Ultraviolet-C Irradiation, Heat, and Storage as Potential Methods of Inactivating SARS-CoV-2 and Bacterial Pathogens on Filtering Facepiece Respirators
Bibliographic record
Abstract
The arrival of SARS-CoV-2 to Aotearoa/New Zealand in February 2020 triggered a massive response at multiple levels. Procurement and sustainability of medical supplies to hospitals and clinics during the then upcoming COVID-19 pandemic was one of the top priorities. Continuing access to new personal protective equipment (PPE) was not guaranteed; thus, disinfecting and reusing PPE was considered as a potential alternative. Here, we describe part of a local program intended to test and implement a system to disinfect PPE for potential reuse in New Zealand. We used filtering facepiece respirator (FFR) coupons inoculated with SARS-CoV-2 or clinically relevant multidrug-resistant pathogens (Acinetobacter baumannii Ab5075, methicillin-resistant Staphylococcus aureus USA300 LAC and cystic-fibrosis isolate Pseudomonas aeruginosa LESB58), to evaluate the potential use of ultraviolet-C germicidal irradiation (UV-C) or dry heat treatment to disinfect PPE. An applied UV-C dose of 1000 mJ/cm2 was sufficient to completely inactivate high doses of SARS-CoV-2; however, irregularities in the FFR coupons hindered the efficacy of UV-C to fully inactivate the virus, even at higher UV-C doses (2000 mJ/cm2). Conversely, incubating contaminated FFR coupons at 65 °C for 30 min or 70 °C for 15 min, was sufficient to block SARS-CoV-2 replication, even in the presence of mucin or a soil load (mimicking salivary or respiratory secretions, respectively). Dry heat (90 min at 75 °C to 80 °C) effectively killed 106 planktonic bacteria; however, even extending the incubation time up to two hours at 80 °C did not completely kill bacteria when grown in colony biofilms. Importantly, we also showed that FFR material can harbor replication-competent SARS-CoV-2 for up to 35 days at room temperature in the presence of a soil load. We are currently using these findings to optimize and establish a robust process for decontaminating, reusing, and reducing wastage of PPE in New Zealand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".