Linoleic Acid Increases Lectin-Like Oxidized LDL Receptor-1 (LOX-1) Expression in Human Aortic Endothelial Cells
Bibliographic record
Abstract
Results from in vitro studies suggest that selected fatty acids, and especially linoleic acid (LA), can elicit endothelial dysfunction (ED). Because LA is increased in all LDL subfractions in patients with type 2 diabetes, this alteration may contribute to ED associated with diabetes. Lectin-like oxidized LDL receptor-1 (LOX-1) is the major endothelial receptor for oxidized LDL (oxLDL), and uptake of oxLDL through LOX-1 induces ED. To evaluate whether LA may contribute to the upregulation of endothelial LOX-1 in diabetes, we studied the effect of LA on LOX-1 expression in cultured human aortic endothelial cells (HAECs). Treatment of HAECs with LA increased, in a time- and dose-dependent manner, endothelial LOX-1 protein expression. Pretreatment of HAECs with antioxidants and inhibitors of NADPH oxidase, protein kinase C (PKC), and nuclear factor-kappaB (NF-kappaB) inhibited the stimulatory effect of LA on LOX-1 protein expression. Furthermore, in LA-treated HAECs, increased expression of classic PKC isoforms was observed. LA also led to a significant increase in LOX-1 gene expression and enhanced the binding of nuclear proteins extracted from HAECs to the NF-kappaB regulatory element of the LOX-1 gene promoter. Finally, LA enhanced, through LOX-1, oxLDL uptake by endothelial cells. Overall, these results demonstrate that LA enhances endothelial LOX-1 expression through oxidative stress-sensitive and PKC-dependent pathways. This effect seems to be exerted at the transcriptional level and to involve the activation of NF-kappaB. Upregulation of LOX-1 by LA may contribute to ED associated with type 2 diabetes.
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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 | 0.001 |
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".