VEGF-E attenuates injury after ischemic stroke by promoting reparative neovascularization
Bibliographic record
Abstract
Post-stroke angiogenesis improves structural and functional recovery, outlining the promises of pro-angiogenic therapies. Unfortunately, vascular endothelial growth factor (VEGF)-A-mediated angiogenesis resulted in mitigated outcomes, as it significantly increases the risk of exacerbating injury via destabilization of the cerebrovascular network. VEGF-E, a non-mammalian VEGF-A homolog, has been reported to promote stable neovascularization upon skin injuries, and thus represents an interesting safe alternative to promote post-stroke angiogenesis. C57BL6/J wildtype mice were subjected to ischemic stroke using transient middle cerebral artery occlusion (MCAo), and recombinant VEGF-E was intranasally delivered throughout the subacute phase. Our results indicate that VEGF-E reduces neuronal loss and improves motor recovery after stroke. VEGF-E attenuates cerebrovascular permeability at the injury site and increases the density of mature CD31+ microvessels. Furthermore, we show that VEGF-E reduces the events of microvascular stalls and improves brain endothelial cell coverage by perivascular cells, required for cerebrovascular stability. VEGF-E increases the density of angiogenic active CD105+ microvessels, while improving the recruitment of CD13+ pericytes, outlining synergistic effects on microvessel formation and stabilization. Using cell-based assays, we demonstrate that VEGF-E activates key pro-survival pathways in brain endothelial cells exposed to ischemia/reperfusion-like conditions, namely extracellular signal-regulated kinase (ERK)1/2 and P38 mitogen-activated protein kinase (MAPK) while preserving the tight junctions. Importantly, we report that the secretome of VEGF-stimulated brain endothelial cells improves perivascular cell migration that is required to mediate the interaction with endothelial cells. Our study indicates that VEGF-E promotes a stable neovascularization after ischemic stroke, paving the way to develop new strategies for therapeutic 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) | 0.000 | 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".