Direct and quantitative capture of viable bacteriophages from experimentally contaminated indoor air: A model for the study of airborne vertebrate viruses including SARS‐CoV‐2
Bibliographic record
Abstract
The air indoors has profound health implications as it can expose us to pathogens, allergens and particulates either directly or via contaminated surfaces. There is, therefore, an upsurge in marketing of air decontamination technologies, but with no proper validation of their claims. We addressed the gap through the construction and use of a versatile room‐sized (25 m3) chamber to study airborne pathogen survival and inactivation. Here, we report on the quantitative recovery and detection of an enveloped (Phi6) and a non‐enveloped bacteriophage (MS2). The two phages, respectively, acted as surrogates for airborne human pathogenic enveloped (e.g., influenza, Ebola and coronavirus SARS‐CoV‐2) and non‐enveloped (e.g., norovirus) viruses from indoor air deposited directly on the lawns of their respective host bacteria using a programmable slit‐to‐agar air sampler. Using this technique, two different devices based on HEPA filtration and UV light were tested for their ability to decontaminate indoor air. This safe, relatively simple and inexpensive procedure augments the use of phages as surrogates for the study of airborne human and animal pathogenic viruses. This simple, safe and relatively inexpensive method of direct recovery and quantitative detection of viable airborne phage particles can greatly enhance their applicattion as surrogates for the study of vertebrate virus survival in indoor air and assessment of technologies for their decontamination. The safe, economical and simple technique reported here can be applied widely to investigate the role of indoor air for virus survival and transmission and also to assess the potential of air decontaminating technologies.
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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 | 0.000 | 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".