Dietary Effects of Fish Oil Containing EPA and DHA on Performance, Fatty Acid Deposition, Plasma Oxylipins, and Cytokines in Response to LPS Challenge in Laying Hens
Bibliographic record
Abstract
BACKGROUND: Including fish oil (FO) in laying hen diets is an attractive strategy to increase egg and tissue omega (ω)-3 PUFA, while simultaneously protecting hens from potential immune challenges. OBJECTIVES: This study compared effects of a standard hen diet with FO containing EPA and DHA on plasma oxylipins and splenic cytokine expression in LPS-challenged layers. METHODS: Twenty-four 40-wk-old Dekalb White layers were randomly assigned to either control (no EPA or DHA) or FO (0.4 g/100 g EPA and DHA) diets. After 6 wk, hens were challenged with LPS (8 mg/kg body weight; i.v.) or saline 2 h before termination. PUFA were analyzed by GC, free oxylipins by HPLC/MS/MS, and splenic cytokines by quantitative reverse transcriptase-polymerase chain reaction. Data were analyzed by principal component analysis and analysis of variance. RESULTS: Yolk, liver, and plasma were enriched in ω-3 PUFA and reduced in ω-6 PUFA in FO group. Interestingly, LPS increased liver α-linolenic acid (ALA) and plasma ALA and DHA in FO-fed hens (P < 0.05). FO-fed hens exhibited higher plasma EPA and DHA oxylipins, and lower ω-6 oxylipins. Because of an interaction with FO, LPS increased ∼14% of oxylipins in control hens, particularly octadecanoids from ALA and linoleic acid, whereas in FO-fed hens, LPS decreased ∼31% of oxylipins derived from ω-6 and also from ω-3 PUFA (P < 0.05). Consistent with these putatively anti-inflammatory effects, FO decreased oxylipins associated with higher soluble epoxide hydrolase activity in saline-treated hens. However, FO increased these oxylipins in LPS-treated hens (P < 0.05). LPS induced the expression of splenic cytokines, and this was not altered by diet. CONCLUSIONS: Dietary EPA and DHA enrich ω-3 PUFA in yolk, liver, and plasma of layers, and modulate plasma oxylipins. Whether oxylipin changes mediate or are a consequence of FO effects on LPS-induced inflammation remains to be elucidated.
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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) | 0.000 | 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 | 0.001 |
| 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".