Alagille syndrome in a Vietnamese cohort: Mutation analysis and assessment of facial features
Bibliographic record
Abstract
Alagille syndrome (ALGS, OMIM #118450) is an autosomal dominant disorder that affects multiple organ systems including the liver, heart, eyes, vertebrae, and face. ALGS is caused by mutations in one of two genes in the Notch Signaling Pathway, Jagged1 (JAG1) or NOTCH2. In this study, analysis of 21 Vietnamese ALGS individuals led to the identification of 19 different mutations (18 JAG1 and 1 NOTCH2), 17 of which are novel, including the third reported NOTCH2 mutation in Alagille Syndrome. The spectrum of JAG1 mutations in the Vietnamese patients is similar to that previously reported, including nine frameshift, three missense, two splice site, one nonsense, two whole gene, and one partial gene deletion. The missense mutations are all likely to be disease causing, as two are loss of cysteines (C22R and C78G) and the third creates a cryptic splice site in exon 9 (G386R). No correlation between genotype and phenotype was observed. Assessment of clinical phenotype revealed that skeletal manifestations occur with a higher frequency than in previously reported Alagille cohorts. Facial features were difficult to assess and a Vietnamese pediatric gastroenterologist was only able to identify the facial phenotype in 61% of the cohort. To assess the agreement among North American dysmorphologists at detecting the presence of ALGS facial features in the Vietnamese patients, 37 clinical dysmorphologists evaluated a photographic panel of 20 Vietnamese children with and without ALGS. The dysmorphologists were unable to identify the individuals with ALGS in the majority of cases, suggesting that evaluation of facial features should not be used in the diagnosis of ALGS in this population. This is the first report of mutations and phenotypic spectrum of ALGS in a Vietnamese population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".