The emerging role of docosahexaenoic acid in neuroinflammation.
Bibliographic record
Abstract
Epidemiological studies have linked fish consumption to lower rates of neurological diseases. Fish contains high levels of omega-3 polyunsaturated fatty acids (n-3 PUFA), and several lines of evidence suggest that the n-3 PUFA docosahexaenoic acid (DHA; 22:6n-3) acts in the brain via anti-apoptotic and neurotrophic pathways. In addition, DHA may act through anti-neuroinflammatory pathways, as DHA possesses anti-inflammatory properties in the periphery. Evidence from animal models has indicated that DHA and its derivatives (resolvin D1 and protectin D1) attenuate colitis, peritonitis and ischemic stroke. n-3 PUFA deprivation in rats decreases brain levels of DHA and increases markers of the brain arachidonic acid (20:4n-6) cascade, a proinflammatory pathway. Thus, chronic low intake of n-3 PUFA may predispose the brain to weak anti-inflammatory, as well as strong proinflammatory signals. Neurological disorders, including Alzheimer's disease, Parkinson's disease and major depression, display a neuroinflammatory component. n-3 PUFA supplementation, as well as drugs targeting brain PUFA metabolism, are promising candidates in the prevention and treatment of neurological disorders.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".