Disinfection of sink drains to reduce a source of three opportunistic pathogens, during Serratia marcescens clusters in a neonatal intensive care unit
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Evaluate the effects of five disinfection methods on bacterial concentrations in hospital sink drains, focusing on three opportunistic pathogens (OPs): Serratia marcescens, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. DESIGN: Over two years, three sampling campaigns were conducted in a neonatal intensive care unit (NICU). Samples from 19 sink drains were taken at three time points: before, during, and after disinfection. Bacterial concentration was measured using culture-based and flow cytometry methods. High-throughput short sequence typing was performed to identify the three OPs and assess S. marcescens persistence after disinfection at the genotypic level. SETTING: This study was conducted in a pediatric hospitals NICU in Montréal, Canada, which is divided in an intensive and intermediate care side, with individual rooms equipped with a sink. INTERVENTIONS: Five treatments were compared: self-disinfecting drains, chlorine disinfection, boiling water disinfection, hot tap water flushing, and steam disinfection. RESULTS: This study highlights significant differences in the effectiveness of disinfection methods. Chlorine treatment proved ineffective in reducing bacterial concentration, including the three OPs. In contrast, all other drain interventions resulted in an immediate reduction in culturable bacteria (4-8 log) and intact cells (2-3 log). Thermal methods, particularly boiling water and steam treatments, exhibited superior effectiveness in reducing bacterial loads, including OPs. However, in drains with well-established bacterial biofilms, clonal strains of S. marcescens recolonized the drains after heat treatments. CONCLUSIONS: Our study supports thermal disinfection (>80°C) for pathogen reduction in drains but highlights the need for additional trials and the implementation of specific measures to limit biofilm formation.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 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 it