The protease inhibitor E64d improves ox-LDL-induced endothelial dysfunction in human aortic endothelial cells
Bibliographic record
Abstract
Oxidized low-density lipoprotein (ox-LDL)-induced endothelial dysfunction in human vascular endothelial cells contributes to the development of atherosclerosis. E64d, a cysteine protease inhibitor, blocks the elastolytic activity of cathepsin essential for vascular matrix remodeling and reduces neurovascular endothelial apoptosis. The objective of this study was to investigate the effects and the underling mechanisms of E64d on ox-LDL-induced endothelial dysfunction in human aortic endothelial cells (HAECs). HAECs were treated with various concentrations of ox-LDL (0-200 mg/L) for 24 h with or without E64d. The results showed that E64d attenuated ox-LDL-induced increase in soluble intercellular adhesion molecule-1 (sICAM-1) concentration and reduction in endothelial nitric oxide synthase (eNOS) expression, prevented ox-LDL-induced reduction in cell viability and migration ability of HAECs. E64d decreased the protein expression of cathepsin B (CTSB), Beclin 1, and microtubule-associated protein light chain 3 (LC3)-II, but not p62. LC3 puncta and autophagosome formation were also reduced by E64d in HAECs. Moreover, E64d decreased the production of MDA and increased the activity of SOD. The results showed that E64d ameliorated ox-LDL-induced endothelial dysfunction in HAECs.
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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) | 0.001 | 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) | 0.001 | 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".