Filtration Performances of Heat and Moisture Exchangers and Filters Against Viral Aerosols: Adaptation of a Wind Tunnel
Bibliographic record
Abstract
Background: The exhaled breath of infected, mechanically ventilated patients poses an infection risk to health care workers. Proper expiratory gas filtration with heat and moisture exchanger filters (HMEF) or filters could reduce that phenomenon. Current laboratory means of assessing the filtration efficiency are limited to the use of monodisperse aerosols at a single humidity level and flow. This study aims to examine the filtration efficiency of various devices under simulated clinical conditions, namely against a broad range of particle sizes containing viruses at different levels of gas humidity and flow. Methods: A wind tunnel was adapted to evaluate the filtration efficiency of 4 devices (HME, HMEF, filters, and HEPA-HMEF) against viral aerosols. Bacteriophages PhiX174 and MS2 were used as a proxy for human viruses. Results: In general, particulate filtration was significantly increased under dry versus humid conditions and with low versus high flows ( P < .05). The HEPA filter significantly outperformed all other devices under all tested conditions in filtration efficiency. Both HMEF and filter showed approximately a 1% decrease in absolute differences compared with the reference method (∼99% vs 99.99%). This difference could represent an emission of as many as 10 2 SARS-CoV-2 copies per hour by an ICU patient, which is enough to spread the infection. Conclusions: Accurate testing of filtration function has long gone unexamined, and in preparation for the next respiratory pandemic, better evaluation of devices that filter potentially dangerous pathogens is vital for health care professionals and systems. Standard filtration testing should be adapted to mimic the clinical usage of HMEs and filters.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".