Detection of carbapenem-resistance genes in bacteria isolated from wastewater in Ontario
Bibliographic record
Abstract
Bacterial carbapenem resistance is a major public health concern since these antimicrobials are often the last resort to treat serious human infections. To evaluate methodologies for detection of carbapenem resistance, carbapenem-tolerant bacteria were isolated from wastewater treatment plants in Toronto, Ottawa, and Arnprior, Ontario. A total of 135 carbapenem-tolerant bacteria were recovered. Polymerase chain reaction (PCR) indicated the presence of carbapenem hydrolysing enzymes KPC ( n = 10), GES ( n = 5), VIM ( n = 7), and IMP ( n = 1), and β-lactamases TEM ( n = 7), PER ( n = 1), and OXA-variants ( n = 16). A subset of 46 isolates were sequenced and analysed using ResFinder and CARD-RGI. Both programs detected carbapenem resistance genes in 35 sequenced isolates and antimicrobial resistance genes (ARGs) conferring resistance to multiple class of other antibiotics. Where β-lactamase resistance genes were not initially identified, lowering the thresholds for ARG detection enabled identification of closely related β-lactamases. However, no known carbapenem resistance genes were found in seven sequenced Pseudomonas spp. isolates. Also of note was a multi-drug-resistant Klebsiella pneumoniae isolate from Ottawa, which harboured resistance to seven antimicrobial classes including β-lactams. These results highlight the diversity of genes encoding carbapenem resistance in Ontario and the utility of whole genome sequencing over PCR for ARG detection where resistance may result from an assortment of genes.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".