Association study between fibronectin and coronary heart disease
Bibliographic record
Abstract
Fibronectin is a known chemoattractant for several cell types that play a role in the wound healing process, including fibroblasts, endothelial cells and macrophages. It also generates a scaffold that allows attachment of other extracellular matrix components. Large amounts of fibronectin have been detected in atherosclerotic plaques, suggesting that it may play a role in the pathogenesis of atherosclerosis. To examine the possible involvement of fibronectin in the etiology of atherosclerotic coronary heart disease, we analyzed four polymorphisms in the human fibronectin gene and determined the plasma fibronectin levels in patients with coronary heart disease (n = 109) and age- and gender-matched controls (n = 123) in Chinese Han people. No significant positive association was observed between these polymorphisms and coronary heart disease. The levels of circulating plasma fibronectin, however, were significantly lower in patients with coronary heart disease (mean +/- SD 245 +/- 87 mg/L) compared with controls (354 +/- 88 mg/L) (p < 0.001). The odds ratio (OR) for plasma fibronectin was 0.94 in a multivariate unconditional logistic regression model (OR = 0.94, 95% CI 0.91-0.96, p < 0.001). We conclude that, in our population, the four fibronectin gene polymorphisms detected are not associated with clinical coronary heart disease. Our data suggest that low circulating fibronectin levels might be a new marker of coronary heart disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".