In Ovo and Oral Administration of Probiotic Lactobacilli Modulate Cell- and Antibody-Mediated Immune Responses in Newly Hatched Chicks
Bibliographic record
Abstract
There is some evidence that lactobacilli can strengthen the immune system of chickens. This study evaluated the effects of in ovo and oral administration of a lactobacilli cocktail on cytokine gene expression, antibody-mediated immune responses, and spleen cellularity in chickens. Lactobacilli were administered either in ovo at embryonic day 18, orally at days 1, 7, 14, 21, and 28 post-hatches, or a combination of both in ovo and post-hatch inoculation. On day 5 and 10 post-hatch, spleen and bursa of Fabricius were collected for gene expression and cell composition analysis. On days 14 and 21 post-hatch, birds were immunized with sheep red blood cells (SRBC) and keyhole limpet hemocyanin (KLH), and sera were collected on days 7, 14, and 21 post-primary immunization. Birds that received lactobacilli (10 7 CFU) via in ovo followed by weekly oral administration showed a greater immune response by enhancing antibody responses, increasing the percentage of CD4 + and CD4 + CD25 + T cells in the spleen and upregulating the expression of interferon (IFN)-α, IFN-β, interleukin (IL)-8, IL-13, and IL-18 in the spleen and expression of IFN-γ, IL-2, IL-6, IL-8, IL-12, and IL-18 in the bursa. These findings suggest that pre-and post-hatch administration of lactobacilli can modulate the immune response in newly hatched chickens.
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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".