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Dataset results
1,319 results for “crohns disease”
AI-Assisted MRE for Intestinal Fibrosis in Crohn's Disease
ClinicalTrials.gov study NCT06858553. IPD Sharing: NO. Countries: 1. Publications: 11.
A Study To Investigate Safety And Efficacy Of CP-690,550 For Induction Therapy In Subjects With Moderate To Severe Crohn's Disease
ClinicalTrials.gov study NCT01393626. IPD Sharing: Not stated. Countries: 18. Publications: 1.
NIH Substudy of AIN457 (Anti-IL-17 Monoclonal Antibody) for Treatment of Moderate to Severe Crohn's Disease
ClinicalTrials.gov study NCT00936585. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Crohn Disease Exclusion Diet After Single Medication De-escalation
ClinicalTrials.gov study NCT02472457. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Induction Study #2 of Oral Ozanimod as Induction Therapy for Moderately to Severely Active Crohn's Disease
ClinicalTrials.gov study NCT03440385. IPD Sharing: Not stated. Countries: 31. Publications: 1.
Remission in Subjects With Crohn's Disease, Open Label Extension
ClinicalTrials.gov study NCT01070303. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Adalimumab for the Maintenance of Clinical Remission in Japanese Subjects With Crohn's Disease
ClinicalTrials.gov study NCT00445432. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Study of the Safety and Efficacy of Ustekinumab (CNTO 1275) in Participants With Crohn's Disease
ClinicalTrials.gov study NCT00265122. IPD Sharing: Not stated. Countries: 3. Publications: 2.
A Dietary Intervention Study on the Microbiome in Crohn's Disease Patients
ClinicalTrials.gov study NCT04065048. IPD Sharing: NO. Countries: 1. Publications: 3.
Single-cell and spatial transcriptomics of stricturing Crohn's disease
<p>This folder contains the spatial transcriptomics data + code. This code was generated by members of the Smillie Lab @ MGH and Harvard Medical School.</p> <ul> <li><strong>github.tar.gz: </strong>spatial analysis code and data</li> <li><strong>anndata.h5ad:</strong> anndata object (scanpy)</li> <li><strong>V*tar.gz:</strong> raw spatial transcriptomics files</li> </ul> <p>The <strong>github.tar.gz</strong> folder contains everything you need to reproduce the spatial transcriptomics figures. It is structured as follows:</p> <ul> <li><strong>1.BayesPrism:</strong> code for running BayesPrism on spatial data</li> <li><strong>2.SparCC:</strong> code for running SparCC on spatial data</li> <li><strong>3.Lasso: </strong>code for running lasso regression on spatial data</li> <li><strong>4.Analysis: </strong>code for reproducing all figures in the paper</li> <li><strong>4.Analysis/1.analysis.r</strong><strong>: </strong>script to reproduce all figures in the paper ***</li> <li><strong>code:</strong> code library containing all necessary functions</li> <li><strong>load_data.r: </strong>code to load the single-cell and spatial datasets</li> <li><strong>sco.rds:</strong> single-cell analysis object (10X Chromium) formatted as an R list</li> <li><strong>vis.rds:</strong> spatial analysis object (10X Visium) formatted as an R list</li> </ul> <p>All scripts are numbered. You need to run everything in order. For convenience, we include the output files for <strong>1.BayesPrism</strong>, <strong>2.SparCC</strong>, and <strong>3.Lasso</strong>, allowing you to skip straight to the analysis code in <strong>4.Analysis.</strong></p> <p>To reproduce all figures in the paper, you need to do the following:</p> <ol> <li>Edit your PROJECT_FOLDER in the header of <strong>load_data.r</strong></li> <li>Install the packages listed at the top of <strong>load_data.r</strong></li> <li>Go to the <strong>4.Analysis</strong> directory, start an interactive R session, and type:<br>> source('1.analysis.r')</li> </ol> <p>This will load the beginning of the <strong>1.analysis.r</strong> script (until the stop() statement on line 68). You can run the code in two different ways:</p> <ol> <li>You can step through the code line by line in your interactive R session (starting at line 68)</li> <li>Alternatively, remove the stop() statement from the script, then run the code start to finish</li> </ol> <p>If you encounter any errors, try to debug them using a combination of Google+ChatGPT. If you still have trouble, please contact the Smillie Lab.</p> <p><strong>Note: </strong>the single-cell and spatial code are also available on GitHub. However, the spatial analysis requires large files that cannot be hosted on GitHub. Therefore, it is better to download the code + files from Zenodo. The GitHub link is provided below:</p> <p><a href="https://github.com/LJ-Kong/fibrosis_scRNA_stRNA">https://github.com/LJ-Kong/fibrosis_scRNA_stRNA</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
Crohn's Disease WES meta results
<p>Synopsis</p> <p>IBD exome-wide assocaition statistics from the meta-analysis of two callsets: Nextera and Twist. We used the fixed-effect meta-analysis in <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2922887/">METAL</a>. Column headers are self-explanatory. Allele2 is the tested/effect allele.</p> <p>Code availability</p> <p>Computer code used in this study:</p> <ul> <li>Hail (quality control, variants effect annotation; <a href="https://hail.is/">https://hail.is</a>)</li> <li>SAIGE (variant-based and gene-based association test; <a href="https://github.com/weizhouUMICH/SAIGE">https://github.com/weizhouUMICH/SAIGE</a>)</li> <li>METAL (meta-analysis; <a href="https://genome.sph.umich.edu/wiki/METAL_Documentation">https://genome.sph.umich.edu/wiki/METAL_Documentation</a>)</li> <li>PLINK (ancestry assignment, IBD relatedness QC and sample heterozygosity QC; plink1.9, <a href="https://www.cog-genomics.org/plink/">https://www.cog-genomics.org/plink/</a>; sample level QC pipeline, <a href="https://github.com/Annefeng/PBK-QC-pipeline">https://github.com/Annefeng/PBK-QC-pipeline</a>)</li> </ul> <p>Citation</p> <p><a href="https://www.medrxiv.org/content/10.1101/2021.06.15.21258641v2">Sazonovs, Stevens, Venkataraman, Yuan et al., MedRxiv, 2021</a></p>
Crohn-s-Disease-WES-meta
<p>Sequencing of over 100,000 individuals identifies multiple genes and rare variants associated with Crohn’s disease susceptibility</p> <p>Synopsis</p> <p>IBD exome-wide assocaition statistics from the meta-analysis of two callsets: Nextera and Twist. We used the fixed-effect meta-analysis in <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2922887/">METAL</a>. Column headers are self-explanatory. Allele2 is the tested/effect allele.</p> <p>Code availability</p> <p>Computer code used in this study:</p> <ul> <li>Hail (quality control, variants effect annotation; <a href="https://hail.is/">https://hail.is</a>) Analysis scripts are available in folder "Hail-scripts" of this repository.</li> <li>SAIGE (variant-based and gene-based association test; <a href="https://github.com/weizhouUMICH/SAIGE">https://github.com/weizhouUMICH/SAIGE</a>)</li> <li>METAL (meta-analysis; <a href="https://genome.sph.umich.edu/wiki/METAL_Documentation">https://genome.sph.umich.edu/wiki/METAL_Documentation</a>)</li> <li>PLINK (ancestry assignment, IBD relatedness QC and sample heterozygosity QC; plink1.9, <a href="https://www.cog-genomics.org/plink/">https://www.cog-genomics.org/plink/</a>; sample level QC pipeline, <a href="https://github.com/Annefeng/PBK-QC-pipeline">https://github.com/Annefeng/PBK-QC-pipeline</a>)</li> </ul> <p>Citation</p> <p><a href="https://www.medrxiv.org/content/10.1101/2021.06.15.21258641v2">Sazonovs, Stevens, Venkataraman, Yuan et al., MedRxiv, 2021</a></p>
IAP in Crohn's disease
<p class="MsoNormal"><span>The study examines the effect of cellular inhibitor of apoptosis protein (cIAP) inhibition on monocyte and PBMC cytokine production and cell death in healthy and Crohn's disease subjects. PBMCs from healthy controls were exposed to 4h of doses ranging from 0.1 to 1.0 micromolar of SMAC mimetics (SM) LCL161 and BV6 and co-incubated with LPS. SM consistently and dose-dependently reduced mRNA production of TNF-a, IL6 and IL-1b as determined by standard qPCR. A similar effect was seen on protein expression of TNF-a as determined by ELISA. To determine if this reduction in cytokine production was secondary to cell death, standard flow cytometry using ANX5 antibodies and nuclear dye 7AAD was performed. PBMCs were exposed from 4-16 hours to SM and/or LPS. Overall, neither of the stimulations LPS, SM or combined LPS/SM induced cell death. To determine if the effect was specific to LPS-induced signaling, PBMCs from healthy controls and from patients with inactive Crohn's disease were exposed to LPS, MDP and entero-adhesive <em>E. coli</em>. Only LPS-induced TNF expression was inhibited by BV6. This effect was seen in both healthy controls and Crohn's patients. Since monocytes are a major source of TNF-a in the intestine and important for intestinal inflammation and treatment responses in Crohn's disease we went on to examine how TNF-a production was affected in primary human monocytes. SMs reduced LPS-induced TNF-a mRNA expression in monocytes as determined by routine qPCR. We screened for different known modulators of IAP signaling (infliximab, zVAD, GSK 872, Nec1s, and NSA) by exposing PBMCs to these inhibitors and/or LPS and SMs. Only the RIPK1 inhibitor Nec1s was able to reverse SM-inhibition of LPS-induced TNF mRNA expression. Further, by standard Western blotting, we found that SM decreased the p-p65/total p65 ratio suggesting downstream inhibition of NFkB. To test if RIPK1 is involved in LPS-mediated TNF-a production we tested TNF-a mRNA expression by routine qPCR in the monocyte cell line THP1. Experiments were done on THP1 wild-type cells and THP1 RIPK1 K/O cells. RIPK1 K/O increased LPS-mediated TNF-a expression. In conclusion, SM can inhibit TNF-a, IL-1b and IL-6 expression without induction of cell death. The effect might in part be mediated RIPK1.</span></p> <p class="MsoNormal"><span>The data set provides the qPCR, ELISA and flow cytometry data in the order mentioned above. Each experiment is labelled according to cells used, exposure to inhibitors, length of stimulation where appropriate. qPCR data are normalized to LPS-only exposed cells, protein levels as pg/mL, flow cytometry data are shown as % cells, and Western blot quantification as pixels (ImageJ). </span></p>
A Comparative Study of MRI, US and CE for Assessing Treatment Response in Known Crohn's Disease
ClinicalTrials.gov study NCT03435016. IPD Sharing: YES. Countries: 1. Publications: 0.
Study of the 12-month Efficacy of Stem Cell Injection in Crohn's Disease With Complex Ano-perineal Fistula
ClinicalTrials.gov study NCT05177003. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Prospective Evaluation of a Scoring System in Patients Newly Diagnosed With Crohn's Disease
ClinicalTrials.gov study NCT02193048. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Crohn's Disease Exclusion Diet With Early Dairy Introduction vs Partial Enteral Nutrition for Crohn's Disease
ClinicalTrials.gov study NCT05606419. IPD Sharing: NO. Countries: 1. Publications: 8.
Safety and Efficacy of Natalizumab in the Treatment of Crohn's Disease
ClinicalTrials.gov study NCT00032799. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Mesenteric SParIng Versus Central mesenterectomY in Ileocolic Resection for Terminal Ileitis in Crohn's Disease
ClinicalTrials.gov study NCT04538638. IPD Sharing: NO. Countries: 1. Publications: 2.
Risk Stratification of Rapid Disease Progression in Children With Crohn's Disease
ClinicalTrials.gov study NCT00790543. IPD Sharing: NO. Countries: 2. 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.
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International Brain Laboratory public data
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OpenNeuro
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