Using DNA microarrays to assess the impact of wastewater treatment processes and disinfection on the prevalence of virulence and antimicrobial resistance genes in «Escherichia coli»
Bibliographic record
Abstract
An increase in the prevalence of virulent and antimicrobial resistance genes-carrying bacteria in water systems could be a threat to public health. The overall objective of this study was to evaluate the impact of municipal wastewater treatment (biological and physicochemical) and disinfection (ultraviolet [UV] and peracetic acid [PAA]) processes on the prevalence of virulence and antimicrobial resistance genes (ARGs) in Escherichia coli. A total of 2,485 E. coli were genotyped using DNA microarrays and PCR/Bioplex assays.The findings of this study showed that both biological and physicochemical wastewater treatment processes reduced the prevalence of pathogenic E. coli. In municipal wastewaters, extraintestinal uropathogenic E. coli (UPEC) were the predominant E. coli pathotypes. The two treatment processes had differential effects on ARGs in E. coli. Biological treatment did not change the prevalence of ARG-carrying E. coli, but it increased the abundance of ARGs in the E. coli genome, while physicochemical treatment reduced both the prevalence of ARG-carrying E. coli and the frequency of ARGs in the E. coli genome.The disinfection experiments showed that the proportions of UPECs were reduced in all samples after UV and PAA treatments; however, UV and PAA had different effects on the prevalence of ARGs in potential UPECs. UV irradiation did not change the prevalence of ARGs in the surviving potential UPECs, while PAA treatment decreased the abundance of ARGs. A positive co-occurrence was found between the ARG and virulence genotypes. Overall, the results of this study suggest that wastewater treatment processes and disinfection reduced the abundance of virulence genes in E. coli; however, they had different effects on the prevalence of ARGs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".