Biological and Physicochemical Wastewater Treatment Processes Reduce the Prevalence of Virulent Escherichia coli
Bibliographic record
Abstract
Effluents discharged from wastewater treatment plants are possible sources of pathogenic bacteria, including Escherichia coli, in the freshwater environment, and determining the possible selection of pathogens is important. This study evaluated the impact of activated sludge and physicochemical wastewater treatment processes on the prevalence of potentially virulent E. coli. A total of 719 E. coli isolates collected from four municipal plants in Québec before and after treatment were characterized by using a customized DNA microarray to determine the impact of treatment processes on the frequency of specific pathotypes and virulence genes. The percentages of potentially pathogenic E. coli isolates in the plant influents varied between 26 and 51%, and in the effluents, the percentages were 14 to 31%, for a reduction observed at all plants ranging between 14 and 45%. Pathotypes associated with extraintestinal pathogenic E. coli (ExPEC) were the most abundant at three of the four plants and represented 24% of all isolates, while intestinal pathogenic E. coli pathotypes (IPEC) represented 10% of the isolates. At the plant where ExPEC isolates were not the most abundant, a large number of isolates were classified as both ExPEC and IPEC; overall, 6% of the isolates were classified in both groups, with the majority being from the same plant. The reduction of the proportion of pathogenic E. coli could not be explained by the preferential loss of one virulence gene or one type of virulence factor; however, the quinolone resistance gene (qnrS) appears to enhance the loss of virulence genes, suggesting a mechanism involving the loss of pathogenicity islands.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".