Preferential aerosolization of bacteria in bioaerosols generated<i>in vitro</i>
Bibliographic record
Abstract
AIMS: Little is known about how bacteria are aerosolized in terms of whether some bacteria will be found in the air more readily than others that are present in the source. This report describes in vitro experiments to compare aerosolization rates (also known as preferential aerosolization) of Gram-positive and Gram-negative bacteria as well as rod- and coccus-shaped bacteria, using two nebulization conditions. METHODS AND RESULTS: A consortium of five bacterial species was aerosolized in a homemade chamber. Aerosols generated with a commercial nebulizer and a homemade bubble-burst aerosol generator were compared. Data suggest that Pseudomonas aeruginosa was preferentially aerosolized in comparison to Moraxella catarrhalis, Lactobacillus paracasei, Staphylococcus aureus and Streptococcus suis, independently of the method of aerosolization. Bacterial integrity of Strep. suis was more preserved compared to other bacteria studied as revealed with PMA-qPCR. CONCLUSION: We reported the design of an aerosol chamber and bubble-burst generator for the in vitro study of preferential aerosolization. In our setting, preferential aerosolization was influenced by bacterial properties instead of aerosolization mechanism. SIGNIFICANCE AND IMPACT OF THE STUDY: These findings could have important implications for predicting the composition of bioaerosols in various locations such as wastewater treatment plants, agricultural settings and health care settings.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".