Colistin in Pig Production: Chemistry, Mechanism of Antibacterial Action, Microbial Resistance Emergence, and One Health Perspectives
Bibliographic record
Abstract
Colistin (Polymyxin E) is one of the few cationic antimicrobial peptides commercialized in both human and veterinary medicine. For several years now, colistin has been considered the last line of defense against infections caused by multidrug-resistant (MDR) Gram-negative such as Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae. Colistin has been extensively used orally since the 1960s in food animals and particularly in swine for the control of Enterobacteriaceae infections. However, with the recent discovery of plasmid-mediated colistin resistance encoded by the mcr-1 gene and the higher prevalence of samples harboring this gene in animal isolates compared to other origins, livestock has been singled out as the principal reservoir for colistin resistance amplification and spread. Co-localization of the mcr-1 gene and Extended-Spectrum- β-lactamase (ESBL) genes on a unique plasmid has been also identified in many isolates from animal origin. The use of colistin in pigs as a growth promoter and for prophylaxis purposes should be banned, and the implantation of sustainable measures in pig farms for microbial infection prevention should be actively encouraged and financed. The scientific research should be encouraged in swine medicine to generate data helping to reduce the exacerbation of colistin resistance in pigs and in manure. The establishment of guidelines ensuring a judicious therapeutic use of colistin in pigs, in countries where this drug is approved, is of crucial importance. The implementation of a microbiological withdrawal period that could reduce the potential contamination of consumers with colistin resistant bacteria of porcine origin should be encouraged. Moreover, the management of colistin resistance at the human-pig-environment interface requires the urgent use of the One Health approach for effective control and prevention. This approach needs the collaborative effort of multiple disciplines and close cooperation between physicians, veterinarians, and other scientific health and environmental professionals. This review is an update on the chemistry of colistin, its applications and antibacterial mechanism of action, and on Enterobacteriaceae resistance to colistin in pigs. We also detail and discuss the One Health approach and propose guidelines for colistin resistance management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".