Therapeutic use of eNOS/Caveolin‐1 antagonistic peptides for endothelial dysfunction and atherogenesis
Bibliographic record
Abstract
Rationale Endothelial dysfunction, which is characterized by a reduction in nitric oxide (NO) bioavailability, is an early pro‐atherogenic step. Endothelial nitric oxide synthase (eNOS), the enzyme responsible for the constitutive production of NO, is negatively regulated by its association to Caveolin‐1 (Cav‐1), the major coat protein of caveolae . Whether antagonizing the eNOS/Cav‐1 interaction is an anti‐atherosclerotic drug target is unknown. Objective We have recently shown that a mutant Cav‐1 derived peptide (CavNoxin) can increase NO release by antagonizing eNOS binding to Cav‐1 (Bernatchez, Sharma et al., JCI 2011). Since upregulating NO production is therapeutically relevant in atherosclerosis we hypothesized that CavNoxin can attenuate atherosclerotic progression in vivo . Methods & Results ApoE knockout mice were placed on a high fat diet for 12 weeks and were injected with either CavNoxin (2.5mg/kg) or vehicle peptide every 3 days. At the end of the 12 weeks, atherosclerotic lesions were analysed. CavNoxin treatment attenuated atherosclerotic lesions in the aorta and aortic sinuses by 42% and 21% respectively, as compared to vehicle controls. In addition, Cavnoxin reduced VCAM‐1 (adhesion molecule) expression and oxidative stress (measured by DHE and nitrotyrosine staining) in vivo. Conclusion These data are the first to show an effect of the eNOS/Cav‐1 antagonism in atherosclerosis.
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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".