 The concentration Q_found was measured, and the recovery rate was calculated as Recovery = (Q_found - Q_blank) / Q_add · 100%, with a requirement of 85%-115%.

[0146] 5. Matrix effect assessment: Compare the peak areas of the pure solvent standard at equal concentration with the matrix-matched standard, and calculate ME = (A_matrix / A_solvent)·100%. A range of 80%-120% indicates no significant matrix effect. All results are compiled into a methodology validation report.

[0147] Calculate the area ratio of the sample to be tested, R_sample = A_sample / A_IS, and substitute it into the standard curve equation to inversely calculate the concentration: C_found = (R_sample - b) / k. Combine the extraction volume V_ext and sample mass W_sam to calculate the content: Content = C_found·V_ext / W_sam, in μg·g -1 Dried feces. 10 μg / ml was inserted into each batch of samples. -1 With 100 μg·ml -1 For quality control, batch data is considered valid only if the RSD of the quality control results is ≤10%. All sample concentrations and quality control data are summarized to form a sample quantification result table.

[0148] The sample quantification results table and the methodology validation report were combined and recorded together in the intestinal metabolite content table. The table includes fields such as sample number, sinapic acid content, butyric acid content, deoxycholic acid content, LOD, LOQ, Recovery, RSD_intra, RSD_inter, and ME, providing a quantitative basis for subsequent construction of gut-brain molecular expression profiles.

[0149] Preferably, the gut-brain axis synergy analysis in step S4 includes:

[0150] A set of key molecular indicators in the gut-brain molecular expression profile was selected based on the comprehensive score of depressive behavior.

[0151] Construct a multidimensional correlation network map based on the set of key molecular indicators;

[0152] Determine the correlation coefficient table of indicators based on the multidimensional correlation network graph;

[0153] Based on the index correlation coefficient table, the key molecular index set is hierarchically indexed to obtain a three-layer system index table.

[0154] The gut-brain axis synergy index was constructed based on the three-layer system index table to obtain the initial gut-brain axis synergy index.

[0155] The gut-brain axis synergy index was obtained by validating the initial synergy index.

[0156] In this embodiment of the invention, the gut-brain molecular expression profile includes sinapic acid Z1, butyric acid Z2, deoxycholic acid Z3, hippocampal BDNF Z4, prefrontal 5-HT Z5, 5-HT conversion rate Z6, IL-6 Z7, and the Depressive Behavioral Scale (DBCS). The correlation coefficient matrix r was calculated using the Pearson method. ij (1≤i≤7, j=DBCS). Let the threshold be |r i DBCS|≥0.30 and P<0.05, all seven items meet the conditions and are included in the key molecular index set.

[0157] Calculate r pairwise for each key indicator ij Construct a 7×7 symmetric matrix R. Let the edge threshold be |r ij |≥0.30; Node pairs that meet the condition have r ij The absolute values ​​are used as weights (w_edge) to plot the network, with solid lines for positive correlations and dashed lines for negative correlations. Nodes are sorted using degree centrality and betweenness centrality, and the NetworkStats table is output.

[0158] DBCS is the dependent variable Y, and the seven indicators are the independent variables X. i The standardized regression coefficients β were obtained using partial least squares regression (PLSR). i: β1=-0.35, β2=-0.28, β3=0.22, β4=-0.40, β5=-0.32, β6=0.25, β7=0.18. Calculate the weight coefficient w i =β i / ∑|β j The resulting table of correlation coefficients for the indicators is compiled, listing the variables and β. i w i .

[0159] The indicators are divided into three layers: Layer 1 (gut metabolism) = {Z1, Z2, Z3}, Layer 2 (brain function) = {Z4, Z5, Z6}, and Layer 3 (immune inflammation) = {Z7}.

[0160] Calculate the system index for the k-th sample: GM_k = ∑{i=1}^{3}(w i ·Z_k i ), BN_k=∑{i=4}^{6}(w i ·Z_k i The three results, INF_k = w7·Z_k7, are imported into the three-level system index table, with the fields being sample number, GM_k, BN_k, and INF_k.

[0161] The synergistic initial index is defined as the product of the three system indices: GBCI0_k = GM_k · BN_k · INF_k. If the sign is negative, the negative sign is retained. The mean GBCI0_blank of the blank group and the model group is calculated. - GBCI0_model - Perform linear standardization: With the value range limited to 0-100, the initial index series of gut-brain axis synergy is obtained.

[0162] Synergy Index Verification:

[0163] 1. Cronbach's α test for internal consistency of the three-system exponents: α = (k / (k-1))·(1-∑σ) 2 _S / σ 2 _T); k=3, σ 2 _S represents the variance of each system, σ 2 _T represents the total variance; α≥0.70 passes.

[0164] 2. Bootstrap resamples 1000 times to calculate the 95% confidence interval of GBCI.

[0165] 3. Sensitivity analysis: (The remaining text appears to be incomplete and contains errors. A more accurate translation would require the full context.) iIf the fluctuation is 10%, recalculate GBCI and calculate the relative deviation RD = (GBCI_var - GBCI_original) / GBCI_original · 100%. If the average |RD| ≤ 5%, it is considered robust.

[0166] After the above conditions are met, GBCI_k is written into the synergistic index data table as the final gut-brain axis synergistic index, listing the sample number, GM_k, BN_k, INF_k, GBCI_k, and validation parameters α, confidence interval, and mean |RD|.

[0167] Of particular importance is the specific construction of the gut-brain axis synergy index:

[0168] Based on the three-layer system index table, a multi-system collaborative model is designed to obtain the basic data for the collaborative model.

[0169] Numerical distribution characteristic analysis was performed on the basic data of the collaborative model to obtain collaborative value calibration data;

[0170] Based on the synergistic value calibration data, a standard scale conversion design was performed to obtain a standardized index conversion table;

[0171] Based on the standardized index conversion table, an index interpretation standard table is established.

[0172] The sample indices were calculated and validated based on the standardized index conversion table and the index interpretation standard table to obtain the initial indices for gut-brain axis synergy.

[0173] In this embodiment of the invention, the intestinal metabolic index GM is listed in the three-layer system index table. k Brain Function Index (BN) k INF (immunoinflammatory index) k (k represents the sample number). To quantify the synergistic effect of the simultaneous deviations of the three systems, a product model is constructed: S k =GM k ·BN k ·INF k S k These are the original values ​​for sample coordination. The within-group means were calculated for both the blank group and the model group. Used for subsequent normalization. Sample number, GM... k BN k 、INF k S k The data is compiled to form the foundational data for the collaborative model.

[0174] For all S k Calculate the population mean μ_S, standard deviation σ_S, skewness Skew_S, and kurtosis Kurt_S. If |Skew_S|>1, perform a logarithmic transformation to ln|S.k | Retain the sign after |; if |Kurt_S|>3, use square root transformation √|S k The sign is retained after |. μ_S' and σ_S' are recalculated after the transformation and used as co-calibration data.

[0175] Using the improved z-score method: Where S k 'This is the calibrated co-value,' The mean of the blank group was calibrated. Then, a linear mapping to the 0-100 interval was performed.

[0176] GBCI k _temp = 100·(Z) k -Z_min) / (Z_max-Z_min) Z_min and Z_max are the values ​​of all samples Z_min. k Minimum and maximum values. The Z-GBCI conversion relationship is written in the standardized index conversion table, listing the Z interval and the corresponding GBCI interval.

[0177] Based on the GBCI_temp distribution of the control and model groups, four threshold levels were defined: 0-20 normal, 20-40 mild disorder, 40-60 moderate disorder, and 60-100 severe disorder. An index interpretation standard table was established, listing the GBCI intervals, functional status descriptions, and recommended research judgments.

[0178] For each sample, the standardized index transformation table is used to map and obtain the initial gut-brain axis synergy index (GBCI). 0k =GBCI k _temp. Perform three verifications: ① Internal consistency, calculate Cronbach's α = (k / (k-1))·(1-∑σ 2 _sub / σ 2 _total), k=3, when α≥0.70, the consistency is considered good; ② Stability, calculate the 95% CI of the GBCI confidence interval after 1000 bootstrapping; ③ Sensitivity, GM k BN k 、INF k Increase each by 10% and then calculate GBCI_new, then calculate the relative deviation RD. k =(GBCI_new-GBCI) 0k ) / GBCI 0k ·100%, if the average |RD k The model is robust if the percentage is ≤5%. GBCI for all samples. 0k The combined output of validation parameters, namely the gut-brain axis synergistic initial index, provides a basis for subsequent linear standardization of the final synergistic index and efficacy evaluation.

[0179] Preferably, the present invention also provides a system for constructing an evaluation model for animal experiments, used to execute the method for constructing an evaluation model for animal experiments as described above, the system comprising:

[0180] The stress sensitivity assessment module is used to group male SD rats and determine baseline data to obtain a group baseline data table; based on the group baseline data table, spatiotemporal stress is implemented and stress sensitivity is assessed to obtain a stress sensitivity index;

[0181] The gut microbiota analysis module is used to perform functional microbiota-specific sequencing on male SD rats based on the stress sensitivity index, and obtain functional microbiota sequencing data packages; the gut microbiota disorder index is obtained by calculating the degree of microbiota disorder on the functional microbiota sequencing data packages.

[0182] The behavioral efficacy evaluation module is used to conduct drug intervention and behavioral testing based on the gut microbiota dysbiosis index to obtain a multidimensional behavioral parameter set; and to conduct a comprehensive behavioral evaluation of the multidimensional behavioral parameter set to obtain a comprehensive depressive behavior score.

[0183] The gut-brain axis correlation measurement module is used to perform targeted detection of gut-brain components based on the comprehensive depressive behavior score to obtain a gut-brain molecular expression profile; to perform gut-brain axis synergy analysis based on the comprehensive depressive behavior score and the gut-brain molecular expression profile to obtain a gut-brain axis synergy index; and to construct an animal experimental evaluation model based on the stress sensitivity index, gut microbiota dysbiosis index, comprehensive depressive behavior score, and gut-brain axis synergy index.

[0184] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0185] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for constructing an evaluation model for animal experiments, characterized in that, Includes the following steps: Step S1: Male SD rats were grouped and baseline measurements were taken to obtain a group baseline data table; based on the group baseline data table, spatiotemporal stress was implemented and stress sensitivity was assessed to obtain a stress sensitivity index; Step S2: Perform functional microbiota-specific sequencing on male SD rats based on the stress sensitivity index to obtain functional microbiota sequencing data packages; The gut microbiota disorder index was obtained by calculating the degree of microecological disorder from the functional microbiota sequencing data package. The calculation of microecological disorder includes: The functional microbial community sequencing data packets were subjected to dual-threshold quality filtering and sequence chimerism processing to obtain chimeric sequences; Microbial taxonomic units were constructed from chimeric sequences to obtain a microbial taxonomic phylogenetic table. Based on the microbial community classification phylogenetic table, key bacterial genera were screened and weighted according to the stress sensitivity index, resulting in a key bacterial genera weight table. The relative abundance data of key bacterial genera were extracted from the blank group rats; the abundance changes were calculated and corrected based on the relative abundance data of key bacterial genera, the bacterial community classification phylogenetic table and the key bacterial genera weight table, and the bacterial genera change correction table was obtained. The gut microbiota disorder index was calculated based on the bacterial genus change correction table and the key bacterial genus weight table. Step S3: Based on the gut microbiota dysbiosis index, conduct drug intervention and behavioral testing to obtain a multidimensional behavioral parameter set; perform a comprehensive behavioral evaluation on the multidimensional behavioral parameter set to obtain a comprehensive depressive behavior score; Step S4: Targeted detection of gut-brain components is performed based on the comprehensive depressive behavior score to obtain a gut-brain molecular expression profile; gut-brain axis synergy analysis is performed based on the comprehensive depressive behavior score and the gut-brain molecular expression profile to obtain a gut-brain axis synergy index; an animal experimental evaluation model is constructed based on the stress sensitivity index, gut microbiota dysbiosis index, comprehensive depressive behavior score, and gut-brain axis synergy index. The gut-brain axis synergy analysis included: A set of key molecular indicators in the gut-brain molecular expression profile was selected based on the comprehensive score of depressive behavior. Construct a multidimensional correlation network map based on the set of key molecular indicators; Determine the correlation coefficient table of indicators based on the multidimensional correlation network graph; Based on the index correlation coefficient table, the key molecular index set is hierarchically indexed to obtain a three-layer system index table. The gut-brain axis synergy index was constructed based on the three-layer system index table to obtain the initial gut-brain axis synergy index. The gut-brain axis synergy index was obtained by validating the initial synergy index.

2. The method for constructing an evaluation model for animal experiments according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Male SD rats were placed in a standard animal room with a temperature set at 20°C to 24°C and a humidity set at 56% to 64% to conduct an experimental adaptability assessment and obtain an animal adaptability score sheet. Step S12: Grouping and baseline determination are performed according to the animal adaptability rating scale to obtain the group baseline data table; Step S13: Using the pre-established stress source library, implement spatiotemporal separation stress according to the group baseline data table to obtain a stress implementation record table; Step S14: Based on the stress implementation record table and the group baseline data table, conduct a stress sensitivity assessment to obtain the stress sensitivity index.

3. The method for constructing an evaluation model for animal experiments according to claim 2, characterized in that, Step S13 includes: Using a pre-established stressor database, stressors are classified and their intensity is assessed based on the grouped baseline data table, resulting in a stressor intensity classification table. The 24 hours are divided into four time periods, and a 4×9 two-dimensional spatiotemporal matrix is ​​constructed based on the stressor intensity classification table. The time periods of the two-dimensional spatiotemporal matrix are listed as stressors. Stress allocation is performed based on a two-dimensional spatiotemporal matrix to obtain a spatiotemporal stress allocation scheme. Individualized stress implementation and monitoring were conducted based on a spatiotemporal stress allocation scheme to obtain individual stress response records; Cumulative stress load is calculated based on individual stress response records to obtain a stress implementation record table.

4. The method for constructing an evaluation model for animal experiments according to claim 1, characterized in that, Step S2, functional microbial community-specific sequencing, includes: Sensitive individuals are screened using the stress sensitivity index to obtain a sensitive individual screening table; Fecal samples were collected and processed according to a sensitive individual screening table to obtain a gut microbiota DNA sample library; Identify conserved regions of functional flora based on gut microbiota DNA sample banks; Extracting regional sequence features from conserved regions of functional bacterial communities; Specific primer pairs were designed based on regional sequence characteristics to obtain primer combinations specific to functional bacterial communities. The multiplex PCR system was optimized based on primer combinations specific to functional bacterial communities, resulting in an optimized multiplex PCR system. Based on the optimized multiplex PCR system, primer amplification efficiency was balanced to obtain a set of PCR amplification products of functional bacterial groups. Specific libraries were constructed from the PCR amplification product set of functional bacterial groups, and library quality control was performed to obtain functional bacterial group sequencing libraries. High-throughput sequencing was performed on the functional microbial community sequencing library, and preliminary data processing was carried out to obtain the functional microbial community sequencing data package.

5. The method for constructing an evaluation model for animal experiments according to claim 1, characterized in that, Step S3, involving drug intervention and behavioral testing, includes: Based on the gut microbiota dysbiosis index, gut microbiota balance groups were formed to obtain a gut microbiota balance grouping table; The drug tolerance index was calculated based on the microecological balance grouping table; A drug intervention tracking table is generated based on the drug tolerance index; Multidimensional behavioral parameters were obtained by conducting multidimensional behavioral tests on male SD rats based on the drug intervention tracking table.

6. The method for constructing an evaluation model for animal experiments according to claim 1, characterized in that, Step S3, the comprehensive behavioral evaluation includes: The multidimensional behavioral parameter set is subjected to behavioral standardization processing to obtain a behavioral standard score data table; Determine the core indicator weight coefficient table based on the behavioral standard score data table; The core indicator standard scores are extracted from the behavioral standard score data table based on the core indicator weight coefficient table. The core indicator standard scores include the standard score for the forced swimming immobility time, the standard score for the dwell time in the open field center area, and the standard score for the dwell time in the elevated cross maze open arm. Calculate the initial depressive behavior comprehensive score based on the standard scores of the core indicators; Obtain initial rating data from the blank group and generate a standardized comprehensive score for depressive behaviors; The depressive behavior composite score for each rat was calculated based on the initial depressive behavior composite score and the standardized depressive behavior composite score.

7. The method for constructing an evaluation model for animal experiments according to claim 1, characterized in that, Step S4, the gut-brain component targeting detection, includes: A representative sample screening table was obtained by screening the comprehensive depressive behavior score. Tissue samples were collected and processed according to a representative sample screening table to obtain a multi-component biobank. Colonic contents, serum, and hippocampal and prefrontal cortical tissue samples were extracted from a multi-component biobank. The colon contents sample was pretreated to obtain an intestinal metabolite extract; The intestinal metabolite extract was subjected to chromatographic-mass spectrometry analysis to obtain raw spectral data. Based on the raw spectral data, intestinal metabolites were quantitatively processed to obtain a table of intestinal metabolite content. Neuromolecular analysis of brain tissue samples from the hippocampus and prefrontal cortex was performed to obtain a table of brain functional molecular data. Serum samples were subjected to multiple detection of serum inflammatory factors to obtain a serum inflammatory factor data table; Gut-brain molecular expression profiles were generated based on intestinal metabolite content tables, brain functional molecular data tables, and serum inflammatory factor data tables.

8. A system for constructing evaluation models for animal experiments, characterized in that, The system for constructing an evaluation model for animal experiments as described in claim 1, comprising: The stress sensitivity assessment module is used to group male SD rats and determine baseline data to obtain a group baseline data table; based on the group baseline data table, spatiotemporal stress is implemented and stress sensitivity is assessed to obtain a stress sensitivity index; The gut microbiota analysis module is used to perform functional microbiota-specific sequencing on male SD rats based on the stress sensitivity index, and obtain functional microbiota sequencing data packages; the gut microbiota disorder index is obtained by calculating the degree of microbiota disorder on the functional microbiota sequencing data packages. The behavioral efficacy evaluation module is used to conduct drug intervention and behavioral testing based on the gut microbiota dysbiosis index to obtain a multidimensional behavioral parameter set; and to conduct a comprehensive behavioral evaluation of the multidimensional behavioral parameter set to obtain a comprehensive depressive behavior score. The gut-brain axis correlation measurement module is used to perform targeted detection of gut-brain components based on the comprehensive depressive behavior score to obtain a gut-brain molecular expression profile; to perform gut-brain axis synergy analysis based on the comprehensive depressive behavior score and the gut-brain molecular expression profile to obtain a gut-brain axis synergy index; and to construct an animal experimental evaluation model based on the stress sensitivity index, gut microbiota dysbiosis index, comprehensive depressive behavior score, and gut-brain axis synergy index.

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