Reduced Expression of Vascular Endothelial Growth Factor Paralleled With the Increased Angiostatin Expression Resulting From the Upregulated Activities of Matrix Metalloproteinase-2 and -9 in Human Type 2 Diabetic Arterial Vasculature
Bibliographic record
Abstract
Impaired angiogenesis could contribute to the increased incidence of coronary and peripheral artery disease in diabetic patients. Angiogenesis is initiated by vascular endothelial growth factor (VEGF), a potent angiogenic cytokine, and suppressed by angiostatin, which is generated by matrix metalloproteinase (MMP)-2 and -9 through proteolytic cleavage of plasminogen. We hypothesized that MMP-2 and -9 were upregulated in the diabetic vasculature, resulting in increased angiostatin production and reduced blood vessel formation. In diabetic internal mammary artery samples (n=32) collected from patients undergoing coronary artery bypass grafting surgery, capillary density was only 30% of that in the nondiabetic vessels (n=32), whereas VEGF expression was reduced by 48%. Diabetes upregulated the expression and the gelatinolytic activity of MMP-2 and -9. Active MMP-2 and -9 were released from diabetic arteries, but not from nondiabetic vessels, during phenylephrine-induced vasoconstriction. Diabetes enhanced transcription and protein expression of tissue inhibitor of MMP (TIMP)-1 but had an opposite effect on TIMP-2. In diabetic vessels angiostatin was increased by 62% and was positively correlated with the activities of MMP-2 and -9 (r2=0.806 and 0.742, respectively). This report indicated a strong correlation between the upregulation of MMP-2 and MMP-9 and the increased angiostatin expression in the human diabetic arterial vasculature. The enhanced angiostatin production with a reduced VEGF formation may explain the pathogenesis of impaired angiogenesis in diabetes mellitus.
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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) | 0.001 | 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".