Potential role of ATF2 and MEF2C in shear stress‐mediated VEGF production
Bibliographic record
Abstract
Increased capillary shear stress in skeletal muscle causes a form of angiogenesis termed luminal splitting. An increased number of capillaries are observed at 7 days of chronic shear stress that is reliant on p38 signaling. Others have shown that VEGF is essential for luminal splitting. We hypothesize that VEGF production is p38 dependent and is increased by p38 dependent transcription factors Creb, ATF2 and MEF2C in response to shear stress. Male Sprague‐Dawley rats were administered prazosin (50 mg/L drinking water) to increase shear stress, and the extensor digitorum longus was extracted at 1, 2, 4 or 7 days. By Western blot, p38 was activated only at 2 days. Skeletal muscle endothelial cells were sheared (12 dynes/cm 2 ) with or without 10 μM SB203580 (p38 inhibitor), then lysed and analyzed by RT‐PCR or Western blot. In vitro, shear stress induced VEGF mRNA and protein elevation at 2 and 6 hours, respectively. These increases were abolished by SB203580. Shear stress increased both ATF2 phosphorylation and MEF2C production at 2 hours, which were abrogated by SB203580. Creb phosphorylation was unchanged by shear stress. Our data show that shear stress‐mediated p38 activation is required for VEGF production. ATF2 phosphorylation and MEF2C production are increased by shear stress, but further investigation is necessary to determine if they mediate p38‐induced VEGF production. Supported by HSF and NSERC. Grant Funding Source The Heart and Stroke Foundation
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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) | 0.002 | 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".