ReviewGastroenterology research and practice2020
COVID-19 and Inflammatory Bowel Diseases: Risk Assessment, Shared Molecular Pathways, and Therapeutic Challenges.
Review in Gastroenterology research and practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Seroprevalence of ANTI-SARS-CoV-2 antibodies in patients with inflammatory bowel disease.Scientific reports · 2023Article
- Are inflammatory bowel diseases associated with an increased risk of COVID-19 susceptibility and severity? A two-sample Mendelian randomization study.Frontiers in genetics · 2023Article
- Susceptibility of Patients with Inflammatory Bowel Disease to COVID-19 Compared with Their Households.Middle East journal of digestive diseases · 2022Article
- Is Having Inflammatory Bowel Disease a Risk Factor for Severe Acute Respiratory Syndrome Coronavirus 2?The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2022Article
- Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes.Computers in biology and medicine · 2021Article
- Text mining approaches for dealing with the rapidly expanding literature on COVID-19.Briefings in bioinformatics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe novel coronavirus SARS-CoV-2 causing COVID-19 disease is yielding a global outbreak with severe threats to public health. In this paper, we aimed at reviewing the current knowledge about COVID-19 infectious risk status in inflammatory bowel disease (IBD) patients requiring immunosuppressive medication. We also focused on several molecular insights that could explain why IBD patients appear not to have higher risks of infection and worse outcomes in COVID-19 than the general population in an attempt to provide scientific support for safer decisions in IBD patient care.
methodsPubMed electronic database was interrogated for relevant articles involving data about common molecular pathways and shared treatment strategies between SARS-CoV-2, SARS-CoV-1, MERS-CoV, and inflammatory bowel diseases. Besides, Neural Covidex, an artificial intelligence tool, was used to answer queries about pathogenic coronaviruses and possible IBD interactions using the COVID-19 Open Research Dataset (CORD-19).
conclusionsUsing the current understanding of SARS-CoV-2 as well as other pathogenic coronaviruses immunopathology, we showed why IBD patients should not be considered at an increased risk of infection or more severe outcomes. Whether our findings are entirely applicable to the pathogenesis, disease susceptibility, and treatment management of SARS-CoV-2 infection in IBD must be further explored.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.