Upregulation of Hypoxia-Inducible Factor (HIF)-1α and HIF-2α in Leptomeningeal Vascular Malformations of Sturge-Weber Syndrome
Bibliographic record
Abstract
Cutaneous and leptomeningeal vascular malformations are hallmarks of the Sturge-Weber Syndrome (SWS), resulting in chronic ischemic tissue damage. The mechanisms underlying the pathobiology of these progressive lesions are unknown. Aberrant expression of angiogenic factors has been implicated in the genesis and maintenance of vascular malformations. To assess the role of angiogenesis in SWS vascular lesions we determined the expression of key angiogenic factors by immunohistochemistry and in situ hybridization in 8 SWS patients (age: 8 months to 18 years). We observed increased expression of vascular endothelial growth factor (VEGF), its cognate receptors VEGFR-1, VEGFR-2, and neuropilin (NP)-1 as well as Tie2 in leptomeningeal SWS blood vessels. Intriguingly, these factors are known to be transcriptionally induced by hypoxia-inducible factor (HIF). The HIF system has emerged as the key regulatory system of responses to hypoxia. Immunohistochemical analysis demonstrated markedly elevated nuclear HIF-1alpha and HIF-2alpha protein levels in SWS vessels. Concomitantly, SWS vessels revealed signs of enhanced endothelial cell (EC) turnover as evidenced by increased EC proliferation and apoptosis. Thus, in terms of angiogenesis, vascular malformations in SWS are not static lesions but constitute dynamic structures. Our observation of a dysregulated HIF-alpha expression in SWS vessels are in agreement with recent findings that EC-specific HIF activation provides a setting which supports and sustains angiogenesis and could be of potential use for developing therapeutic strategies to treat these currently incurable lesions.
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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 | 0.000 |
| 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".