Effect of treatment with Lactococcus lactis NZ9000 on intestinal microbiota and mucosal immune responses against Clostridium perfringens in broiler chickens
Bibliographic record
Abstract
Alterations in intestinal microbiota can modulate the developing avian intestinal immune system and, subsequently, may impact on resistance to enteric pathogens. The aim was to demonstrate that early life exposure to Lactococcus lactis, could affect either susceptibility or resistance of broilers to necrotic enteritis (NE). L. lactis NZ9000 (rL. lactis) pre-treatment at 1, 7, 14 and 21 days of age (DOA) led to a significant decrease in NE lesion scores in Clostridium perfringens infected chickens. C. perfringens Infection was associated with spatial and temporal decreases in mononuclear phagocytes and CD4+ αβ T cells. However, rL. Lactis pre-treatment and subsequent C. perfringens infection led to a significant increase in mononuclear phagocytes, CD8α + γδ T, αβ T cells (CD4+ and CD8α+) and B cells (IgM+, IgA+ and IgY+), as well as IL-12p40, IFN-γ and CD40. Differential expression of interleukin (IL)-6, IL-8, IL-10, IL-13, IL-18, IL-22, and transforming growth factor (TGF)-β were observed in L. lactis treated chickens when compared to C. perfringens infected chickens. Microbiota analysis in C. perfringens infected chickens demonstrated an increase in abundance of Bacillota, Bacteroidota, Pseudomonadota and Actinomycetota. These findings suggests that modulation of the chicken intestinal immune system by L. lactis confers partial protection 30 against NE.
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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".