The successful experimental induction of necrotic enteritis in chickens by Clostridium perfringens: a critical review
Bibliographic record
Abstract
Necrotic enteritis (NE) is one of the most important enteric diseases in poultry and is a high cost to the industry worldwide. It is caused by avian-specific, Necrotic Enteritis Beta toxin (NetB)-producing, strains of Clostridium perfringens that also possess in common other virulence-associated genes. In Europe the disease incidence has increased since the ban on in-feed "growth promoting" antibiotics. Because of this, many recent studies of NE have focused on finding different ways to control the disease, and on understanding its pathogenesis. Frustratingly, reproduction of the disease has proven impossible for some researchers. This review describes and discusses factors known to be important in reproducing the disease experimentally, as well as other considerations in reproducing the disease. The critical bacterial factor is the use of virulent, netB-positive, strains; virulence can be enhanced by using tpeL- positive strains and by the use of young rather than old broth cultures to increase toxin expression. Intestinal damaging factors, notably the use of concurrent or preceding coccidial infection, or administration of coccidial vaccines, combined with netB-positive C. perfringens administration, can also be used to induce NE. Nutritional factors, particularly feeding high percentage of cereals containing non-starch polysaccharides (NSP) (wheat, rye, and barley) enhance disease by increasing digesta viscosity, mucus production and bacterial growth. Animal proteins, especially fish meal, enhance C. perfringens proliferation and toxin production. Other factors are discussed that may affect outcome but for which evidence of their importance is lacking. The review compares the different challenge approaches; depending on the aim of particular studies, the different critical factors can be adjusted to affect the severity of the lesions induced. A standardized scoring system is proposed for international adoption based on gross rather than histopathological lesions; if universally adopted this will allow better comparison between studies done by different researchers. Also a scoring system is provided to assist decisions on humane euthanasia of sick birds.
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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 | 0.000 |
| 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".