Characterization of bacterial antimicrobial peptides active against <i>Campylobacter jejuni</i>
Bibliographic record
Abstract
Campylobacter jejuni is one of the major causes of food poisoning, often resulting from the consumption of improperly cooked poultry products. The emergence of C. jejuni strains resistant to conventional antibiotics necessitates the evaluation of other possible treatments or preventative measures to minimize the impact and prevalence of infections. Antimicrobial peptides produced by bacteria have begun to emerge as a potential means of decreasing the levels of C. jejuni in poultry, thereby limiting Campylobacter contamination in associated food products. A number of bacteriocins produced by Gram-positive bacteria have unexpectedly been described as having antimicrobial activity against the Gram-negative C. jejuni. Additionally, some nonribosomal lipopeptides produced by Bacillus and Paenibacillus spp. show efficacy against this pathogen. This review will describe the bacterial antimicrobial peptides reported to be active against C. jejuni, with an emphasis on the characterization of their primary structures. However, for many of these peptides, little is known about their amino acid sequences and structures. Furthermore, there are unusual inconsistencies associated with the reported amino acid sequences for several of the more well-studied bacteriocins. Clarifying the chemical nature of these promising antimicrobial peptides is necessary before their potential utility for livestock protection from C. jejuni can be fully explored. Once these peptides are better characterized, they may prove to be strong candidates for minimizing the impact of Campylobacter on human health.
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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) | 0.000 | 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 | 0.000 |
| 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".