Therapeutic Angiogenesis in Diabetes and Hypercholesterolemia: Influence of Oxidative Stress
Bibliographic record
Abstract
Despite significant improvements in the medical, percutaneous, and surgical management, numerous patients are first seen with non-revascularizable coronary artery disease (CAD). The growth of new blood vessels to improve myocardial perfusion (i.e., therapeutic angiogenesis) is an attractive treatment option for these patients. However, the successes of angiogenic therapy, observed in preclinical studies, have not been realized in clinical trials. Increasing evidence suggests that this discrepancy between animal and human studies may be due to the nature of the substrate, or the molecular and cellular environment within which the angiogenic agent acts. Antiangiogenic influences, including endothelial dysfunction, hypercholesterolemia, and diabetes, are present in virtually all patients with advanced CAD. Recent studies have better characterized the abnormalities associated with these disease states, providing novel targets for intervention. These substrate-modifying interventions can potentially enhance the response to protein-, gene-, or cell-based angiogenic therapy. In this review, we discuss key aspects of the angiogenic process and the pathophysiologic and molecular mechanisms that contribute to an impaired angiogenic response in the setting of endothelial dysfunction, hypercholesterolemia, and diabetes, with a focus on the role of oxidative stress. Last, we briefly explore substrate modifying agents that have been evaluated in preclinical and clinical studies to improve the angiogenic response.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".