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189 results for “gut health”
Dataset: Fractyl Health, Inc. (GUTS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Low molecular weight seaweed–derived polysaccharides lead to increased faecal bulk but do not alter human gut health markers
<p>Differential analysis of taxa before and after the consumption of either agar, alginate or maltodextrin showed no significant change at phylum or family level (<strong>Supplementary tables 1-2</strong>). </p> <p><em>Supplementary Table 1: Differential abundance analysis with ALDEX2 on family level</em></p> <p><em>Supplementary Table 2: Differential abundance analysis with ALDEX2 on phylum level</em></p>
TableS1 of ML-based predictive gut microbiome analysis for health assessment
<p>Complete list of species associated with the COVID and Control cohort (ANOVA F score > 1). Comparison with respect to the original set of species used by Gupta <em>et al.</em>, and ANOVA-F values are reported.</p>
Data from: Association between gut health and gut microbiota in a polluted environment
<p>Animals host complex bacterial communities in their gastrointestinal tracts, with which they share a mutualistic interaction. The numerous effects these interactions grant to the host include regulation of the immune system, defense against pathogen invasion, aid in digestion of otherwise indigestible foodstuffs, and even changing host behaviour. Stress, such as environmental pollution, parasites, predators, and intraspecies competition, can alter the composition of the gut microbiome, which in turn can change host-microbiome interactions in ways that are detrimental to the host such as causing metabolic dysfunction and inflammation. While host-microbiome interactions have been extensively studied in humans and captive animals, studies into wild animal microbiomes have been scarce. We assessed the effects of disturbed environment on gut health of bank voles exposed to radionuclides in natural habitat using a combined approach of transcriptomics, microbial community analysis by 16S amplicon sequencing, histological staining analyses of colon tissue, and quantification of gut microbiota -produced short-chain fatty acids in faecal matter and blood that act as mediators of host-microbiome interactions. We found signs of weakened mucus layer and related changes in <em>Clca1</em> and <em>Agr2</em> gene expression and microbiome composition in animals exposed to radionuclides. These results imply that disturbed environment can have widely reaching effects through gut health.</p>
Exploring Beneficial Properties of Haskap Berry Leaf Compounds for Gut Health Enhancement
<p>Data and the publication to which they are related</p>
MicrobiomeHD: the human gut microbiome in health and disease
<p><strong>Overview</strong></p> <p>MicrobiomeHD is a standardized database of human gut microbiome studies in health and disease. This database includes publicly available 16S data from published case-control studies and their associated patient metadata. Raw sequencing data for each study was downloaded and processed through a standardized pipeline.</p> <p>To be included in MicrobiomeHD, datasets have:</p> <ul> <li>publicly available raw sequencing data (fastq or fasta)</li> <li>publicly available metadata with at least case and control labels for each patient</li> </ul> <p>Currently, MicrobiomeHD is focused on stool samples. Additional samples may be included in certain datasets, as indicated in the metadata.</p> <p><strong>Files</strong></p> <p>Additional information about the datasets included in this MicrobiomeHD release are in the MicrobiomeHD github repo <a href="https://github.com/cduvallet/microbiomeHD">https://github.com/cduvallet/microbiomeHD</a>, in the file <em>db/dataset_info.yaml</em>. Top-level identifiers correspond to dataset IDs labeled by disease_first-author. For the most part, sample sizes in the yaml file are those that were described in the papers, and may not exactly reflect the actual data (due to missing/extra data, samples which didn't pass quality control, etc).</p> <p>Each dataset was downloaded and processed through a standardized pipeline. The raw processing results are available in the *.tar.gz files here. Each file has the same directory structure and files, as described in the pipeline documentation: <a href="http://amplicon-sequencing-pipeline.readthedocs.io/en/latest/output.html">http://amplicon-sequencing-pipeline.readthedocs.io/en/latest/output.html</a>.</p> <p>Specific files of interest in each *.tar.gz folder include:</p> <ul> <li><strong>summary_file.txt</strong>: this file contains a summary of all parameters used to process the data</li> <li><strong>datasetID.metadata.txt</strong>: the metadata associated with the samples. Note that some samples in the metadata may not have sequencing data, and vice versa.</li> <li><strong>RDP/datasetID.otu_table.100.denovo.rdp_assigned</strong>: the 100% OTU tables with Latin taxonomic names assigned using the RDP classifier (c = 0.5).</li> <li><strong>datasetID.otu_seqs.100.fasta</strong>: representative sequences for each OTU in the 100% OTU table. OTU labels in the OTU table end with <code>d__denovoID</code> - these denovoIDs correspond to the sequences in this file.</li> <li><strong>README.txt</strong>: additional information about steps taken to download and process each dataset, as needed.</li> </ul> <p>The raw data was acquired as described in the supplementary materials of Duvallet et al.'s "Meta analysis of microbiome studies identifies shared and disease-specific patterns" and, when available, the respective dataset README files.</p> <p>Raw sequencing data was processed with the Alm lab's in-house 16S processing pipeline: <a href="https://github.com/thomasgurry/amplicon_sequencing_pipeline">https://github.com/thomasgurry/amplicon_sequencing_pipeline</a></p> <p>Pipeline documentation is available at: <a href="http://amplicon-sequencing-pipeline.readthedocs.io/">http://amplicon-sequencing-pipeline.readthedocs.io/</a></p> <p>Metadata was extracted from the original papers and/or data sources, and formatted manually. When possible, these steps are documented in each dataset's associated README.txt file.</p> <p><strong>Contributing</strong></p> <p>MicrobiomeHD is a resource that can be used to extract disease-specific microbiome signals in individual case-control studies. Many microbes respond non-specifically to health and disease, and the majority of bacterial associations within individual studies overlap with this non-specific response. Researchers should cross-check their results with the data presented here to ensure that their identified microbial associations are specific to their disease under study.</p> <p>We provide an updated list of non-specific microbes here, as well as the raw OTU tables for anyone who wishes to reproduce and adapt this analysis to their study question.</p> <p>If you would like to include your case-control dataset in MicrobiomeHD, please email ejalm[at]mit.edu and duvallet[at]mit.edu.</p> <p>For us to process your data through our standard pipeline, you will need to provide the following files and information about your data:</p> <ul> <li>raw sequencing data in fastq or fasta format (preferably fastq)</li> <li>information about which processing steps will be required (e.g. removing primers or barcodes, merging paired-end reads, etc)</li> <li>sample IDs associated with the sequencing data (either mapped to barcodes still in the sequences, or to each de-multiplexed sequencing file)</li> <li>case/control metadata of each sample</li> <li>other relevant metadata (e.g. sampling site, if not all samples are stool; sampling time point, if multiple samples per patient were taken; etc)</li> </ul> <p>By using MicrobiomeHD in your own analyses, you agree to contribute your dataset to this database and to make your raw sequencing data (i.e. fastq files) publicly available.</p> <p><strong>Citing MicrobiomeHD</strong></p> <p>The MicrobiomeHD database and original publications for each of these datasets are described in Duvallet et al. (2017): <a href="http://dx.doi.org/10.1038/s41467-017-01973-8">http://dx.doi.org/10.1038/s41467-017-01973-8</a></p> <p>Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A., & Alm, E. J. (2017). Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. <em>Nature communications</em>, 8(1), 1784.</p> <p>If you use any of these datasets in your analysis, please cite both MicrobiomeHD (Duvallet et al. (2017)) and the original publication for each dataset that you use.</p> <p>The code used to process and analyze this data in the paper is available on github: <a href="https://github.com/cduvallet/microbiomeHD">https://github.com/cduvallet/microbiomeHD</a></p> <p><strong>Files</strong></p> <p><em>Data files</em></p> <p><strong>file-S3.nonspecific_genera.txt</strong>: Supplemental Table 3 from Duvallet et al. (2017), listing the non-specific health- and disease-associated microbes.<br> <strong>dataset_info.yaml</strong>: yaml file with additional dataset metadata.</p> <p><em>Datasets</em></p> <p>Note that MicrobiomeHD contains all 28 datasets from Duvallet et al. (2017), as well as additional datasets which did not meet the inclusion criteria for the meta-analysis presented in the paper. Additional information about the datasets included in this MicrobiomeHD release are in the original publications and the MicrobiomeHD github repo https://github.com/cduvallet/microbiomeHD, and in the file <em>dataset_info.yaml</em>.</p> <p>The sample sizes listed here reflect what was reported in the original publications. Some may have discrepancies between what is reported and what is in the actual data due to missing data, quality issues, barcode mismatches, etc.</p> <ul> <li><strong>asd_son_results.tar.gz</strong> (<em>asd_son</em>): NT: 44, ASD: 59 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0137725</li> </ul> </li> <li><strong>autism_kb_results.tar.gz</strong> (<em>asd_kang</em>): H: 20, ASD: 20 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0068322</li> </ul> </li> <li><strong>cdi_schubert_results.tar.gz</strong> (<em>cdi_schubert</em>): H: 155, nonCDI: 89, CDI: 94 <ul> <li>http://dx.doi.org/10.1128/mBio.01021-14</li> </ul> </li> <li><strong>cdi_vincent_v3v5_results.tar.gz</strong> (<em>cdi_vincent</em>): H: 25, CDI: 25 <ul> <li>http://dx.doi.org/10.1186/2049-2618-1-18</li> </ul> </li> <li><strong>cdi_youngster_results.tar.gz</strong> (<em>cdi_youngster</em>): H: 4, CDI: 19 <ul> <li>http://dx.doi.org/10.1093/cid/ciu135</li> </ul> </li> <li><strong>crc_baxter_results.tar.gz</strong> (<em>crc_baxter</em>): adenoma: 198, H: 172, CRC: 120 <ul> <li>http://dx.doi.org/10.1186/s13073-016-0290-3</li> </ul> </li> <li><strong>crc_xiang_results.tar.gz</strong> (<em>crc_chen</em>): H: 22, CRC: 21 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0039743</li> </ul> </li> <li><strong>crc_zackular_results.tar.gz</strong> (<em>crc_zackular</em>): adenoma: 30, H: 30, CRC: 30 <ul> <li>http://dx.doi.org/10.1158/1940-6207.CAPR-14-0129</li> </ul> </li> <li><strong>crc_zeller_results.tar.gz</strong> (<em>crc_zeller</em>): H: 75, CRC: 41 <ul> <li>http://dx.doi.org/10.15252/msb.20145645</li> </ul> </li> <li><strong>crc_zhao_results.tar.gz</strong> (<em>crc_wang</em>): H: 56, CRC: 46 <ul> <li>http://dx.doi.org/10.1038/ismej.2011.109}</li> </ul> </li> <li><strong>edd_singh_results.tar.gz</strong> (<em>edd_singh</em>): STEC: 28, CAMP: 71, SALM: 66, SHIG: 34, H: 75 <ul> <li>http://dx.doi.org/10.1186/s40168-015-0109-2</li> </ul> </li> <li><strong>hiv_dinh_results.tar.gz</strong> (<em>hiv_dinh</em>): H: 16, HIV: 21 <ul> <li>http://dx.doi.org/10.1093/infdis/jiu409</li> </ul> </li> <li><strong>hiv_lozupone_results.tar.gz</strong> (<em>hiv_lozupone</em>): H: 13, HIV: 25 <ul> <li>http://dx.doi.org/10.1016/j.chom.2013.08.006</li> </ul> </li> <li><strong>hiv_noguerajulian_results.tar.gz</strong> (<em>hiv_noguerajulian</em>): H: 34, HIV: 206 <ul> <li>https://doi.org/10.1016%2Fj.ebiom.2016.01.032</li> </ul> </li> <li><strong>ibd_alm_results.tar.gz</strong> (<em>ibd_papa</em>): IBDundef: 1, nonIBD: 24, UC: 43, CD: 23 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0039242</li> </ul> </li> <li><strong>ibd_engstrand_maxee_results.tar.gz</strong> (<em>ibd_willing</em>): CCD: 12, H: 35, ICD: 15, UC: 16, ICCD: 2 <ul> <li>http://dx.doi.org/10.1053/j.gastro.2010.08.049</li> </ul> </li> <li><strong>ibd_gevers_2014_results.tar.gz</strong> (<em>ibd_gevers</em>): H: 31, CD: 224 <ul> <li>http://dx.doi.org/10.1016/j.chom.2014.02.005</li> </ul> </li> <li><strong>ibd_huttenhower_results.tar.gz</strong> (<em>ibd_morgan</em>): H: 18, UC: 48, CD: 62 <ul> <li>http://dx.doi.org/10.1186/gb-2012-13-9-r79</li> </ul> </li> <li><strong>mhe_zhang_results.tar.gz</strong> (<em>liv_zhang</em>): CIRR: 25, H: 26, MHE: 26 <ul> <li>http://dx.doi.org/10.1038/ajg.2013.221</li> </ul> </li> <li><strong>nash_chan_results.tar.gz</strong> (<em>nash_wong</em>): H: 22, NASH: 16 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0062885</li> </ul> </li> <li><strong>nash_ob_baker_results.tar.gz</strong> (<em>nash_ob_zhu</em>): H: 16, NASH: 22, OB: 25 <ul> <li>http://dx.doi.org/10.1002/hep.26093</li> </ul> </li> <li><strong>ob_escobar_results.tar.gz</strong> (<em>ob_escobar</em>): OW: 10, H: 10, OB: 10 <ul> <li>https://doi.org/10.1186/s12866-014-0311-6</li> </ul> </li> <li><strong>ob_goodrich_results.tar.gz</strong> (<em>ob_goodrich</em>): OW: 322, H: 433, OB: 183 <ul> <li>http://dx.doi.org/10.1016/j.cell.2014.09.053</li> </ul> </li> <li><strong>ob_gordon_2008_v2_results.tar.gz</strong> (<em>ob_turnbaugh</em>): H: 61, OB: 219 <ul> <li>http://dx.doi.org/10.1038/nature07540</li> </ul> </li> <li><strong>ob_jumpertz_results.tar.gz</strong> (<em>ob_jumpertz</em>): H: 12, OB: 9 <ul> <li>http://ajcn.nutrition.org/content/early/2011/05/03/ajcn.110.010132</li> </ul> </li> <li><strong>ob_ross_results.tar.gz</strong> (<em>ob_ross</em>): H: 26, OB: 37 <ul> <li>http://dx.doi.org/10.1186/s40168-015-0072-y</li> </ul> </li> <li><strong>ob_wu_results.tar.gz</strong> (<em>ob_wu</em>): bmi_data: 101 <ul> <li>http://dx.doi.org/10.1126/science.1208344</li> </ul> </li> <li><strong>ob_zeevi_results.tar.gz</strong> (<em>ob_zeevi</em>): bmi_data: 870 <ul> <li>http://dx.doi.org/10.1016/j.cell.2015.11.001</li> </ul> </li> <li><strong>ob_zupancic_results.tar.gz</strong> (<em>ob_zupancic</em>): H: 167, OB: 117 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0043052</li> </ul> </li> <li><strong>par_scheperjans_results.tar.gz</strong> (<em>par_scheperjans</em>): H: 72, PAR: 72 <ul> <li>http://dx.doi.org/10.1002/mds.26069</li> </ul> </li> <li><strong>ra_littman_results.tar.gz</strong> (<em>art_scher</em>): H: 28, NORA: 44, CRA: 26, PSA: 16 <ul> <li>http://dx.doi.org/10.7554/eLife.01202</li> </ul> </li> <li><strong>t1d_alkanani_results.tar.gz</strong> (<em>t1d_alkanani</em>): T1D: 21, H: 55, T1D_new-onset: 35 <ul> <li>http://dx.doi.org/10.2337/db14-1847</li> </ul> </li> <li><strong>t1d_mejialeon_results.tar.gz</strong> (<em>t1d_mejialeon</em>): T1D: 21, H: 8 <ul> <li>http://dx.doi.org/10.1038/srep03814</li> </ul> </li> </ul> <p><strong>Version changes</strong></p> <p>Version 3</p> <ul> <li>added missing ob_escobar metadata</li> <li>added ob_jumpertz, ob_zeevi, and ob_wu</li> <li>added README.txt files to all folders, with info about data downloading and processing steps</li> <li>removed deprecated quality_control folders from all dataset results</li> <li>changed Supplemental File S3 to the most updated version of non-specific genera (as published in Duvallet et al 2017)</li> </ul> <p>Version 2</p> <ul> <li>added crc_zhu and ob_escobar datasets</li> <li>added list of core genera and dataset_info.yaml</li> </ul>
In‑vitro screening of compatible synbiotics and (introducing) "prophybiotics" as a tool to improve gut health
Open the record for dataset details and reuse information.
Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease
<p><strong><em>Data associated with the paper "Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease".</em></strong></p> <p><strong>Content:</strong></p> <ul> <li><strong>Metagenomic curated data considering healthy and diseased state of the human individuals. Aligned against RefSeq with Kaiju.</strong></li> <li><strong>Curated metadata with anonymised physiological and medical information</strong></li> </ul> <p><strong>Paper authors</strong>: Jacopo Pasqualini, Sonia Facchin, Andrea Rinaldo, Amos Maritan, Edoardo Vincenzo Savarino, Samir Suweis</p> <p><strong>Paper preprint</strong>: https://www.biorxiv.org/content/10.1101/2023.10.19.563037v2</p> <p><strong>Data Curator</strong>: Jacopo Pasqualini.</p> <p><strong>Pipeline used to generate the data</strong>: https://github.com/jacopopasqualini/MetaGym</p> <p><strong>Complete description of data generation</strong>: https://www.biorxiv.org/content/10.1101/2023.10.19.563037v2</p>
Assessing Gut Microbiota Mediated Health Outcomes of Whole Wheat and Its Major Bioactive Components
ClinicalTrials.gov study NCT05318183. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Use of a Novel Synbiotic to Change Human Gut Bacteria and Improve Health in Obese Adults
ClinicalTrials.gov study NCT02355210. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Association between gut health and gut microbiota in a polluted environment
Open the record for dataset details and reuse information.
Species-rich old grasslands have beneficial effects on the health and gut microbiome of bumblebees
Open the record for dataset details and reuse information.
Diet Supplementation With Prebiotics to Improve Gut Health in Egyptian Children
ClinicalTrials.gov study NCT06561724. IPD Sharing: YES. Countries: 1. Publications: 3.
Diet-induced Arrangement of the Gut Microbiome for Improvement of Cardiometabolic Health
ClinicalTrials.gov study NCT03071718. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Effect of Non-nutritive Sweeteners of High Sugar Sweetened Beverages on Metabolic Health and Gut Microbiome
ClinicalTrials.gov study NCT03259685. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Gut Health Enhancement by Eating Favourable Food
ClinicalTrials.gov study NCT05900609. IPD Sharing: NO. Countries: 1. Publications: 4.
Red Palm Olein on Inflammation and Gut Health
ClinicalTrials.gov study NCT05791370. IPD Sharing: NO. Countries: 1. Publications: 1.
Eggs for Gut Health
ClinicalTrials.gov study NCT06002438. IPD Sharing: YES. Countries: 1. Publications: 1.
Wild Blueberries for Gut, Brain, and Cardiometabolic Health in Prediabetes
ClinicalTrials.gov study NCT06735651. IPD Sharing: NO. Countries: 1. Publications: 6.
A Study to Evaluate the Effects of a Butyrate-Polyphenol Formulation on Gut Health and Associated Symptoms
ClinicalTrials.gov study NCT07371975. IPD Sharing: NO. Countries: 1. Publications: 13.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.