Genetic and epigenetic instability induced by betel quid associated chemicals
Bibliographic record
Abstract
Over the years, betel quid chewing and tobacco use have attracted considerable interest as they are implicated as the most likely causative risk factors of oral and esophageal cancer. Although areca nut use and betel quid chewing may lead to apoptosis, chronic exposure to areca nut and slaked lime may promote pre-malignant and malignant transformation of oral cells. The putative mutagenic and carcinogenic mechanisms may involve endogenous nitrosation of areca and tobacco alkaloids as well as the presence of direct alkylating agents in betel quid and smokeless tobacco. Metabolic activation of carcinogenic N-nitrosamines by phase-I enzymes is required not only to elicit the genotoxicity via the reactive intermediates but also to potentiate the mutagenicity with the sporadic alkylations of nucleotide bases, resulting in the formation of diverse DNA adducts. Persistent DNA adducts provides the impetus for genetic and epigenetic lesions. The genetic and epigenetic factors cumulatively influence the development and progression of disorders such as cancer. Accumulation of numerous genetic and epigenetic aberrations due to long-term betel quid (with or without tobacco) chewing and tobacco use culminates into the development of head and neck cancers. We review recent evidence that supports putative mechanisms for mutagenicity and carcinogenicity of betel quid chewing along with tobacco (smoking and smokeless) use. The detailed molecular mechanisms of the extent of accumulation and patterns of genetic alterations, indicative of the prior exposure to carcinogens and alkylating agents because of BQ chewing and tobacco use, have not yet been elucidated.
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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 | 0.001 | 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) | 0.001 | 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".