Differential involvement of MMP-2 and VEGF during muscle stretch- versus shear stress-induced angiogenesis
Bibliographic record
Abstract
Capillary growth in skeletal muscle occurs via the dissimilar processes of abluminal sprouting or longitudinal splitting, which can be initiated by muscle stretch and elevated shear stress, respectively. The distinct morphological hallmarks of these types of capillary growth suggest that discrete sets of angiogenic mediators play a role in each situation. Because proteolysis and proliferation are two key steps associated with capillary growth, we tested whether differences in the regulation of matrix metalloproteinases (MMPs) or VEGF may be associated with the two types of capillary growth. We found significant increases in MMP-2 total protein and percent activation, and membrane type-1 MMP mRNA levels, compared with controls after muscle stretch but not after shear stress stimulation. In contrast, VEGF protein and endothelial cell proliferation increased after either angiogenic stimulus. We observed that MMP-2 regulation occurs independent of VEGF signaling, because VEGF did not induce MMP-2 production or activation in isolated endothelial cells. Our data suggest that the involvement of MMPs in capillary growth is dependent on the nature of the angiogenic stimulus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".