Treatment of chickens with lactobacilli prior to challenge with Clostridium perfringens modifies innate responses and gut morphology
Bibliographic record
Abstract
Necrotic enteritis caused by Clostridium perfringens (CP), is a common enteric disease of poultry that has been previously controlled by in-feed antibiotics. However, due to the rapid emergence of antimicrobial resistance, alternatives to antibiotics such as probiotics have received considerable attention because of their immunomodulatory and intestinal health benefits. The present study investigated the effects of probiotic lactobacilli on gut histomorphology and intestinal innate responses in chickens. Day-old male broiler chickens were treated with 1 × 107 or 1 × 108 colony-forming units (CFU) of a lactobacilli cocktail on days 1, 7, 14, and 20 post-hatch, while control groups were not treated with lactobacilli. On day 21, birds in all groups (except the negative control) were challenged with 3 × 108 CFU of CP for 3 days. Intestinal tissue samples were collected before and after the CP challenge to assess gene expression and for histomorphological analysis. Lactobacilli treatment at a dose of 1 × 108 CFU conferred partial protection against NE by lowering lesion scores, increasing villus height in the ileum and reducing crypt depth in the jejunum. In addition, 1 × 108 CFU of lactobacilli enhanced the expression of Toll-like receptor (TLR) 2, interferon-gamma (IFN-γ), interleukin (IL)-10, IL-12, and IL-13 in both the jejunum and ileum at different timepoints and subsequently decreased the expression of transforming growth factor beta (TGF-β) and IL-1β post-CP challenge. In conclusion, the results indicate that treatment with lactobacilli mitigated NE in a dose-dependent manner via improvement of intestinal morphology and modulation of innate immune response in 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".