Impact of Diabetes-Associated Lipoproteins on Generation of Fibrinolytic Regulators from Vascular Endothelial Cells
Bibliographic record
Abstract
Increased thrombotic tendency and decreased fibrinolytic activity have been frequently found in patients with diabetes mellitus (DM). Previous studies by our group indicated that nonenzymatically glycated low density lipoprotein (LDL) increased plasminogen activator inhibitor-1 (PAI-1) production and decreased the generation of tissue plasminogen activator (tPA) from cultured human umbilical vein endothelial cells (HUVEC). The present study demonstrates that plasma levels of PAI-1 or PAI-1/tPA were significantly increased in patients with type 1 (n = 10) and type 2 DM (n = 14) compared with those in healthy controls (n = 10; P < 0.05 or 0.01). LDL from patients with type 1 or type 2 DM, and very low density lipoprotein (VLDL) from patients with type 2 DM induced significantly greater increases in the release of PAI-1 and more profound reduction in tPA generation from HUVEC compared with corresponding lipoproteins from healthy controls (P < 0.05 or 0.01). HDL from diabetic patients did not significantly alter the generation of PAI-1 or tPA from endothelial cells (EC) compared with HDL from controls. Comparable effects of lipoproteins from DM patients on the generation of PAI-1 and tPA were found in human coronary artery EC. LDL and VLDL from patients with type 2 DM enhanced the activation of PAI-1 promoter (-1528/+55)/luciferase reporter gene transiently transfected in HUVEC (P < 0.01). The results of the present study suggest that LDL and VLDL from patients with DM reduce the generation of tPA and increase PAI-1 production through the activation of the PAI-1 promoter in vascular EC.
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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 | 0.000 | 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".