Contamination of Aerosol with Pseudomonas Aeruginosa Introduced via Mouthpiece in Different Nebulizer Designs
Bibliographic record
Abstract
Background: Nebulizers have been associated with bacterial and viral contamination likely from drooling or expulson of oral secretions into the nebulizer mouthpiece. We hypothesized that simulated “drooling” could result in contamination of the nebulizer medication resulting in aerosolization of potential pathogens. Method: We evaluated four nebulizers: Continuous jet nebulizer (CJN: MistyMax, Allegiance, USA), breath enhanced (BEN:LC Sprint; Pari, Germany), breath actuated (BAN; AeroEclipse Monaghan/Trudell, Canada) and vibrating mesh nebulizer (VMN; Aerogen with Ultra, Aerogen Ltd, Galway, Ireland) operated per manufacturer recommendations with 3 mL of NSS. Pseudomonas aeruginosa broth (2 mL) was pipetted into the mouthpiece of each nebulizer in an upright postion simulating a patient drooling into the device. Aerosol was produced for 30-60 seconds and collected on Triptic Soy Agar (TSA) plate, prior, immeadiately, and 4-5 hours post instillation. Colony counts were done post incubation (3-5 days). Results: P. aeruginosa colony counts prior, immediately, and four hours after instillation; BAN (0, 110, and 122 CFU/m); and BEN (0, Too Numerous To Count (TNC), and TNC), VMN: (0, 0, and 0 CFU/mL) and CJN (0, 0, and 0 CFU/mL), respectively. Conclusions: Nebulizer type and design influence impact of pathogen containing fluids passing through the mouthpiece contaminating the aerosol generated.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".