Study on the persistence of ciprofloxacin and sulfamethoxazole in simulated drinking water systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it