System and method for predicting efficacy of fmt donors for adolescent depression

Abstract

This invention discloses a fecal transplant donor efficacy prediction system and model construction method for adolescent depression, belonging to the field of combining biomedicine and artificial intelligence. It includes a detection module for obtaining quantitative detection results of preset microbial markers in fecal samples of the donor to be tested, including *Holmania fibrosum*; and a comparison module for comparing the quantitative detection results with preset thresholds to determine whether the donor to be tested is a high-efficiency or low-efficiency donor. The product provided by this invention has good feasibility and accuracy, and can effectively distinguish between high-efficiency and low-efficiency donors, providing a new diagnostic / predictive tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine and artificial intelligence, and specifically relates to a FMT donor efficacy prediction system and model construction method for adolescent depression. Background Technology

[0002] Fecal microbiota transplantation (FMT) has gained widespread acceptance as a novel, fast, stable, broad-based, and safe treatment strategy for adolescent depression by reconstructing the gut microbiota. However, significant individual differences exist in the clinical efficacy of FMT, with donor microbiota quality being a key determinant. Different donors exhibit significant differences in microbiota composition and metabolic function, directly impacting the efficiency of recipient gut microbiota remodeling.

[0003] Currently, there is a lack of specific biomarker systems for donor screening and efficacy prediction for adolescent depression, making it difficult to accurately identify donors who are more effective for this group in clinical practice.

[0004] Therefore, there is an urgent need in this field to develop a FMT donor prediction system specifically for adolescent depression, which can effectively distinguish between high-efficiency and low-efficiency donors by detecting specific microbial markers in donors, thereby improving the accuracy and reliability of FMT treatment. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a FMT donor efficacy prediction system and its construction method for adolescent depression. By screening donor microbial biomarkers that are specifically related to the treatment efficacy of adolescent depression, a model can be constructed to predict donor efficacy in advance, enabling precise screening of efficient donors for adolescent depression patients.

[0006] The technical solution provided by this invention is as follows: In a first aspect, the present invention provides a predictive system related to adolescent depression, the predictive system being used to distinguish whether a potential donor is a high-efficiency donor or a low-efficiency donor, comprising: The detection module is used to obtain quantitative detection results of preset microbial markers in the fecal sample of the donor to be tested, wherein the microbial markers include Holdemania filiformis; The comparison module is used to compare the quantitative detection results with a preset threshold, and determine whether the donor to be tested is a high-efficiency donor or a low-efficiency donor based on the comparison results.

[0007] Secondly, the present invention provides a FMT donor efficacy prediction system for adolescents with depression, the prediction system being used to distinguish whether a donor to be tested is a high-efficiency donor or a low-efficiency donor, including: The detection module is used to obtain the quantitative detection results of preset microbial markers in the fecal sample of the donor to be tested. The microbial markers include one or more of the following: Holdemania filiformis, Desulfovibrio piger, Lachnospiraceae bacterium, and Coprococcus catus. The calculation module is used to calculate the probability that the donor to be tested is a high-efficiency donor based on the quantitative detection results and the preset calculation formula, and denoted as the high-efficiency donor probability value. The comparison module is used to compare the probability value of the high-efficiency donor with a preset threshold, and determine whether the donor to be tested is a high-efficiency donor or an inefficient donor based on the comparison result.

[0008] Thirdly, the present invention provides a method for constructing a predictive model for FMT donor efficacy in adolescents with depression, the method comprising the following steps: Fecal samples from healthy individuals were collected according to pre-set standards for intake and discharge. Based on the pre-set intestinal flora transplantation procedure and the Hamilton Depression Rating Scale, the fecal samples of the healthy individuals were divided into a high-efficiency donor group and a low-efficiency donor group. High-throughput sequencing was performed on the high-efficiency donor group and the low-efficiency donor group to identify the differentially expressed microorganisms between the two groups, which were then denoted as microbial biomarkers. The microbial biomarkers were used as a predictive model for FMT donor efficacy in adolescent depression, distinguishing between high-efficiency and low-efficiency donors.

[0009] Fourthly, the present invention provides an application of a reagent for quantitatively detecting microbial markers in the preparation of products for treating adolescent depression, wherein the microbial markers include one or more of the following: Holdemania filiformis, Desulfovibrio piger, Lachnospiraceae bacterium, and Coprococcus catus.

[0010] In the above technical solution, the product is one or more of the following: reagent kit, lyophilized powder, capsule, FMT bacterial solution, and treatment system for treating adolescent depression.

[0011] It should be noted that this invention newly discovered and verified a strong correlation between the aforementioned gut microbiota and adolescent depression, which can be used to distinguish between highly efficient and inefficient donors. Based on this, although the embodiments of this invention only list how to achieve quantitative detection of the test sample through relative abundance values, other methods for quantitative detection of microorganisms are also feasible (such as absolute abundance or total microbial load information), and can also be used to assist in distinguishing between highly efficient and inefficient donors. Users can choose according to their own needs, and these will not be elaborated upon here.

[0012] It should also be noted that the donors mentioned in this invention refer to natural persons who can provide fecal samples to patients in fecal microbiota transplantation (FMT) and who meet the relevant requirements and specifications of FMT; high-efficiency donors mainly refer to natural persons whose adolescent depression patients show significant symptom improvement after FMT treatment (HAMD-17 score reduction rate ≥ 50%); low-efficiency donors mainly refer to natural persons whose adolescent depression patients show poor efficacy after FMT treatment (HAMD-17 score reduction rate < 50%). The efficacy classification of adolescent depression in this invention (e.g., significant symptom improvement or poor efficacy) is mainly based on the Hamilton Depression Rating Scale, which is common knowledge in the field and will not be elaborated upon here. (See Examples 1-3 for details) The beneficial effects of this invention are as follows: 1. This invention newly discovers six gut microbes associated with adolescent depression, including: Holdemania filiformis, Desulfovibrio piger, Lachnospiraceae bacterium, Coprococcus catus, Clostridium aldenense, and Bacteroides vulgatus.

[0013] Research and verification have shown that, for adolescents with depression, one or more of the above six gut microbiota can be used to distinguish whether the tested donor is a high-efficiency donor or a low-efficiency donor.

[0014] 2. This invention uses one or more of the above 6 bacterial species as detection markers (i.e., microbial markers) to distinguish whether the test donor is a high-efficiency donor or a low-efficiency donor. It is completely non-invasive and highly accurate.

[0015] Metagenomic sequencing provides higher resolution, enabling the analysis of microbial communities to reach the species and even strain level, thereby improving the accuracy and reliability of diagnosis. The six species mentioned can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0016] 3. This invention also provides a predictive system / model for diagnosing adolescent depression. This system / model can distinguish between highly effective and ineffective donors based on the aforementioned microbial biomarkers, thereby determining whether the donor is a highly effective donor with better therapeutic efficacy, and thus improving the accuracy and reliability of FMT treatment.

[0017] Preferably, the present invention can also calculate the probability that the test donor is a high-efficiency donor based on the quantitative detection results (such as relative abundance values) of the above-mentioned microbial markers, and then distinguish whether the test donor is a high-efficiency donor or a low-efficiency donor based on the high-efficiency donor probability value. The present invention has good feasibility and accuracy, and can effectively assess the risk of adolescent depression in the test sample, providing a new tool for clinical diagnosis.

[0018] 4. Given the good therapeutic effect of the high-efficiency donor group on adolescent depression, fecal samples from the high-efficiency donor group can be used to treat adolescent depression with good efficacy. Figure 1 As described above, the levels of *Holdemania filiformis*, *Desulfovibrio piger*, *Lachnospiraceae bacterium*, and *Coprococcus catus* in the high-efficiency donor group were significantly higher than those in the low-efficiency donor group. Therefore, fecal samples containing these four microorganisms can be used to treat adolescent depression through intestinal microbiota transplantation (FMT).

[0019] Furthermore, if the content of one or more of the above-mentioned fibrous Holdemania filiformis, desulfovibrio piger, Lachnospiraceae bacterium, and Coprococcus catus is within the range determined by the mean and standard deviation in Table 4, then the donor is a highly efficient donor.

[0020] Furthermore, if a donor contains four microorganisms (also known as four single species), namely Holdemania filiformis, Desulfovibrio piger, Lachnospiraceae bacterium, and Coprococcus catus, and the content of each of these four microorganisms is within the range determined by the mean and standard deviation in Table 4, then this donor is the most effective in treating adolescent depression and can be called a super donor. Attached Figure Description

[0021] Figure 1 This is a graph showing the results of the linear discriminant analysis; Figure 2 Box plot of microbial biomarkers; Figure 3 ROC curve; Figure 4 A schematic diagram of the structure of a predictive system related to adolescent depression; Figure 5 A schematic diagram of the FMT donor efficacy prediction system for adolescent depression. Figure 6 Flowchart for constructing a predictive model for FMT donor efficacy in adolescents with depression. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can understand it.

[0023] Adolescent depression is a complex metabolic disorder caused by insulin resistance and progressive decline in pancreatic β-cell function, and has become one of the most serious public health problems worldwide. Its harm lies not only in its persistently high prevalence, but also in the serious systemic complications it causes, placing a heavy burden on patients, their families, and society.

[0024] Adolescent depression has become a global public health focus: the World Health Organization (WHO) estimates that 5%-8% of adolescents suffer from depressive disorders, with the first episode often occurring during adolescence. Compared to adults, the adolescent brain is still maturing, and the interplay of hormones, neurotransmitters, and social stress makes the condition more prone to chronicity and associated with self-harm risks. The causes can be attributed to genetic predisposition, childhood trauma, school bullying, and gut-brain axis dysbiosis, all of which trigger abnormalities in the development of the prefrontal-limbic system, leading to persistent low mood, cognitive decline, and increased suicide risk. Without systematic intervention, the relapse rate within two years can exceed 60%.

[0025] For adolescents with depression, the main treatment options currently include psychotherapy and medication. Psychotherapy includes cognitive behavioral therapy (CBT) and interpersonal therapy (IPT), which can correct negative cognitions and interpersonal conflicts; however, its effectiveness is limited by therapist resources, family support, and patient motivation, with about 40% of adolescents responding poorly. As for medication, the FDA has only approved fluoxetine and escitalopram for adolescents; however, it takes 4 to 8 weeks to take effect, and 30% to 50% of patients do not respond to multiple antidepressants. In addition, the black box warning indicates that early treatment may increase suicidal ideation, so clinical medication use is generally conservative.

[0026] To address the aforementioned issues, gut microbiota transplantation (FMT) not only bypasses the monoamine pathway bottleneck and remains effective for drug-resistant patients, but also simultaneously improves accompanying symptoms such as sleep and gastrointestinal discomfort. The capsule formulation is non-invasive and well-tolerated, and when combined with low-dose SSRIs, it can reduce drug dosage and parents' concerns about black box warnings, providing a safe and accessible new strategy for adolescent depression.

[0027] However, long-term research has revealed significant individual differences in the clinical efficacy of gut microbiota transplantation (FMT), with donor microbiota quality being one of the key factors influencing FMT transplantation outcomes. Different donors often exhibit significant differences in microbiota composition, metabolic function, and ecological interaction networks, and these differences directly affect the efficiency of recipient gut microbiota remodeling and disease prognosis.

[0028] It is noteworthy that the gut microbiota of the same donor can exhibit drastically different therapeutic effects on different diseases. For example, the gut microbiota of donor A may show a good clinical response in patients with depression, but have limited improvement in patients with inflammatory bowel disease (IBD); while the gut microbiota of donor B may be significantly effective for IBD, but less effective in patients with depression. This disease-specific difference in response suggests that general donor quality assessments alone are insufficient to accurately predict transplant outcomes for specific diseases.

[0029] Currently, there is no specific biomarker system for donor screening and efficacy prediction for the specific population of adolescents with depression, which makes it difficult to accurately identify the most effective donors for this group in clinical practice.

[0030] To address the aforementioned problems, this invention provides a microbial biomarker for FMT donors associated with adolescent depression and its application, to assess whether gut microbiota can serve as a predictor of FMT donors in adolescent depression, thereby distinguishing between highly efficient and inefficient donors. Specifically, the technical approach of this invention is as follows: First, based on the clinical need for more suitable FMT donors (high-efficiency donors) for adolescent patients with depression, this invention first collects fecal samples from healthy individuals and uses a standardized experimental testing procedure (specific experimental methods are described in Examples 1-4) to screen out FMT donors (i.e., high-efficiency donors) with better treatment effects and FMT donors (i.e., low-efficiency donors) with poor treatment effects for adolescent patients with depression.

[0031] Then, the present invention compared the fecal samples of the above-mentioned high-efficiency donors with the fecal samples of low-efficiency donors, thereby screening out 6 donor microorganisms associated with adolescent depression (i.e., microorganisms present in donor feces), including: Holdemania filiformis, Desulfovibrio piger, Lachnospiraceae bacterium, Coprococcus catus, Clostridium aldenense, and Bacteroides vulgatus.

[0032] Next, ROC curve analysis revealed that the above six biomarkers have high specificity and sensitivity as detection variables. One or more of these six bacterial species can be used as detection biomarkers (i.e., microbial biomarkers) to distinguish between high-efficiency and low-efficiency donors, thereby determining whether the donor is a high-efficiency donor with better therapeutic effect, and thus improving the accuracy and reliability of FMT treatment.

[0033] Finally, given that the high-efficiency donor group showed better therapeutic effects on adolescent patients with depression, and that the inventors of this application found that the content of *Holdemania filiformis*, *Desulfovibrio piger*, *Lachnospiraceae bacterium*, and *Coprococcus catus* in the high-efficiency donor group was significantly higher than that in the low-efficiency donor group, fecal samples containing the above four microorganisms can be used to treat adolescent depression through intestinal microbiota transplantation (FMT).

[0034] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions.

[0035] Example 1: Sample Collection

[0036] This study included 195 healthy adolescent donors. All healthy donors and their legal guardians signed written informed consent forms. The donor (fecal) samples used in this invention were collected from Hubei Province, and the source and inclusion criteria for the (fecal) samples are as follows: 1. Source: The screening targets are from non-urbanized areas with stable living environments and no record of major infectious disease outbreaks in the past 5 years; the content of heavy metals and organic pollutants in soil and water must meet the risk control requirements; areas with high-risk environmental factors such as industrial pollution sources and excessive use of agricultural chemicals are excluded.

[0037] 2. Information Collection: 2.1. Age 12-18 years old. 2.2. Family medical history investigation: no family history of disease, with particular attention to hereditary diseases, digestive system diseases, infectious diseases, any family history of mental illness or tumors; confirm the average lifespan of immediate family members of the candidate's gut microbiota. 2.3. Personal medical history investigation: no gastrointestinal diseases (inflammatory bowel disease, irritable bowel syndrome, chronic constipation or diarrhea, malignant tumors or known polyposis, celiac disease, congenital or chronic liver disease, rectal bleeding, major surgery), autoimmune diseases, atopic diseases (asthma, atopic dermatitis, eczema, gastrointestinal eosinophilic diseases), cardiovascular or metabolic diseases (such as diabetes, hypertension, heart disease, etc.), neurological diseases (anxiety, multiple sclerosis, Parkinson's disease, etc.), immunosuppression, chronic pain, infectious diseases, community-acquired pneumonia, etc. 2.4. Candidates should have good on-site verbal communication skills, be in good mental condition, and have no tattoos, puncture wounds, or history of blood transfusions or other high-risk behaviors.

[0038] 3. Scale Assessment: 3.1 Interviews with psychiatrists or counselors indicated that the selected subjects were in good mental condition. 3.2 Scores on the Self-Rating Mental Health Scale (SCL-90), Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Pittsburgh Sleep Quality Index (PSQI) were all normal.

[0039] 4. Health Check-up: 4.1. A comprehensive health check-up conducted by a professional medical institution, with satisfactory results. Blood routine tests, liver and kidney function tests, hepatitis A / E tests, cytomegalovirus tests, EBV (IgM + IgG), hepatitis B tests (HBsAg + anti-Hbcore), HCV hepatitis C tests, HIV tests (anti-HIV), syphilis tests, C13 breath tests, and endocrine indicator tests all meet relevant requirements. 4.2. Testing for exons related to hereditary diseases in the selected candidates, screening for single-gene hereditary diseases, and excluding pathogenic and suspected pathogenic mutations. 4.3. Sensitivity testing for common allergens. The selected candidates have no obvious history of allergic reactions, and test results show no positive reaction to common allergens.

[0040] 5. Stool Testing: 5.1 The stool characteristics of the selected candidates should conform to Bristol Stool Classification Type III and IV. 5.2 The selected candidates should be excluded from carrying pathogenic bacteria, drug-resistant bacteria, and potential pathogenic microorganisms. This includes Clostridium difficile, Salmonella (which easily causes bacterial gastroenteritis), Campylobacter, Yersinia, and Shiga toxin-producing Escherichia coli; antibiotic-resistant bacteria such as vancomycin-resistant enterococci (VRE), extended-spectrum β-lactamase (ESBL), and methicillin-resistant Staphylococcus aureus (MRSA); and viral pathogens such as norovirus (types I and II), enteroviruses, and hepatitis E virus. 5.3 The stool of the selected candidates should be free of parasitic infections. Exclude parasites such as Clonorchis sinensis, Clonorchis sinensis, Balantidium coli, Hookworm, Giardia lamblia, Cyclospora cayetta, Trichodina, Strongyloides stercoralis, Intestinal nematodes, Mesozoa natans, Taenia spp., Cryptosporidium spp., Ascaris spp., Entamoeba histolytica, and Entamoeba histolytica.

[0041] Exclusion criteria for donors: 1. Volunteers have any concerns about this study or face other risks; 2. Volunteers have participated in other clinical trials; 3. Volunteers have received any other gut microbiota therapy before enrollment; 4. Volunteers have taken antibiotics (e.g., neomycin, rifaximin) or probiotics / prebiotics before and during the study; 5. Circumstances identified by the researchers during the study that may affect volunteer compliance and / or completion of study-related procedures.

[0042] Example 2: Screening of patients with depression, donor matching and gut microbiota transplantation

[0043] 2.1 Screening of patients with depression and donor matching The patients with depression were selected from stool samples collected in Hubei Province, and the screening criteria were as follows: Screening criteria: a) Age 12-18 years; b) Clinical diagnosis of depression by two or more physicians; c) Hamilton Depression Rating Scale (HAMD-17) total score ≥17; d) Exclusion of subjects with bipolar disorder, other mental illnesses, substance abuse, serious physical illness, or who are pregnant or lactating.

[0044] Table 1 Information on Patients with Moderate to Severe Depression .

[0045] 2.2 Donor matching for patients with depression After the screening process described in section [2.1 Screening of Depressed Patients and Donor Matching], as shown in Table 1, we obtained 195 eligible depressed patients. Thus, this invention obtained 195 eligible depressed patients. Finally, this invention used fecal samples from healthy donors collected in Example 1 to perform randomized fecal-to-molecular (FMT) matching on these patients. During the FMT matching process, each depressed patient was assigned a fecal sample from one healthy donor as described in Example 1. There was a one-to-one correspondence between the depressed patient and the FMT donor fecal samples, and the matching was randomized.

[0046] 2.3. Fecal microbiota transplantation (FMT) Currently, fecal microbiota transplantation (FMT), also known as fecal microbiota transplantation (technology) or gut microbiota transplantation (technology), is gradually gaining widespread attention. In 2023, the National Health Commission officially included FMT in the "Notice on Issuing the National Technical Specifications for Medical Service Projects".

[0047] Flavouring (FMT), as an innovative treatment method, offers a new approach to treating adolescent depression by restoring the balance of the gut microbiota. Given that performing FMT on adolescents with depression is a routine procedure in this field, this invention will not elaborate on that.

[0048] After completing gut microbiota transplantation (FMT) in adolescent patients with depression and conducting post-transplant monitoring and follow-up in accordance with FMT guidelines, Hamilton Depression Rating Scale (HAMD-17) scores were collected before and after treatment. The donor efficacy was classified by calculating the reduction rate of the Hamilton Depression Rating Scale (HAMD-17) score before and after transplantation. If the reduction rate of the HAMD-17 score was ≥50%, the donor was considered a high-efficiency donor; if the reduction rate of the HAMD-17 score was <50%, the donor was considered an inefficient donor.

[0049] In summary, based on the HAMD-17 deduction rate table, this invention divides the donor population in Example 1 into high-efficiency donors and low-efficiency donors. Finally, as shown in Table 2, this invention collected fecal samples from 93 high-efficiency donors (hereinafter referred to as the high-efficiency donor group) and collected fecal samples from 102 low-efficiency donors (hereinafter referred to as the low-efficiency donor group).

[0050] This invention explores the characteristics of donor flora that have a positive impact on the treatment of adolescent depression by comparing the differences in gut microbiota between the two groups of donors (high-efficiency donor group or low-efficiency donor group). The calculation method of the reduction rate of HAMD-17 is common knowledge in the field and will not be described in detail here.

[0051]

[0052] Example 3 DNA Sequencing and Data Validation

[0053] 3.1 Perform DNA sequencing on the data in the training set. Microbial genomic DNA was extracted from the samples using the CTAB (hexadecyltrimethylammonium bromide) method, followed by PCR amplification. The amplification primers were used to construct the library using the TruSeq@DNA PCR-Free Sample Preparation Kit, and the library was then sequenced using an Illumina Miseq PE250.

[0054] 3.2 Perform statistical analysis on the data in the training set. KneadData software was used for quality control (based on Trimmomatic) and host removal (based on Bowtie2) of the raw data. Kraken2 alignment was used to calculate the sequence number of each species in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% abundance data were randomly selected for analysis using LEfSe software, with the default LDAScore filter value set to 2. The results are as follows: Figure 1 As shown.

[0055] Depend on Figure 2 It was found that the researchers identified four biomarkers that showed significant differences between the high-efficiency and low-efficiency donor groups: *Desulfovibrio piger*, *Holdemania filiformis*, *Coprococcus catus*, and *Lachnospiraceaebacterium*. Two biomarkers, *Bacteroides vulgatus* and *Clostridium aldenense*, were significantly increased in the low-efficiency donor group. Box plots of these six biomarkers are shown in [reference needed]. Figure 2 .

[0056] Depend on Figures 1-2 It was found that the relative abundance of *Desulfovibrio laziensis*, *Holdermania fibrosum*, *Factococcus regularus*, and *Trichophyton* bacteria in the high-efficiency donor group was significantly higher than that in the low-efficiency donor group. Conversely, the relative abundance of *Bacteroides commonis* and *Clostridium aldoni* in the low-efficiency donor group was significantly higher than that in the high-efficiency donor group. These differential characteristics, validated by machine learning algorithms, demonstrated stable inter-group discriminatory power, indicating that the composition of these gut symbiotic microbiota can serve as a predictor of donors for adolescent depression.

[0057] Depend on Figures 2-3It is known that these six biomarkers are associated with the efficacy of donor therapy in adolescent depression. These six microorganisms can be used to predict and distinguish whether the tested donor is a high-efficiency or low-efficiency donor. Specifically, Holdemania filiformis has the highest predictive effect on high-efficiency donors, followed by Coprococcus catus, Lachnospiraceae bacterium, Bacteroides vulgatus, and Clostridium aldenense. Finally, Desulfovibrio piger has the best predictive effect.

[0058] 3.3 The reliability of the above six microbial biomarkers was verified using validation set data. 3.31. First, the remaining 20% ​​of the test subjects in Example 4 (i.e., the test set) are used as the validation set. The abundance data of each sample in the validation set are first subjected to binary logistic regression, and then the receiver operating characteristic (ROC curve) is analyzed to obtain the cutoff value (optimal cutoff value).

[0059] 3.32. Use the R language statistical software (v4.4.1) to complete the specificity and sensitivity calculation and ROC curve plotting. The software first calculates the threshold of the actual measurement value, and then calculates the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold.

[0060] Specificity (true negative rate) = TN / (TN + FP).

[0061] Sensitivity (true positive rate) = TP / (TP + FN).

[0062] 3.33. The ROC curve can be constructed using 1 minus specificity and sensitivity. The integral of the ROC curve is the AUC value. To calculate the specificity and sensitivity of an indicator, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of the indicator.

[0063] 3.34. The relative abundance values ​​of microbial biomarkers for single strains were directly analyzed using receiver operating characteristic (ROC) curve testing to determine the cutoff value. The ROC curve for predictive scoring is shown below. Figure 3 As shown in Table 3, the AUC, optimal cutoff value, sensitivity, and specificity of the predicted mimicry markers (markers formed by a combination of 6 single bacterial species) and gut microbiota are shown in Table 3.

[0064]

[0065] As shown in Table 3, for single bacterial species, *Holdermania fibrosum* has the highest AUC value (approximately 0.9591), while bacteria from the Trichophyceae family have the lowest AUC value (approximately 0.7576). The AUC value of the mimicry marker (a marker formed by the combination of 6 single bacterial species) (approximately 1) is higher than that of the single bacterial species.

[0066] As can be seen from the above, one or more of the six newly discovered bacterial species in this application can be used as detection variables, and they all have high specificity and sensitivity. Moreover, the AUC of the six microbial markers is greater than 75%. Therefore, one or more of the six microbial markers can be used to distinguish whether the test donor is a high-efficiency donor or a low-efficiency donor.

[0067] Example 4: Logistic Regression Model Establishment and Validation

[0068] Based on the microbial biomarkers selected above and the relative abundance value of each metabolic biomarker obtained, the first probability of each sample (i.e., the logarithm y of the donor's dominance) was calculated using the binary logistic regression algorithm in RStudio software. Then, the probability Z of the sample was calculated using the probability optimization formula Z = exp(y) / {1 + exp(y)}. Finally, this probability Z was compared with the actual disease status (e.g., severity) of each sample to verify the accuracy of the probability calculation equation. Specifically: a. Establishing a model Based on the biomarkers identified above, and using the ratio of high-efficiency to low-efficiency donors in adolescent depression within the training set, the relative abundance of the six detected bacterial species was further analyzed as a single variable. The linear relationship between the relative abundance of these six single bacteria and the sample probability was discussed, and the logarithm y (also known as the first probability value y) of the donor's dominance was calculated using a binary logistic regression equation.

[0069] Where A is the intercept term, B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bacteroides vulgatus, x2 is the relative abundance value of Desulfovibrio piger, x3 is the relative abundance value of Holdemania filiformis, x4 is the relative abundance value of Coprococcus catus, x5 is the relative abundance value of Lachnospiraceae bacterium, and x6 is the relative abundance value of Clostridium aldenense.

[0070] b. Determine the values ​​of A and B1 to B6 above. After statistical analysis of the sample data, the values ​​of A and B1 to B5 are as follows: A is -0.26, B1 is -8.25, B2 is 185.66, B3 is 1689.84, B4 is 82.65, B5 is 22.97, and B6 is -685.16. At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is:

[0071] c. Calculate the probability Z of the donor to be tested. Substitute the first probability value y into the following formula to calculate the probability Z that the test donor is an efficient donor: Z = exp(y) / {1 + exp(y)}; where Z is the probability value that the test donor is an efficient donor, and exp(y) is the natural exponential function of the first probability value y.

[0072] After simplification, the formula for calculating probability Z is:

[0073] d. Validation set data calculation and statistical analysis Based on the validation set data, the relative abundance of each single bacterial species in the disease group and the healthy group of each sample was obtained. Then, the first probability value y was obtained by using the aforementioned binary logistic regression method. The probability Z of the test sample being a high-efficiency donor was then calculated by the formula. The results are shown in Tables 4 and 5. Here, a high-efficiency donor refers to the population with better FMT efficacy for adolescent patients with depression.

[0074] Table 4 shows the relative abundance mean and standard deviation for each bacterial species. The relative abundance mean determines the central location of the data distribution, while the standard deviation reflects the dispersion of the data relative to the mean. The p-value is a statistic calculated using the rank-sum test formula. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0075] It should be noted that the combination of the six bacterial species—*Holmania fibrousa*, *Vibrio laziensis*, *Trichophyton*, *Factococcus regularis*, *Clostridium altonense*, and *Bacteroides vulgaris*—can be called the first mimicry marker, while the combination of the four bacterial species—*Holmania fibrousa*, *Vibrio laziensis*, *Trichophyton*, and *Factococcus regularis*—can be called the second mimicry marker. The first and second mimicry markers can be collectively referred to as mimicry markers.

[0076] In Table 5, the first high-efficiency donor probability is the probability that the test donor is a high-efficiency donor when the first mimicry marker is detected as a whole; similarly, the second high-efficiency donor probability is the probability that the test donor is a high-efficiency donor when the second mimicry marker is detected as a whole.

[0077] In Tables 4 and 5, the mimicry markers refer to the evaluation of p-value, sensitivity, and specificity of *Holmania fibrosum*, *Desulfovibrio desulfurization*, *Trichophyton* bacteria, *Factococcus regularis*, *Clostridium aldoni*, and *Bacteroides vulgaris* as a whole. The first high-efficiency donor probability refers to the probability that the test donor is a high-efficiency donor when the above mimicry markers are used as a baseline. The second high-efficiency donor probability refers to the probability that the test donor is a high-efficiency donor when *Holmania fibrosum* + *Factococcus regularis* + *Trichophyton* bacteria + *Desulfovibrio desulfurization* are used as a baseline.

[0078] In practice, the first and second efficient donor probabilities calculated by this invention are based on the donor samples described in this invention. People can also use their own collected samples to summarize and generalize the calculation formula suitable for their own samples, or even calculate the corresponding data parameters themselves using R language statistical software (v4.4.1), which will not be elaborated here.

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] Note: E represents 10 to the power of 10. For example, 9.76514818612e-05 means 9.76514818612 × 10⁻⁵. -5 .

[0085] Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similar.

[0086] e. Results and Analysis 1) Based on the results of Examples 4 and 5, it can be seen that any one of the six newly discovered single bacterial species in this application can be used as a high- or low-efficacy biomarker for adolescent depression. Each single bacterial species has sensitivity and specificity for adolescent depression. Therefore, the high- or low-efficacy biomarker for adolescent depression can be selected from any one or more of the six newly discovered single bacterial species in this application. Specifically, the high- or low-efficacy biomarker for adolescent depression can be selected from one or more of the following: *Holmania longiflora*, *Desulfovibrio laziensis*, *Trichophyton* bacteria, *Factococcus regularus*, *Clostridium aldoni*, and *Bacteroides vulgaris*.

[0087] 2) In Tables 4 and 5, it is normal for only one or a few species of bacteria to be detected when the intestinal sample is tested. This is because there are individual differences. The probability Z in this application is calculated. Therefore, even if a sample contains only a single species of bacteria, this application can still calculate the probability that the test donor is a high-efficiency donor.

[0088] 3) Combining the results of Examples 4 and 5, the AUC of the mimicry biomarker's predictive score is approximately 1, the optimal cutoff value is approximately 0.561, the sensitivity is approximately 1, and the specificity is 1. Therefore, applying the mimicry biomarker as a detection biomarker to the diagnosis of adolescent depression patients yields the highest accuracy.

[0089] Based on the results obtained from the description in Table 5 above and the calculation formula for probability Z, it can be seen that the calculation formula for calculating the probability of the sample to be tested summarized in this application is basically correct and can be used to distinguish whether the donor to be tested is a high-efficiency donor or a low-efficiency donor. The probabilities in Table 5 above may not fully meet the diagnostic criteria. The reason is that the intestinal samples of the person to be tested may produce false positive or false negative results. Further testing using other methods is required. Other methods include mental health testing and drug testing, which will not be elaborated here.

[0090] Example 5: Examining and verifying the probability of centralized high-efficiency and low-efficiency donors.

[0091] Based on the product and method of Example 4, the probability of the test donors in the verification set being high-efficiency donors is checked and verified. The specific steps are as follows: S1. Collect intestinal samples from the individuals to be tested and detect the relative abundance of each single bacterial species in the intestine; among which, the single bacterial species include Holdemania filiformis, Desulfovibriopiger, Lachnospiraceae bacterium, Coprococcus catus, Clostridium aldenense, and Bacteroides vulgatus; S2. Calculate the logarithm y of the dominance of the donor under test based on the binary logistic regression equation;

[0092] Wherein, x1 is the relative abundance value of *Bacteroides vulgatus*, x2 is the relative abundance value of *Desulfovibrio piger*, x3 is the relative abundance value of *Holdemania filiformis*, x4 is the relative abundance value of *Coprococcus catus*, x5 is the relative abundance value of *Lachnospiraceae bacterium*, and x6 is the relative abundance value of *Clostridium aldenense*. S3. Calculate the probability Z that the donor to be tested is an efficient donor based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y; The probability Z can also be expressed as: .

[0093] S4. Based on the comparison between the patient's probability Z-value and the reference value, determine whether the donor to be tested is a high-efficiency donor.

[0094] In practical work, to make the technical solution of this embodiment easier to understand, the probability Z of each sample in the validation set being a highly efficient donor is recorded in Table 5. This allows people to more intuitively determine whether the conclusions obtained by this invention are correct. Specifically: when the Z value is greater than 0.5, it indicates that the probability of the donor being a highly efficient donor is relatively high; when the Z value is less than 0.5, it indicates that the probability of the donor being an inefficient donor is relatively high; when the Z value is 0.5, it indicates that the donor may be a highly efficient donor or an inefficient donor. In this case, further testing using other methods is required, such as mental health testing and drug testing. Furthermore, the closer the Z value is to 0.5, the more necessary it is to use other methods for testing.

[0095] It should be noted that this embodiment only illustrates how to calculate the probability that the test donor is a highly efficient donor using six single bacterial species (i.e., the first mimicry marker). In actual work, one can also calculate the probability that the test donor is a highly efficient donor using four single bacterial species (such as the second mimicry marker), or even other combinations of markers. One can choose different markers to calculate the probability that the test donor is a highly efficient donor according to one's own needs. Even the calculation formula and its independent variable coefficients and other parameters can be selected as needed, which will not be elaborated here.

[0096] It should also be noted that although this embodiment only lists the methods and approaches for quantitative detection of the sample to be tested through relative abundance values, other means for quantitative detection of microorganisms are also feasible for this invention (such as absolute abundance or total microbial load information, etc.), and can also be used to distinguish whether the donor to be tested is a high-efficiency donor or a low-efficiency donor. People can choose the appropriate quantitative detection method of microorganisms according to their own needs, which will not be elaborated here.

[0097] Example 6: Predictive System for Evaluating Test Donors

[0098] Based on the above embodiments and the representations in Tables 3-5, this embodiment provides a prediction system for evaluating whether a test donor is a high-efficiency or low-efficiency donor, including a detection module and a comparison module, specifically: The detection module is used to obtain quantitative detection results of a single bacterial species in the fecal sample of the donor to be tested; wherein, the single bacterial species may include one or more of the following: Holdemania filiformis, Desulfovibriopiger, Lachnospiraceae bacterium, Coprococcus catus, Clostridium aldenense, and Bacteroides vulgatus; The comparison module is used to compare the quantitative detection results with a preset threshold, and determine whether the donor to be tested is a high-efficiency donor or a low-efficiency donor based on the comparison results.

[0099] In practical work, to more clearly illustrate how the prediction system of this embodiment evaluates and distinguishes between efficient and inefficient donors, this embodiment uses abundance values ​​as an example to illustrate the specific technical solution, specifically: The detection module obtains the relative abundance value of Holdemania filiformis in the fecal sample of the donor to be tested, which is recorded as the first abundance value. This first abundance value is also the quantitative detection result obtained by the detection module. The comparison module compares the first abundance value with the data for *Holdemania filiformis* in Table 4 to assess and distinguish whether the tested donor is a highly efficient or inefficient donor. For example, if the first abundance value falls within the range defined by the mean and standard deviation of the highly efficient donor group, the probability that the tested donor belongs to the highly efficient donor group is relatively high. Similarly, referring to the detection and evaluation method of Holdemania filiformis mentioned above, the prediction system can also perform similar detection and evaluation on microorganisms such as Desulfovibrio piger, Lachnospiraceaebacterium, Coprococcus catus, Clostridium aldenense, and Bacteroides vulgatus, thereby assessing whether the test donor is a highly efficient or inefficient donor.

[0100] Due to factors such as genetics, region, and environment, it is normal that not all six newly discovered bacterial species can be detected in fecal samples. One or more of the six newly discovered bacterial species can be used as detection markers to assess whether the donor being tested is a highly efficient or inefficient donor.

[0101] Since relative abundance values ​​and other quantitative detection data (such as absolute abundance or total microbial load information) are all conventional methods in this field, if one wants to use other quantitative detection methods to evaluate whether the test donor is a high-efficiency donor or a low-efficiency donor, one can refer to the description above, which will not be repeated here.

[0102] Example 7: FMT Donor Efficiency Prediction System for Adolescent Depression

[0103] Based on the above embodiments, this embodiment provides a predictive system for donor efficacy in adolescent depression using femtosecond chemoradiotherapy (FMT). This predictive system can also be referred to as a computer program product related to adolescent depression. The predictive system (computer program product) is used to distinguish between a tested donor and an inefficient donor, including the following steps: Obtain the relative abundance value of each single bacterial species in the feces of the donor to be tested; the single bacterial species include one or more of the following: Holdemania filiformis, Desulfovibrio piger, Lachnospiraceae bacterium, Coprococcus catus, Clostridium aldenense, and Bacteroides vulgatus; Substitute the relative abundance value of each individual bacterial species into the binary logistic regression equation to calculate the logarithm y of the dominance of the donor to be tested (i.e., the first probability value y). y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6; Where A is the intercept term, B1 to B5 are the regression coefficients of the independent variables; x1 is the relative abundance value of Bacteroides vulgatus, x2 is the relative abundance value of Desulfovibrio piger, x3 is the relative abundance value of Holdemania filiformis, x4 is the relative abundance value of Coprococcus catus, x5 is the relative abundance value of Lachnospiraceae bacterium, and x6 is the relative abundance value of Clostridium aldenense. S3. Calculate the probability Z that the donor to be tested is an efficient donor based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y; The probability Z can also be expressed as: ; S4. Based on the comparison between the probability Z of a high-efficiency donor and the reference value, diagnose or predict the risk of the tested donor having adolescent depression.

[0104] It should be noted that one can choose a single bacterial species as the calculation variable to calculate the probability Z of a highly efficient donor of the test donor, or one can choose several single bacterial species (microorganisms) as a combination to calculate the probability Z of a highly efficient donor of the test donor. For example, according to Example 5 and Table 5, one can use the first or second mimicry marker as the independent variable in the above calculation formula to calculate the probability Z of the test donor being a highly efficient donor. When using the second mimicry marker as the independent variable, only the data of x2, x3, x4, and x6 need to be used. The specific calculation formula is as follows: ; In practice, a Z-score greater than 0.5 indicates a higher probability that the donor is a highly efficient donor; a Z-score less than 0.5 indicates a higher probability that the donor is an inefficient donor; a Z-score of 0.5 indicates that the donor may be either highly efficient or inefficient, requiring further testing using methods such as mental health testing and drug testing. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to utilize additional testing methods.

[0105] Example 8: Method for constructing a prediction model

[0106] Based on the descriptions in Examples 1-7, the present invention provides a method for constructing a prediction model for FMT donor efficacy prediction in adolescents with depression, comprising the following steps: S1. Collect fecal samples from healthy individuals according to preset intake and discharge standards; In this step, given that different groups of people have different definitions of "healthy person," such as some people considering family members of long-lived families as healthy people (passing physical examinations), some people considering adults aged 20-30 as healthy people (passing physical examinations), some people considering college students of sports colleges as healthy people (passing physical examinations), etc., people can use the healthy person screening method listed in Example 1 of this invention to screen for healthy people, or they can use other standards to screen for healthy people. This embodiment does not impose any restrictions here, as long as the collected fecal samples conform to people's general understanding.

[0107] S2. Based on the preset intestinal flora transplantation steps and the reduction rate of the Hamilton Depression Rating Scale, the fecal samples of the healthy individuals are divided into a high-efficiency donor group and a low-efficiency donor group. In this step, after selecting specific fecal samples from healthy individuals, the fecal samples should be divided into a high-efficiency donor group and a low-efficiency donor group based on the preset gut microbiota transplantation steps and the reduction rate of the Hamilton Depression Rating Scale. The specific experimental steps can be referred to the descriptions in Examples 1 to 4 above, and will not be repeated here.

[0108] S3. Perform high-throughput sequencing on the high-efficiency donor group and the low-efficiency donor group to identify the differentially expressed microorganisms between the high-efficiency donor group and the low-efficiency donor group. The differentially expressed microorganisms include the microbial biomarkers described in the prediction system described in Example 6 or Example 7. The steps for performing high-throughput sequencing and identifying differentially expressed microorganisms can be found in the descriptions of Examples 1-4 above, and will not be repeated here.

[0109] S4. Use the differentially expressed microorganisms as a predictive model for FMT donor efficacy in adolescent depression to distinguish between high-efficiency and low-efficiency donors.

[0110] In conjunction with Examples 6 and 7, this step provides two methods for using the differentially expressed microorganisms as predictive models, specifically including: Referring to Example 6, the first method of using the differential microorganisms as a prediction model is as follows: S41, obtain the quantitative detection results of preset microbial markers in the fecal sample of the donor to be tested; S42, compare the quantitative detection results with preset thresholds, and determine whether the donor to be tested is a high-efficiency donor or a low-efficiency donor based on the comparison results.

[0111] Referring to Example 7, the second method of using the differential microorganisms as a prediction model is as follows: S41, obtain the quantitative detection results of preset microbial markers in the fecal sample of the donor to be tested; S42, calculate the probability that the donor to be tested is a high-efficiency donor based on the quantitative detection results, and use the probability value of the high-efficiency donor as the quantitative detection result; S43, compare the quantitative detection result with a preset threshold, and determine whether the donor to be tested is a high-efficiency donor or a low-efficiency donor based on the comparison result.

[0112] Example 9: Products for detecting gut microbiota

[0113] Based on the description of embodiments 1 to 4 above, it can be seen that the biomarkers selected in this application have good predictive effects. Medical personnel can use a single bacterial species as a biomarker to detect and diagnose the sample to be tested, so as to distinguish whether the donor to be tested is a high-efficiency donor or a low-efficiency donor. Medical personnel can also combine multiple single bacterial species together as biomarkers (such as a first mimicry biomarker or a second mimicry biomarker) to detect and diagnose the sample to be tested, thereby distinguishing whether the donor to be tested is a high-efficiency donor or a low-efficiency donor.

[0114] Therefore, this embodiment also provides a product for detecting microbial biomarkers. The product may include one or more of reagents, test strips, aptamers, and chips. The reagents may contain primers, probes, antibodies, etc. The product is specific to the microbial biomarkers described in Examples 1 to 4 and can be used for quantitative detection of the microbial biomarkers described in Examples 1 to 4.

[0115] In practical applications, the product described in this embodiment can be used in the preparation of products for diagnosing adolescent depression to distinguish between high-efficiency and low-efficiency donors. Simultaneously, the microbial markers may include the six single bacterial species found in this application that are associated with adolescent depression. That is, the microbial markers in the test reagent may include (or be selected from) one or more of the following: *Holmania fibrosum*, *Desulfovibrio desulfurization*, *Trichophyton* bacteria, *Factococcus regularus*, *Clostridium aldoni*, and *Bacteroides vulgaris*.

[0116] It should be noted that, as described in Examples 1 to 4, one or more of the six single bacterial species discovered in the invention have sensitivity and specificity, and can be used to distinguish whether the test donor is a high-efficiency donor. Therefore, products containing the above-mentioned single bacterial species will also have sensitivity and specificity, which will not be elaborated here.

[0117] Example 10: Use of the reagent in predicting / diagnosing products related to adolescent depression

[0118] As described in the above embodiments, the detection reagent containing six single bacterial species has high sensitivity and specificity, and can be used to distinguish whether a test donor is a highly efficient donor. This detection reagent may contain one or more of the six single bacterial species. Similarly, products containing the above-mentioned detection reagent can also be used to predict / diagnose whether a test donor is a highly efficient donor. These products can be kits and prediction systems, etc., which will not be elaborated upon here.

[0119] This embodiment also provides a first reagent kit, which may contain the detection reagents described in the above embodiments. Since the detection reagents can be used to distinguish between high-efficiency and low-efficiency donors, the reagent kit can also be used to distinguish between high-efficiency and low-efficiency donors. The limitations and technical solutions of the reagent kit in this application can be referred to the above description, and will not be repeated here. Similarly, the above reagent kit can also be used in the preparation of products for detecting adolescent depression, and will not be repeated here either.

[0120] As a preferred option, the microbial marker in the first kit may be only the first mimicry marker or the second mimicry marker, so that the prediction results of the kit are more accurate and can more accurately distinguish whether the test donor is a high-efficiency donor or a low-efficiency donor.

[0121] As a preferred embodiment, the reagents / kits of this embodiment can be used in the FMT donor efficacy prediction system for adolescent depression described in Example 8 to more accurately distinguish whether the tested donor is a high-efficiency donor or a low-efficiency donor.

[0122] Example 11: Reagents for treating adolescent depression and their applications

[0123] As described in Examples 1-4, the high-efficiency donor group showed good therapeutic effects on adolescent patients with depression. Therefore, fecal samples from the high-efficiency donor group can be used to treat adolescent depression with good efficacy. Figure 1 As described above, the levels of *Holdemania filiformis*, *Desulfovibrio piger*, *Lachnospiraceae bacterium*, and *Coprococcus catus* in the high-efficiency donor group were significantly higher than those in the low-efficiency donor group. Therefore, fecal samples containing these four microorganisms can be used to treat adolescent depression through intestinal microbiota transplantation (FMT).

[0124] In fecal microbiota transplantation (FMT), related products include reagents, lyophilized powders, capsules, FMT bacterial solutions, and treatment systems for adolescent depression. Since all of these products can be obtained from human fecal samples through filtration (or enrichment), and reagents may contain primers, probes, antibodies, etc., products containing the four types of microorganisms mentioned above (including reagents, lyophilized powders, capsules, FMT bacterial solutions, and treatment systems for adolescent depression) can also be used to treat adolescent depression. Furthermore, because capsules and FMT bacterial solutions are obtained directly from human fecal samples through filtration (or enrichment), they have the best therapeutic effect on adolescent patients with depression, which will not be elaborated upon here.

[0125] Furthermore, in conjunction with Table 4 and the descriptions of Examples 1-4, it can be seen that if the content of one or more of the above-mentioned fibrous Holdmannii, lazy desulfovibrio, Trichophyton family bacteria, and regular fecal cocci are within the range determined by the mean and standard deviation in Table 4, then the donor is a highly efficient donor.

[0126] Furthermore, if a donor contains four microorganisms (also known as four single species or second mimicry markers): Fibrinous Hallermannii, Lazy Desulfovibrio, Trichophyton, and Regular Fecal Entomopathogens, and the content of each of these four microorganisms is within the range determined by the mean and standard deviation in Table 4, then the donor is the most effective in treating adolescent depression, and this donor can be called a super donor.

[0127] Based on the above description, this embodiment also provides a second kit containing reagents for the quantitative detection of microbial markers, including second mimicry markers. Preferably, the content of one or more single bacterial species in the second mimicry marker is within the range determined by the mean and standard deviation in Table 4; optimally, the content of each single bacterial species in the second mimicry marker is within the range determined by the mean and standard deviation in Table 4.

[0128] Furthermore, a second kit can be used in the preparation of products related to adolescent depression, wherein the product is a capsule or FMT bacterial solution made from human fecal samples.

[0129] It should be noted that, in conjunction with the descriptions of Examples 1-4 and conventional methods in the art, it is feasible for those skilled in the art to produce corresponding, specific products (primers, probes, antibodies, aptamers, or chips, etc.) when using the six newly discovered single bacterial species as microbial markers, and will not be elaborated here.

[0130] Similarly, based on the descriptions of Examples 1-4 and conventional methods in the art, it is common practice in the art to prepare corresponding reagents, lyophilized powders, capsules, and FMT bacterial solutions from fecal samples. Therefore, it should be feasible for those skilled in the art to prepare corresponding products (including reagents, lyophilized powders, capsules, and FMT bacterial solutions) from one or more of Fibrous Hallemannii, Lazy Desulfovibrio, Trichophyton, and Regular Fecal Entomopathogens, and will not be elaborated here.

[0131] It should be noted that, as described in Examples 1 to 4, once it is known that highly effective donors are better at treating adolescent depression, and that the content of one or more of the following in highly effective donors—such as *Holmania fibrosum*, *Desulfovibrio lavans*, *Trichophyton* bacteria, and *Stenococcus punctatus*—is significantly higher than that in ineffective donors, then it is also possible for those skilled in the art to create a complementary treatment system (such as an app) for treating adolescent depression using the above four microorganisms, which will not be elaborated here.

[0132] Example 12: Determining whether the donor to be tested is a high-efficiency donor

[0133] If it is necessary to distinguish between high-efficiency and low-efficiency donors to further determine whether the donor is a high-efficiency donor, medical personnel can use the methods or products described in Examples 1-11 above to diagnose the individuals being tested, in addition to conventional testing methods, to assist them in making a more accurate judgment. (1) If, after continuous observation over multiple time periods, the content of one or more of the following bacteria—Lazy Desulfovibrio, Fibrous Hallermannii, Regular Fecal Entoloma, and Trichophyceae—is found to be high (compared to the mean and standard deviation of the high-efficiency donor group in Table 4), or even shows a significant increasing trend, then the person being tested is likely to be a high-efficiency donor.

[0134] (2) If, after observation over multiple consecutive periods, the content of one or more of Bacteroides commonis and Clostridium aldonis is found to be high (compared to the mean and standard deviation of the inefficient donor group in Table 4), then the person being tested is more likely to be an inefficient donor.

[0135] (3) If, after observation over multiple consecutive periods, the person being tested does not exhibit the patterns described in (1) and (2) above, then medical staff can combine [the above information with further details]. Figures 1-3 According to Tables 3-5, make a biased judgment on whether the tested donor is a high-efficiency donor or a low-efficiency donor.

[0136] (4) If medical staff want to distinguish more accurately and intuitively whether the donor to be tested is a high-efficiency donor, they can calculate whether the donor to be tested is a high-efficiency donor based on the relative abundance values ​​of the six single bacterial species and with reference to the technical solutions described in Examples 5 to 7.

[0137] Conclusion and explanation: 1. By Figures 1-3 As shown in Tables 3-5, any one of the six newly discovered single bacterial species in this application can be used as a microbial marker for FMT donors. Each single bacterial species has sensitivity and specificity for adolescent depression and can be used to distinguish whether the donor to be tested is a high-efficiency donor or a low-efficiency donor. Therefore, the microbial marker for FMT donors can be selected from any one or more of the six newly discovered single bacterial species in this application.

[0138] 2. As shown in Tables 3-5, it is normal for only one or a few species of bacteria to be detected when testing intestinal samples. This is because there are individual differences. The probability Z in this application is calculated. Therefore, even if a sample contains only a single species of bacteria, this application can still calculate the probability that the sample to be tested has adolescent depression.

[0139] 3. Predictive effect: For single bacterial species, the AUC value of *Holdermania fibrosum* was the highest (approximately 0.9591), while the AUC value of *Trichophyton* bacteria was the lowest (approximately 0.7576). The AUC value of the mimicry marker (a marker formed by the combination of 6 single bacterial species) (approximately 1) was significantly higher than that of the single bacterial species.

[0140] Meanwhile, the six newly discovered bacterial species in this application can all be used as detection variables, with AUC values ​​all greater than 60%, exhibiting high specificity and sensitivity. Therefore, one or more of the six microbial markers can be used to distinguish between high-efficiency and low-efficiency donors, thereby more efficiently screening donors for FMT specifically for adolescent depression, determining whether the donor to be tested belongs to the high-efficiency donor with better efficacy, and thus improving the accuracy and reliability of FMT treatment, thereby increasing the efficacy of FMT for adolescent depression.

[0141] Finally, given the good therapeutic effect of the high-efficiency donor group on adolescent depression, fecal samples from the high-efficiency donor group can be used to treat adolescent depression with good efficacy. Figure 1 As described above, the levels of *Holdemania filiformis*, *Desulfovibrio piger*, *Lachnospiraceae bacterium*, and *Coprococcus catus* in the high-efficiency donor group were significantly higher than those in the low-efficiency donor group. Therefore, fecal samples containing these four microorganisms can be used to treat adolescent depression through intestinal microbiota transplantation (FMT).

[0142] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. A predictive system for adolescent depression, characterized in that, The prediction system is used to distinguish whether the donor under test is a high-efficiency donor or a low-efficiency donor, including: The detection module is used to obtain quantitative detection results of preset microbial markers in the fecal sample of the donor to be tested, wherein the microbial markers include Holdemania filiformis; The comparison module is used to compare the quantitative detection results with a preset threshold, and determine whether the donor to be tested is a high-efficiency donor or a low-efficiency donor based on the comparison results.

2. The prediction system according to claim 1, characterized in that, The microbial markers also include Desulfovibrio piger and / or Lachnospiraceae bacterium.

3. The prediction system according to claim 2, characterized in that, The microbial markers also include one or more of Coprococcus catus, Clostridium aldenense, and Bacteroides vulgatus.

4. A FMT donor efficacy prediction system for adolescent depression, characterized in that: The prediction system is used to distinguish whether the donor under test is a high-efficiency donor or a low-efficiency donor, including: The detection module is used to obtain the quantitative detection results of preset microbial markers in the fecal sample of the donor to be tested. The microbial markers include one or more of the following: Holdemania filiformis, Desulfovibrio piger, Lachnospiraceae bacterium, and Coprococcus catus. The calculation module is used to calculate the probability that the donor to be tested is a high-efficiency donor based on the quantitative detection results, and denoted as the high-efficiency donor probability value; The comparison module is used to compare the probability value of the high-efficiency donor with a preset threshold, and determine whether the donor to be tested is a high-efficiency donor or an inefficient donor based on the comparison result.

5. A FMT donor efficacy prediction system for adolescents with depression, characterized in that: The microbial markers also include Clostridium aldenense and / or Bacteroides vulgatus.

6. A method for constructing a predictive model for FMT donor efficacy in adolescents with depression, characterized in that, The method for constructing the prediction model includes the following steps: Fecal samples from healthy individuals were collected according to pre-set standards for intake and discharge. Based on the pre-set intestinal flora transplantation steps and the reduction rate of the Hamilton Depression Rating Scale, the fecal samples of the healthy individuals were divided into a high-efficiency donor group and a low-efficiency donor group. High-throughput sequencing was performed on the high-efficiency donor group and the low-efficiency donor group to identify differentially expressed microorganisms between the high-efficiency donor group and the low-efficiency donor group, wherein the differentially expressed microorganisms include the microbial biomarkers described in the prediction system of any one of claims 1 to 3; The differentially expressed microorganisms were used as a predictive model for FMT donor efficacy in adolescent depression to distinguish between high-efficiency and low-efficiency donors.

7. The construction method according to claim 6, characterized in that, The step of using the microbial biomarker as a predictive model for FMT donor efficacy in adolescent depression includes: Obtain quantitative detection results of pre-defined microbial markers in fecal samples from the donor to be tested; The quantitative detection results are compared with a preset threshold, and the donor to be tested is determined to be either a high-efficiency donor or a low-efficiency donor based on the comparison results.

8. The construction method according to claim 6, characterized in that, The step of using the microbial biomarker as a predictive model for FMT donor efficacy in adolescent depression includes: Obtain quantitative detection results of pre-defined microbial markers in fecal samples from the donor to be tested; The probability that the donor to be tested is a high-efficiency donor is calculated based on the quantitative detection results, and the probability value of the high-efficiency donor is used as the quantitative detection result; The quantitative detection results are compared with a preset threshold, and the donor to be tested is determined to be either a high-efficiency donor or a low-efficiency donor based on the comparison results.

9. The application of a reagent for quantitative detection of microbial markers in the preparation of products for treating adolescent depression, characterized in that, The microbial markers include one or more of the following: Holdemania filiformis, Desulfovibrio piger, Lachnospiraceae bacterium, and Coprococcus catus.

10. The application according to claim 9, characterized in that, The product is one or more of the following: a reagent kit, lyophilized powder, capsules, FMT bacterial culture, and a treatment system for treating adolescent depression.

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