Decontamination Efficacy of Ultraviolet Radiation against Biofilms of Common Nosocomial Bacteria.
Bibliographic record
Abstract
BACKGROUND: Ultraviolet radiation (UV) is commonly used to destroy microorganisms in the health-care environment. However, the efficacy of UV radiation against bacteria growing within biofilms has never been studied. OBJECTIVE: To measure the sterilization effectiveness of UV radiation against common healthcare associated pathogens growing within biofilms. MATERIAL AND METHOD: Staphylococcus aureus, Methicillin-resistant S. aureus (MRSA), Streptococcus epidermidis, Escherichia coli, ESBL-producing E. coli, Pseudomonas aeruginosa and Acinetobacter baumannii were cultivated in the Calgary Biofilm Device. Their biofilms were placed 50 cm from the UV lamp within the Biosafety Cabinet. Viability test, crystal violet assay and a scanning electron microscope were used to evaluate the germicidal efficacy. RESULTS: Within 5 minutes, UV radiation could kill S. aureus, MRSA, S. epidermidis, A. baumannii and ESBL-producing E. coli completely while it required 20 minutes and 30 minutes respectively to kill E. coli and P. aeruginosa. However, the amounts of biomass and the ultrastructure between UV-exposed biofilms and controls were not significantly different. CONCLUSION: UV radiation is effective in inactivating nosocomial pathogens grown within biofilms, but not removing biofilms and EPS. The biofilm of P. aeruginosa was the most durable.
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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 | 0.000 |
| 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) | 0.001 | 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".