Effect of UV-C light or hydrogen peroxide wipes on the inactivation of methicillin-resistant <i>Staphylococcus aureus</i>, <i>Clostridium difficile</i> spores and norovirus surrogate
Bibliographic record
Abstract
Abstract Aims The current study aimed to assess the potential of a new high dose ultraviolet (UV) disinfection device to inactivate methicillin-resistant Staphylococcus aureus (MRSA), Clostridium difficile and a norovirus surrogate on handheld mobile devices, and to compare the efficacy of the UV-C device to hydrogen peroxide disinfection wipes. Methods and Results Suspensions of MRSA, C. difficile spores and a surrogate for norovirus (MS2) were inoculated onto glass or plastic coupons, with or without organic contamination and were exposed to continuous UV-C light for 15–60 s (165–646 mJ cm−2) in a self-contained UV-C chamber or treated with hydrogen peroxide wipes. Increasing the UV-C dose from 310 to 650 mJ cm−2 did not result in greater levels of inactivation. UV-C light inactivated all three micro-organisms, in the absence of organic contamination, by &gt;2·9 log. Treatment of MRSA, C. difficile spores or MS2, in the presence of organic contamination, with UV-C light (310–646 mJ cm−2) resulted in 2·3–3·7 log reductions. Treatment of MRSA with UV-C light provided levels of inactivation comparable to treatment with hydrogen peroxide wipes used following the manufacturer’s instructions. Conclusions UV-C light and hydrogen peroxide wipes had strong antimicrobial activity against MRSA, C. difficile spores and a norovirus surrogate, in the presence or absence of organic contamination. Significance and Impact of the Study Chemical disinfection wipes are widely used in healthcare facilities, but they are not recommended for use on handheld mobile devices which may harbour pathogenic micro-organisms. The powerful bactericidal, sporicidal and virucidal activity of this high dose UV-C light device, shows that this technology is a promising alternative to chemical disinfectants, particularly for control of MRSA.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".