<i>NOTCH2</i> mutations in Alagille syndrome
Bibliographic record
Abstract
BACKGROUND: Alagille syndrome (ALGS) is a dominant, multisystem disorder caused by mutations in the Jagged1 (JAG1) ligand in 94% of patients, and in the NOTCH2 receptor in <1%. There are only two NOTCH2 families reported to date. This study hypothesised that additional NOTCH2 mutations would be present in patients with clinical features of ALGS without a JAG1 mutation. METHODS: The study screened a cohort of JAG1-negative individuals with clinical features suggestive or diagnostic of ALGS for NOTCH2 mutations. RESULTS: Eight individuals with novel NOTCH2 mutations (six missense, one splicing, and one non-sense mutation) were identified. Three of these patients met classic criteria for ALGS and five patients only had a subset of features. The mutations were distributed across the extracellular (N=5) and intracellular domains (N=3) of the protein. Functional analysis of four missense, one nonsense, and one splicing mutation demonstrated decreased Notch signalling of these proteins. Subjects with NOTCH2 mutations demonstrated highly variable expressivity of the affected systems, as with JAG1 individuals. Liver involvement was universal in NOTCH2 probands and they had a similar prevalence of ophthalmologic and renal anomalies to JAG1 patients. There was a trend towards less cardiac involvement in the NOTCH2 group (60% vs 100% in JAG1). NOTCH2 (+) probands exhibited a significantly decreased penetrance of vertebral abnormalities (10%) and facial features (20%) when compared to the JAG1 (+) cohort. CONCLUSIONS: This work confirms the importance of NOTCH2 as a second disease gene in ALGS and expands the repertoire of the NOTCH2 related disease phenotype.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".