A new multidrug‐resistant enterotoxigenic <i>Escherichia coli</i> pulsed‐field gel electrophoresis cluster associated with enrofloxacin non‐susceptibility in diseased pigs
Bibliographic record
Abstract
AIMS: To describe the temporal trends in Escherichia coli pathotypes and antimicrobial resistance detected in isolates from diseased-pig cases submitted to the EcL from 2008 to 2016, in Quebec, Canada, and to investigate the presence of spatiotemporal and phylogenetic clusters. METHODS AND RESULTS: Detection of 12 genes coding for virulence factors in pathogenic E. coli in pigs by PCR and antimicrobial resistance standard disc diffusion assay were performed. Demographic and clinical data were entered in the Animal Pathogenic and Zoonotic E. coli (APZEC) database. ETEC:F4 was the most prevalent pathovirotype among the 3773 cases submitted. The LT:STb:F4 virotype was predominant until 2014, then was overtaken by the LT:STb:STa:F4 virotype. More than 90% of the ETEC:F4 isolates were multidrug resistant. A spatiotemporal cluster of LT:STb:STa:F4 isolates non-susceptible to enrofloxacin was detected between 4/2015 and 9/2016. Pulsed-field gel electrophoresis analysis of 137 ETEC:F4 isolates revealed the presence of a cluster composed mainly of LT:STb:STa:F4 isolates non-susceptible to enrofloxacin. CONCLUSIONS: The APZEC database was useful to highlight temporal trends in E. coli pathotypes. A high-risk ETEC:F4 clone might disseminate in the pig population in Quebec since 2015. SIGNIFICANCE AND IMPACT OF THE STUDY: Surveillance is crucial to identify new clones and develop control strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".