Role of RNF213 in Guiding Treatment of Moyamoya Disease with Unusual Phenotypic Presentation
Bibliographic record
Abstract
BACKGROUND: Moyamoya disease (MMD) is characterized by progressive carotid fork steno-occlusion and the development of "puff-of-smoke" collaterals on angiography. However, a subset of patients present with similar vascular changes but lack these hallmark collaterals, complicating both diagnosis and management. This "smokeless" phenotype, associated with ring finger protein 213 (RNF213) gene variants, challenges the traditional description of MMD. We describe a series of such patients who responded favorably to revascularization. METHODS: In this ambispective observational study, we evaluated 12 patients with carotid fork steno-occlusive disease but without "puff-of-smoke" collaterals. Clinical, radiological and genetic assessments were assessed. Structural modeling of RNF213 protein variants was conducted through 3D homology modeling, validated via Ramachandran plots and further refined with COOT and PyMOL. Functional insights were derived through ConSurf analysis. RESULTS: Of the 12 patients, 9 carried the RNF213 p.R4810K variant, 1 harboured a novel variant, 1 had both p.R4810K and a novel variant and 1 had p.R4859K. Initial misclassification as intracranial atherosclerosis or vasculitis led to inappropriate treatment. Following genetic confirmation, 9 patients underwent revascularization, with no stroke recurrence and a favorable clinical outcome. Structural modeling revealed minimal functional impact for the Val1529Met variant, whereas other variants significantly disrupted RNF213 stability and functionality. CONCLUSIONS: "Smokeless moyamoya," characterized by carotid fork steno-occlusion without typical angiographic collaterals, represents a distinct clinical phenotype responsive to revascularization. RNF213 genetic screening enhances diagnostic precision, reshaping traditional paradigms and supporting tailored therapeutic approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".