VEGFR2 regulates p38 but not ERK1/2 in response to shear stress
Bibliographic record
Abstract
Increased capillary shear stress in skeletal muscle causes luminal splitting. Signaling mechanisms initiating this form of angiogenesis are not known. We hypothesize that shear stress‐dependent activation of vascular endothelial growth factor receptor 2 (VEGFR2) causes p38 and ERK1/2 phosphorylation. Skeletal muscle endothelial cells were sheared (12 dynes/cm 2 , 2 or 6 hrs) in the presence or absence of 10 μM VEGFR2 kinase inhibitor I or 30 μM LNNA (nitric oxide synthase inhibitor) then lysed. Cells were treated with 20 ng/ml vascular endothelial growth factor 165 (VEGF) for 30 minutes then lysed. Male Sprague‐Dawley rats were administered prazosin (50 mg/L drinking water) to increase shear stress, the extensor digitorum longus was extracted at 2, 4 and 7 days. Lysates were analyzed by Western blot. In vitro, p38 and ERK1/2 phosphorylation increased at 2 hrs of shear stress but only p38 remained phosphorylated at 6 hrs. Sustained phosphorylation of p38 was not blocked by LNNA. VEGFR2 inhibition abrogated p38 but not ERK1/2 phosphorylation. Similarly, VEGF treatment of static cultures increased phosphorylation of p38 but not ERK1/2. In vivo, p38 phosphorylation was increased significantly in muscles exposed to increased shear stress for 7 days. Our data imply that VEGFR2 is a shear stress sensitive receptor necessary for p38 phosphorylation, and it may play a role in luminal splitting. Supported 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".