Challenge of mechanical and antimicrobial filters against infectious phages artificially agglomerated with inorganic dust with a known particle-size distribution
Bibliographic record
Abstract
Air filtration to remove viruses is considered for use in the swine industry to reduce epidemic episodes in Canada. The capture efficiencies of commercially available air filters against biological particles (bioaerosols) such as viruses needs to be determined in standardized and controlled conditions such as the ones required by the ASHRAE Standard 52.2. Artificially nebulized viruses may not accurately represent the bioaerosols present in swine buildings as associated viruses are likely to be transported on dust particles. The present study seeks to develop an infectious phages - carrying dust characterized by a similar particle size distribution of bioaerosols in swine buildings. A test duct was used to challenge MERV-16 and antimicrobial filters against the aerosolized infectious phages – dust mixtures (artificial viral aerosols). ISO 12103-1 A3 medium test dust, phages, and sucrose were lyophilized to form dried infectious phages – dust preparations. Observations of TEM imaging and results from Electrical Low Pressure Impactor (ELPI) samplings support MS2 phages were aggregated with dust particles. MS2 genomes were detectable on particles sized from 0.017 to 10 µm. The reduction efficiency of the MERV-16 filter challenged against the artificial viral aerosols was 99.4% for infectious MS2 phages (culture), 99.4% for total MS2 phages (qPCR), and 96.4% for dust. The antimicrobial filters had efficacies of 97.9% (culture), 83.4% (qPCR), and 48.5% (dust). The present study supports the possibility of making an infectious phages - bearing dust for use in environmentally controlled experiments evaluating reduction efficiency of air filters against viruses. Copyright © 2020 American Association for Aerosol Research
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".