Effect of the thymine‐DNA glycosylase rs4135050 variant on Saudi smoker population
Bibliographic record
Abstract
BACKGROUND: Thymine-DNA glycosylase (TDG) is an essential DNA-repair enzyme which works in both epigenetic regulation and genome maintenance. It is also responsible for efficient correction of multiple endogenous DNA lesions which occur commonly in mammalian genomes. Research of genetic variants such as SNPs, resulting in disease, is predicted to yield clinical advancements through the identification of sensitive genetic markers and the development of disease prevention and therapy. To that end, the main objective of the present study is to identify the possible interactions between cigarette smoking and the rs4135050 variant of the TDG gene, situated in the intron position, among Saudi individuals. METHODS: TDG rs4135050 (A/T) was investigated by genotyping 239, and 235 blood specimens were obtained from nonsmokers and smokers of cigarette respectively. RESULTS: T allele frequency was found which showed a significant protective effect on Saudi male smokers (OR = 0.64, p = 0.0187) compared to nonsmoking subjects, but not in female smokers. Furthermore, smokers aged less than 29 years, the AT and AT+TT genotypes decreased more than four times the risk of initiation of smoking related-diseases compare to the ancestral AA homozygous genotype. Paradoxically, the AT (OR = 3.88, p = 0.0169) and AT+TT (OR = 2.86, p = 0.0420) genotypes were present at a higher frequency in smoking patients aged more than 29 years as compared to nonsmokers at the same ages. CONCLUSION: Depending on the gender and age of patients, TDG rs4135050 may provide a novel biomarker for the early diagnosis and prevention of several diseases caused by cigarette smoking.
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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 | 0.000 | 0.001 |
| 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".