[The application of DNA sequencing in studying haplotypes of PAI-1 gene in patients with coronary artery disease].
Bibliographic record
Abstract
OBJECTIVE: Using simple and efficient DNA sequencing method to determine the haplotypes derived from 4G/5G and G/A polymorphisms in promoter of plasminogen activator inhibitor-1 (PAI-1) gene and trying to associate those haplotypes with coronary artery disease(CAD). METHODS: The frequencies and genotype distribution of the two PAI-1 gene variants in 93 CAD patients and 145 control subjects were determined via denaturing high-performance liquid chromatography (DHPLC) method. The heterozygotes for both polymorphism sites were directly sequenced to define the haplotypes based on the principle that heterozygotes of 4G/5G genotype were one guanine deletion and therefore one base shift from this site in sequencing graph results in particular wave pattern on -844G/A site. RESULTS: Only A-4G and G-5G haplotypes were detected in carriers heterozygous for both -6754G/5G and -844G/A polymorphisms sites though there are G-4G and A-5G haplotypes in carriers homozygous for both sites. No significant differences in haplotypes distribution were seen between CAD patients and control subjects. CONCLUSION: The DNA sequencing is objective and correct in determining haplotypes derived from two polymorphisms with distance less than about 400 bp apart and with one insertion/deletion polymorphism. And the haplotypes derived from -675 4G/5G and -844G/A polymorphisms are not associated with pathogenosis of CAD.
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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 | 0.000 |
| 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 | 0.001 |
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".