Bibliographic record
Abstract
Infection with Campylobacter jejuni is now considered to be the most common cause of acute bacterial gastroenteritis in humans worldwide. It occurs more frequently than infections caused by Salmonella species, Shigella species, or Escherichia coli O157:H7. Although C. jejuni is also recognized for its association with serious post-infection neurological complications, most patients with C. jejuni infections have a self-limited illness. Nevertheless, a substantial proportion of these infections are treated with antibiotics. These include severe and prolonged cases of enteritis, infections in immune-suppressed patients, septicaemia and other extra-intestinal infections. Under these circumstances, erythromycin is often recommended as the drug of first choice. However, erythromycin-resistant Campylobacter have emerged during therapy with macrolides. Moreover, the widespread use of macrolides, including erythromycin, in veterinary medicine has accelerated this resistance trend. Several countries including Canada, Japan and Finland have reported C. jejuni isolates with low and stable rates of macrolide resistance. In contrast, the increasing level of macrolide resistance in C. jejuni is becoming a major public health concern in other parts of the world such as the United States, Europe and Taiwan. Macrolide resistance in Campylobacter is mainly associated with point mutation(s) occurring in the peptidyl-encoding region in domain V of the 23S rRNA gene, the target of macrolides. Several rapid and practical techniques have recently been developed for the identification of macrolide-resistant isolates of C. jejuni. The aim of this mini-review is to give an overview of the worldwide distribution of macrolide resistance in C. jejuni and Campylobacter coli as well as its possible association with the massive use of these agents in food animals. Mechanisms implicated in macrolide resistance in C. jejuni and also techniques that have been developed for the efficient detection of macrolide-associated mutation(s) will be discussed in detail.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".