Wastewater Treatment Works: A Last Line of Defense for Preventing Antibiotic Resistance Entry Into the Environment
Bibliographic record
Abstract
With their large, diverse microbial communities chronically exposed to sub-inhibitory antibiotic concentrations, wastewater treatment works (WWTW) have been deemed hotspots for the emergence and dissemination of antimicrobial resistance, with growing concern about the transmission of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB) into receiving surface waters. This study explored (1) the prevalence of ARG and ARB in local WWTW, (2) the effect of sub-inhibitory antimicrobial exposure on ARG copy numbers in pure cultures from WWTW, and (3) two WWTW with different treatment configurations. For each WWTW, qPCR determined the prevalence of mcr3, sul1, sul2, and bla(KPC) during the treatment process, and culture methods were used to enumerate and identify ARB. Bacterial colonies isolated from effluent samples were identified by 16S rDNA sequencing and their respective minimum inhibitory concentrations (MIC) were determined. These were compared to the MICs of whole community samples from the influent, return activated sludge, and effluent of each WWTW. Resistance genes were quantified in 11 isolated cultures before and after exposure to sub-MIC concentrations of target antibiotics. The numbers of ARG and ARB in both WWTW effluents were notably reduced compared to the influent. Sul1 and sul2 gene copies increased in cultures enriched in sub-MIC concentrations of sulfamethoxazole, while bla(KPC) decreased after exposure to amoxicillin. It was concluded, within the parameters of this study, that WWTW assist in reducing ARG and ARB, but that sub-inhibitory exposure to antimicrobials has a varied effect on ARG copy number in pure cultures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".