The contribution of DNA apurinic/apyrimidinic endonuclease genotype and smoking habit to Taiwan lung cancer risk.
Bibliographic record
Abstract
To evaluate the association and interaction of genotypic polymorphism the gene for DNA-apurinic/apyrimidinic endonuclease (APEX1) with personal smoking habit and lung cancer risk in Taiwan, the polymorphic variants of APEX1, Asp(148)Glu (rs1130409), were analyzed in association with lung cancer risk, and their joint effect with personal smoking habits on lung cancer susceptibility was discussed. In this hospital-based case-control study, 358 patients with lung cancer and 716 cancer-free controls, frequency-matched by age and sex, were recruited and genotyped by polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP). The results showed that the percentages of TT, TG and GG APEX1 Asp(148)Glu genotypes were not significantly different at 43.0%, 41.1% and 15.9% in the lung cancer patient group and 39.9%, 46.1% and 14.0% in non-cancer control group, respectively. We further analyzed the genetic-lifestyle effects on lung cancer risk and found the contribution of APEX1 Asp(148)Glu genotypes to lung cancer susceptibility was neither enhanced in the cigarette smokers nor in the non-smokers (p=0.3550 and 0.8019, respectively). Our results provide evidence that the non-synonymous polymorphism of APEX1 Asp(148)Glu may not be directly associated with lung cancer risk, nor enhance the effects of smoking habit on lung cancer development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".