Impact of ultraviolet radiation on filtering facepiece respirators and SARS-CoV-2 detection
Bibliographic record
Abstract
Introduction The global coronavirus disease (COVID-19) pandemic of 2020 caused by SARS-CoV-2 resulted in a shortage of filtering facepiece respirators (FFRs), such as N95 and KN95 masks. Ultraviolet-C (UV-C) irradiation has been proposed as a potential decontamination method to enable FFR reuse and mitigate the shortage. This study aims to evaluate the impact of UV-C irradiation on the filtration efficiency (FE) of various FFR types and SARS-CoV-2 RNA degradation. Method We evaluated the effect of UV-C irradiation (60–900 mJ/cm 2 ) on the FE using various particle sizes (PSs, 0.3–5 μm) representing seven common FFR types (3M-8210, 3M-1860, Gerson 1730, Medline, Benehal, KN95 “duck shape,” and KN95 “molded shape”) and the stability of the SARS-CoV-2 RNA genes ( E, RdRp2, RdRp4 , and N ) using reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). Results Following UV-C exposure, the FE of the FFRs at all PSs was >98%, with no significant differences among them ( p > 0.05). UV-C irradiation significantly increased the RT-qPCR cycle threshold values (ΔCt) for the E , RdRp2 , and RdRp4 SARS-CoV-2 genes ( p ≤ 0.001) compared with the control, indicating marked RNA degradation; however, it did not significantly affect N gene stability ( p = 0.612). Discussion These results support the use of UV-C as an effective decontamination technique for FFRs, particularly during periods of shortage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".