Evolving Strategies in Manipulating VEGF/VEGFR Signaling for the Promotion of Angiogenesis in Ischemic Muscle
Bibliographic record
Abstract
Peripheral artery disease is characterized by reduced blood flow to the lower limb, resulting in chronic ischemia in these muscles, which can lead to eventual amputation of the affected limb. Stimulation of angiogenesis in the ischemic region would be of therapeutic benefit; however, attempts to increase angiogenesis through delivery of vascular endothelial growth factor (VEGF) largely have been unsuccessful. Recent studies have shown that VEGF signaling through its receptors, VEGFR1 and VEGFR2, is much more complex than previously appreciated. This review will examine current research into the function of VEGFR1 and -2 signaling pathways, and evidence of cross-talk between these two receptors. The potential impact of endothelial cell co-stimulation via other growth factors/cell surface receptors (such as angiopoietins and ephrins) on angiogenesis also will be discussed. Evidence suggesting deficiencies in VEGF pathway signaling in individuals with chronic ischemia and diabetes will be discussed. Numerous pro-angiogenic therapies for ischemia have been employed. The successes and limitations of these therapies will be illustrated, emphasizing more recent angiogenesis therapies that focus on activating co-ordinated patterns of pro-angiogenic genes as the most promising direction in the treatment of ischemic muscle tissue in peripheral artery disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".