Disinfection of sink drains to reduce a source of three opportunistic pathogens, during Serratia marcescens clusters in a neonatal intensive care unit
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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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) | 0.000 | 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) | 0.001 | 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".