Semaphorin3A elevates vascular permeability and contributes to cerebral ischemia-induced brain damage
Bibliographic record
Abstract
Semaphorin 3A (Sema3A) increased significantly in mouse brain following cerebral ischemia. However, the role of Sema3A in stroke brain remains unknown. Our aim was to determine wether Sema3A functions as a vascular permeability factor and contributes to ischemic brain damage. Recombinant Sema3A injected intradermally to mouse skin, or stereotactically into the cerebral cortex, caused dose- and time-dependent increases in vascular permeability, with a degree comparable to that caused by injection of a known vascular permeability factor vascular endothelial growth factor receptors (VEGF). Application of Sema3A to cultured endothelial cells caused disorganization of F-actin stress fibre bundles and increased endothelial monolayer permeability, confirming Sema3A as a permeability factor. Sema3A-mediated F-actin changes in endothelial cells were through binding to the neuropilin2/VEGFR1 receptor complex, which in turn directly activates Mical2, a F-actin modulator. Down-regulation of Mical2, using specific siRNA, alleviated Sema3A-induced F-actin disorganization, cellular morphology changes and endothelial permeability. Importantly, ablation of Sema3A expression, cerebrovascular permeability and brain damage were significantly reduced in response to transient middle cerebral artery occlusion (tMCAO) and in a mouse model of cerebral ischemia/haemorrhagic transformation. Together, these studies demonstrated that Sema3A is a key mediator of cerebrovascular permeability and contributes to brain damage caused by cerebral ischemia.
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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".