17β-Estradiol Increases Astrocytic Vascular Endothelial Growth Factor (VEGF) in Adult Female Rat Hippocampus
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) is critical to angiogenesis and vascular permeability. It is also important in the endocrine system, in which VEGF mediates the vascular effects of estrogens in target tissues such as the uterus, a response attributed to an estrogen response element on the VEGF gene. Here we asked whether 17β-estradiol increases VEGF levels in the brain. We focused on the hippocampus, in which 17β-estradiol and VEGF both have important actions, and used immunocytochemistry to evaluate VEGF protein. VEGF immunoreactivity was compared in adult female rats sampled during the estrous cycle when serum levels of 17β-estradiol peak (proestrous morning) as well as when they are low (metestrous morning). In addition, adult rats were ovariectomized and compared after treatment with 17β-estradiol or vehicle. The results demonstrated that VEGF immunoreactivity was increased when serum levels of 17β-estradiol were elevated. Confocal microscopy showed that VEGF immunofluorescence was predominantly nonneuronal, often associated with astrocytes. Glial VEGF labeling was primarily punctate rather than diffuse and labile because glial VEGF immunoreactivity was greatly reduced if tissue sections were left in an aqueous medium overnight. We conclude that VEGF protein in normal female hippocampus is primarily nonneuronal rather than neuronal and suggest that glial VEGF immunoreactivity has been underestimated by past studies with other methods because there is a labile extracellular pool. We suggest that estrogens may exert actions on female hippocampal structure and function by increasing hippocampal VEGF.
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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".