Genomic Epidemiology of Major Extraintestinal Pathogenic Escherichia coli Lineages Causing Urinary Tract Infections in Young Women Across Canada
Bibliographic record
Abstract
Abstract Background A few extraintestinal pathogenic Escherichia coli (ExPEC) multilocus sequence types (STs) cause the majority of community-acquired urinary tract infections (UTIs). We examine the genomic epidemiology of major ExPEC lineages, specifically factors associated with intestinal acquisition. Methods A total of 385 women with UTI caused by E. coli across Canada were asked about their diet, travel, and other exposures. Genome sequencing was used to determine both ST and genomic similarity. Logistic regression was used to identify factors associated with the acquisition of and infection with major ExPEC STs relative to minor ExPEC STs. Results ST131, ST69, ST73, ST127, and ST95 were responsible for 54% of all UTIs. Seven UTI clusters were identified, but genomes from the ST95, ST127, and ST420 clusters exhibited as few as 3 single nucleotide variations across the entire genome, suggesting recent acquisition. Furthermore, we identified a cluster of UTIs caused by 6 genetically-related ST1193 isolates carrying mutations in gyrA and parC. The acquisition of and infection with ST69, ST95, ST127, and ST131 were all associated with increased travel. The consumption of high-risk foods such as raw meat or vegetables, undercooked eggs, and seafood was associated with acquisition of and infection with ST69, ST127, and ST131, respectively. Conclusions Reservoirs may aid in the dissemination of pandemic ExPEC lineages in the community. Identifying ExPEC reservoirs may help prevent future emergence and dissemination of high-risk lineages within the community setting.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".