The joint effect of hOGG1 single nucleotide polymorphism and smoking habit on lung cancer in Taiwan.
Bibliographic record
Abstract
AIM: To evaluate the association and interaction among human 8-oxoguanine DNA glycosylase 1 (hOGG1) genotypic polymorphism, smoking status and lung cancer risk in Taiwan. MATERIALS AND METHODS: The gene for hOGG1 was analyzed via polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 358 patients with lung cancer and 716 healthy controls recruited from the China Medical Hospital. RESULTS: The hOGG1 codon 326 genotypes were not differently distributed between the lung cancer and control groups (p=0.0809). However, the C allele of hOGG1 codon 326 was significantly (p=0.0198) more frequently found in controls than in cancer patients. We further analyzed the joint effect of genetics and smoking on lung cancer risk and found an interaction between hOGG1 codon 326 genotypes and smoking status. The hOGG1 codon 326 C allele-bearing genotypes were significantly associated with lung cancer risk only in the smoker group (p=0.0132), but not in the non-smoker group (p=0.06588). CONCLUSION: Our results provide evidence that the C allele of hOGG1 codon 326 may have a joint effect with smoking on the development of lung cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".