Associations between Antimicrobial Resistance Phenotypes, Antimicrobial Resistance Genes, and Virulence Genes of Fecal <i>Escherichia coli</i> Isolates from Healthy Grow-Finish Pigs
Bibliographic record
Abstract
Escherichia coli often carries linked antimicrobial resistance genes on transmissible genetic elements. Through coselection, antimicrobial use may select for unrelated but linked resistance or virulence genes. This study used unconditional statistical associations to investigate the relationships between antimicrobial resistance phenotypes and antimicrobial resistance genes in 151 E. coli isolates from healthy pigs. Phenotypic resistance to each drug was significantly associated with phenotypic resistance to at least one other drug, and every association found that the probability of observing the outcome resistance was increased by the presence of the predictor resistance. With one exception, each statistical association that was identified between a pair of resistance genes had a corresponding significant association identified between the phenotypes mediated by those genes. This suggests that associations between resistance phenotypes might predict coselection. If this hypothesis is confirmed, evaluation of the associations between resistance phenotypes could improve our knowledge of coselection dynamics and provide a cost-effective way to evaluate existing data until large-scale genotypic data collection becomes feasible. This could enable policy makers and users of antimicrobials to consider coselection in antimicrobial use decisions. This study also considered the unconditional relationships between resistance and virulence genes in E. coli from healthy pigs (aidA-1, eae, elt, estA, estB, fedA1, stx1, and stx2). Positive statistical associations would suggest that antimicrobial use may select for virulence in bacteria that may contaminate food or cause diarrhea in pigs. Fortunately, the odds of detecting a virulence gene were rarely increased by the presence of an antimicrobial resistance gene. This suggests that on-farm antimicrobial use did not select for the examined virulence factors in E. coli carried by this population of healthy pigs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".