What Do Influenza and COVID-19 Represent for Patients With Inflammatory Bowel Disease?
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel diseases (IBD) are a group of immune and inflammatory diseases; and patients seem to be more vulnerable to influenza and coronavirus disease 2019 (COVID-19). These conditions are characterized by the augmented release of inflammatory cytokines that have been suggested as potential triggers for the acute respiratory distress syndrome, which may favor severe and even fatal outcomes. For these reasons, this review aims to evaluate what influenza and COVID-19 may represent for patients with IBD. METHODS: The search was performed in MEDLINE/PubMed, EMBASE, and Cochrane databases. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to build the review. RESULTS: The conventional therapies used by IBD patients may also interfere in the outcomes of influenza and COVID-19. Immune-suppressors agents are associated with a higher risk of infections due to the inhibition of intracellular signals necessary to the host act against pathogens. On the other hand, drugs related to the suppression of the production of cytokines in IBD could bring benefits to reduce mucosal inflammation, and for preventing pneumonia. Moreover, coronaviruses can bind to the target cells through angiotensin-converting enzyme 2 (ACE-2) receptor that is expressed in epithelial cells of the lung and largely the colon and the terminal ileum suggesting that human intestinal tract could be an alternative route for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). CONCLUSIONS: Once the cytokine storm observed in influenza and COVID-19 is similar to the cytokine pattern observed in IBD patients during the disease flares, the advice is that avoiding the infections is still an optimal option for IBD subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".