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315 results for “human intestine”
Distribution and activity of nitrate and nitrite reductases in the microbiota of the human intestinal tract
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Single cell RNA sequencing of human tissue along the stomach-intestinal tract
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Data from: Tipping elements in the human intestinal ecosystem
The microbial communities living in the human intestine can have profound impact on our well-being and health. However, we have limited understanding of the mechanisms that control this complex ecosystem. Here, based on a deep phylogenetic analysis of the intestinal microbiota in a thousand western adults, we identify groups of bacteria that exhibit robust bistable abundance distributions. These bacteria are either abundant or nearly absent in most individuals, and exhibit decreased temporal stability at the intermediate abundance range. The abundances of these bimodally distributed bacteria vary independently, and their abundance distributions are not affected by short-term dietary interventions. However, their contrasting alternative states are associated with host factors such as ageing and overweight. We propose that the bistable groups reflect tipping elements of the intestinal microbiota, whose critical transitions may have profound health implications and diagnostic potential.
OMAP-15 Human Intestine with CODEX
<p>Datasets are CODEX experiments performed on TMA ffpe sections (composed of human duodenum, proximal jejunum, 3 mid jejunum samples, ileum, ascending colon, and sigmoid colon samples) labeled with nuclear marker DAPI and anitbodies directed against the indicated markers in OMAP-15. Images were acquired with the Akoya Phenocycler. </p> <p>Markers not included in OMAP-15 are not representative images.</p> <p>See "S16-0513-B13-TMA_Scan1.qptiff" for all markers except SPIB. See "S7-B017-011624_Scan1.qptiff" for SPIB.</p>
Data from: Epidemiological interactions between urogenital and intestinal human schistosomiasis in the context of praziquantel treatment across three West African countries
Background: In many parts of sub-Saharan Africa, urogenital and intestinal schistosomiasis co-occur, and mixed species infections containing both Schistosoma haematobium and S. mansoni can be common. During co-infection, interactions between these two species are possible, yet the extent to which such interactions influence disease dynamics or the outcome of control efforts remains poorly understood. Methodology/Principal Findings: Here we analyse epidemiological data from three West African countries co-endemic for urogenital and intestinal schistosomiasis (Senegal, Niger and Mali) to test whether the impact of praziquantel (PZQ) treatment, subsequent levels of re-infection or long-term infection dynamics are altered by co-infection. In all countries, positive associations between the two species prevailed at baseline: infection by one species tended to predict infection intensity for the other, with the strength of association varying across sites. Encouragingly, we found little evidence that co-infection influenced PZQ efficacy: species-specific egg reduction rates (ERR) and cure rates (CR) did not differ significantly with co-infection, and variation in treatment success was largely geographical. In Senegal, despite positive associations at baseline, children with S. mansoni co-infection at the time of treatment were less intensely re-infected by S. haematobium than those with single infections, suggesting competition between the species may occur post-treatment. Furthermore, the proportion of schistosome infections attributable to S. mansoni increased over time in all three countries examined. Conclusions/Significance: These findings suggest that while co-infection between urinary and intestinal schistosomes may not directly affect PZQ treatment efficacy, competitive interspecific interactions may influence epidemiological patterns of re-infection post-treatment. While re-infection patterns differed most strongly according to geographic location, interspecific interactions also seem to play a role, and could cause the community composition in mixed species settings to shift as disease control efforts intensify, a situation with implications for future disease management in this multi-species system.
Single cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis
<p>Codes and single cell data for "<strong>Single cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis</strong>" paper.</p> <p><strong>File: Tunel_Lyve1_Data.xlsx </strong>– quantifications of Tunel+Lyve1+ cells for n=4 NECs and n=4 neonatal samples.</p> <p><strong>Folder: Cell_ranger_output</strong></p> <p>Data produced using 10x sequencing.</p> <p>This folder contains all the Cell Ranger output raw files from all 11 subjects used for single cell RNA sequencing (5x Neonatal- S1056, S1127, S1212, S1214, S1082, 6x NECs- S1021, S1074, S1095, S1109, S1155, S1193).</p> <p>The neonatal subjects had two 10X runs (except subject 1082) - one done on CD45 negatively selected small intestinal cells and the other on non-selected small intestinal cells.</p> <p>The NEC subjects had one run each (subject 1074 was sequenced twice) with no selection due to low yield in the selected runs.</p> <p><strong>Folder: Seurat_pipeline</strong></p> <p>Done with Seurat package version 3.2.2</p> <p>This folder contains R scripts for Seurat analysis and script for background (BG) subtraction of the data.</p> <ol> <li>BG_subtraction_before_seurat.R - Script that creates a BG subtracted matrix for each CellRanger output before Seurat analysis.</li> <li>seurat_pipline_all_cells_NEC.R - Script for Seurat analysis for the full atlas.</li> <li>Final_seurat_obj_after_filtration_all_cells.rds – rds file of final Seurat object of all cells after pipeline filtration</li> <li>Files description in “RDS_files_for_seurat_pipeline” folder: <ol> <li>list_UMIs_after_BG_subtruction.rds – rds file containing a list of all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script.</li> <li>list_metadata_after_BG_subtruction.rds – rds file containing a list of metadata for all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script.</li> </ol> </li> </ol> <p><strong>Folder: Deconvolution_analysis</strong></p> <p>Done on Matlab R2019b</p> <p>This folder contains the script for deconvolution analysis and its input files.</p> <ol> <li>Deconvolution_parsing_NEC_for_zenodo.m - Matlab script to create box plots of deconvolution results and their q- values.</li> <li>Files description: <ol> <li>deconvolution_original8_clusters.mat – structure of deconvolution results. Signature cells were split into the 8 major cell type groups shown in figure 1.</li> <li>cellanneal_split_clust_results.mat – structure of deconvolution results. Signature cells were split into sub groups for each cell type group.</li> </ol> </li> </ol> <p><strong>Folder: Ligand-receptor_interaction_analysis</strong></p> <p>This folder contains matlab and R scripts for ligand-receptor interaction analysis.</p> <ol> <li>run_permutations_for_lig_rec_ratio_tensor_Zenodo.m – main script to run ligand-receptor analysis.</li> <li>calculate_ratio_ligand_receptor.m – function used by the main script.</li> <li>Interactions_visualizations_heatmaps.R – R script for heatmap visualization.</li> <li>Files description in “Mat_files_for_analysis” folder: <ol> <li>all_cells_split_for_interactions.mat – single cell structure of all cells. Cells are split into subgroups for ligand receptor analysis.</li> <li>ramilowsky.mat – ligand-receptor lists taken from Ramilowski et al. 2015 to use in main script and function.</li> <li>tensor_real_q100permutations.mat – output mat file of the interactions ratio, q values and ligand-receptor names for heatmaps visualization in R (input file for “Interactions_visualizations_heatmaps.R”).</li> </ol> </li> </ol>
Fig. 8 in Metabolites isolated from the human intestinal fungus Penicillium oxalicum SL2 and their agonistic effects on PXR and FXR
Fig. 8. (A) The 3D structure and hydrogen bond interaction of compound 18 with FXR at the 50th ns MD stimulation. (B) The agonistic activity of compound 18 against the wild-type, mutant R341V, or S342V FXR. Data were shown as the mean ± SD, n = 4 (*p <0.05, **p <0.01, ***p <0.001 compared to the Ctrl group; #p <0.05, ##p <0.01, ###p <0.001 compared to the WT group).
Fig. 7 in Metabolites isolated from the human intestinal fungus Penicillium oxalicum SL2 and their agonistic effects on PXR and FXR
Fig. 7. The RMSD (A) and RMSF (B) of compound 18 with FXR in 50 ns MD stimulation. (C) The volume of pock for a complex of compound 18 and FXR in 50 ns MD stimulation. (D–F) The energy of the complex (D), energy of contribution (E), and hydrogen bond number (F) of compound 18 with FXR in the 50 ns MD stimulation. (G) The distance of compound 18 with amino acid residues Val325, Met328, and Phe329. (H) The distance of compound 18 with amino acid residues Ser332 and Tyr369.
Fig. 5 in Metabolites isolated from the human intestinal fungus Penicillium oxalicum SL2 and their agonistic effects on PXR and FXR
Fig. 5. (A) Effects of compound 18 (2 μM) towards FXR, SHP1, and BSEP mRNA levels. (B) Effects of compound 18 (2 μM) towards FXR, SHP1, FGF, and BSEP expression levels. (C) Quantitative analysis of FXR, SHP1, FGF, and BSEP expression levels. Data were shown as the mean ± SD, n = 3 (*p <0.05, **p <0.01, ***p <0.001 compared to the Ctrl group). CDCA (80 μM) was used as the positive control.
Fig. 4 in Metabolites isolated from the human intestinal fungus Penicillium oxalicum SL2 and their agonistic effects on PXR and FXR
Fig. 4. Experimental and calculated ECD spectra of 1 (A) and 5–7 (B–D) at the CAM-B3LYP/def-tzvp level.
Fig. 3 in Microbial transformation of capsaicin by several human intestinal fungi and their inhibitory effects against lysine-specific demethylase 1
Fig. 3. The metabolites of capsaicin transformed by Rhizopus oryzae R2701 and its hypothetical biotransformation pathway.
Fig. 1 in Microbial transformation of capsaicin by several human intestinal fungi and their inhibitory effects against lysine-specific demethylase 1
Fig. 1. The metabolites of capsaicin transformed by Aspergillus fumigatus PB4204, Aspergillus japonicus Y4009A, and the hypothetical biotransformation pathway.
Ceftriaxone and Cefotaxime Have Similar Effects on the Intestinal Microbiota in Human Volunteers
<p><strong>Pour ce TP, nous allons déterminer l’impact d’un traitement à la ceftriaxone sur la flore intestinale (<a href="https://aac.asm.org/content/63/6/e02244-18.abstract">https://aac.asm.org/content/63/6/e02244-18.abstract</a>). 22 volontaires sains ont reçu par intraveineuse de la ceftriaxone (1g / 24 h) ou de la cefotaxime ( 1g / 8h) pendant 3 jours. Le consortium CEREMI a collecté des échantillons de selles de ces volontaires et réalisé une étude de métagénomique ciblée sur le gène 16S rRNA. En raison des limitations de vos machines, nous allons analyser les échantillons du groupe B uniquement à 2 temps: Jm1 (un jour avant le traitement) et J4</strong></p>
Establishment of the Human Intestinal and Salivary Microbiota Biobank - Kidney Diseases
ClinicalTrials.gov study NCT04689074. IPD Sharing: NO. Countries: 1. Publications: 5.
Establishment of the Human Intestinal and Salivary Microbiota Biobank - Gastrointestinal Diseases
ClinicalTrials.gov study NCT04698148. IPD Sharing: NO. Countries: 1. Publications: 5.
Human Milk and Infant Intestinal Microbiome Study
ClinicalTrials.gov study NCT03181269. IPD Sharing: NO. Countries: 1. Publications: 11.
Calibrated Diets and Human Intestinal Microflora
ClinicalTrials.gov study NCT00639561. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Investigation of the Ability of a Supplement to Increase Good Bacteria in the Human Intestine and Blood Sugar Levels
ClinicalTrials.gov study NCT01944904. IPD Sharing: NO. Countries: 1. Publications: 1.
Establishment of the Human Intestinal and Salivary Microbiota Biobank - Oncologic Diseases
ClinicalTrials.gov study NCT04698161. IPD Sharing: NO. Countries: 1. Publications: 5.
Intestinal Glucagon-like Peptide-1 (GLP-1) and the Physiological Role in Eating in Humans
ClinicalTrials.gov study NCT01900340. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
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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.