Effects of dietary supplementation with fish oil on in vivo production of inflammatory mediators in clinically normal dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of diets enriched with eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) on in vivo production of interleukin (IL)-1, IL-6, tumor necrosis factor (TNF)-alpha, prostaglandin E2 (PGE2), and platelet-activating factor (PAF) in dogs. ANIMALS: 15 young healthy dogs. PROCEDURES: Dogs were randomly allocated to receive an isocaloric ration supplemented with sunflower oil (n=5), fish oil (5), or fish oil plus vitamin E (5) for 12 weeks. At week 12, in vivo production of inflammatory mediators was evaluated in serum at multiple time points for 6 hours following stimulation with IV administration of lipopolysaccharide (LPS). RESULTS: Serum activity or concentration (area under the curve) of IL-1, IL-6, and PGE2 significantly increased after LPS injection in all groups but to a lesser extent in dogs receiving the fish oil diet, compared with results for dogs receiving the sunflower oil diet. Serum activity of TNF-alpha and PAF concentration also increased significantly after LPS injection in all groups but did not differ significantly among groups. CONCLUSIONS AND CLINICAL RELEVANCE: A fish oil-enriched diet consisting of 1.75 g of EPA/kg of diet and 2.2 g of DHA/kg of diet (dry-matter basis) with an n-6:n-3 fatty acid ratio of 3.4:1 was associated with significant reductions in serum PGE2 concentrations and IL-1 and IL-6 activities. Results supported the use of EPA- and DHA-enriched diets as part of antiinflammatory treatments for dogs with chronic inflammatory diseases. Additional studies in affected dogs are warranted to further evaluate beneficial anti-inflammatory effects of EPA- and DHA-enriched diets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".