Particle Size, Mass Concentration, and Microbiota in Dental Aerosols
Bibliographic record
Abstract
Many dental procedures are considered aerosol-generating procedures that may put the dental operator and patients at risk for cross-infection due to contamination from nasal secretions and saliva. This aerosol, depending on the size of the particles, may stay suspended in the air for hours. The primary objective of the study was to characterize the size and concentrations of particles emitted from 7 different dental procedures, as well as estimate the contribution of the nasal and salivary fluids of the patient to the microbiota in the emitted bioaerosol. This cross-sectional study was conducted in an open-concept dental clinic with multiple operators at the same time. Particle size characterization and mass and particle concentrations were done by using 2 direct reading instruments: Dust-Trak DRX (Model 8534) and optical particle sizer (Model 3330). Active bioaerosol sampling was done before and during procedures. Bayesian modeling (SourceTracker2) of long-reads of the 16S ribosomal DNA was used to estimate the contribution of the patients’ nasal and salivary fluids to the bioaerosol. Aerosols in most dental procedures were sub-PM 1 dominant. Orthodontic debonding and denture adjustment consistently demonstrated more particles in the PM 1 , PM 2.5 , PM 4 , and PM 10 ranges. The microbiota in bioaerosol samples were significantly different from saliva and nasal samples in both membership and abundance ( P < 0.05) but not different from preoperative ambient air samples. A median of 80.15% of operator exposure was attributable to sources other than the patients’ salivary or nasal fluids. Median operator’s exposure from patients’ fluids ranged from 1.45% to 2.75%. Corridor microbiota showed more patients’ nasal bioaerosols than oral bioaerosols. High-volume saliva ejector and saliva ejector were effective in reducing bioaerosol escape. Patient nasal and salivary fluids are minor contributors to the operator’s bioaerosol exposure, which has important implications for COVID-19. Control of bioaerosolization of nasal fluids warrants further investigation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".