A systematic review from basics to omics on bacteriophage applications in poultry production and processing
Bibliographic record
Abstract
The growing human population is currently facing an unprecedented challenge on global food production and sustainability. Despite recognizing poultry as one of the most successful and rapidly growing food industries to address this challenge; poultry health and safety remain major issues that entail immediate attention. Bacterial diseases including colibacillosis, salmonellosis, and necrotic enteritis have become increasingly prevalent during poultry production. Likewise, outbreaks caused by consumption of undercooked poultry products contaminated with zoonotic bacterial pathogens such as Salmonella, Campylobacter and Listeria, are a serious public health concern. With antimicrobial resistance problem and restricted use of antibiotics in food producing animals, bacteriophages are increasingly recognized as an attractive natural antibacterial alternative. Bacteriophages have recently shown promising results to treat diseases in poultry, reduce contamination of carcasses, and enhance the safety of poultry products. Omics technologies have been successfully employed to accurately characterize bacteriophages and their genes/proteins important for interaction with bacterial hosts. In this review, the potential of using lytic bacteriophages to mitigate the risk of major poultry-associated bacterial pathogens are explored. This study also explores challenges associated with the adoption of this technology by industries. Furthermore, the impact of omics approaches on studying bacteriophages, their host interaction and applications is discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".