Association of the TNFRSF1B-rs1061622 variant with nonresponse to infliximab in ulcerative colitis
Bibliographic record
Abstract
For severe forms of ulcerative colitis (UC), a chronic inflammatory bowel disease (IBD), biological therapies, including tumor necrosis factor inhibitors (anti-TNF), are often used. However, these drugs have a high variability in treatment response. Multiple factors, such as genetic variants, can affect this variability. The goal of the study was to verify if selected candidate variants could affect response to anti-TNF in UC treatment. This association study included 76 participants suffering from UC and past or current users of anti-TNF. Clinical data for phenotyping was collected through a single visit with the participant and a medical chart review. Blood or saliva samples were collected to extract DNA and to genotype eight selected candidate variants in genes TNF, TNFAIP3, TNFRSF1 A and TNFRSF1B. For anti-TNF users, 30% of individuals were non-responders, 70% suffered from AE and none of the studied variants was associated with the response's phenotype. However, for infliximab users only (n = 44), the TNFRSF1B-rs1061622 variant was associated with nonresponse to infliximab for the first time in a cohort of UC patients (p-value = 0.028). Next steps are to replicate this association in independent cohorts and to perform functional studies to gain more evidence on the variant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".