Glucose Enhances Endothelial LOX-1 Expression
Bibliographic record
Abstract
Endothelial dysfunction is an early and key determinant of diabetic vascular complications that is elicited at least in part by oxidized LDL (oxLDL). The recent observation that lectin-like oxLDL receptor-1 (LOX-1) expression is increased in the vascular endothelium of diabetic rats suggests a role for LOX-1 in the pathogenesis of diabetic vascular dysfunction. Because postprandial plasma glucose has been recently proposed as an independent risk factor for cardiovascular diseases in patients with diabetes, we evaluated, in the current study, the in vitro effect of high glucose on LOX-1 expression by human aortic endothelial cells (HAECs) and the role of this receptor in glucose-induced human monocyte adhesion to endothelium. Exposure of HAECs to high D-glucose concentrations (5.6-30 mmol/l) enhanced, in a dose- and time-dependent manner, LOX-1 expression, both at the gene and protein levels. The stimulatory effect of glucose on LOX-1 gene expression in HAECs was abolished by antioxidants and inhibitors of nuclear factor (NF)-kappaB, protein kinase C (PKC), and mitogen-activated protein kinases (MAPKs). Electrophoretic mobility shift assay data demonstrated that high glucose enhanced, in HAECs, the nuclear protein binding to the NF-kappaB regulatory element of the LOX-1 gene. Finally, our results showed that incubation of HAECs with high glucose increased human monocyte adhesion to endothelium through a LOX-1-dependent signaling mechanism. Overall, these results demonstrate that high glucose induces endothelial LOX-1 expression. This effect appears to be exerted at the transcriptional level through increased oxidant stress and NF-kappaB, PKC, and MAPK activation. The study also suggests a role for LOX-1 as mediator of the stimulatory effect of high glucose on monocyte adhesion.
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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".