Immunization of broiler chickens with five newly identified surface-exposed proteins unique to Clostridium perfringens causing necrotic enteritis
Bibliographic record
Abstract
Since the ban or reduction on the use of antibiotic growth promoters (AGPs) in commercial broiler chickens in many countries, avian necrotic enteritis (NE) caused by Clostridium perfringens has re-emerged as one of the biggest threats for the poultry industry worldwide. While the toolbox for controlling NE in the absence of antibiotics consists of a limited number of alternatives for which the overall effectiveness has yet proven to be suboptimal, an effective vaccine would represent the best control strategy for this often-deadly disease. Using a comparative and subtractive reverse vaccinology approach, we previously identified 14 putative antigenic proteins unique to NE-causing strains of C. perfringens. In the current work, the in silico findings were confirmed by PCR and sequencing, and five vaccine candidate proteins were produced and purified subsequently. Among them, two candidates were hypothetical proteins, two candidates were prepilin proteins which are predicted to form the subunits of a pilus structure, and one candidate was a non-heme iron protein. Western blotting and ELISA results showed that immunization of broiler chickens with five of these proteins raised antibodies which can specifically recognize both the recombinant and native forms of the protein in pathogenic 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".