The Haplotype of the TGFβ1 Gene Associated with Cerebral Infarction in Chinese
Bibliographic record
Abstract
BACKGROUND: Transforming growth factor beta1 (TGFβ1) is a multifunctional cytokine involved in inflammation and pathogenesis of atherosclerosis. The aim of the present study was to investigate the relationship between human TGFβ1 gene +869T>C (rs1800470), -509C>T (rs1800469) single nucleotide polymorphisms (SNPs) and haplotypes and cerebral infarction (CI) in a Chinese population. METHODS: The genetic association study was performed in 450 Chinese patients (306 male and 144 female) with CI and 450 control subjects (326 male and 124 female). TGFβ1 gene +869T>C and -509C>T polymorphisms were identified with amplification refractory mutation system polymerase chain reaction and DNA sequencing method. RESULTS: The individual SNPs analysis showed the +869T and -509C in an additive model (+869T vs +869C; -509 C vs T), +869TT genotype in a recessive model (TT vs TC+CC) and 509CC genotype in a dominant model (CC+ CT vs TT) were identified to be related to CI (P<0.05). +869T>C and -509C>T SNPs were in strong linkage disequilibrium (d'=0.87, R2=0.75). Haplotype analysis showed that +869C/-509T haplotype was associated with a significant decreased risk of CI (OR= 0.86, 95%CI, 0.70-0.92; P=0.007). Furthermore,+869T/-509C haplotype was associated with a significant increased risk of CI (OR=1.31, 95%CI, 1.10-2.03; P=0.019). CONCLUSIONS: The results of this study indicate that polymorphisms and the haplotypes in the TGFβ1 gene might be genetic markers for CI in the Chinese population.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".