Involvement of fibrinolytic regulators in adhesion of monocytes to vascular endothelial cells induced by glycated LDL and to aorta from diabetic mice
Bibliographic record
Abstract
Diabetes mellitus accelerates the development of atherosclerotic cardiovascular diseases. Monocyte adhesion is an early cellular event of atherogenesis. Elevated levels of glyLDL were common in diabetic patients. Our previous studies indicated that HSF1 and p22-phox (a subunit of the NOX complex) were involved in glyLDL-induced up-regulation of PAI-1 in vascular EC. The present study demonstrated that glyLDL significantly increased the adhesion of monocytes to the surface of cultured human umbilical vein or PAEC. Transfection of siRNA for PAI-1, p22-phox, or HSF1 in EC prevented glyLDL-induced monocyte adhesion to EC. uPA siRNA increased monocyte adhesion to EC. Exogenous uPA reduced monocyte adhesion induced by glyLDL or uPA siRNA. Exogenous PAI-1 restored monocyte adhesion to EC inhibited by PAI-1 siRNA or uPA. GlyLDL-induced monocyte adhesion to EC was inhibited by treatment of EC with RAP, an antagonist for LRP, and enhanced by uPAR antibody. The adhesion of monocytes to aorta from leptin db/db diabetic mice was significantly greater than to that from control mice, which was associated with elevated contents of PAI-1, uPA, p22-phox, and HSF1 in hearts of db/db mice. The results suggest that oxidative stress and fibrinolytic regulators (PAI-1, uPA, and uPAR) are implicated in the modulation of glyLDL-induced monocyte adhesion to vascular endothelium, which may play a crucial role in vascular inflammation under diabetes-associated metabolic disorder.
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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 | 0.001 |
| 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 | 0.001 |
| 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".