Omega-3 fatty acids: molecular approaches to optimal biological outcomes
Bibliographic record
Abstract
Purpose of review This review discusses recent advances in delineating basic mechanisms underlying the beneficial effects of ω-3 fatty acids on health and on disease. Recent findings While a substantial number of studies have delineated many differences between the biological effects of saturated versus polyunsaturated fatty acids, less is known about the long-chain ω-3 fatty acids commonly present in certain fish oils. In this review, we focus on recent studies relating to basic mechanisms whereby ω-3 fatty acids modulate cellular pathways to exert beneficial effects on promoting health and decreasing risks of certain diseases. We will use, as examples, conditions of the cardiovascular, neurological, and immunological systems as well as diabetes and cancer, and then discuss basic regulatory pathways. Summary ω-3 Fatty acids are major regulators of multiple molecular pathways, altering many areas of cellular and organ function, metabolism and gene expression. Generally, these regulatory events lead to ‘positive’ endpoints relating to health and disease. Abbreviations CVD: cardiovascular disease; DHA: docosahexaenoic acid; EPA: eicosapentaenoic acid; PPAR: peroxisomal proliferating-activated receptor; PUFA: polyunsaturated fatty acid; RXR: retinoid X receptor; SREBP: sterol regulatory element-binding protein; Th: T-helper cell.
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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 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".