Differential Expression of Vascular Endothelial Growth Factor-A (VEGF-A) and VEGF-B After Brain Injury
Bibliographic record
Abstract
Our previous study demonstrated that vascular endothelial growth factor (VEGF), now referred to as VEGF-A, plays a significant role in blood-brain barrier (BBB) breakdown and angiogenesis after brain injury. In this study, VEGF-A expression was compared with that of VEGF-B in the rat cortical cold injury model over a period of 6 hours to 6 days post-injury. VEGF-A and VEGF-B mRNA were detected by in situ hybridization and their protein was detected by immunohistochemistry. The presence of VEGF-A and VEGF-B proteins in endothelium of lesion vessels was related to BBB breakdown by double labeling for either of these growth factors and fibronectin, which was used as a marker of BBB breakdown. Significant induction of both VEGF-A and VEGF-B mRNA occurred at the lesion site during the period of maximal endothelial proliferation. VEGF-A mRNA levels peaked at 3 and 4 days post-injury and returned to basal expression by day 6, while VEGF-B mRNA levels remained elevated up to day 6. VEGF-B protein was constitutively expressed in endothelium of all cerebral vessels. After brain injury, there was increased immunoreactivity for VEGF-B at the lesion site, this protein being present in the endothelium and vascular smooth muscle cells of pial vessels, in inflammatory cells, and later in proliferating endothelial cells, endothelium of neovessels, and astrocytes. Lesion vessels showing BBB breakdown to fibronectin showed endothelial VEGF-A protein but not VEGF-B protein. Constitutive expression of VEGF-B in normal endothelium suggests that it may have a role in maintenance of the BBB in steady states, while its induction at both the gene and protein level post-injury indicates that it has an essential role in angiogenesis and the repair processes after brain injury.
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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 | 0.001 | 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".