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 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) | 0.001 | 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) | 0.001 | 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".