Anti-angiogenic therapeutic strategies in hereditary hemorrhagic telangiectasia
Bibliographic record
Abstract
Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant vascular dysplastic disorder, characterized by recurrent nosebleeds (epistaxis), multiple telangiectases and arteriovenous malformations (AVMs) in major organs. Mutations in Endoglin (ENG or CD105) and Activin receptor-like kinase 1 (ACVRL1 or ALK1) genes of the TGF-β superfamily receptors are responsible for HHT1 and HHT2 respectively and account for the majority of HHT cases. Haploinsufficiency in ENG and ALK1 is recognized at the underlying cause of HHT. However, the mechanisms responsible for the predisposition to and generation of AVMs, the hallmark of this disease, are poorly understood. Recent data suggest that dysregulated angiogenesis contributes to the pathogenesis of HHT and that the vascular endothelial growth factor, VEGF, may be implicated in this disease, by modulating the angiogenic-angiostatic balance in the affected tissues. Hence, anti-angiogenic therapies that target the abnormal vessels and restore the angiogenic-angiostatic balance are candidates for treatment of HHT. Here we review the experimental evidence for dysregulated angiogenesis in HHT, the anti-angiogenic therapeutic strategies used in animal models and some patients with HHT and the potential benefit of the anti-angiogenic treatment for ameliorating this severe, progressive vascular disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".