Brain arteriovenous malformation in hereditary hemorrhagic telangiectasia: Recent advances in cellular and molecular mechanisms
Bibliographic record
Abstract
Hereditary hemorrhagic telangiectasia (HHT) is a genetic disorder characterized by vessel dilatation, such as telangiectasia in skin and mucosa and arteriovenous malformations (AVM) in internal organs such as the gastrointestinal tract, lungs, and brain. AVMs are fragile and tortuous vascular anomalies that directly connect arteries and veins, bypassing healthy capillaries. Mutations in transforming growth factor β (TGFβ) signaling pathway components, such as ENG (ENDOGLIN), ACVRL1 (ALK1), and SMAD4 (SMAD4) genes, account for most of HHT cases. 10–20% of HHT patients develop brain AVMs (bAVMs), which can lead to vessel wall rupture and intracranial hemorrhages. Though the main mutations are known, mechanisms leading to AVM formation are unclear, partially due to lack of animal models. Recent mouse models allowed significant advances in our understanding of AVMs. Endothelial-specific deletion of either Acvrl1, Eng or Smad4 is sufficient to induce AVMs, identifying endothelial cells (ECs) as primary targets of BMP signaling to promote vascular integrity. Loss of ALK1/ENG/SMAD4 signaling is associated with NOTCH signaling defects and abnormal arteriovenous EC differentiation. Moreover, cumulative evidence suggests that AVMs originate from venous ECs with defective flow-migration coupling and excessive proliferation. Mutant ECs show an increase of PI3K/AKT signaling and inhibitors of this signaling pathway rescue AVMs in HHT mouse models, revealing new therapeutic avenues. In this review, we will summarize recent advances and current knowledge of mechanisms controlling the pathogenesis of bAVMs, and discuss unresolved questions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".