Antimicrobial Resistance in<i>Campylobacter</i>
Bibliographic record
Abstract
This chapter provides a review of prevalence and trends of resistance in Campylobacter jejuni and Campylobacter coli isolated from humans in different parts of the world and a more thorough description of the mechanisms of resistance, origin, spread, and clinical consequences of resistance. Aminoglycosides exhibit rapid and significant bactericidal effects in vitro and should initially be included for the treatment of Campylobacter bacteremia in patients who appear very ill. The only mechanism of chloramphenicol resistance identified in Campylobacter occurs through modification of chloramphenicol by chloramphenicol acetyltransferase, which prevents its binding to the ribosome. The majority of contacts between Tet(O) and the ribosome are mediated by the rRNA, and one interaction with ribosomal protein S12. Most of the antimicrobials used in veterinary medicine are tetracyclines and macrolides, which result in high and continuous selective pressure for the animal-colonizing bacteria, ultimately resulting in the acquisition of antimicrobial resistance genes. Investigation into the mechanisms of action of antimicrobials, as well as the transfer of resistance determinants, is necessary to gain effective control of antimicrobial resistance. Epidemiological and microbiological studies show that poultry is the most important source for quinolone-susceptible and quinolone-resistant Campylobacter infections in humans. Trends over time for macrolide resistance show stable low rates in most countries, and macrolides should remain the drug class of choice for C. jejuni and C. coli enteritis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".