Novel Antiinflammatory Vascular Benefits of Systemic and Stent-Based Delivery of Ethylisopropylamiloride
Bibliographic record
Abstract
BACKGROUND: Recently, we demonstrated that the amiloride derivative ethylisopropylamiloride (EIPA) limits vascular smooth muscle cell growth and migration. The purpose of the present experiments was to determine whether EIPA can also reduce the inflammatory component of atherogenesis and stent neointima formation. METHODS AND RESULTS: To determine the effect of EIPA on the early inflammatory stages of atherogenesis, apolipoprotein E null mice (apoE-/-) fed an atherogenic diet received a subcutaneous pump infusion of either EIPA (3 mg x kg(-1)d(-1)) or the control vehicle for 4 weeks. The en face aortic area of atherosclerotic lesions and the subendothelial accumulation of macrophages were reduced by 46% and 38%, respectively, in EIPA-treated mice. Moreover, the number of vascular cell adhesion molecule-1 (VCAM-1) immunopositive lumenal endothelial cells was 59% less in the EIPA treatment group. In vitro, there was a concentration-dependent inhibition of lipopolysaccharide (LPS)-induced VCAM-1 expression with a corresponding 37% reduction in U-937 cell adhesion to endothelial cells. EIPA also reduced LPS-stimulated nuclear factor-kappaB (NF-kappaB) activation as reflected by a 66% reduction in NF-kappaB nuclear translocation. Finally, to test the effect of EIPA on the early inflammatory reaction to stent implantation, stents coated with jelly alone or jelly plus EIPA were implanted into rabbit iliac arteries. Four weeks later, the stent neointimal area, abundance of peristrut macrophages, and density of intimal smooth muscle cells were reduced by 38%, 47%, and 37%, respectively, for EIPA stents. CONCLUSIONS: EIPA downregulates endothelial cell activation of NF-kappaB and VCAM-1 expression and attenuates the early inflammatory stages of atherogenesis and stent intimal formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".