Impact of Antioxidants and HDL on Glycated LDL–Induced Generation of Fibrinolytic Regulators From Vascular Endothelial Cells
Bibliographic record
Abstract
Hyperglycemia and dyslipoproteinemia are biochemical markers of diabetes mellitus (DM). Elevated levels of plasminogen activator inhibitor-1 (PAI-1) with and without reduction of tissue plasminogen activator (tPA) in plasma have been frequently found in patients with DM. Our previous studies indicated that glycation enhances low density lipoprotein (LDL)-induced production of PAI-1 and further decreases tPA generation in vascular endothelial cells (ECs). The present study demonstrated that treatment with antioxidants, butylated hydroxytoluene or vitamin E, blocked native LDL- and glycated LDL-induced changes in PAI-1 and tPA generation in ECs. Native or glycated high density lipoprotein (HDL) did not significantly alter tPA generation in ECs. Glycated but not native HDL (>/=100 microg/mL) moderately increased PAI-1 release from ECs. Cotreatment with native or glycated HDL inhibited LDL-induced or glycated LDL-induced changes in PAI-1 and tPA generation in ECs. The abundance of conjugated dienes was increased in glycated or EC-modified LDL. Treatment with butylated hydroxytoluene, vitamin E, or HDL reduced the abundance of conjugated dienes in glycated or EC-modified LDL. The effects of antioxidants and HDL on LDL-induced or its glycated LDL-induced changes in the generation of PAI-1 and tPA were also found in cultured human coronary artery ECs. The findings of the present study suggest that antioxidants and HDL may attenuate native LDL- or glycated LDL-induced changes in the generation of fibrinolytic regulators from vascular ECs, which possibly results from their inhibition on the lipid peroxidation of LDL particles. Treatment with antioxidants or hypolipidemic agents potentially improves fibrinolytic activity and reduces thrombotic tendencies in patients with DM.
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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".