Differential survival of potentially pathogenic, septicemia- and meningitis-causing E. coli across the wastewater treatment train
Bibliographic record
Abstract
Abstract A growing body of evidence indicates that extraintestinal pathogenic E. coli (ExPEC) readily survive wastewater treatment, raising concerns about the public health risks associated with exposure to wastewater-contaminated environments. In this study, E. coli isolates recovered from chlorinated sewage or treated wastewater effluents in Canada were screened for ExPEC virulence markers. Eighty-six isolates were identified as presumptive ExPEC, clustering within major pandemic lineages including ST131, ST95, and ST73 according to multilocus sequence typing analyses. Across the whole, core, and accessory genome, 37 isolates were extremely similar to clinical bloodborne E. coli (BBEC) and neonatal meningitic E. coli (NMEC) strains, suggesting that these wastewater isolates may exhibit a similar phenotypically related pathogenic potential. Interestingly, ExPEC strains also shared accessory gene content with naturalized wastewater strains, suggesting a common genetic capacity for surviving water treatment. Collectively, these findings suggest that E. coli strains that may cause septicemia and meningitis are surviving wastewater treatment and may be transmissible through wastewater effluents.
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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 | 0.000 | 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".