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Dysbiosis

Dysbiosis is a term used to describe an imbalance or disruption in the normal composition and diversity of the gut microbiome.
This condition has been associated with a variety of health conditions, including inflammatory bowel diseases, metabolic disorders, and neurological disorders.
The PubCompare.ai platform can help researchers optimize research protocols and enhance reproducibility in Dysbiosis studies by providing easy access to relevant protocols from literature, preprints, and patents.
Leveraging advanced AI-driven analysis, the tool can assist in identifying the best approaches for your Dysbiosis research needs, taking your work to new heights and accelerating the discovery of potential treatments or interventions.

Most cited protocols related to «Dysbiosis»

1
Identifying Dysbiotic Gut Microbiomes in IBD
To identify samples with highly divergent (dysbiotic) metagenomic microbial compositions, as a complement to baseline disease diagnosis, we defined a dysbiosis score based on Bray–Curtis dissimilarities to non-IBD metagenomes. First, a ‘reference set’ of samples was constructed from non-IBD subjects by taking all samples after the 20th week after the subject’s first stool sample. This was chosen because a subset of the non-IBD subjects at the start of their respective time series may not yet have overcome any gastrointestinal symptoms that triggered the initial visit to a doctor, though these were ultimately not caused by IBD. The dysbiosis score of a given sample was then defined as the median Bray–Curtis dissimilarity to this reference sample set, excluding samples that came from the same subject (Fig. 2c).
To identify samples that were highly divergent from the reference set, we thresholded the dysbiosis score at the 90th percentile of this score for non-IBD samples. This therefore identifies samples with a feature configuration that has a less than 10% probability of occurring in a participant without IBD. By this measure, 272 metagenomes were classified as dysbiotic. Samples from participants with CD or UC were overrepresented in the dysbiotic set, with 24.3% and 11.6% of their samples classified as dysbiotic, respectively. As expected, these samples also tended to locate in the extremes of the taxonomic ordination based on metagenomes (Extended Data Fig. 3b, c). Dysbiosis was unevenly distributed among subjects (Extended Data Fig. 3d), with some subjects remaining dysbiotic for all or most of their time series, while others remained non-dysbiotic for their entire time series.
To lend additional support to the definition of dysbiosis (that is, as outliers by one type of microbiome profile), we tested the concordance between dysbiosis classifications made using the same statistical definition, but applied to metabolomic rather than taxonomic profiles. That is, we defined a metabolomic dysbiosis score as the median Bray–Curtis dissimilarity of one metabolomic profile to the non-IBD metabolomic profiles (after the 20th week), and defined the dysbiosis threshold as the 90th percentile of this distribution among non-IBD metabolomic profiles. We then compared these dysbiosis classifications with those from the nearest metagenomic sample (up to two weeks, see ‘Cross-measurement type temporal matching’) and found that dysbiotic samples identified by metagenomics were 4.6 times more likely to be dysbiotic by metabolomics (Fisher’s exact P = 5.9 × 10−9), showing that dysbiosis measurements are highly consistent across measurement types.
To test the sensitivity of the dysbiosis classification to the choice of reference data set, we also performed the dysbiosis classification using the HMP1-II stool samples10 (link) as the reference sample set instead of the non-IBD samples. The resulting dysbiosis scores (Extended Data Fig. 3e) were highly concordant (Spearman ρ = 0.86; P < 2.2 × 10−16), as were the dysbiosis classifications (odds ratio of 56; Fisher’s exact P < 2.2 × 10−16). This shows that, despite the inclusion of subjects with other conditions in the non-IBD group here, as well as large differences in measurement technologies between the data sets, the dysbiosis classification is highly robust. Furthermore, 43 out of 426 (10.1%) of non-IBD samples were classified as dysbiotic using the HMP1-II samples as reference, falling remarkably close to the 10% expected by the definition and showing that the enrichment of IBD samples in the dysbiotic set is not simply a consequence of the definition.
Lloyd-Price J., Arze C., Ananthakrishnan A.N., Schirmer M., Avila-Pacheco J., Poon T.W., Andrews E., Ajami N.J., Bonham K.S., Brislawn C.J., Casero D., Courtney H., Gonzalez A., Graeber T.G., Hall A.B., Lake K., Landers C.J., Mallick H., Plichta D.R., Prasad M., Rahnavard G., Sauk J., Shungin D., Vázquez-Baeza Y., White RA I.I.I., Braun J., Denson L.A., Jansson J.K., Knight R., Kugathasan S., McGovern D.P., Petrosino J.F., Stappenbeck T.S., Winter H.S., Clish C.B., Franzosa E.A., Vlamakis H., Xavier R.J, & Huttenhower C. (2019). Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature, 569(7758), 655-662.
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Publication 2019
Diagnosis Dysbiosis Feces Hypersensitivity Metagenome Microbiome Physicians PITRM1 protein, human
2
Identifying Dysbiotic Gut Microbiomes in IBD
To identify samples with highly divergent (dysbiotic) metagenomic microbial compositions, as a complement to baseline disease diagnosis, we defined a dysbiosis score based on Bray-Curtis dissimilarities to non-IBD metagenomes. First, a “reference set” of samples was constructed from non-IBD subjects by taking all samples after the 20th week after the subject’s first stool sample. This was chosen since a subset of the non-IBD subjects at the start of their respective time series may not yet have overcome any gastrointestinal symptoms that triggered the initial visit to a doctor, though ultimately not caused by IBD. The dysbiosis score of a given sample was then defined as the median Bray-Curtis dissimilarity to this reference sample set, excluding samples that came from the same subject (Fig. 2C).
To identify samples which were highly divergent from the reference set, we thresholded the dysbiosis score at the 90th percentile of this score for non-IBD samples. This therefore identifies samples with a feature configuration that has a <10% probability of occurring in a non-IBD subject. By this measure, 272 metagenomes were classified as dysbiotic. Samples from CD and UC subjects are overrepresented in the dysbiotic set, with 24.3% and 11.6% of their samples classified as dysbiotic, respectively. As expected, these samples also tended to locate in the extremes of the taxonomic ordination based on metagenomes (Extended Data Figs. 3B, 3C). Dysbiosis was unevenly distributed among subjects (Extended Data Fig. 3D), with some subjects remaining dysbiotic for all or most of their time series, while others remained non-dysbiotic for their entire time series.
To lend additional support to the dysbiosis definition (i.e. as outliers by one type of microbiome profile), we tested the concordance between dysbiosis classifications made using the same statistical definition, but applied to metabolomic rather than taxonomic profiles. That is, we defined a metabolomic dysbiosis score as the median Bray-Curtis dissimilarity of one metabolomic profile to the non-IBD metabolomic profiles (after the 20th week), and defined the dysbiosis threshold as the 90th percentile of this distribution among non-IBD metabolomic profiles. We then compared these dysbiosis classifications with those from the nearest metagenomic sample (up to two weeks, see the “Cross-measurement type temporal matching” section) and found that dysbiotic samples identified by metagenomics were 4.6 times more likely to be dysbiotic by metabolomics (Fisher’s exact p = 5.9 × 10−9), showing that dysbiosis measurements are highly consistent across measurement types.
To test the sensitivity of the dysbiosis classification to the choice of reference dataset, we also performed the dysbiosis classification using the HMP1-II stool samples10 (link) as the reference sample set instead of the non-IBD samples. The resulting dysbiosis scores (Extended Data Fig. 3E) were highly concordant (Spearman rho = 0.86; p < 2.2 × 10−16), as were the dysbiosis classifications (odds ratio of 56; Fisher’s exact p < 2.2 × 10−16). This shows that, despite the inclusion of subjects with other conditions in the non-IBD group here, as well as large differences in measurement technologies between the datasets, the dysbiosis classification is highly robust. Further, 43/426 (10.1%) of non-IBD samples were classified as dysbiotic using the HMP1-II samples as reference, falling remarkably close to the 10% expected by the definition and showing that the enrichment of IBD samples in the dysbiotic set is not simply a consequence of the definition.
Lloyd-Price J., Arze C., Ananthakrishnan A.N., Schirmer M., Avila-Pacheco J., Poon T.W., Andrews E., Ajami N.J., Bonham K.S., Brislawn C.J., Casero D., Courtney H., Gonzalez A., Graeber T.G., Hall A.B., Lake K., Landers C.J., Mallick H., Plichta D.R., Prasad M., Rahnavard G., Sauk J., Shungin D., Vázquez-Baeza Y., White RA I.I.I., Braun J., Denson L.A., Jansson J.K., Knight R., Kugathasan S., McGovern D.P., Petrosino J.F., Stappenbeck T.S., Winter H.S., Clish C.B., Franzosa E.A., Vlamakis H., Xavier R.J, & Huttenhower C. (2019). Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature, 569(7758), 655-662.
Publication 2019
Diagnosis Dysbiosis Feces Hypersensitivity Metagenome Microbiome Physicians PITRM1 protein, human
3
Conditional Knockout Mice for IL-18 Signaling

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Publication 2015
Alleles Clustered Regularly Interspaced Short Palindromic Repeats Deletion Mutation Dysbiosis Homologous Recombination Institutional Animal Care and Use Committees Interleukin-18 interleukin 18 protein, human Mice, Knockout Mus
4
Differential Abundance Analysis of Multi-Omic Microbiome Data
Differential abundance (DA) analysis of all microbial measurement types (except for viruses, which were modelled as presence/absence binary features) were tested as follows. First, an appropriate transformation/normalization method was applied: arcsine square-root transformation for microbial taxonomic and functional relative abundances, log transformation (with pseudo count 1 for zero values) for metabolite profiles and protein abundances, and log transform with no pseudocount for expression ratios (non-finite values removed). Transformed abundances were then fit with the following per-feature linear mixed-effects model: feature~intercept+diagnosis+diagnosis/dysbiosis+antibioticuse+consentage+1∣recruitmentsite+1∣subject
That is, in each per-feature multivariable model, recruitment sites and subjects were included as random effects to account for the correlations in the repeated measures (denoted by (1 | recruitment site) and (1 | subject), respectively) and the transformed abundance of each feature was modelled as a function of diagnosis (a categorical variable with non-IBD as the reference group) and dysbiosis state as a nested binary variable (with non-dysbiotic as reference) within each IBD phenotype (UC, CD, and non-IBD), while adjusting for consent age as a continuous covariate, and antibiotics as as binary covariate. Pearson’s residual values from the above linear mixed effects models were retained for use in subsequent analyses (see ‘Cross-measurement type interaction testing’).
Fitting was performed with the nlme package in R79 (using the lme function), where significance of the association was assessed using Wald’s test (except for viruses, where a logistic random effects model was considered with the glmer function from the lmer R package). Nominal P values were adjusted for multiple hypothesis testing with a target FDR of 0.25. In order to reduce the effect of zero-inflation in microbiome data, features with no variance or with >90% zeros were removed before fitting linear models. In addition, a variance filtering step was applied to remove features with very low variance. To further remove the effect of redundancy in KO gene family abundances (explainable by at most a single taxon), only features (both DNA and RNA) with low correlation with individual microbial abundances (Spearman correlation coefficient <0.6) were retained.
Lloyd-Price J., Arze C., Ananthakrishnan A.N., Schirmer M., Avila-Pacheco J., Poon T.W., Andrews E., Ajami N.J., Bonham K.S., Brislawn C.J., Casero D., Courtney H., Gonzalez A., Graeber T.G., Hall A.B., Lake K., Landers C.J., Mallick H., Plichta D.R., Prasad M., Rahnavard G., Sauk J., Shungin D., Vázquez-Baeza Y., White RA I.I.I., Braun J., Denson L.A., Jansson J.K., Knight R., Kugathasan S., McGovern D.P., Petrosino J.F., Stappenbeck T.S., Winter H.S., Clish C.B., Franzosa E.A., Vlamakis H., Xavier R.J, & Huttenhower C. (2019). Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature, 569(7758), 655-662.
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Publication 2019
Antibiotics, Antitubercular Diagnosis Dysbiosis Genes Microbiome Phenotype Plant Roots Proteins Strains Virus
5
Evaluating Metagenomic Data for Disease Classification
The experimental evaluation can be summarized into five main steps: 1) cross-validation analysis was done on the six disease-association datasets for evaluating the capabilities of metagenomic data for disease classification; 2) cross-stage studies were performed on the cirrhosis and T2D datasets in order to test the generalization of the model on independent collection batches from the same study; 3) in terms of T2D, the analysis was extended by taking into account also samples from completely distinct cohorts; 4) cross-studies were also done to model the features of the “healthy” gut microbiome for use as a dysbiosis prediction model for syndromes where few or no training samples are available; 5) cross-validation and cross-study analysis were applied to deal with different classification problem such as gender and body site discrimination. We note that all the investigated classification problems, excluding the body site discrimination, represented binary classification problems. Moreover, most of the analysis was done in terms of disease classification, in which the objective was to discriminate between “healthy” and “diseased” subjects.
Pasolli E., Truong D.T., Malik F., Waldron L, & Segata N. (2016). Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLoS Computational Biology, 12(7), e1004977.
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Publication 2016
Discrimination, Psychology Dysbiosis Gastrointestinal Microbiome Gender Generalization, Psychological Human Body Liver Cirrhosis Metagenome Syndrome

Most recents protocols related to «Dysbiosis»

1
Treating Gut Dysbiosis with Bacterial Mixture
Not available on PMC !

Example 4

A subject having gut dysbiosis is administered a pharmaceutical composition comprising a bacterial mixture of the present invention to treat the gut dysbiosis.

For subjects who have gut dysbiosis as a side effect of an anti-cancer therapeutic agent and/or a side effect of an anti-cancer therapy, the pharmaceutical composition helps reduce or treating the side effect.

For subjects who have undergone or are undergoing an anti-cancer therapeutic agent and/or a side effect of an anti-cancer therapy, the pharmaceutical composition increases the efficacy of the anti-cancer therapeutic agent and/or anti-cancer therapy.

US11865145B2. Compositions and methods for maintaining and restoring a healthy gut barrier (2024-01-09). Finch Therapeutics Holdings LLC [US]. Inventors: Marina Santiago [US], Kevin Roelofs [US].
Full text: Click here
Patent 2024
Bacteria Dysbiosis Malignant Neoplasms Pharmaceutical Preparations Therapeutic Effect Therapeutics
2
Prevention of Gut Dysbiosis in Cancer Patients
Not available on PMC !

Example 5

A subject at risk for gut dysbiosis is administered a pharmaceutical composition comprising a bacterial mixture of the present invention to prevent gut dysbiosis.

For subjects who are at risk for gut dysbiosis as a side effect of an anti-cancer therapeutic agent and/or a side effect of an anti-cancer therapy, the pharmaceutical composition helps prevents the likelihood of getting the side effect.

US11865145B2. Compositions and methods for maintaining and restoring a healthy gut barrier (2024-01-09). Finch Therapeutics Holdings LLC [US]. Inventors: Marina Santiago [US], Kevin Roelofs [US].
Full text: Click here
Patent 2024
Bacteria Dysbiosis Malignant Neoplasms Pharmaceutical Preparations Therapeutic Effect Therapeutics
3
Gut Microbiome and Lung Cancer Connections
Web of Science Core Collection (WoSCC) is widely used for visualization and quantitative analyses, which is the most authoritative citation-based database with the function of a powerful index (18 (link)–20 (link)). All data were retrieved from the WoSCC in this study, with the timespan from 1 January 2011 to 25 October 2022.
The search strategy employed was as follows: TS=(((intestin) OR (gastrointestin) OR (gut) OR (gastro-intestin)) AND ((Microbiot) OR (Microbiome) OR (Flora) OR (Microflora) OR (Bacteria) OR (antibiotic) OR (probiotic) OR (prebiotic) OR (dysbiosis))) AND (lung) AND ((cancer) OR (tumor) OR (tumour) OR (carcinoma) OR (neoplasm)). Two authors (HT Chen and YB Lai) completed the data extraction on 26 October 2022 to reduce the deviation caused by data extraction. Any disagreement was resolved by discussion or by seeking the assistance of a third author (QH Yao). Additionally, only articles or review articles were included, while there were no strict language restrictions.
Chen H., Lai Y., Ye C., Wu C., Zhang J., Zhang Z, & Yao Q. (2023). Global research trends between gut microbiota and lung cancer from 2011 to 2022: A bibliometric and visualization analysis. Frontiers in Oncology, 13, 1137576.
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Publication 2023
Antibiotics Bacteria Carcinoma Dysbiosis Lung Malignant Neoplasms Microbiome Neoplasms Prebiotics Probiotics Stomach
4
Longitudinal Microbiome Profiling with Linear Mixed Effects
The HMP2:IBDMDB study provides longitudinal measurements for each subject. After data filtering described in the “IBDMDB data processing” section, we observed in some features that most subjects only have one longitudinal observation, while in other features many subjects have multiple longitudinal observations. Thus we first used a model selection procedure to determine if a linear model with only fixed effects or a linear-mixed effect model is needed for each feature. The two models differ in that the linear-mixed effect model includes a random effect indicating which subject each longitudinal observation was collected. For each feature we fitted both models and compared their model fittings using a likelihood ratio test. Throughout the study, the linear-mixed effect model was fitted using lmerTest::lmer() function in R and the ordinary linear model was fitted using stats::lm() function in R. The p-values were obtained with asymptotic chi-squared tests (lmerTest::anova() function in R) and adjusted by BH procedure [16 ] (stats::p.adjust() function in R) to obtain FDRs. For all features with FDR < 0.05, linear-mixed effect models were used. For all features with FDR > 0.05, linear models with only fixed effects were used.
For simulation studies based on HMP2:IBDMDB data, the linear mixed-effect models are RNA∼DNA+Taxa+group+(1|subject) for the DNA+Taxa model, RNA∼DNA+group+(1|subject) for the DNA model, and RNA∼Taxa+group+(1|subject) for the Taxa model. For model selection described above, their corresponding ordinary linear regression models are RNA∼DNA+Taxa+group , RNA∼DNA+group , and RNA∼Taxa+group . Here RNA, DNA, and Taxa represent the processed RNA, DNA, and taxonomic abundances. group indicates the two sample groups being compared with, and subject indicates the subjects from which samples are longitudinally collected. The p-values of the group differences (group) are of primary interest and extracted from the model fitting (lmerTest::summary() function in R).
For real data analysis of HMP2:IBDMDB data, the linear mixed-effect models are RNA∼DNA+Taxa+active+age+antibiotics+(1|subject) for the DNA+Taxa model, RNA∼DNA+active+age+antibiotics+(1|subject) for the DNA model, and RNA∼Taxa+active+age+antibiotics+(1|subject) for the Taxa model. For model selection described above, their corresponding ordinary linear regression models are RNA∼DNA+Taxa+active+age+antibiotics , RNA∼DNA+active+age+antibiotics , and RNA∼Taxa+active+age+antibiotics . Here active indicates the dysbiotic and non-dysbiotic sample groups being compared with, and age and antibiotics are the age and antibiotics status for each subject, similar to a previous study [9 (link)]. The p-values of the differences between dysbiotic and non-dysbiotic sample groups (active) are of primary interest and extracted from the model fitting (lmerTest::summary() function in R).
For simulation studies based on a previous study [10 (link)], we used ordinary linear regression since there is no longitudinal design. The DNA+Taxa model is: RNA∼DNA+Taxa+group . The DNA model is: RNA∼DNA+group . The Taxa model is: RNA∼Taxa+group . The p-values of the group differences (group) are of primary interest and extracted from the model fitting (stats::summary() function in R).
DE analysis was performed only in features with at least 10 observations in both sample groups after data filtering. To test for the fixed effect of interest, the Satterthwaite method [17 (link)] was used to estimate degrees of freedoms and p-values were obtained with t-tests. These procedures are already implemented in lmerTest::summary() function in R. FDRs were obtained using BH procedure [16 ] (stats::p.adjust() function in R).
Ji Z, & Ma L. (2023). Controlling taxa abundance improves metatranscriptomics differential analysis. BMC Microbiology, 23, 60.
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Publication 2023
Antibiotics, Antitubercular Dysbiosis neuro-oncological ventral antigen 2, human
5
Microbial Profiling of Appendicitis Patients
Quality filtering was performed on the raw reads to obtain high-quality clean reads. According to Cutadapt (v1.9.1) [18 (link)] (http://cutadapt.readthedocs.io/en/stable/), the reads were compared with the GOLD reference database (http://drive5.com/uchime/uchime_download.html) with the UCHIME algorithm (http://www.drive5.com/usearch/manual/uchime_algo.html) to detect and remove chimaeric sequences to obtain clean reads [19 (link), 20 (link)].
Sequence analysis was performed with UPARSE software (Uparse v7.0.1001) (http://drive5.com/uparse/) [21 (link)]. Sequences with ≥ 97% similarity were assigned to the same operational taxonomic units (OTUs). Representative sequences for each OTU were screened for further annotation. For each representative sequence, the SSU rRNA [22 (link)] database of Silva (http://www.arb-silva.de/) [23 (link)] was used based on the Mothur algorithm to annotate taxonomic information (set threshold from 0.8 to 1). For determination of the phylogenetic relationships of different OTUs and the difference in the dominant species in different samples (groups), multiple sequence alignments were conducted using MUSCLE (http://www.drive5.com/muscle/) Software (v3.8.31) [24 (link)]. OTUs abundance information was normalized using a standard sequence number corresponding to the sample with the fewest sequences. Subsequent analyses of alpha diversity and beta diversity were all performed based on these output normalized data.
Data are expressed as the mean ± standard error of the mean. Alpha diversity was applied to analyse the complexity of species diversity for a sample through 2 indices, observed species and Chao1 indices. Both of these indices in our samples were calculated with QIIME (Version 1.7.0). The Wilcox test in the agricolae package of R software (Version 2.15.3) was used to analyse the between-group difference in alpha diversity. Beta diversity was applied with Permutational multivariate analysis of variance (Adonis) analysis and the nonmetric multidimensional scaling (NMDS) analysis. NMDS analysis was based on Bray–Curtis dissimilarity and performed by the vegan software package of R software. The correlation between microbiome taxa and rosuvastatin effectiveness was assessed using linear discriminant analysis (LDA) effect size (LEfSe) at various taxonomic ranks [25 (link)]. An LDA score greater than 4.0 was defined as significant by default. LEfSe data were analysed using R software, and analysis of variance (ANOVA) was used to identify the relative abundance differences between groups. Tukey’s test was applied to perform post hoc tests, with P < 0.05 considered a significant difference. PICRUSt2 was performed using the OmicStudio Analysis (https://www.omicstudio.cn/analysis/) to predict the functional profiles of intestinal microbiome. T-test was used for analysing the OTU abundance from the same gut segment between the two groups OmicStudio tools (https://www.omicstudio.cn/tool) was utilized for statistical analyses and visualization of the identified pathways. R software was used for permutational multivariate analysis of variance (Adonis) to analyse the between-group differences in beta diversity. Group comparisons of histological scores were statistically analysed using independent-samples t-tests (SPSS 19.0). Statistical significance was accepted at P < 0.05. Twenty-five appendicitis-associated taxa reported previously (Table 1), such as Actinobacteria, Proteobacteria, and Fusobacteria, were analysed from our samples with/without dysbiosis [13 (link), 26 (link)–29 (link)].

Appendicitis-associated taxa reported in previous studies

PhylumGenusSpecies

Firmicutes

Bacteroidetes

Actinobacteria

Proteobacteria

Fusobacteria

Streptococcus

Gemella

Bacteroides

Faecalibacterium

Proteus

Fusobacterium

Rhizobium

Porphyromonas

Mogibacterium

Prevotella

Bilophila

Dialister

Anaerofilum

Bergeyella

Peptostreptococcus

Fusibacter

Parvimonas

Escherichia coli

Bacteroides fragilis

Porphyromonas endodontalis

Shao C., Song X., Wang L., Zhang H., Liu Y., Wang C., Chen S., Ren B., Wen S., Xiao J, & Tang L. (2023). Microbiome Structure and Mucosal Morphology of Jejunum Appendix and Colon of Rats in Health and Dysbiosis. Current Microbiology, 80(4), 127.
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Publication 2023
Actinomycetes Adonis Appendicitis Chimera Dysbiosis Fusobacterium Gold Intestinal Microbiome Microbiome Muscle Tissue Proteobacteria Ribosomal RNA Rosuvastatin Sequence Alignment Sequence Analysis Strains Vegan

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More about "Dysbiosis"

Dysbiosis, a term used to describe an imbalance or disruption in the normal composition and diversity of the gut microbiome, has been linked to a variety of health conditions, including inflammatory bowel diseases (IBDs), metabolic disorders, and neurological disorders.
This microbial imbalance can be influenced by various factors, such as diet, antibiotics (e.g., Ampicillin, Metronidazole, Vancomycin, Neomycin), and other environmental exposures.
Researchers studying Dysbiosis can leverage the PubCompare.ai platform to optimize their research protocols and enhance reproducibility.
The tool provides easy access to relevant protocols from literature, preprints, and patents, allowing researchers to identify the best approaches for their Dysbiosis research needs.
By utilizing advanced AI-driven analysis, the platform can assist in selecting the most appropriate methodologies, such as the Indican Assay Kit and the MoBio PowerSoil DNA Isolation Kit, to analyze the gut microbiome.
Additionally, researchers can use statistical software like JMP Pro 14 to analyze and interpret their Dysbiosis data, which may include information from animal models like the C57BL/6J mouse strain or studies involving anesthetic agents like Zoletil 100.
The PSP Spin Stool DNA Plus Kit can also be used to efficiently extract high-quality DNA from stool samples for microbial profiling.
By leveraging these tools and resources, researchers can take their Dysbiosis studies to new heights, accelerating the discovery of potential treatments or interventions and contributing to a better understanding of this complex condition and its impact on human health.