Molecular epidemiology and in silico prediction of ciprofloxacin resistance in <i>Salmonella enterica</i> in Canada, 2017–2022
Bibliographic record
Abstract
Ciprofloxacin is important for treatment of severe or invasive Salmonella infections in humans. As laboratories transition from phenotypic to genomics-based methods for determining ciprofloxacin non-susceptibility, it is important to define the correlation between genetic determinants of resistance and phenotypic outcomes. Here, we examined ciprofloxacin resistance mechanisms in Salmonella and tested the hypothesis that isolates containing only one mechanism had intermediate resistance while isolates containing two or more mechanisms had full resistance according to breakpoints from the Clinical Laboratory Standards Institute. Among 13 750 human and food/animal Salmonella enterica isolates, 2325 were predicted to be non-susceptible to ciprofloxacin using whole genome sequencing and Staramr. The most common mechanisms of resistance were mutations in gyrA (especially S83F and D87N/D87Y) and the qnrB19 allele. Only 28% of ciprofloxacin resistant isolates had two or more resistance mechanisms; the remainder contained only one mechanism. Of isolates with two or more mechanisms, only 63% were resistant. Thus, the number of genetic determinants of ciprofloxacin resistance in an isolate could not reliably differentiate the ciprofloxacin intermediate or resistant categories when using North American breakpoints. Predicting ciprofloxacin intermediate/resistant as a single non-susceptible category would facilitate global standardization of data to inform public health surveillance, treatment guidelines, and stewardship.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".