A Narrative Review on The Beneficial Effects of <i>Lactobacillus</i> Probiotics Against Necrotic Enteritis in Poultry
Bibliographic record
Abstract
Necrotic enteritis is an important disease of poultry that causes economic loss to the broiler industry. Clostridium perfringens is an important bacterium that is responsible for causing necrotic enteritis. Antibiotics are mainly used to control C. perfringens but due to resistance antibiotics are banned in many countries like Canada, Hong Kong, and the European Union. Many alternatives such as probiotics, essential oils, and postbiotics have been developed to control C. perfringens. Among them, probiotics are very important because they can increase beneficial bacteria in the intestine, create a competitive environment in the gut region, and prevent the adhesion and colonization of pathogenic bacteria such as C. perfringens. Probiotics cause immune system modulation, reducing inflammatory markers such as cytokines. Lactobacillus based probiotics also cause weight gain, improve feed conversion ratio, and decrease mortality in poultry which in turn increase profit margin. Several studies have reported that when poultry populations were challenged with C. perfringens then these probiotics prevented intestinal lesions, provided anti-inflammatory effects to the intestine, prevented damage to the villi, and did not allow C. perfringens to form its colony in the intestine. The main aim of this review paper is to explain the updated information on necrotic enteritis, the damages caused to the gut, and the mechanism of actions through which Lactobacilli work against C. perfringens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".