Responses of reconstituted human bronchial epithelia from normal and health-compromised donors to non-volatile particulate matter emissions from an aircraft turbofan engine
Bibliographic record
Abstract
Health effects of particulate matter (PM) from aircraft engines have not been adequately studied since controlled laboratory studies reflecting realistic conditions regarding aerosols, target tissue, particle exposure and deposited particle dose are logistically challenging. Due to the important contributions of aircraft engine emissions to air pollution, we employed a unique experimental setup to deposit exhaust particles directly from an aircraft engine onto reconstituted human bronchial epithelia (HBE) at air-liquid interface under conditions similar to in vivo airways to mimic realistic human exposure. The toxicity of non-volatile PM (nvPM) from a CFM56-7B26 aircraft engine was evaluated under realistic engine conditions by sampling and exposing HBE derived from donors of normal and compromised health status to exhaust for 1 h followed by biomarker analysis 24 h post exposure. Particle deposition varied depending on the engine thrust levels with 85% thrust producing the highest nvPM mass and number emissions with estimated surface deposition of 3.17 × 109 particles cm−2 or 337.1 ng cm−2. Transient increase in cytotoxicity was observed after exposure to nvPM in epithelia derived from a normal donor as well as a decrease in the secretion of interleukin 6 and monocyte chemotactic protein 1. Non-replicated multiple exposures of epithelia derived from a normal donor to nvPM primarily led to a pro-inflammatory response, while both cytotoxicity and oxidative stress induction remained unaffected. This raises concerns for the long-term implications of aircraft nvPM for human pulmonary health, especially in occupational settings.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".