p38 MAPK activity is stimulated by vascular endothelial growth factor receptor 2 activation and is essential for shear stress‐induced angiogenesis
Bibliographic record
Abstract
Increased capillary shear stress induces angiogenesis in skeletal muscle, but the signaling mechanisms underlying this response are not known. We hypothesize that shear stress-dependent activation of vascular endothelial growth factor receptor 2 (VEGFR2) causes p38 and ERK1/2 phosphorylation, which contribute to shear stress-induced angiogenesis. Skeletal muscle microvascular endothelial cells were sheared (12 dynes/cm(2), 0.5-24 h). VEGFR2-Y1214 phosphorylation increased in response to elevated shear stress and VEGF stimulation. p38 and ERK1/2 phosphorylation increased at 2 h of shear stress but only p38 remained phosphorylated at 6 and 24 h of shear stress. VEGFR2 inhibition abrogated p38, but not ERK1/2 phosphorylation. VEGF production was increased in response to shear stress at 6 h, and this increased production was abolished by p38 inhibition. Male Sprague-Dawley rats were administered prazosin (50 mg/L drinking water, 1, 2, 4, or 7 days) to induce chronically elevated capillary shear stress in skeletal muscle. In some experiments, mini-osmotic pumps were used to dispense p38 inhibitor SB203580 or its inactive analog SB202474, to the extensor digitorum longus (EDL) of control and prazosin-treated rats. Immunostaining and Western blotting showed increases in p38 phosphorylation in capillaries from rats treated with prazosin for 2 days but returned to basal levels at 4 and 7 days. p38 inhibition abolished the increase in capillary to muscle fiber ratio seen after 7 days of prazosin treatment. Our data suggest that p38 activation is necessary for shear stress-dependent angiogenesis.
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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 | 0.000 |
| 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) | 0.002 | 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".