Whole cigarette smoke promotes human gingival epithelial cell apoptosis and inhibits cell repair processes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Smoking cigarettes increases the risk of developing various types of human diseases, including cancers and periodontitis. As gingival epithelial cells are known to play an active role in innate immunity via the secretion of a wide variety of mediators, and as these cells are the first ones exposed to environmental stimuli such as cigarette smoke, we sought to investigate the effects of whole cigarette smoke on normal human gingival epithelial cells and tissue. MATERIAL AND METHODS: Human gingival epithelial cells were extracted from healthy nonsmokers and used either as a monolayer or as an engineered human oral mucosa to investigate the effect of whole cigarette smoke on cell growth, apoptosis and wound repair/migration. RESULTS: Our findings show that when gingival epithelial cells were exposed once to whole cigarette smoke, this resulted in a significant inhibition of cell growth through an apoptotic pathway, as confirmed by an increase of Bax and a decrease of Bcl-xL and caspase-3 activity. Cigarette smoke also inhibited epithelial cell migration. These effects may explain the disorganization of the engineered human oral mucosa tissue when exposed to whole cigarette smoke. CONCLUSION: Exposure to whole cigarette smoke markedly inhibits epithelial cell growth through an apoptosis/necrosis pathway that involves Bax and Bcl-xL proteins and caspase-3 activity. Cigarette smoke also disrupts epithelial cell migration, which may negatively affect periodontal wound healing.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".