Understanding the Impact of E-cigarette Aerosol Exposure on Human Airway Epithelial Cell Function
Bibliographic record
Abstract
Electronic cigarettes or e-cigarettes were introduced as a “healthier alternative” to conventional cigarettes and as a tool to facilitate smoking cessation. However, their safety and effectiveness as a smoking cessation tool are still unknown. The use of e-cigarettes has soared among Canadian youth, making this the leading cause of nicotine addiction in adolescence. Nicotine, a common psychoactive substance found in e-cigarettes, has been shown to cause irritation and inflammation in the airways. However, the effects of e-cigarettes, particularly concerning the base components such as propylene glycol (PG) and vegetable glycerin (VG), on the airways are less well understood. We hypothesized that PG and VG aerosol exposure negatively impacts mucociliary clearance and induces an inflammatory response in airway epithelial cells, potentially due to changes in membrane properties. To test this hypothesis, human nasal epithelial cells isolated from the inferior turbinate of healthy donors were exposed to an e-cigarette aerosol containing PG and VG. Exposure led to an increase in ciliary beat frequency, a key factor in mucociliary clearance. An LDH cytotoxicity assay revealed an increase in cell damage following exposure. Taken together, this work suggests that e-cigarette aerosol generated from the carrier substances is not innocuous.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".