Diverse Escherichia coli lineages from domestic animals carrying colistin resistance gene mcr-1 in an Ecuadorian household
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to detect potential animal reservoirs of Escherichia coli carrying the mcr-1 gene in an Ecuadorian household. METHODS: The mobile colistin-resistance gene, mcr-1, was first detected in Ecuador in a commensal E. coli isolate from a boy. A cross-sectional study was performed to detect the possible source of colistin-resistant E. coli in the boy's household. Faecal swabs and soil faecal samples were collected from companion animals. Samples were plated on selective media to isolate colistin-resistant E. coli and isolates were submitted to PCR detection of mcr-1, pulsed field gel electrophoresis (PFGE), and multi-locus sequences typing (MLST). Moreover, the genomes of all the isolates were sequenced. RESULTS: Three different colistin-resistant E. coli sequence types (ST3941, 1630 and 2170), corresponding to three PFGE patterns, were obtained from a chicken and two dogs; these isolates were different from the human isolate (ST609). By whole-genome sequencing, the mcr-1.1 gene was found on IncI2 plasmids with very high nucleotide identity. CONCLUSIONS: Our results indicate a polyclonal dissemination of mcr-1.1 in the environment surrounding the first MCR-producing E. coli strain reported in Ecuador. Our findings support the idea of lateral dissemination of mcr-1.1 gene between unrelated E. coli isolates.
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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".