Nutritional Lipids and Mucosal Inflammation
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is characterized by chronic relapsing inflammation in the intestine. Given their role in regulation of inflammation, long-chain n-3 polyunsaturated fatty acids (PUFAs) represent a potential supplementary therapeutic approach to current drug regimens used for IBD. Mechanistically, there is ample evidence for an anti-inflammatory and pro-resolution effect of long-chain n-3 PUFAs after they incorporate into cell membrane phospholipids. They disrupt membrane rafts and when released from the membrane suppress inflammatory signaling by activating PPAR-γ and free fatty acid receptor 4; furthermore, they shift the lipid mediator profile from pro-inflammatory eicosanoids to specialized pro-resolving mediators. The allocation of long-chain n-3 PUFAs also leads to a higher microbiome diversity in the gut, increases short-chain fatty acid-producing bacteria, and improves intestinal barrier function by sealing epithelial tight junctions. In line with these mechanistic studies, most epidemiological studies support a beneficial effect of long-chain n-3 PUFAs intake on reducing the incidence of IBD. However, the results from intervention trials on the prevention of relapse in IBD patients show no or only a marginal effect of long-chain n-3 PUFAs supplementation. In light of the current literature, international recommendations are supported that adequate diet-derived n-3 PUFAs might be beneficial in maintaining remission in IBD patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".