Insights Into Inflammatory Bowel Disease and Effects of Dietary Fatty Acid Intake With a Focus on Polyunsaturated Fatty Acids Using Preclinical Models
Bibliographic record
Abstract
While the aetiology of inflammatory bowel disease (IBD) has been linked to genetic susceptibility coupled with environmental factors, the underlying molecular mechanisms remain unclear. Among the environmental factors, diet and the gut microbiota have been implicated as drivers of immune dysregulation in IBD. Indeed, epidemiologic studies have highlighted that the increase in incidence of IBD parallels the increase in dietary intake of omega-6 (n-6) polyunsaturated fatty acids (PUFAs) and the change in balance of intake of n-6 to n-3 fatty acids. Experimental evidence suggests that the increase in n-6 PUFA intake increases cell membrane arachidonic acid, which is accompanied by the production of pro-inflammatory mediators as well as increased oxidative stress; together, this contributes to the development of chronic inflammation. However, it is also increasingly clear that some of the n-6 PUFA-derived mediators exert beneficial effects depending on the settings and timing of ingestion. In contrast to n-6, when n-3 PUFA eicosapentaenoic acid and docosahexaenoic acid are incorporated into the cell membrane and are metabolized into less pro-inflammatory eicosanoids, as well as strong specialized pro-resolving mediators, which play a role in inflammation cessation. With a focus on preclinical models, we explore the relationship between dietary lipid, the gut microbiome, and intestinal inflammation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".