Inhalation of Nebulized Diesel Particulate Matter: A Safety Trial in Healthy Humans
Bibliographic record
Abstract
Diesel particulate matter (DPM) has adverse health effects. Examining the underlying pathophysiological mechanisms would be facilitated by the introduction of an exposure method that is safe, portable, and cost-effective. The purpose of this study was to establish a novel method to study DPM exposure via nebulization and an inhalation dose that was safe, yet capable of eliciting an inflammatory response. Ten participants enrolled in this nonblinded, nonrandomized study. Subjects inhaled nebulized 0.9% saline and increasing doses of DPM suspended in 0.9% saline (75, 150, and 300 μg) in a sequential manner. FEV1was measured repeatedly during the first 2 h after exposure and blood, oximetry, sputum, and heart rate were taken before, 2 h, and 24 h after inhalation challenge. DPM inhalation was well-tolerated at all doses. A decrease in FEV1was observed after each inhalation challenge (including saline). Inhalation of 300 μg DPM produced a significantly different FEV1response curve. An increase in particle inclusion-positive sputum macrophages for all DPM doses confirmed that the nebulized particles were reaching the lower airways. Serum GM-CSF was elevated after exposures to 150 and 300 μg DPM. No other inflammatory changes were detected. DPM inhalation via nebulizer is a safe method of delivering low doses of DPMs in healthy people.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".