From bench to bedside: murine models of inherited and sporadic brain arteriovenous malformations
Bibliographic record
Abstract
Brain arteriovenous malformations are abnormal vascular structures in which an artery shunts high pressure blood directly to a vein without an intervening capillary bed. These lesions become highly remodeled over time and are prone to rupture. Historically, brain arteriovenous malformations have been challenging to treat, using primarily surgical approaches. Over the past few decades, the genetic causes of these malformations have been uncovered. These can be divided into (1) familial forms, such as loss of function mutations in TGF-β (BMP9/10) components in hereditary hemorrhagic telangiectasia, or (2) sporadic forms, resulting from somatic gain of function mutations in genes involved in the RAS-MAPK signaling pathway. Leveraging these genetic discoveries, preclinical mouse models have been developed to uncover the mechanisms underlying abnormal vessel formation, and thus revealing potential therapeutic targets. Impressively, initial preclinical studies suggest that pharmacological treatments disrupting these aberrant pathways may ameliorate the abnormal pathologic vessel remodeling and inflammatory and hemorrhagic nature of these high-flow vascular anomalies. Intriguingly, these studies also suggest uncontrolled angiogenic signaling may be a major driver in bAVM pathogenesis. This comprehensive review describes the genetics underlying both inherited and sporadic bAVM and details the state of the field regarding murine models of bAVM, highlighting emerging therapeutic targets that may transform our approach to treating these devastating 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.002 |
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".