In ovo co-administration of vitamins (A and D) and probiotic lactobacilli modulates immune responses in broiler chickens
Bibliographic record
Abstract
There is evidence that probiotic lactobacilli, in addition to essential vitamins, such as vitamin A and D, have immunomodulatory properties that enhance immune response of neonatal chickens against infections. The present study evaluated the effects of in ovo administration of retinoic acid (RA), 25-Hydroxyvitamin D3 (VitD), and a lactobacilli cocktail on cytokine gene expression, antibody responses and spleen cell subsets in chickens. RA (90 µmol/egg) and VitD (0.6 μg/egg) were administered in ovo, either alone or in combination with lactobacilli (107 CFU/egg), at embryonic d 18. On d 5 and 10 posthatch, gene expression and cellular composition were analyzed in the bursa of Fabricius and spleen. Birds were immunized on d 14 and 21 posthatch with 2 T-dependent antigens, sheep red blood cells (SRBC) and keyhole limpet hemocyanin (KLH), to assess their antibody responses. Sera were collected from the immunized chickens on d 14, 21, 28, and 35 posthatch. The results demonstrated that lactobacilli treatment increased the number of monocyte/macrophages (KUL01+) and CD3+CD4+ T cells in the spleen, and enhanced serum anti-KLH IgM and IgY on d 14 postprimary immunization (P < 0.05). RA significantly increased serum IgY and IgM titers to KLH and enhanced the expression of interferon (IFN)-α, interleukin (IL)-1β, IL-6, IL-8, IL-12, IL-13, and transforming growth factor-β (TGF-β) in the bursa of Fabricius (P < 0.05). The percentage of CD3+CD8+ T cells, and monocyte/macrophages (KUL01+) was elevated in the spleen as well (P < 0.05). These findings reveal that prehatch administration of RA improves immunocompetency of neonatal chickens by increasing the production of cytokines that regulate innate immunity and through enhancing antibody-mediated response against T-dependent antigens.
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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) | 0.000 | 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 | 0.001 |
| 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".