A comparative assessment of e-cigarette aerosols and cigarette smoke on in vitro endothelial cell migration
Bibliographic record
Abstract
Cigarette smoking is a risk factor for several diseases. There has been a steep increase in the use of e-cigarettes that may offer a safer alternative to cigarette smoking. In vitro models of smoking-related diseases may provide valuable insights into disease mechanisms associated with tobacco use and could be used to assess e-cigarettes. We previously reported the application of a 'scratch wound' assay, measuring endothelial cell migration rate following artificial wounding, in the presence or absence of cigarette smoke extracts. This study reports the comparative effects of two commercial e-cigarette products (Vype ePen and Vype eStick) and a scientific reference cigarette (3R4F) on endothelial migration in vitro. Puff-matched extracts were generated using the Health Canada Intense (HCI) regime for cigarettes and a modified HCI for e-cigarettes. Exposure to 3R4F extract (20h) induced concentration-dependent inhibition of endothelial cell migration, with complete inhibition at concentrations >20%. E-cigarette extracts did not inhibit migration, even at double the 3R4F extract nicotine concentration, allowing cells to migrate into the wounded area. Our data demonstrate that e-cigarettes do not induce the inhibition of endothelial cell migration in vitro when compared to 3R4F. The scratch wound assay enables the comparative assessment between tobacco and nicotine products in vitro.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".