Arginine and Vitamin E improve the antibody responses to infectious bursal disease virus (IBDV) and sheep red blood cells in broiler chickens
Bibliographic record
Abstract
1. Dietary arginine (ARG) and vitamin E (VE) have been shown to improve immune responses in broiler chickens, but their combined effects have not been well documented. The objective of this study was to evaluate the combined effects of dietary ARG and VE on antibody responses to sheep red blood cell (SRBC, agglutination assay) inoculation in 13-d-old chicks, and antibody titres (ELISA) to infectious bursal disease virus (IBDV) before and after vaccination of 20-d-old chicks. 2. One-day-old broiler chicks were fed diets with normal (NARG, 12 g/kg of feed) or high (HARG, 22 g/kg of feed) inclusion rates of ARG, and three rates of VE (40, 80, or 200 mg/kg of feed; 40 mg being the supplement used in commercial diets) in a factorial arrangement. 3. Antibody titres to SRBC at 5, 8, and 12 d after inoculation were higher in chicks fed on the HARG diet than in those on NARG, and in chickens on VE80 compared with those on VE200 at 5, 8, and 12 d after inoculation. Antibody titres to the IBDV 2 days before and 19 d after vaccination were higher in chickens on HARG compared with those on NARG, and in chicks on VE80 compared to those on VE40 but similar to those on VE200. Conversely, 5 d after vaccination titres against IBDV were higher in chicks on NARG than in those on HARG, and in chickens on VE40 compared with those on VE80, yet similar to those on VE200. 4. These results show that diets with high ARG and high VE (80 mg/kg) improved the humoral-mediated immune response of broilers to IBDV and SRBC, suggesting it could be a strategy to improve vaccination protection and resistance to diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".