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/cm2) 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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".