VEGF‐E Attenuates Injury After Ischemic Stroke by Promoting Reparative Revascularization
Bibliographic record
Abstract
The angiogenic response after stroke correlates with mild injury and an improved recovery. Stimulation of post-stroke angiogenesis using vascular endothelial growth factor (VEGF)-A is associated with an increased risk of vascular destabilization, leading to life-threatening complications. The non-mammalian VEGF-A homolog, VEGF-E, stimulates stable cutaneous vascularization and promotes wound healing. Herein, we posit that VEGF-E represents a potential mediator of reparative revascularization after ischemic stroke. C57BL6/J wildtype mice were subjected to experimental stroke, and VEGF-E or VEGF-A were intranasally delivered during the subacute phase. Our results indicate that VEGF-E improves neurological recovery and increases vascular density without compromising permeability, more efficiently than VEGF-A. We show that VEGF-E-mediated revascularization correlates with normal restoration of brain perfusion, whereas VEGF-A induces cerebral hyperperfusion, indicative of vascular dysfunction. Furthermore, VEGF-E reduces microvascular stalls, increases the density of angiogenic vasculature, and improves the interaction of brain endothelial cell with pericytes, which is critical for vascular stabilization. Using cell-based assays, we demonstrate that stimulation of brain endothelial cells with VEGF-E, but not with VEGF-A, increases the expression of platelet-derived growth factor (PDGF)-D, a potent ligand of PDGFRβ that plays critical roles in regulating the survival and functions of perivascular cells, including pericytes. These effects are associated with activation of extracellular signal-regulated kinase (ERK)1/2 and P38 mitogen-activated protein kinase (MAPK). Finally, we confirm that the secretome of VEGF-E-stimulated brain endothelial cells ameliorates pericyte migration required for vascular recruitment. Our study indicates that VEGF-E promotes a stable and functional revascularization after ischemic stroke, outlining its promises for therapeutic purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".