Sildenafil Increases Endothelial Progenitor Cell Function and Improves Ischemia-Induced Neovascularization in Hypercholesterolemic Apolipoprotein E–Deficient Mice
Bibliographic record
Abstract
Hypercholesterolemia is associated with impaired neovascularization in response to ischemia. Potential mechanisms include defective NO bioactivity and a reduction in the number/function of endothelial progenitor cells (EPCs). Here we tested the hypothesis that sildenafil, a phosphodiesterase 5 inhibitor that increases NO-driven cGMP levels, could stimulate EPC function and improve ischemia-induced neovascularization in hypercholesterolemic conditions. Apolipoprotein E-deficient (ApoE(-/-)) mice were treated (or not treated) with sildenafil (40 mg/kg per day in water), and hindlimb ischemia was surgically induced by femoral artery removal. Sildenafil treatment led to an improved blood flow recovery, an increased capillary density, and a reduction of oxidative stress levels in ischemic muscles at day 7 after surgery. Sildenafil therapy is associated with an increased activation of angiogenic transduction pathways, including Akt, p44/42 mitogen-activated protein kinase, and p38. In vitro, sildenafil increases cellular migration and tubule formation of mature endothelial cells (human umbilical vascular endothelial cells) in a cGMP-dependent manner. In vivo, ApoE(-/-) mice treated with sildenafil exhibit a significant increase in the number of bone marrow-derived EPCs. Moreover, the angiogenic activities of EPCs (migration and adhesion) are significantly improved in ApoE(-/-) mice treated with sildenafil. In summary, this study demonstrates that sildenafil treatment is associated with improved ischemia-induced neovascularization in hypercholesterolemic ApoE(-/-) mice. The mechanisms involve beneficial effects on angiogenic transduction pathways together with an increase in the number and the functional activity of EPCs. Sildenafil could constitute a novel therapeutic strategy to reduce tissue ischemia in atherosclerotic diseases.
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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".