Interaction between feed management systems and omega-3 fatty acid supplementation on bovine immunization parameters (VET1P.1116)
Bibliographic record
Abstract
Abstract A trial was designed to investigate impacts of omega-3 fatty acid feeding regimes on bovine immunization responses. Dietary supplements contained either fish oil or microalgae as omega-3 fatty acid sources, and cattle were divided into 2 feed management systems; they either grazed pasture or were kept in barn tie stalls and fed total mixed rations (TMR). Cattle were immunized with keyhole limpet hemocyanin (KLH), a standard T cell dependent antigen. Dietary supplements and feeding systems influenced the response of cattle to immunization with KLH. Anti-KLH IgM, IgG and IgA antibody levels in serum were significantly higher in cattle grazing pasture than those fed TMR. Cattle consuming microalgae had consistently higher anti-KLH IgG2 and IgM antibody levels compared to cattle fed fish oil. Effects of omega-3 fatty acid supplements and TMR versus pasture-based diets were also apparent in cytokine profiles from spleen and cecal tissues. Levels of transforming growth factor-beta (TGF-β) were highest in cecal tissue of pasture-grazing cattle (P < 0.0001), and spleen TGF-β levels were highest in pasture-grazing cattle consuming microalgae (P = 0.0073).These findings suggest that effects of omega-3 supplements on bovine immunity are influenced by the feed management system, with heightened effects on antibody production observed with a pasture system relative to a confinement and TMR-based diet.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".