Epigenomic Dysregulation in Youth Vapers: Implications for Disease Risk Assessment
Bibliographic record
Abstract
Abstract Despite the ongoing epidemic of youth vaping, the long-term health consequences of electronic cigarette use are largely unknown. We report the effects of vaping versus smoking on the oral cell methylome of healthy young vapers and smokers relative to nonusers. Whereas vapers and smokers differ in the number of differentially methylated regions (DMRs) (831 vs. 2,863), they share striking similarities in the distribution and patterns of DNA methylation, chromatin states, transcription factor binding motifs, and pathways. There is substantial overlap in DMR-associated genes between vapers and smokers, with the shared subset of genes enriched for transcriptional regulation, signaling, tobacco use disorders, and cancer-related pathways. Of significance is the identification of a common hypermethylated DMR at the promoter of HIC1 (hypermethylated in cancer 1), a tumor suppressor gene frequently silenced in smoking-related cancers. Our data support a potential link between epigenomic dysregulation in youth vapers and disease risk. These novel findings have significant implications for public health and tobacco product regulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".