Effect of Glycated LDL on Monocyte Adhesion on Vascular Endothelial Cells: Role of Plasminogen Activator Inhibitor‐1
Bibliographic record
Abstract
Diabetes accelerates the development of atherosclerotic cardiovascular diseases. Inflammation on endothelium plays a key role in atherosclerosis. Monocyte adhesion is the earliest event of vascular inflammation. Elevated levels of glycated low density lipoprotein (gLDL) were detected in diabetic patients. We previously demonstrated that gLDL increased the expression of plasminogen activator inhibitor‐1 (PAI‐1) in vascular endothelial cells (EC). PAI‐1 is the physiological inhibitor of tissue and urokinase activators, and a marker of inflammation and endothelial dysfunction. The results of the present study demonstrated that pre‐incubation with gLDL significantly increased the adhesion of THP‐1 monocytes on the surface of cultured human umbilical vein EC (HUVEC) with a peak at 100 μg/ml for 6 h. Transfection of short interference RNA (siRNA) for PAI‐1 to EC prevented gLDL‐induced monocyte adhesion on EC. Scrumble siRNA did not inhibit gLDL‐induced monocyte adhesion on EC. Increased monocyte adhesion on aorta was detected in leptin receptor‐deficient (db/db) diabetic mice. The results of the present study suggest that diabetes‐associated metabolic disorders may promote the adhesion of monocytes to vascular endothelium, and PAI‐1 is required for glyLDL‐induced monocyte adhesion to vascular EC (supported by CDA and CIHR).
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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 | 0.001 |
| 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".