Dietary Ethyl-eicosapentaenoic Acid but not Soybean Oil Reverses Central Interleukin-1-induced Changes in Behavior, Corticosterone and Immune Response in Rats
Bibliographic record
Abstract
Omega (n)-3 and n-6 fatty acids are important membrane components of neurons and immune cells, and related to psychiatric and inflammatory diseases. Increased ratio of n-6/n-3 in the blood has been reported in depressed patients and in students following stress exposure. The n-3 fatty acid, eicosapentaenoic acid (ethyl-EPA) suppresses inflammation and has antidepressant properties. Interleukin (IL)-1beta can stimulate corticosterone secretion, induce anxiety and stress-like behavior and inflammatory responses. This study was to evaluate the effect of diets enriched with coconut oil, ethyl-EPA and soybean oil on central IL-1beta induced stress and anxiety-like behavior, induced changes in the concentration of prostaglandin (PG) E2 and corticosterone and the release of IL-10. Groups of rats were fed with either 5% coconut oil (as control diet), 0.2% EPA with 4.8% coconut oil or 1% EPA with 4% coconut oil and 5% soybean oil for 7 weeks. The central administration of IL-1beta induced sickness, stress and anxiety-like behavior as indicated by a reduction in body weight, decreased time spent, and the number of entries, into the open arms of the elevated plus maze and decreased exploration and entry into the central zone of the "open field" apparatus. IL-1beta also increased PGE2 and corticosterone concentrations and decreased the release of IL-10 from leucocytes. Food enriched with ethyl-EPA but not soybean oil, significantly attenuated most of these changes. These results demonstrate that ethyl-EPA has anti-inflammatory, anti-stress and anti-anxiety effects in rats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".