Impact of UV and Peracetic Acid Disinfection on the Prevalence of Virulence and Antimicrobial Resistance Genes in Uropathogenic Escherichia coli in Wastewater Effluents
Bibliographic record
Abstract
Wastewater discharges may increase the populations of pathogens, including Escherichia coli, and of antimicrobial-resistant strains in receiving waters. This study investigated the impact of UV and peracetic acid (PAA) disinfection on the prevalence of virulence and antimicrobial resistance genes in uropathogenic Escherichia coli (UPEC), the most abundant E. coli pathotype in municipal wastewaters. Laboratory disinfection experiments were conducted on wastewater treated by physicochemical, activated sludge, or biofiltration processes; 1,766 E. coli isolates were obtained for the evaluation. The target disinfection level was 200 CFU/100 ml, resulting in UV and PAA doses of 7 to 30 mJ/cm(2) and 0.9 to 2.0 mg/liter, respectively. The proportions of UPECs were reduced in all samples after disinfection, with an average reduction by UV of 55% (range, 22% to 80%) and by PAA of 52% (range, 11% to 100%). Analysis of urovirulence genes revealed that the decline in the UPEC populations was not associated with any particular virulence factor. A positive association was found between the occurrence of urovirulence and antimicrobial resistance genes (ARGs). However, the changes in the prevalence of ARGs in potential UPECs were different following disinfection, i.e., UV appears to have had no effect, while PAA significantly reduced the ARG levels. Thus, this study showed that both UV and PAA disinfections reduced the proportion of UPECs and that PAA disinfection also reduced the proportion of antimicrobial resistance gene-carrying UPEC pathotypes in municipal wastewaters.
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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 | 0.000 | 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".