Effect of Urban Wastewater Discharge on the Abundance of Antibiotic Resistance Genes and Antibiotic-Resistant Escherichia coli in Two Italian Rivers
Bibliographic record
Abstract
Background: Wastewater treatment plants (WWTPs) are microbial factories aimed to reduce the amount of nutrients and pathogenic microorganisms in the treated wastewater before its discharge into the environment. We studied the impact of urban WWTP effluents on the abundance of antibiotic resistance genes (ARGs) and antibiotic-resistant Escherichia coli (AR-E. coli) in the last stretch of two rivers (Arrone and Tiber) in Central Italy that differ in size and flow volume. Methods: Water samples were collected in three seasons upstream and downstream of the WWTP, at the WWTP outlet, and at sea sites near the river mouth, and analyzed for the abundance of ARGs by qPCR and AR-E. coli using cultivation followed by disk diffusion assays. Results: For all studied genes (16S rRNA, intI1, sul1, ermB, blaTEM, tetW and qnrS), absolute concentrations were significantly higher in the Tiber than in the Arrone at all sampling sites, despite their collection date, but the prevalence of target ARGs within bacterial communities in both rivers was similar. The absolute concentrations of most ARGs were also generally higher in the WWTP effluent with median levels between log 4 and log 6 copies per ml but did not show differences along the studied stretches of rivers. Statistically significant site effect was found for E. coli phenotypic resistance to tetracycline and ciprofloxacin in the Arrone but not in the Tiber. Conclusions: In both rivers, diffuse or point pollution sources other than the studied WWTP effluents may account for the observed resistance pattern, although the Arrone appears as more sensitive to the wastewater impact considering its lower flow volume.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".