Muscle-derived vascular endothelial growth factor regulates microvascular remodelling in response to increased shear stress in mice
Bibliographic record
Abstract
AIM: The source of vascular endothelial growth factor-A (VEGF-A) may influence vascular function. Exercise-induced vascular growth has been attributed to elevated metabolic demand and to increased blood flow, involving the production of VEGF-A by skeletal muscle and by endothelial cells respectively. We hypothesized that muscle-derived VEGF-A is not required for vascular adaptations to blood flow in skeletal muscle, as this remodelling stimulus originates within the capillary. METHODS: Myocyte-specific VEGF-A (mVEGF(-/-) ) deleted mice were treated for 7-21 days with the vasodilator prazosin to produce a sustained increase in skeletal muscle blood flow. RESULTS: Capillary number increased in the extensor digitorum longus (EDL) muscle in response to prazosin in wild type but not mVEGF(-/-) mice. Prazosin increased the number of smooth muscle actin-positive blood vessels in the EDL of wild-type but not mVEGF(-/-) mice. The average size of smooth muscle actin-positive blood vessels also was smaller in knockout mice after prazosin treatment. In response to prazosin treatment, VEGF-A mRNA was elevated within the EDL of wild-type but not mVEGF(-/-) mice. Ex vivo incubation of wild-type EDL with a nitric oxide donor increased VEGF-A mRNA. Likewise, we demonstrated that nitric oxide donor treatment of cultured myoblasts stimulated an increase in VEGF-A mRNA and protein. CONCLUSION: These results suggest a link through which flow-mediated endothelial-derived signals may promote myocyte production of VEGF-A. In turn, myocyte-derived VEGF-A is required for appropriate flow-mediated microvascular remodelling. This highlights the importance of the local environment and paracrine interactions in the regulation of tissue perfusion.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".