Effect of e-cigarette aerosol on gingival mucosa structure and proinflammatory cytokine response
Bibliographic record
Abstract
We evaluated the effect of multiple exposures to electronic cigarettes on human oral mucosa structure and proinflammatory cytokine secretion. A 3D air-liquid interface human gingival mucosa was produced and exposed 10 min twice a day for 2 and 4 days for a total of 4 or 8 exposure times to e-cigarette aerosol. The vaped e-liquid contained 18 mg/ml of nicotine. Results show that 4 and 8 exposures to the e-cigarettes with and without nicotine-induced structural tissue damage decreased Laminin and type IV collagen production but increased the secretions of several metalloproteinases (MMPs), and lactate dehydrogenase (LDH). The e-cigarette reduced the number of proliferative epithelial cells, as ascertained by the low number of Ki-67+ cells. Exposure to e-cigarette aerosol increased proinflammatory cytokines IL-6, IL-8, GM-CSF, MCI-1, and TNFα. However, the e-cigarette aerosol effects were lower than combustible cigarette smoke (CS). Although e-cigarette aerosols produced less tissue damage than CS, they still induce critical damage to the engineered human gingival mucosa. E-cigarette users and oral health professionals should be aware of the potential adverse effects of e-cigarettes.
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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 | 0.000 |
| 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".