Effects of Aging and Hypoxia-Inducible Factor-1 Activity on Angiogenic Cell Mobilization and Recovery of Perfusion After Limb Ischemia
Bibliographic record
Abstract
Ischemia is a stimulus for production of angiogenic cytokines that activate local vascular cells and mobilize angiogenic cells to the circulation. These responses are impaired in elderly patients with peripheral arterial disease. Hypoxia-inducible factor (HIF)-1 mediates adaptive responses to ischemia, including production of angiogenic cytokines. In this study, we demonstrate that aging and HIF-1 loss-of-function impair the expression of multiple angiogenic cytokines, mobilization of angiogenic cells, maintenance of tissue viability, and recovery of limb perfusion following femoral artery ligation. We show that HIF-1 directly activates transcription of the gene encoding stem cell factor and that mice lacking the cognate receptor C-KIT have impaired recovery from ischemia. Administration of AdCA5, an adenovirus encoding a constitutively active form of HIF-1alpha, improved the recovery of perfusion in older mice to levels similar to those in young mice. Injection of AdCA5 into nonischemic limb was sufficient to increase the number of circulating angiogenic cells. These results indicate that HIF-1 activity is necessary and sufficient for the mobilization of angiogenic cells and that HIF-1alpha gene therapy can counteract the pathological effects of aging in a mouse model of limb ischemia.
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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) | 0.000 | 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) | 0.001 | 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".