[Prevalence of CYP2C19 polymorphisms involved in clopidogrel metabolism in Fujian Han population].
Bibliographic record
Abstract
OBJECTIVE: To investigate the frequency of CYP2C19 polymorphisms involved in clopidogrel metabolism in Fujian Han population. METHODS: Frequencies of CYP2C19* 2, CYP2C19*3 and CYP2C19*17 in 1001 unrelated Fujian Han volunteers were determined with polymerase chain reaction-restriction fragment length polymorphism and direct sequencing method. RESULTS: The frequencies of CYP2C19*2, *3 and *17 were 32.4%, 5.8% and 0.4%, respectively. According to genotyping results, intermediate metabolizers (CYP2C19 *1/*2 or *1/*3) and poor metabolizers (CYP2C19 *2/*2 and *2/*3) respectively accounted for 47.95% and 13.99% of all subjects. Above frequencies were similar to those of Japan, Korea, Singapore, Malaysia, Thailand and Chinese Dai, Mongolian,Li and Hui ethnics (P>0.05), but were significantly different from those of Chinese Kazakh and Uygur ethnics, and people from Iran, Russia, Italy, Poland, Norway, Canada native Indians, Bolivia, Egypt or Tanzania (P<0.05). CONCLUSION: Ethnic/regional diversity exist with regard to the prevalence of CYP2C19 polymorphisms. No significant difference were found between Fujian Han Chinese and Dai, Mongolian, Li and Hui from China or other populations from East and Southeast Asia, but higher frequencies of intermediate metabolizers and poor metabolizers compared with populations of Kazakh and Uygur in China, and people from Europe, South America and Africa.
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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) | 0.002 | 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".