Chloraminated drinking water does not generate bacterial resistance to antibiotics in Pseudomonas aeruginosa biofilms
Bibliographic record
Abstract
AIM: To determine if exposure of Pseudomonas aeruginosa biofilms to chloraminated drinking water can lead to individual bacteria with resistance to antibiotics. METHODS AND RESULTS: Biofilms of P. aeruginosa PA14 were grown in drinking water in a Kadouri drip-fed reactor; the biofilms were treated with either 0.5 mg l(-1) or 1.0 mg l(-1) of chloramine for 15 or 21 days; control biofilms were grown in water without chloramine. Fewer isolates with antibiotic resistance were obtained from the chloramine-treated biofilms as compared to the control. Minimum inhibitory concentrations (MIC) for selected antibiotic-resistant isolates were determined using ciprofloxacin, tobramycin, gentamicin, rifampicin and chloramphenicol. All of the isolates tested had increased resistance over the wildtype to ciprofloxacin, rifampicin and chloramphenicol, but were not resistant to tobramycin or gentamicin. CONCLUSIONS: Under these test conditions, there was no detectable increase in antibiotic resistance in P. aeruginosa exposed as biofilms to disinfectant residues in chloraminated drinking water. SIGNIFICANCE AND IMPACT OF THE STUDY: Chloramine in drinking water, while unable to kill biofilm bacteria, does not increase the potential of P. aeruginosa to become resistant to antibiotics.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".