Respiratory virus deterrence induced by modified mask filter
Bibliographic record
Abstract
Airborne transmission of infectious respiratory pathogens is a significant health hazard for the general public as well as healthcare professionals. Face masks have been frequently utilized as safety measures to limit the transmission of these infectious aerosolized particles. However, the efficacy of face masks in reducing respiratory virus infectivity and pathogenicity is unknown. Improving the effectiveness of masks in blocking viruses is urgently needed. In this study, surgical mask filters were modified by coating the filters with 1, 3, or 5 M of sodium dihydrogen phosphate, and subsequently exposed to the aerosolized respiratory influenza viruses (A/H3N2, A/H5N1) generated by a nebulizer set. Mask filter modification significantly reduced the size and counts of filter pores, which enabled entrapment of 40-60% of aerosolized viruses (captured viruses) with more than 90% of the captured viruses losing their infectivity. Upon contact with the coated mask filters, both the captured viruses and the viruses that managed to bypass the filter pore (passed viruses) were found to be inactivated. Passed viruses demonstrated significantly reduced pathogenicity in mice as indicated by significantly reduced lung virus titers, bodyweight loss, and prolonged survival compared to bare control. These findings highlight the potential of modified mask filters for reducing viral activity and pathogenicity, which contributes to improving facial mask efficacy as well as limiting airborne pathogen transmission.
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 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".