E-liquid alters oral epithelial cell function to promote epithelial to mesenchymal transition and invasiveness in preclinical oral squamous cell carcinoma
Bibliographic record
Abstract
The gaining popularity of tobacco and nicotine delivery products, such as electronic cigarettes (e-cigarettes) being perceived as relatively safe is of a medical concern. The long-term safety of these new products remains uncertain for oral health. In this study, in vitro effects of e-liquid were assessed in a panel of normal oral epithelium cell lines (NOE and HMK), oral squamous cell carcinoma (OSCC) human cell lines (CAL27 and HSC3), and a mouse oral cancer cell line (AT84) using cell proliferation, survival/cell death, and cell invasion assays. In addition, signaling pathways underlying the pro-invasive activity of e-cigarettes were evaluated by gene and protein expression analysis. We demonstrated that e-liquid promotes proliferation and anchorage-independent growth of OSCC and induces morphological changes associated with enhanced motility and invasive phenotypes. Furthermore, e-liquid-exposed cells express significantly reduced cell viability, regardless of e-cigarette flavour content. At the gene expression level, e-liquid induces changes in gene expression consistent with epithelial to mesenchymal transition (EMT) revealed by reduced expression of cell epithelial markers such as E-cadherin and enhanced expression of mesenchymal proteins like vimentin and B-catenin seen both in OSCC cell lines and normal oral epithelium cells. In summary, the ability of e-liquid to induce proliferative and invasive properties along the activation of the EMT process can contribute to the development of tumorigenesis in normal epithelial cells and promote aggressive phenotype in pre-existing oral malignant cells.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".