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 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 | 0.001 |
| 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".