Effect of estrogen on the blood supply of pituitary autografts in rats
Bibliographic record
Abstract
Estrogens are known to cause pituitary enlargement and lactotroph proliferation. They also modulate pituitary angiogenesis and induce tumor formation. Pituitary grafts, due to the loss of hypothalamic dopamine, also show lactotroph hyperplasia. We investigated the role of estrogen on rat pituitary autograft vascularization by light and transmission electron microscopy, and assessed prolactin (PRL) blood levels, microvessel density (MVD) and cell proliferation using the BrdU labeling index. All adenohypophysial cell types were identified by immunohistochemistry (streptavidin-biotin-peroxidase complex method). The proangiogenic factors, vascular endothelial growth factor (VEGF), its receptor Flk-1, and hypoxia inducible factor-1alpha (HIF-1alpha) were similarly demonstrated. The prevalence of lactotrophs, as well as more intense staining for VEGF, Flk-1 and HIF-1alpha, was noted in those grafts exposed to estrogen, mainly in the area surrounding the central necrotic core. Immunostaining showed Flk-1 expression increased in endothelial cells of the estrogen-exposed grafts as compared with those unexposed. In contrast to the grafts not exposed to estrogen, in the estrogen-exposed grafts, only fenestrated endothelium could be demonstrated, suggesting that estrogen induces fenestration of newly formed capillaries. There was an increase in blood PRL levels in the estrogen-treated groups as compared with controls. Both MVD and BrdU labeling indices were higher in grafts exposed to estrogen, especially after 4 weeks. Our results suggest that estrogen administration not only enhances the expression of proangiogenic factors in the pituitary grafts but also induces their expression at earlier stages, leading to rapid neoformation of purely fenestrated capillaries.
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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) | 0.001 | 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".