CN116434845B - Microbial marker, application thereof in depression diagnosis and evaluation device - Google Patents

Microbial marker, application thereof in depression diagnosis and evaluation device Download PDF

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CN116434845B
CN116434845B CN202310389237.2A CN202310389237A CN116434845B CN 116434845 B CN116434845 B CN 116434845B CN 202310389237 A CN202310389237 A CN 202310389237A CN 116434845 B CN116434845 B CN 116434845B
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曹美群
曹永凯
范大华
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Shenzhen Second Peoples Hospital
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Abstract

The invention discloses a microbial marker and application thereof in depression diagnosis and an evaluation device, and belongs to the technical field of biology. The depression evaluation device is based on a new depression diagnosis model constructed by macrogenomics, and the model is based on intestinal microorganisms at a seed level and a genus level. The diagnosis model constructed by the invention can be used as an objective detection index for early diagnosis of depression, and has high accuracy, specificity and sensitivity. The depression evaluation device based on the diagnosis model can rapidly and accurately diagnose depression.

Description

Microbial marker, application thereof in depression diagnosis and evaluation device
Technical Field
The invention relates to the technical field of biology, in particular to a microbial marker, and application and an evaluation device thereof in depression diagnosis.
Background
Depression (Depression), also known as depressive disorder, is a major type of mood disorder characterized by a significant and persistent Depression in the mood. World Health Organization (WHO) data shows that the total number of currently depressed patients worldwide exceeds 3 billion, increasing by about 18% in the last decade, and it is expected that depression will be the first disease in the overall burden of disease by 2030. Depression is also the leading cause of disability worldwide and is the "blackhand behind the scenes" driving an increase in suicide rate.
At present, the clinical diagnosis and typing of the depression are mainly evaluated by a depression scale, are determined according to subjective description of patients and mental examination of psychiatric doctors, and lack objective examination means, biological indexes and basis. In recent years, many studies have shown that depression exists in vivo in multiple systems, multiple index changes, and that these structural and biological index changes can be used for early prediction and diagnostic typing of depression and are directly related to the efficacy and selection of antidepressants.
The metagenomics is a novel microorganism research method which takes microorganism group genome in an environment sample as a research object, takes functional gene screening and sequencing analysis as research means, and takes microorganism diversity, population structure, evolutionary relationship, functional activity, mutual cooperation relationship and relationship with the environment as research purposes. Analysis of depression using means of metagenomics helps to explore objective diagnostic indices that can diagnose depression.
Disclosure of Invention
The invention aims to provide a depression evaluation device which uses a depression diagnosis model as a biological index for depression diagnosis and has high accuracy.
In order to achieve the aim of the invention, the following technical scheme is adopted.
The invention discloses a depression evaluation device, which comprises a data input module, a data analysis module and a result output module;
the data input module is used for inputting detection results of the relative abundance of microorganisms at the seed level and/or the genus level, wherein the relative abundance of microorganisms is the sequence ratio of single-seed or genus microorganisms in all detected microorganisms;
the horizontal microorganisms include at least one of the following:
clostridium praecox (Faecalibacterium prausnitzii); streptomyces sp.AC541; salinibacillus sp.ZJ450; pseudomonas brassicacearum; streptomyces platyphyllus (Streptomyces platensis); burkholderia oklahomensis; lysobacter sp.h23m41; aerococcus urinaehominis; osmophilic tetragenic coccus (Tetragenococcus osmophilus);
wherein Faecalibacterium prausnitzii can be abbreviated as f.prausnitzii; streptomyces sp.AC541 can be abbreviated as S.sp.AC541; salinibacillus sp.ZJ450 may be abbreviated as S.sp.ZJ450; pseudomonas brassicacearum may be abbreviated as p.brissicacearum; streptomyces platensis can be abbreviated as s.
The genus-level microorganisms include at least one of:
Faecalis (Faecalibacterium); chaetomium (Lachnospira); rogowski (Roseburia); monomonas (Areniminonas).
The data analysis module is used for substituting the data of the input module into a pre-constructed model to calculate a risk index;
wherein the model comprises a logistic regression analysis model and/or a subject work characteristic curve (ROC) analysis model;
and the result output module compares the risk index with a cut-off value (cutoff value) of the test subject working characteristic curve analysis model and outputs a numerical value comparison result.
Preferably, the comparison result output by the result output module is judged, and if the risk index is larger than the cutoff value, the patient suffers from depression, otherwise, the patient does not suffer from depression.
Preferably, when the detection results of more than two types of horizontal microorganisms are input into the data input module, the logistic regression analysis model adopts the following formula:
Logit(P)＝a+bi×Vi+…+bi×Vi；
wherein a and b are constants, a is 0-1;
i=1, 2, …,13, i takes different values in the same formula, and the value range of bi is-94-92;
b1 to b13 respectively correspond to horizontal microorganism clostridium praecox in sequence; streptomyces sp.AC541; salinibacillus sp.ZJ450; pseudomonas brassicacearum; streptomyces platyphyllus; burkholderia oklahomensis; lysobacter sp.h23m41; aerococcus urinaehominis; osmophilic tetragenic cocci and genus horizontal microorganisms, the genus faecium; the genus chaetomium; genus rochanteria; parameters of the genus monad;
V1-V13 respectively correspond to horizontal microorganism clostridium praecox in sequence; streptomyces sp.AC541; salinibacillus sp.ZJ450; pseudomonas brassicacearum; streptomyces platyphyllus; burkholderia oklahomensis; lysobacter sp.h23m41; aerococcus urinaehominis; osmophilic tetragenic cocci and genus horizontal microorganisms, the genus faecium; the genus chaetomium; genus rochanteria; detection results of the genus monad;
logit (P) represents a risk index;
the cut-off value of the result output module is 0-1.
The evaluation device analyzes the detection result of the related intestinal microorganisms of the depression through the data input module, the data analysis module and the result output module and outputs the result. The invention has high accuracy, specificity and sensitivity of the evaluation device.
The microorganisms are obtained by performing a macrogenomic analysis on a stool sample from a patient suffering from depression. The application of macrogenomic analysis to the human intestinal microbiota can generate a large amount of data that can be used to characterize the composition and function of the microbiota, and thus analyze the type and quantity of intestinal microorganisms in patients with depression to find their association with the onset of depression.
More preferably, the microorganism consists of Faecalibacterium prausnitzii, streptomyces sp.AC541, pseudomonas brassicacearum, salinibacillus sp.ZJ450. The detection result of the microorganism combination is input to diagnose the depression, the accuracy, the specificity and the sensitivity are high, the AUC can reach more than 0.92, the accuracy can reach more than 0.85, and the specificity and the sensitivity are respectively higher than 0.92 and 0.87.
The content of Faecalibacterium prausnitzii, streptomyces sp.ac541, salinomyces sp.zj450, pseudomonas brassicacearum, streptomyces platensis, burkholderia oklahomensis, lysobacters p.h23m41, aerococcus urinaehominis, tetragenococcus osmophilus was reduced in intestinal microorganisms in depressed patients compared to normal subjects.
Preferably, when the data input module inputs the detection result of clostridium prasugrel, the cut-off value in the result analysis module is 0.530+/-0.001; and/or
When the data input module inputs a detection result of Streptomyces sp.AC541, a cutoff value in the result analysis module is 0.586+/-0.001; and/or
When the data input module inputs a detection result of Salinibacillus sp.ZJ450, a cutoff value in the result analysis module is 0.543+/-0.001; and/or
When the data input module inputs a detection result of Pseudomonas brassicacearum, a cutoff value in the result analysis module is 0.408+/-0.001; and/or
When the data input module inputs the detection result of the streptomyces planus, the cut-off value in the result analysis module is 0.568+/-0.001 and/or
When the data input module inputs a detection result of Burkholderia oklahomensis, a cutoff value in the result analysis module is 0.457+/-0.001; and/or
When the data input module inputs a detection result of the lysobacterium sp.H23M41, a cutoff value in the result analysis module is 0.398+/-0.001; and/or
When the data input module inputs a detection result of Aerococcus urinaehominis, a cutoff value in the result analysis module is 0.632+/-0.001; and/or
When the data input module inputs the detection result of the tetragenic osmophilic coccus, the cut-off value in the result analysis module is 0.669+/-0.001; and/or
When the data input module inputs the detection result of the clostridium prasugrel, the cut-off value in the result analysis module is 0.221+/-0.001; and/or
When the data input module inputs the detection result of clostridium, streptomyces sp.ac541, the cut-off value in the result analysis module is 0.553 +/-0.001; and/or
When the data input module inputs the detection results of clostridium prasugrel and Pseudomonas brassicacearum, the cut-off value in the result analysis module is 0.595+/-0.001; and/or
When the data input module inputs the detection result of clostridium praecox, salinibacillus sp.ZJ450, the cut-off value in the result analysis module is 0.45+/-0.001; and/or
When the data input module inputs the detection result of Streptomyces sp.AC541, pseudomonas brassicacearum, the cut-off value in the result analysis module is 0.484+/-0.001; and/or
When the data input module inputs the detection results of Streptomyces sp.AC541 and Salinibacillus sp.ZJ450, the cut-off value in the result analysis module is 0.545+/-0.001; and/or
When the data input module inputs a detection result of Pseudomonas brassicacearum and Salinibacillus sp.ZJ450, a cutoff value in the result analysis module is 0.369+/-0.001; and/or
When the data input module inputs the detection results of clostridium, streptomyces sp.ac541, pseudomonas brassicacearum, the cut-off value in the result analysis module is 0.680 plus or minus 0.001; and/or
When the data input module inputs the detection result of clostridium, streptomyces sp.AC541, salinibacteriumsp.ZJ450, the cut-off value in the result analysis module is 0.461+/-0.001; and/or
When the data input module inputs the detection results of clostridium praecox, pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the cut-off value in the result analysis module is 0.731+/-0.001; and/or
When the data input module inputs the detection results of Streptomyces sp.AC541, pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the cut-off value in the result analysis module is 0.52+/-0.001; and/or
When the data input module inputs the detection results of clostridium praecox, streptomyces sp.ac541, pseudomonas brassicacearum and salinomyces sp.zj450, the cut-off value in the result analysis module is 0.819+/-0.001; and/or
When the data input module inputs the detection result of the genus faecium, the cut-off value in the result analysis module is 0.529+/-0.001; and/or
When the data input module inputs the detection result of the chaetomium, the cut-off value in the result analysis module is 0.674+/-0.001; and/or
When the data input module inputs the detection result of the genus Roche, the cutoff value in the result analysis module is 0.455+/-0.001; and/or
When the data input module inputs the detection result of the monad genus, the cut-off value in the result analysis module is 0.510+/-0.001; and/or
When the data input module inputs the detection result of the fecal and the chaetobacter, the cut-off value in the result analysis module is 0.689+/-0.001; and/or
When the data input module inputs the detection result of the genus faecium and the genus rogowski, the cut-off value in the result analysis module is 0.472+/-0.001; and/or
When the data input module inputs the detection result of the genus faecium and the genus monad, the cut-off value in the result analysis module is 0.523+/-0.001; and/or
When the data input module inputs the detection result of the chaetomium and the rogowski, the cut-off value in the result analysis module is 0.657+/-0.001; and/or
When the data input module inputs the detection result of the Chaetomium and the Monomonas, the cut-off value in the result analysis module is 0.632+/-0.001; and/or
When the data input module inputs the detection result of the genus Roche and the genus Monomonas, the cut-off value in the result analysis module is 0.564+/-0.001; and/or
When the data input module inputs detection results of the faecal bacillus, the chaetobacter and the rogowski, the cut-off value in the result analysis module is 0.618+/-0.001; and/or
When the data input module inputs detection results of the faecal bacillus, the chaetobacter and the monad, the cut-off value in the result analysis module is 0.534+/-0.001; and/or
When the data input module inputs detection results of the faecal bacillus genus, the rogowski genus and the monad genus, the cut-off value in the result analysis module is 0.537+/-0.001; and/or
When the data input module inputs detection results of the chaetomium, the rogowski and the monad, the cut-off value in the result analysis module is 0.655+/-0.001; and/or
When the data input module inputs detection results of the fecal genus, the chaetobacter genus, the rogowski genus and the monad genus, the cut-off value in the result analysis module is 0.618+/-0.001;
the data input module inputs the detection result of the fecal bacillus, and the cut-off value in the result analysis module is 0.573+/-0.001;
when the data input module inputs the detection result of the genus faecium, S.sp.AC541, the cut-off value in the result analysis module is 0.554+/-0.001;
when the data input module inputs the detection result of the genus faecium, S.sp.AC541, the cut-off value in the result analysis module is 0.553 +/-0.001;
when the data input module inputs the detection results of the clostridium, the clostridium pratensis and the S.sp.AC541, the cut-off value in the result analysis module is 0.530+/-0.001.
More preferably, the logistic regression analysis model of the data analysis module employs at least one of the following formulas:
logit (P) = -2.907 clostridium praecox+0.765;
Logit(P)＝-2.773*Streptomyces sp.AC541+0.534；
Logit(P)＝-2.255*Pseudomonas brassicacearum+0.517；
Logit(P)＝-3.066*Salinibacterium sp.ZJ450+0.419；
logit (P) = -2.363 Streptomyces platyphyllus+0.533;
Logit(P)＝-2.603*Burkholderia oklahomensis+0.485；
Logit(P)＝-2.017*Lysobacter sp.H23M41+0.579；
Logit(P)＝-2.042*Aerococcus urinaehominis+0.479；
logit (P) = -2.131. Osmophilic tetragenic coccus+0.457;
logit (P) =0.616-2.073 clostridium prasugrel-2.358*Streptomyces sp.AC541;
Logit (P) =0.695-2.463 clostridium prasugrel-2.02*Pseudomonas brassicacearum;
logit (P) = 0.553-2.365 Clostridium praecox-3.103*Salinibacterium sp.ZJ450;
Logit(P)＝0.559-2.47*Streptomyces sp.AC541-1.882*P.brassicacearum；
Logit(P)＝0.453-1.891*Streptomyces sp.AC541-1.888*Salinibacteriumsp.ZJ450；
Logit(P)＝0.419-1.782*Pseudomonas brassicacearum-2.659*Salinibacteriumsp.ZJ450；
logit (P) =0.552-2.213 clostridium prasugrel-1.978*Streptomyces sp.AC541-1.849 P.brassiaceae;
logit (P) =0.537-2.22 x Clostridium praecox-1.428 x Streptomyces sp.AC541-2.276*Salinibacterium sp.ZJ450;
logit (P) =0.531-2.498 clostridium praecox-1.609P.
brassicacearum-2.691*Salinibacterium sp.ZJ450；
Logit(P)＝0.472-1.755*Streptomyces sp.AC541-1.654*P.
brassicacearum-1.448*Salinibacterium sp.ZJ450；
Logit (P) =0.477-2.353 clostridium prasugrel-1.155*Streptomyces sp.AC541-1.502 P.brassicaceae-1.918*Salinibacterium sp.ZJ450;
logit (P) = -3.395. Faecalis +0.798;
logit (P) = -2.411 Chaetomium +0.593;
logit (P) = -2.919 Ralstonia+0.466;
logit (P) = -2.48. Times. Monad+0.56;
logit (P) = 0.713-2.604. Faecalis-1.445. Trichosporon;
logit (P) =0.64-2.46. Faecalis genus-1.746. Roxburghii genus;
logit (P) =0.764-3.224. Faecalis-2.662. Sp;
logit (P) =0.602-1.575. Chaetomium-1.936. Rochanterium;
logit (P) =0.762-2.161 Chaetomium-2.319. Mu.m;
logit (P) =0.525-2.708 Proteus-2.193 Acidovorax;
logit (P) =0.664-2.179. Faecalis-1.065. Chaetomium-1.164. Rochanterium;
Logit (P) =0.789-2.6. Faecalis-1.23. Chaetomium-2.672. Monad;
logit (P) =0.672-2.56. Faecalis-1.325. Roxburgh-2.647. Monad;
logit (P) =0.688-1.475 Chaetomium-1.777 rochanterium-2.327 Monomonas;
logit (P) =0.763-2.351. Faecalis-0.967. Chaetomium-0.842. Rochanterium-2.67. Monad;
logit (P) =0.815-39.527. Faecalis + 36.507. Praecox;
logit (P) =0.753-2.407. Faecalis genus-2.707*Streptomyces sp.AC541;
logit (P) = -2.402 clostridium prasugrel-2.705*Streptomyces sp.AC541+0.752
Logit (P) =0.756-93.399. Faecalis + 91.02. Prasux-2.797. Streptomyces sp.AC541.
The analysis formula of the data analysis module has extremely high specificity and sensitivity, and extremely high authenticity and prediction accuracy.
More preferably, the method for constructing the ROC analysis model includes the steps of:
sample data are analyzed: and drawing a working characteristic curve of the test subject, and calculating the area under the curve, the cut-off value, the specificity and the sensitivity.
The method is used for analyzing and processing the sample data and constructing the model, and the obtained model has high accuracy and strong specificity.
The invention also discloses a computer readable medium, which stores a computer program, and the computer program realizes the functions of the data analysis module when being executed.
The invention also discloses application of the evaluation device in preparing products for diagnosing or assisting in diagnosing depression.
The invention also discloses application of the reagent for detecting the intestinal microorganisms in preparing products for diagnosing depression, wherein the reagent comprises reagents for detecting the relative abundance of microorganisms at a seed level and/or microorganisms at a genus level:
the horizontal microorganisms include at least one of the following:
clostridium praecox; streptomyces sp.AC541; salinibacillus sp.ZJ450; pseudomonas brassicacearum; streptomyces platyphyllus; burkholderia oklahomensis; lysobactersp.h23m41; aerococcus urinaehominis;
the genus-level microorganisms include at least one of:
faecalis genus; the genus chaetomium; genus rochanteria; the genus monad.
Preferably, the above-mentioned agent comprises at least one of the following:
a genomic DNA extraction reagent; a DNA amplification reagent; DNA quantitative detection reagent.
Compared with the prior art, the invention has the beneficial effects that:
the invention analyzes intestinal microorganisms of depression patients through metagenomics and screens out microorganisms Faecaliberium suitable for depression diagnosis; streptomyces; salinibacillus; pseudomonas; burkholderia; lysobacter; aerococcus; tetragenococcus; lachnospira; roseburia; areniminonas. And then analyzing the genus level of the microorganism, and screening out four intestinal flora of Faecalibacterium, lachnospira, roseburia, arenimonas which can be used for diagnosis of depression. Then constructing a depression diagnosis model; the constructed model has extremely high authenticity and accuracy. The depression evaluation device of the invention is based on the microorganism and the diagnosis model, so that depression can be predicted, and the reality, the specificity and the sensitivity of the depression can be predicted.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below.
FIG. 1 shows the ROC analysis of Faecalibacterium prausnitzii;
FIG. 2 shows the result of ROC analysis of Streptomyces sp.AC541;
FIG. 3 shows the result of ROC analysis of Salinibacillus sp.ZJ450;
FIG. 4 is a ROC analysis of Pseudomonas brassicacearum;
FIG. 5 is a ROC analysis of Streptomyces platensis;
FIG. 6 is a ROC curve and prediction accuracy results for 2 genus combinations of Faecalibacterium, areniminonas;
FIG. 7 is a ROC curve and prediction accuracy results for 3 genus combinations of Faecalibacterium, roseburia, areniminonas;
FIG. 8 is a ROC curve and prediction accuracy results for 4 genus combinations of Faecalibacterium, lachnospira, roseburia, areniminonas;
FIG. 9 is a ROC curve and prediction accuracy results for the species combination Faecalibacterium, F.prausnitzii;
FIG. 10 is a ROC curve and prediction accuracy results for the species combination Faecalibacterium, S.sp.AC541;
FIG. 11 is a ROC curve and prediction accuracy results for the species combination Faecalibacterium, F.prausnitzii, S.sp.AC541;
FIG. 12 results of ROC curves, cross-validation accuracy, and validation set accuracy for horizontal intestinal microbiology diagnostic models;
FIG. 13 is a overfitting test result of a species level intestinal microbial diagnostic model based on a supervisory model;
FIG. 14 is a ROC curve, cross-validation accuracy and validation set accuracy results for a horizontal intestinal microbiology diagnostic model;
FIG. 15 is a fitting test result of a horizontal intestinal microbial diagnosis model based on a supervision model;
FIG. 16 is a ROC curve of a species level joint genus level intestinal microorganism diagnostic model, the accuracy of cross validation, and the accuracy results of the validation set;
FIG. 17 is a result of a supervised model-based overfitting test for a species-level joint genus-level intestinal microbiology diagnostic model.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, the implementations described in the following exemplary embodiments not being representative of all implementations consistent with the present disclosure. Rather, they are merely examples of methods consistent with some aspects of the present disclosure.
The clinical samples related to the project are all approved by the ethical of Shenzhen second people hospital (lot number: KS20191031001 and KS20191031001-GZ 2020), and are recorded in the China clinical trial registry (registration number: chiCTR 1900027456).
Example 1
Screening of intestinal microbial markers and construction of models
1. Intestinal microorganism detection analysis
1. Sample collection
The 58 patients with depression are taken into the depression group, and 40 healthy volunteers are taken into the healthy control group; case inclusion criteria and exclusion criteria were the same as in example 1.
(1) Collecting a fecal sample:
a) Preparing a bedpan and a fecal container, washing hands, and taking gloves to collect fresh fecal samples; b) The fecal sample of the subject is packaged in a laboratory, and is immediately packaged and marked; c) Intercepting the inner part of the middle section of the sample by using a sterile toothpick or a fecal sampler (the surface layer of the fecal contains intestinal mucosa exfoliated cells; the outside is easy to pollute, and after the part of bacterial DNA begins to degrade after being exposed to air, about 50-100 mg (about peanut size, filled into a 2.0mL centrifuge tube with not more than 1/3 volume) is taken into a sterile 2.0mL centrifuge tube, and 3-5 tubes of each sample are taken for backup. d) After sub-packaging, immediately quick-freezing with liquid nitrogen or directly storing at-80 deg.C.
(2) DNA extraction:
the extraction of DNA was performed using the kit according to the instructions. Taking 200-300mg of the excrement sample in a crushing tube, grinding by a machine, and then cracking. Centrifuging at 12000rpm at room temperature for 10min, adding SL3 into supernatant, and incubating at-20deg.C for 5min. After centrifugation at 12000rpm for 10min, the supernatant was washed with 700. Mu.L of SW1 and SW2, respectively, and 100. Mu.L of the eluent was added thereto for incubation at room temperature for 5min, followed by centrifugation at 12000rpm for 5min at room temperature for eluting DNA.
(3) Metagenomic sequencing
Metagenome sequencing was performed on the delegated Hua megagenes. Detecting the concentration of the DNA sample by using a Qubit fluorescent quantitative instrument; detecting the integrity of the DNA sample by 1% agarose gel electrophoresis, interrupting the DNA sample by using a Covaris instrument ultrasonic wave to select DNA fragments, and concentrating the fragments to about 300-400 bp; the amount of DNA sample was measured using QubitdsDNA HS Assay Kit 500 assays. Repairing the end of double-stranded DNA, and preparing a linker connecting reaction system to connect the linker with the DNA. Preparing a PCR reaction system to amplify the connection product. The amplified products were subjected to fragment screening using the reagent Agencourt AMPure XP-Medium. PCR products were detected with an Agilent 2100 Bioanalyzer. After the PCR product is denatured into single strands, preparing a cyclization reaction system to obtain single-strand annular products, and digesting linear DNA molecules which are not cyclized to obtain a final DNA library. Fragment size and concentration of the library were measured using an Agilent 2100Bioanalyzer (Agilent DNA 1000 Reagents). Metagenomic sequencing was performed using the Illumina HiSeq X T en platform and low quality DNA data and impurity information was removed.
(4) Sample assembly and genetic analysis
Samples were assembled using the assembly software MEGAHIT. MetaGeneMark software was used for metagenomic gene prediction. Based on the given sequence characteristics, known and unknown genes are predicted. The gene alignment was processed using MEGAN (version 5) to obtain species annotation information for each sequence. Summing the abundance of genes annotated to the same species gives the species content of that species in the sample.
(5) Species diversity analysis
Using the PCA (Principal Components Analysis) principal component analysis method, a first large variance of the normal and model set data is dimensionality reduced on a first coordinate (referred to as a first principal component) and a second large variance is projected on a second coordinate (a second principal component). And evaluate whether there is a discrepancy between the two sets of data, which can be used for subsequent analysis.
(6) Species abundance analysis
The species classification was performed on each sample at several classification levels of phylum, class, order, family, genus, species, respectively, by comparison with the database. The first 30 species are selected from the abundance of the species according to the mean value to draw a histogram, and the composition and proportion of each sample species and the variation of the species among groups are intuitively displayed and analyzed through the species histogram.
(7) Statistical analysis
Wilcoxon rank-sum test is a method of nonparametric testing of two independent sets of samples to find the species characteristic of the two sets of samples that best accounts for the differences between the sets under different biological conditions or environments. All statistics are done by R language software.
2. Results of intestinal microbial analysis
A total of 156 microorganisms were detected; further performing univariate ROC analysis on the microbial content; to avoid redundancy, only the results of the detection of intestinal microorganisms with AUC >0.8 obtained in ROC analysis are shown here, and are shown in table 1.
TABLE 1 intestinal flora species and relative abundance
As can be seen from table 1, the content of Faecalibacterium prausnitzii, streptomyces sp.ac541, salinomyces sp.zj450, pseudomonas brassicacearum, streptomyces platensis, burkholderia oklahomensis, lysobacter sp.h23m41, aerococcus urinaehominis, tetragenococcus osmophilus was reduced in intestinal microorganisms in depression patients compared to normal subjects. The microorganisms described above therefore have potential as diagnostic markers for depression.
A univariate model was constructed for each of 156 microorganisms, and the microorganisms with area under the curve (AUC) >0.8 showed the ROC analysis results. The single variable ROC analysis results are shown in table 2. To avoid redundancy, only ROC curves of Faecalibacterium prausnitzii, streptomyces sp.ac541, salinomyces sp.zj450, pseudomonas brassicacearum, streptomyces platensis are shown, as shown in fig. 1-5, respectively. The univariate model formula is as follows:
Logit(P)＝-2.907*Faecalibacterium prausnitzii+0.765；
Logit(P)＝-2.773*Streptomyces sp.AC541+0.534；
Logit(P)＝-2.255*Pseudomonas brassicacearum+0.517；
Logit(P)＝-3.066*Salinibacterium sp.ZJ450+0.419；
Logit(P)＝-2.363*Streptomyces platensis+0.533；
Logit(P)＝-2.603*Burkholderia oklahomensis+0.485；
Logit(P)＝-2.017*Lysobacter sp.H23M41+0.579；
Logit(P)＝-2.042*Aerococcus urinaehominis+0.479；
Logit(P)＝-2.131*Tetragenococcus osmophilus+0.457。
TABLE 2 Single variable ROC analysis results
The values in parentheses in table 2 are 95% confidence intervals. As can be seen from Table 2, the AUC of the strains in the table is higher than 0.8, which indicates that the diagnosis of depression using the above strains has higher authenticity.
Further performing multivariate ROC analysis, predicting the contribution degree of each data to the model through an importance index (VIP) of projection variables, selecting VIP & gt 1, and adopting t-test (t-test) to screen components with P & lt 0.05, wherein the components represent statistical significance and can be used as preliminary potential biomarkers. Finally 4 intestinal microbial species were selected for constructing a model for diagnosis of depression:
Faecalibacterium prausnitzii、Streptomyces sp.AC541、Pseudomonas brassicacearum、Salinibacterium sp.ZJ450。
the combined model and ROC analysis results are shown in table 3.
TABLE 3 bacterial species combination model and ROC analysis results
As can be seen from table 3, the AUC of the combinations of microorganisms in the table is close to or even exceeds 0.9, demonstrating the extremely high realism of the diagnosis of depression using the combinations of microorganisms; the specificity and the sensitivity are extremely high, and the diagnosis model of the microorganism combination is suitable for diagnosis of depression.
Further subordinate levels were analyzed for intestinal microorganisms, a total of 65 genera were analyzed, and only representative genera were shown for redundancy avoidance, as shown in table 4. The model formula for the individual genus is as follows:
Logit(P)＝-3.395*Faecalibacterium+0.798；
Logit(P)＝-2.411*Lachnospira+0.593；
Logit(P)＝-2.919*Roseburia+0.466；
Logit(P)＝-2.48*Arenimonas+0.56。
TABLE 4 results of ROC analysis of microorganisms of various genus
The values in brackets in table 4 are 95% confidence intervals. As can be seen from table 4, when Faecalibacterium, lachnospira, roseburia, arenimonas intestinal flora alone was used to construct a model for diagnosis of depression, AUC was higher than 0.83, indicating that the diagnosis was extremely truly; the specificity is higher than 0.72, the sensitivity is higher than 0.79, and Faecalibacterium, lachnospira, roseburia, arenimonas genera are suitable for being independently used for constructing a depression diagnosis model, so that early diagnosis of depression can be performed.
And further carrying out permutation and combination on the 4 genera, constructing a multivariate detection model, and carrying out ROC analysis. The analysis results are shown in Table 9. To avoid redundancy, only representative combined ROC curves and prediction accuracy results are shown.
2 genus combinations of Faecaliberia, areniminonas, as shown in FIG. 6;
3 genus combinations of Faecaliberia, roseburia, areniminonas, as shown in FIG. 7;
4 genus combinations of Faecalibacterium, lachnospira, roseburia, areniminonas, as shown in FIG. 8;
table 5 horizontal microorganism group model and ROC analysis results
As can be seen from table 5, the AUC of all combinations in the table was 0.87, up to 0.928, indicating that the combination was extremely truly useful for depression diagnosis; and all combinations above have a prediction accuracy of 0.78, a specificity and sensitivity of 0.77, indicating that the combinations are accurate and reliable for use in depression diagnosis.
Further using species and genus level intestinal microorganism combinations, a multivariate model was constructed and ROC analysis results are shown in table 6 and fig. 9-11: FIG. 9 is a ROC curve and prediction accuracy results for the species combination Faecalibacterium, F.prausnitzii; FIG. 10 is a ROC curve and prediction accuracy results for the species combination Faecalibacterium, S.sp.AC541; FIG. 11 is a ROC curve and prediction accuracy results for the combination of species, faecalibacterium, F.prausnitzii, S.sp.AC541.
TABLE 6 results of ROC analysis of microorganisms of genus and species level
As can be seen from Table 6, the AUC of the combinations in the tables are above 0.9, indicating the use of Faecaliberia, F.prausnitzii; the 3 combinations of Faecalibacterium, S.sp.AC541 and Faecalibacterium, F.prausnitzii, S.sp.AC541 are extremely realistic for diagnosis of depression; the prediction accuracy, specificity and sensitivity are high, and the combination and the diagnosis model constructed by using the combination are accurate and reliable when used for diagnosis of depression.
Test example 1
The relative intestinal microorganism content of 29 depressed patients and 20 normal persons was selected to construct a training set, and the relative intestinal microorganism content of 29 depressed patients and 20 normal persons was used as a validation set. And respectively carrying out model evaluation on the training set and the verification set on the microbial markers at the species level, the genus level, the species level and the genus level, carrying out 100 times of cross verification prediction accuracy by adopting a linear SVM algorithm, and carrying out fitting inspection based on a supervision model.
1. Seed level
Model evaluation results of the training set and the verification set of the species level microorganisms are shown in table 7, the area under the curve (AUC), sensitivity, specificity and prediction accuracy of the training set and the verification set are all high, the over fit test p of all models is less than 0.05, the models are not over fit, and the models are available. FIG. 12 is a graph of ROC curve and cross-validation accuracy for a representative species level microorganism Faecalibacterium prausnitzii, streptomyces sp.AC541, and FIG. 13 is a graph of the over-fit test results.
TABLE 7 model of horizontal microorganisms in training set and validation set
2. Belongs to the level
Model evaluation results of a training set and a verification set of the horizontal microorganisms are shown in table 8, areas Under Curves (AUC), sensitivity, specificity and prediction accuracy of the training set and the verification set are high, the over-fitting test p of all models is less than 0.05, the models are not over-fitted, and the models are available. FIG. 14 is a graph showing the ROC curve and the accuracy of cross-validation of a representative genus level microorganism, faecaliberia, arenimmonas, and FIG. 15 is a graph showing the results of a overfitting test.
TABLE 8 model of horizontal microorganisms in training and validation sets
3. The level is a level
Model evaluation results of the training set and the verification set of the species and genus level microorganisms are shown in table 9, area Under Curve (AUC), sensitivity, specificity and prediction accuracy of the training set and the verification set are all high, the over fit test p of all models is less than 0.05, the models are not over fit, and the models are available. FIG. 16 is a graph of ROC curves and cross-validation accuracy for representative species level microorganisms, faecaliberia, faecalibacterium prausnitzii, streptomyces sp.AC541, and FIG. 17 is a graph of the over-fit test results.
TABLE 9 model of microorganisms at genus and species level in training set and validation set
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention, but various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (6)

1. The depression evaluation device is characterized by comprising a data input module, a data analysis module and a result output module;
the data input module is used for inputting detection results of the relative abundance of the species level microorganisms and/or genus level microorganisms;
the relative abundance of the microorganism is the ratio of the microorganism gene sequences of a single species or genus to all the detected microorganism gene sequences;
the species level of microorganism includes at least one of:
clostridium praecox; streptomyces sp.AC541; salinibacillus sp.ZJ450; pseudomonas brassicacearum; streptomyces platyphyllus; burkholderia oklahomensis; lysobacter sp.h23m41; aerococcus urinaehominis;
the genus-level microorganism comprises at least one of:
faecalis genus; the genus chaetomium; genus rochanteria; the genus monad;
The data analysis module is used for substituting the data of the input module into a pre-constructed model and calculating a risk index;
the model comprises a logistic regression analysis model and/or a subject working characteristic curve analysis model;
the result output module compares the risk index with a cut-off value of a test subject working characteristic curve analysis model and outputs a numerical value comparison result;
when the detection result of at least one species level microorganism and/or genus level microorganism is input into the data input module, the logistic regression analysis model of the data analysis module adopts the following formula:
Logit(P)＝a+bi×Vi+…+bi×Vi；
wherein a and b are constants, a is 0-1;
i=1, 2, …,13, i takes different values in the same formula, and the value range of bi is-94-92;
b1 to b13 respectively correspond to horizontal microorganism clostridium praecox in sequence; streptomyces sp.AC541; salinibacillus sp.ZJ450; pseudomonas brassicacearum; streptomyces platyphyllus; burkholderia oklahomensis; lysobacter sp.h23m41; aerococcus urinaehominis; osmophilic tetragenic cocci and genus horizontal microorganisms, the genus faecium; the genus chaetomium; genus rochanteria; parameters of the genus monad;
V1-V13 respectively correspond to horizontal microorganism clostridium praecox in sequence; streptomyces sp.AC541; salinibacillus sp.ZJ450; pseudomonas brassicacearum; streptomyces platyphyllus; burkholderia oklahomensis; lysobacter sp.h23m41; aerococcus urinaehominis; osmophilic tetragenic cocci and genus horizontal microorganisms, the genus faecium; the genus chaetomium; genus rochanteria; detection results of the genus monad;
Logit (P) represents a risk index;
the cutoff value of the result output module is 0-1;
the logistic regression analysis model of the data analysis module adopts at least one of the following formulas:
logit (P) = -2.907 clostridium praecox+0.765;
Logit(P)＝-2.773*Streptomyces sp.AC541+0.534；
Logit(P)＝-2.255*Pseudomonas brassicacearum+0.517；
Logit(P)＝-3.066*Salinibacterium sp.ZJ450+0.419；
logit (P) = -2.363 Streptomyces platyphyllus+0.533;
Logit(P)＝-2.603*Burkholderia oklahomensis+0.485；
Logit(P)＝-2.017*Lysobacter sp.H23M41+0.579；
Logit(P)＝-2.042*Aerococcus urinaehominis+0.479；
logit (P) = -2.131. Osmophilic tetragenic coccus+0.457;
logit (P) =0.616-2.073 clostridium prasugrel-2.358*Streptomyces sp.AC541;
logit (P) =0.695-2.463 clostridium prasugrel-2.02*Pseudomonas brassicacearum;
logit (P) = 0.553-2.365 Clostridium praecox-3.103*Salinibacterium sp.ZJ450;
Logit(P)＝0.559-2.47*Streptomyces sp.AC541-1.882*P.brassicacearum；
Logit(P)＝0.453-1.891*Streptomyces
sp.AC541-1.888*Salinibacterium sp.ZJ450；
Logit(P)＝0.419-1.782*Pseudomonas
brassicacearum-2.659*Salinibacterium sp.ZJ450；
logit (P) =0.552-2.213 clostridium prasugrel-1.978 streptomyces
sp.AC541-1.849*P.brassicacearum；
Logit (P) =0.537-2.22 clostridium prasugrel-1.428 streptomyces
sp.AC541-2.276*Salinibacterium sp.ZJ450；
Logit (P) =0.531-2.498 clostridium praecox-1.609P.
brassicacearum-2.691*Salinibacterium sp.ZJ450；
Logit(P)＝0.472-1.755*Streptomyces sp.AC541-1.654*P.
brassicacearum-1.448*Salinibacterium sp.ZJ450；
Logit (P) =0.477-2.353 Clostridium praecox-1.155 Streptomyces
sp.AC541-1.502*P.brassicacearum-1.918*Salinibacterium sp.ZJ450；
Logit (P) = -3.395. Faecalis +0.798;
logit (P) = -2.411 Chaetomium +0.593;
logit (P) = -2.919 Ralstonia+0.466;
logit (P) = -2.48. Times. Monad+0.56;
logit (P) = 0.713-2.604. Faecalis-1.445. Trichosporon;
logit (P) =0.64-2.46. Faecalis genus-1.746. Roxburghii genus;
logit (P) =0.764-3.224. Faecalis-2.662. Sp;
Logit (P) =0.602-1.575. Chaetomium-1.936. Rochanterium;
logit (P) =0.762-2.161 Chaetomium-2.319. Mu.m;
logit (P) =0.525-2.708 Proteus-2.193 Acidovorax;
logit (P) =0.664-2.179. Faecalis-1.065. Chaetomium-1.164. Rochanterium;
logit (P) =0.789-2.6. Faecalis-1.23. Chaetomium-2.672. Monad;
logit (P) =0.672-2.56. Faecalis-1.325. Roxburgh-2.647. Monad;
logit (P) =0.688-1.475 Chaetomium-1.777 rochanterium-2.327 Monomonas;
logit (P) =0.763-2.351. Faecalis-0.967. Chaetomium-0.842. Rochanterium-2.67. Monad;
logit (P) =0.815-39.527. Faecalis + 36.507. Praecox;
logit (P) =0.753-2.407. Faecalis genus-2.707*Streptomyces sp.AC541;
logit (P) = -2.402 clostridium prasugrel-2.705*Streptomyces sp.AC541+0.752
Logit (P) =0.756-93.399. Faecalis + 91.02. Praecox-2.797*Streptomyces sp.AC541.
2. The evaluation device according to claim 1, wherein when the data input module inputs the detection result of clostridium prasugrel, the cutoff value in the result output module is 0.530±0.001; and/or
When the data input module inputs a detection result of Streptomyces sp.AC541, a cutoff value in the result output module is 0.586+/-0.001; and/or
When the data input module inputs a detection result of Salinibacillus sp.ZJ450, a cutoff value in the result output module is 0.543+/-0.001; and/or
When the data input module inputs a detection result of Pseudomonas brassicacearum, a cutoff value in the result output module is 0.408+/-0.001; and/or
When the data input module inputs the detection result of the streptomyces planus, the cut-off value in the result output module is 0.568+/-0.001 and/or
When the data input module inputs a detection result of Burkholderia oklahomensis, a cutoff value in the result output module is 0.457+/-0.001; and/or
When the data input module inputs a detection result of the lysobacterium sp.H23M41, a cutoff value in the result output module is 0.398+/-0.001; and/or
When the data input module inputs a detection result of Aerococcus urinaehominis, a cutoff value in the result output module is 0.632+/-0.001; and/or
When the data input module inputs the detection result of the osmophilic tetragenic coccus, the cut-off value in the result output module is 0.669+/-0.001; and/or
When the data input module inputs the detection result of the clostridium prasugrel, the cut-off value in the result output module is 0.221+/-0.001; and/or
When the data input module inputs the detection result of clostridium, streptomyces sp.ac541, the cut-off value in the result output module is 0.553 +/-0.001; and/or
When the data input module inputs the detection results of clostridium prasugrel and Pseudomonas brassicacearum, the cut-off value in the result output module is 0.595+/-0.001; and/or
When the data input module inputs the detection result of clostridium praecox, salinibacillus sp.ZJ450, the cut-off value in the result output module is 0.45+/-0.001; and/or
When the data input module inputs the detection result of Streptomyces sp.AC541, pseudomonas brassicacearum, the cut-off value in the result output module is 0.484+/-0.001; and/or
When the data input module inputs the detection results of Streptomyces sp.AC541 and Salinibacillus sp.ZJ450, the cut-off value in the result output module is 0.545+/-0.001; and/or
When the data input module inputs a detection result of Pseudomonas brassicacearum and Salinibacillus sp.ZJ450, a cutoff value in the result output module is 0.369+/-0.001; and/or
When the data input module inputs the detection result of clostridium, streptomyces sp.ac541, pseudomonas brassicacearum, the cut-off value in the result output module is 0.680 plus or minus 0.001; and/or
When the data input module inputs the detection results of clostridium sp.ac541 and salmonella sp.zj450, the cut-off value in the result output module is 0.461+/-0.001; and/or
When the data input module inputs the detection results of clostridium praecox, pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the cut-off value in the result output module is 0.731+/-0.001; and/or
When the data input module inputs the detection results of Streptomyces sp.AC541, pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the cut-off value in the result output module is 0.52+/-0.001; and/or
When the data input module inputs the detection results of clostridium sp.ac541, pseudomonas brassicacearum and salinomyces sp.zj450, the cut-off value in the result output module is 0.819+/-0.001; and/or
When the data input module inputs the detection result of the genus faecium, the cut-off value in the result output module is 0.529+/-0.001; and/or
When the data input module inputs the detection result of the chaetomium, the cut-off value in the result output module is 0.674+/-0.001; and/or
When the data input module inputs the detection result of the genus Roche, the cutoff value in the result output module is 0.455+/-0.001; and/or
When the data input module inputs the detection result of the monad genus, the cut-off value in the result output module is 0.510+/-0.001; and/or
When the data input module inputs the detection result of the fecal and the chaetobacter, the cutoff value in the result output module is 0.689+/-0.001; and/or
When the data input module inputs the detection result of the genus faecal bacillus and the genus rochanterium, the cutoff value in the result output module is 0.472+/-0.001; and/or
When the data input module inputs the detection result of the genus faecium and the genus monad, the cutoff value in the result output module is 0.523+/-0.001; and/or
When the data input module inputs the detection result of the chaetomium and the rogowski, the cut-off value in the result output module is 0.657+/-0.001; and/or
When the data input module inputs the detection result of the Chaetomium and the Monomonas, the cut-off value in the result output module is 0.632+/-0.001; and/or
When the data input module inputs the detection result of the genus Roche and the genus Monomonas, the cutoff value in the result output module is 0.564+/-0.001; and/or
When the data input module inputs detection results of the genus faecalis, the genus chaetomium and the genus rogowski, the cutoff value in the result output module is 0.618+/-0.001; and/or
When the data input module inputs detection results of the faecal bacillus, the chaetobacter and the monad, the cut-off value in the result output module is 0.534+/-0.001; and/or
When the data input module inputs detection results of the faecal bacillus genus, the rogowski genus and the monad genus, the cutoff value in the result output module is 0.537+/-0.001; and/or
When the data input module inputs detection results of the chaetomium, the rogowski and the monad, the cut-off value in the result output module is 0.655+/-0.001; and/or
When the data input module inputs detection results of the fecal genus, the chaetobacter genus, the rogowski genus and the monad genus, the cutoff value in the result output module is 0.618+/-0.001;
the data input module inputs the detection result of the fecal bacillus, and the cutoff value in the result output module is 0.573+/-0.001;
When the data input module inputs the detection result of the genus faecium, S.sp.AC541, the cut-off value in the result output module is 0.554+/-0.001;
when the data input module inputs the detection result of the genus faecium, S.sp.AC541, the cut-off value in the result output module is 0.553 +/-0.001;
when the data input module inputs the detection results of the clostridium, the clostridium pratensis and the S.sp.AC541, the cut-off value in the result output module is 0.530+/-0.001.
3. The evaluation device according to claim 1, wherein when the data input module inputs the detection result of clostridium prasugrel, the sensitivity of the result output module is 0.897±0.001; and/or
When the data input module inputs a detection result of Streptomyces sp.AC541, the sensitivity of the result output module is 0.862+/-0.001; and/or
When the data input module inputs the detection result of Salinibacillus sp.ZJ450, the sensitivity of the result output module is 0.828+/-0.001; and/or
When the data input module inputs a detection result of Pseudomonas brassicacearum, the sensitivity of the result output module is 0.845+/-0.001; and/or
When the data input module inputs the detection result of the streptomyces planus, the sensitivity of the result output module is 0.828+/-0.001 and/or
When the data input module inputs the detection result of Burkholderia oklahomensis, the sensitivity of the result output module is 0.81+/-0.001; and/or
When the data input module inputs a detection result of the lysobacterium sp.H23M41, the sensitivity of the result output module is 0.621+/-0.001; and/or
When the data input module inputs a detection result of Aerococcus urinaehominis, the sensitivity of the result output module is 0.741+/-0.001; and/or
When the data input module inputs the detection result of the osmophilic tetragenic coccus, the sensitivity of the result output module is 0.69+/-0.001; and/or
When the data input module inputs the detection result of the clostridium prasugrel, the sensitivity of the result output module is 0.221+/-0.001; and/or
When the data input module inputs the detection result of clostridium, streptomyces sp.ac541, the sensitivity of the result output module is 0.825+/-0.001; and/or
When the data input module inputs the detection result of clostridium prasugrel and Pseudomonas brassicacearum, the sensitivity of the result output module is 0.900+/-0.001; and/or
When the data input module inputs the detection result of clostridium praecox, salinibacillus sp.ZJ450, the sensitivity of the result output module is 0.750+/-0.001; and/or
When the data input module inputs the detection result of Streptomyces sp.AC541, pseudomonas brassicacearum, the sensitivity of the result output module is 0.825+/-0.001; and/or
When the data input module inputs the detection results of Streptomyces sp.AC541 and Salinibacillus sp.ZJ450, the sensitivity of the result output module is 0.800+/-0.001; and/or
When the data input module inputs the detection result of Pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the sensitivity of the result output module is 0.725+/-0.001; and/or
When the data input module inputs the detection result of clostridium, streptomyces sp.ac541, pseudomonas brassicacearum, the sensitivity of the result output module is 0.925+/-0.001; and/or
When the data input module inputs the detection results of clostridium sp.ac541 and salmonella sp.zj450, the sensitivity of the result output module is 0.825+/-0.001; and/or
When the data input module inputs the detection results of clostridium praecox, pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the sensitivity of the result output module is 0.950+/-0.001; and/or
When the data input module inputs the detection results of Streptomyces sp.AC541, pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the sensitivity of the result output module is 0.825+/-0.001; and/or
When the data input module inputs the detection results of clostridium praecox, streptomyces sp.ac541, pseudomonas brassicacearum and salinomyces sp.zj450, the sensitivity of the result output module is 0.975+/-0.001; and/or
When the data input module inputs the detection result of the genus faecium, the sensitivity of the result output module is 0.862+/-0.001; and/or
When the data input module inputs the detection result of the chaetomium, the sensitivity of the result output module is 0.793 +/-0.001; and/or
When the data input module inputs the detection result of the genus Roche, the sensitivity of the result output module is 0.793 +/-0.001; and/or
When the data input module inputs the detection result of the monad genus, the sensitivity of the result output module is 0.845+/-0.001; and/or
When the data input module inputs the detection result of the fecal bacterium genus and the chaetomium genus, the sensitivity of the result output module is 0.810+/-0.001; and/or
When the data input module inputs the detection result of the genus faecium and the genus rochanterium, the sensitivity of the result output module is 0.914+/-0.001; and/or
The data input module inputs the detection result of the genus faecium and the genus monad, and the sensitivity of the result output module is 0.914+/-0.001; and/or
The data input module inputs the detection result of the Chaetomium and the Roche, and the sensitivity of the result output module is 0.793 +/-0.001; and/or
The data input module inputs the detection result of the Chaetomium, and the sensitivity of the result output module is 0.776+/-0.001; and/or
The data input module inputs the detection result of the genus Roche and the genus Monomonas, and the sensitivity of the result output module is 0.914+/-0.001; and/or
When the data input module inputs detection results of the genus faecalis, the genus chaetobacter and the genus rochanterium, the sensitivity of the result output module is 0.875+/-0.001; and/or
When the data input module inputs detection results of the faecal bacillus, the chaetobacter and the monad, the sensitivity of the result output module is 0.897+/-0.001; and/or
The data input module inputs detection results of the faecal bacillus, the rogowski and the monad, and the sensitivity of the result output module is 0.875+/-0.001; and/or
When the data input module inputs detection results of the chaetomium, the rogowski and the monad, the sensitivity of the result output module is 0.810+/-0.001; and/or
When the data input module inputs detection results of the genus faecalis, the genus chaetobacter, the genus rochanterium and the genus monad, the sensitivity of the result output module is 0.925+/-0.001;
the data input module inputs the detection result of the fecal bacillus, and the cutoff value in the result output module is 0.865+/-0.001;
when the data input module inputs the detection result of the genus faecium, S.sp.AC541, the cut-off value in the result output module is 0.875+/-0.001;
when the data input module inputs a detection result of the Streptomyces sp.AC541, a cutoff value in the result output module is 0.875+/-0.001;
when the data input module inputs the detection results of the clostridium, the clostridium and the Streptomyces sp.AC541, the cut-off value in the result output module is 0.90+/-0.001.
4. The evaluation device according to claim 1, wherein the specificity of the result output module is 0.825±0.001 when the data input module inputs the detection result of clostridium prasugrel; and/or
When the data input module inputs a detection result of Streptomyces sp.AC541, the specificity of the result output module is 0.825+/-0.001; and/or
When the data input module inputs the detection result of Salinibacillus sp.ZJ450, the specificity of the result output module is 0.75+/-0.001; and/or
When the data input module inputs the detection result of Pseudomonas brassicacearum, the specificity of the result output module is 0.75+/-0.001; and/or
When the data input module inputs the detection result of the streptomyces planus, the specificity of the result output module is 0.75+/-0.001 and/or
When the data input module inputs the detection result of Burkholderia oklahomensis, the specificity of the result output module is 0.75+/-0.001; and/or
When the data input module inputs a detection result of the lysobacterium sp.H23M41, the specificity of the result output module is 0.875+/-0.001; and/or
When the data input module inputs the detection result of Aerococcus urinaehominis, the specificity of the result output module is 0.8+/-0.001; and/or
When the data input module inputs the detection result of the osmophilic tetragenic coccus, the specificity of the result output module is 0.825+/-0.001; and/or
When the data input module inputs the detection result of the clostridium prasugrel, the specificity of the result output module is 0.221+/-0.001; and/or
When the data input module inputs the detection result of clostridium, streptomyces sp.ac541, the specificity of the result output module is 0.931+/-0.001; and/or
When the data input module inputs the detection results of clostridium prasugrel and Pseudomonas brassicacearum, the specificity of the result output module is 0.914+/-0.001; and/or
When the data input module inputs the detection result of clostridium praecox, salinibacillus sp.ZJ450, the specificity of the result output module is 0.948+/-0.001; and/or
When the data input module inputs the detection result of Streptomyces sp.AC541, pseudomonas brassicacearum, the specificity of the result output module is 0.931+/-0.001; and/or
When the data input module inputs the detection results of Streptomyces sp.AC541 and Salinibacillus sp.ZJ450, the specificity of the result output module is 0.862+/-0.001; and/or
When the data input module inputs the detection result of Pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the specificity of the result output module is 0.966+/-0.001; and/or
When the data input module inputs the detection result of clostridium, streptomyces sp.ac541, pseudomonas brassicacearum, the specificity of the result output module is 0.879 plus or minus 0.001; and/or
When the data input module inputs the detection results of clostridium sp.ac541 and salmonella sp.zj450, the specificity of the result output module is 0.948+/-0.001; and/or
When the data input module inputs the detection results of clostridium praecox, pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the specificity of the result output module is 0.810+/-0.001; and/or
When the data input module inputs the detection results of Streptomyces sp.AC541, pseudomonas brassicacearum and Salinibacillus sp.ZJ450, the specificity of the result output module is 0.914+/-0.001; and/or
When the data input module inputs the detection results of clostridium praecox, streptomyces sp.ac541, pseudomonas brassicacearum and salinomyces sp.zj450, the specificity of the result output module is 0.810+/-0.001; and/or
When the data input module inputs the detection result of the genus faecium, the specificity of the result output module is 0.85+/-0.001; and/or
When the data input module inputs the detection result of the chaetomium, the specificity of the result output module is 0.75+/-0.001; and/or
When the data input module inputs the detection result of the genus Roche, the specificity of the result output module is 0.75+/-0.001; and/or
When the data input module inputs the detection result of the monad genus, the specificity of the result output module is 0.725+/-0.001; and/or
The data input module inputs the detection result of the genus faecalis and the genus chaetobacter, and the specificity of the result output module is 0.950+/-0.001; and/or
The data input module inputs the detection result of the genus faecium and the genus Roche, and the specificity of the result output module is 0.800+/-0.001; and/or
The data input module inputs the detection result of the genus faecium and the genus monad, and the specificity of the result output module is 0.900+/-0.001; and/or
The data input module inputs the detection result of the chaetomium and the rogowski, and the specificity of the result output module is 0.875+/-0.001; and/or
The data input module inputs the detection result of the Chaetomium, and the specificity of the result output module is 0.875+/-0.001; and/or
The data input module inputs the detection result of the genus Roche and the genus Monomonas, and the specificity of the result output module is 0.800+/-0.001; and/or
The data input module inputs the detection results of the fecal bacteria, the chaetomium and the rogowski bacteria, and the specificity of the result output module is 0.862+/-0.001; and/or
The data input module inputs detection results of the faecal bacillus, the chaetobacter and the monad, and the specificity of the result output module is 0.875+/-0.001; and/or
The data input module inputs the detection results of the faecal bacillus, the rogowski and the monad, and the specificity of the result output module is 0.897+/-0.001; and/or
The data input module inputs detection results of the chaetomium, the rogowski and the monad, and the specificity of the result output module is 0.925+/-0.001; and/or
The data input module inputs detection results of the genus faecium, the genus chaetobacter, the genus rochanterium and the genus monad, and the specificity of the result output module is 0.879+/-0.001;
the data input module inputs the detection result of the fecal bacillus, and the cutoff value in the result output module is 0.875+/-0.001;
When the data input module inputs the detection result of the genus faecium, S.sp.AC541, the cutoff value in the result output module is 0.948+/-0.001;
when the data input module inputs a detection result of the Streptomyces sp.AC541, a cutoff value in the result output module is 0.948+/-0.001;
when the data input module inputs the detection results of the clostridium, the clostridium and the Streptomyces sp.AC541, the cut-off value in the result output module is 0.966+/-0.001.
5. A computer readable medium, characterized in that the computer readable medium has stored thereon a computer program which, when executed, implements the functionality of the data analysis module of any of claims 1-4.
6. Use of an assessment device according to any one of claims 1 to 4 for the manufacture of a product for diagnosis and/or for the assisted diagnosis of depression.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106554998A (en) * 2016-10-18 2017-04-05 深圳市康宁医院 Depression biomarker and its application
WO2019049960A1 (en) * 2017-09-07 2019-03-14 国立大学法人千葉大学 Test method for depression symptoms and screening method of therapeutic agent therefor
CN111505288A (en) * 2020-05-15 2020-08-07 首都医科大学附属北京安定医院 Novel depression biomarker and application thereof

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106554998A (en) * 2016-10-18 2017-04-05 深圳市康宁医院 Depression biomarker and its application
WO2019049960A1 (en) * 2017-09-07 2019-03-14 国立大学法人千葉大学 Test method for depression symptoms and screening method of therapeutic agent therefor
CN111505288A (en) * 2020-05-15 2020-08-07 首都医科大学附属北京安定医院 Novel depression biomarker and application thereof

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