<i>NAT2*7</i> Allele Is a Potential Risk Factor for Adult Brain Tumors in Taiwanese Population
Bibliographic record
Abstract
Arylamine N-acetyltransferase-2 (NAT2) displays extensive genetic polymorphisms that affect the rates of acetylation of drugs and toxic compounds such as amine carcinogens. The association of NAT2 polymorphisms with adult brain tumors has been unclear. To investigate whether the NAT2 genotype is a risk factor of brain tumors, we determined the frequencies of three common polymorphisms in the NAT2 gene, NAT2*5 (T341C), NAT2*6 (G590A), and NAT2*7(G857A), in brain tumor patients and in age- and gender-matched control subjects (n = 27 in each group). Genotyping was carried out using PCR-RFLP and subjects were phenotyped as a fast or slow acetylator based on the genotypes. The odds ratio of NAT2*7 allele frequency was significantly higher in patients with brain tumor than in controls (odds ratio, 6.786; 95% confidence interval, 2.06-22.37; P = 0.003); in the mean time, NAT2*4/*7 genotype was significantly more common in the patient group than in controls (odds ratio, 6.19; 95% confidence interval, 1.68-22.79; P = 0.0039). The tumors in the patients with NAT2*7 allele tended to be high-grade astrocytoma or glioblastoma multiforme (P = 0.016). In conclusion, these data suggest that the presence of NAT2*7 allele might be a potential risk factor for the development of brain tumors in Taiwan.
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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 | 0.000 |
| 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".