E-Cigarettes Increase Candida albicans Growth and Modulate its Interaction with Gingival Epithelial Cells
Bibliographic record
Abstract
Electronic cigarette (e-cigarette) vapor comes in contact with the different constituents of the oral cavity, including such microorganisms as Candida albicans. We examined the impact of e-cigarettes on C. albicans growth and expression of different virulent genes, such as secreted aspartic proteases (SAPs), and the effect of e-cigarette vapor-exposed C. albicans on gingival epithelial cell morphology, growth, and lactate dehydrogenase (LDH) activity. An increase in C. albicans growth was observed with nicotine-rich e-cigarettes compared with non-exposed cultures. Following exposure to e-cigarette vapor, C. albicans produced high levels of chitin. E-cigarettes also increased C. albicans hyphal length and the expression of SAP2, SAP3, and SAP9 genes. When in contact with gingival epithelial cells, e-cigarette-exposed C. albicans adhered better to epithelial cells than the control. Indirect contact between e-cigarette-exposed C. albicans and gingival epithelial cells led to epithelial cell differentiation, reduced cell growth, and increased LDH activity. Overall, results indicate that e-cigarettes may interact with C. albicans to promote their pathogenesis, which may increase the risk of oral candidiasis in e-cigarette users.
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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 | 0.001 |
| 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".