Strategies for Controlling Campylobacter in Poultry Production: A Comprehensive Review of Challenges and Potential Solutions
Bibliographic record
Abstract
Campylobacteriosis, a major foodborne illness caused by Campylobacter, poses significant challenges in poultry production. This review examines strategies to mitigate Campylobacter prevalence and foodborne outbreaks in poultry. Antibiotic therapy is limited due to Campylobacter's antibiotic resistance. Natural alternatives, including bacteriocins, phages, probiotics, essential oils, and plant-derived compounds, show promise in combating Campylobacter and improving meat safety. Biosecurity measures and hygiene practices are crucial in preventing Campylobacter introduction and colonization. Strict protocols and cleanliness reduce contamination. Nutritional interventions and vaccination strategies enhance disease resistance and immune responses in poultry. Nanotechnology, particularly ZnO nanoparticles, exhibits antimicrobial efficacy against Campylobacter and other bacteria. Electrostatic interaction with cell walls and the production of reactive oxygen species disrupt bacterial membranes and intracellular components. A comprehensive approach integrating natural alternatives, biosecurity, nutrition, and nanotechnology is necessary for effective Campylobacter control. Continued research and adherence to hygiene practices can reduce Campylobacter contamination, improve food safety, and protect public health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".