Dietary n-3 PUFA May Attenuate Experimental Colitis
Bibliographic record
Abstract
Background. Inflammatory bowel diseases (IBD) occurred in genetically predisposed people exposed to environmental triggers. Diet has long been suspected to contribute to the development of IBD. Supplementation with n-3 polyunsaturated fatty acids (PUFA) protects against intestinal inflammation in rodent models while clinical trials showed no benefits. We hypothesized that intervention timing is crucial and dietary fatty acid pattern may influence intestinal environment to modify inflammation genesis. The aim of this study was to evaluate the dietary effect of PUFA composition on intestinal inflammation.Methods. Animals received diet varying in their PUFA composition for four weeks before TNBS-induced colitis. Colon inflammatory markers and gut barrier function parameters were assessed. Inflammatory pathway PCR arrays were determined.Results. n-3 diet significantly decreased colon iNOS, COX-2 expression, IL-6 production, and LTB4 production but tended to decrease colon TNFαproduction ( P=0.0617 ) compared to control diet. Tight junction protein (claudin-1, occludin) expressions and MUC2 and TFF3 mRNA levels were not different among groups. n-9 diet also decreased colon IL-6 production ( P<0.05 ).Conclusions. Dietary n-3 PUFA influence colitis development by attenuating inflammatory markers. Further research is required to better define dietary advice with a scientific rationale.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".