Probiotics as Alternatives to Antibiotics for the Prevention and Control of Necrotic Enteritis in Chickens
Bibliographic record
Abstract
Necrotic enteritis (NE) in poultry is an economically important disease caused by Clostridium perfringens type A bacteria. A global trend on restricting the use of antibiotics as feed supplements in food animal production has caused a spike in the NE incidences in chickens, particularly in broiler populations. Amongst several non-antibiotic strategies for NE control tried so far, probiotics seem to offer promising avenues. The current review focuses on studies that have evaluated probiotic effects on C. perfringens growth and NE development. Several probiotic species, including Lactobacillus, Enterococcus, Bacillus, and Bacteroides bacteria as well as some yeast species have been tested in chickens against C. perfringens and NE development. These findings have shown to improve bird performance, reduce C. perfringens colonization and NE-associated pathology. The underlying probiotic mechanisms of NE control suggest that probiotics can help maintain a healthy gut microbial balance by modifying its composition, improve mucosal integrity by upregulating expression of tight-junction proteins, and modulate immune responses by downregulating expression of inflammatory cytokines. Collectively, these studies indicate that probiotics can offer a promising platform for NE control and that more investigations are needed to study whether these experimental probiotics can effectively prevent NE in commercial poultry operational settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".