Critical role of HIF1α and HIF2α in stretch‐induced angiogenesis
Bibliographic record
Abstract
Hypoxia Inducible Factors, HIF1α and HIF2α are transcription factors responsible for upregulation of many genes involved in angiogenesis. Previously we demonstrated increased HIF1,2α mRNA levels in response to muscle stretch. We hypothesize that HIF1,2α are critical for muscle stretch induced angiogenesis and reduction of HIF levels will attenuate this response. A geldanamycin‐derived HSP90 inhibitor, 17‐DMAG, was used to destabilize HIF protein. 17‐DMAG efficacy in endothelial cells was confirmed as exposure to 17‐DMAG (3μM) significantly reduced the cobalt‐stimulated levels on HIF1, 2α and VEGF protein as assessed by western blotting. To stimulate stretch induced angiogenesis in vivo , rat extensor digitorium longus (EDL) muscles were overloaded through removal of the tibialis anterior. EDL were treated with 17‐DMAG (12.5mg/ml) or vehicle continuously via osmotic pump with 17‐DMAG‐treated muscles having decreased HIF1, 2α protein. Capillary to fibre (C:F) ratio was 1.47±0.04 and 1.36±0.06 for vehicle and 17‐DMAG treated unstretched muscles, respectively. After 14 days of overload, C:F was increased to 1.93±0.10 in vehicle treated but this increase was attenuated significantly in overload + 17‐DMAG muscles (1.36±0.05). These results indicate a significant role for HIFs in stretch‐induced angiogenesis distinct from their well defined roles in hypoxia stimulated angiogenesis. Funding by CIHR.
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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".