Study on the persistence of ciprofloxacin and sulfamethoxazole in simulated drinking water systems
Bibliographic record
Abstract
Abstract The antibiotics ciprofloxacin and sulfamethoxazole are well-known to be persistent in drinking water, as they have been detected at the highest concentration and frequency, respectively. These antibiotics persist despite their residence time, water treatment, and environmental conditions encountered in drinking water distribution systems. To better understand this phenomenon, the objectives of this study were to determine their degradation kinetics at a residual, sub-minimum inhibitory concentration while exposed to multi-species biofilms in polyvinyl chloride (PVC) pipe, as well as examine their effect on total cell count (TCC). The results revealed that both antibiotics continued to be detected after the experimental period of 12 days. Ciprofloxacin concentrations decreased by 31.1% (± 3.9%) and 27.4% (± 7.7%) during exposure to the biofilm and PVC-only control respectively, whereas sulfamethoxazole concentrations decreased by 87.2% (± 15.8%) and 3.6% (± 8.6%) during exposure to the biofilm and PVC-only control, respectively. Biofilm TCC increased significantly when exposed to ciprofloxacin ( p -value < 0.005), but showed no significant change when exposed to sulfamethoxazole ( p -value > 0.05), which suggested that ciprofloxacin enhanced biofilm formation. These results address the gap in antibiotic persistence research by tracing their concentrations, elucidating the mechanisms of sorption and degradation, and discussing their relative implications. As antibiotics continue to persist in drinking water, their interaction with biofilms may contribute to the long-term selection of antibiotic-resistant bacteria, posing potential risks to drinking water safety and public health.
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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 | 0.000 |
| 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) | 0.000 | 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".