Propylene glycol and vegetable glycerin e-cigarette aerosols impact mucociliary function and cause cytotoxicity in human airway epithelium
Bibliographic record
Abstract
Electronic cigarettes (e-cigarettes) have emerged as a “healthier alternative” to conventional cigarettes, gaining popularity due to their perceived reduced harm and modern appeal. 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-cigarette aerosols, particularly 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 would negatively impact mucociliary clearance and induce an inflammatory response in airway epithelial cells. To test this hypothesis, cultured human airway 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 (CBF), 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 carrier substances is not innocuous.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".