Whole genome sequencing of increased number of azithromycin-resistant Shigella flexneri 1b isolates in Ontario
Bibliographic record
Abstract
Abstract Azithromycin (AZM) resistance among Shigella is a major public health concern. Here, we investigated the epidemiology of Shigella flexneri serotype 1b recovered during 2016–2018 in Ontario, to describe the prevalence and spread of AZM resistance. We found that 72.3% (47/65) of cases were AZM–resistant (AZMR), of which 95.7% (45/47) were among males (P < 0.001). Whole-genome based phylogenetic analysis showed three major clusters, and 56.9% of isolates grouped within a single closely-related cluster (0–10 ∆SNP). A single AZMR clonal population was persistent over 3 years and involved 67.9% (36/53) of all male cases, and none reported international travel. In 2018, a different AZMR cluster appeared among adult males not reporting travel. A proportion of isolates (10.7%) with reduced susceptibility to ciprofloxacin (CIP) due to S83L mutation in gyrA were AZM susceptible, and 71.4% reported international travel. Resistance to AZM was due to the acquisition of mph gene-bearing incFII plasmids having > 95% nucleotide similarity to pKSR100. Plasmid-borne resistance limiting treatment options to AZM, ceftriaxone (CRO) and CIP was noted in a single isolate. We characterized AZMR isolates circulating locally among males and found that genomic analysis can support targeted prevention and mitigation strategies against antimicrobial-resistance.
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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 | 0.001 | 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".