Tyrosinemia type <scp>II</scp>: Mutation update, 11 novel mutations and description of 5 independent subjects with a novel founder mutation
Bibliographic record
Abstract
BACKGROUND: Tyrosinemia type II, also known as Richner-Hanhart Syndrome, is an extremely rare autosomal recessive disorder, caused by mutations in the gene encoding hepatic cytosolic tyrosine aminotransferase, leading to the accumulation of tyrosine and its metabolites which cause ocular and skin lesions, that may be accompanied by neurological manifestations, mostly intellectual disability. AIMS: To update disease-causing mutations and current clinical knowledge of the disease. MATERIALS AND METHODS: Genetic and clinical information were obtained from a collection of both unreported and previously reported cases. RESULTS: We report 106 families, represented by 143 individuals, carrying a total of 36 genetic variants, 11 of them not previously known to be associated with the disease. Variants include 3 large deletions, 21 non-synonymous and 5 nonsense amino-acid changes, 5 frameshifts and 2 splice variants. We also report 5 patients from Gran Canaria, representing the largest known group of unrelated families sharing the same P406L mutation. CONCLUSIONS: Data analysis did not reveal a genotype-phenotype correlation, but stressed the need of early diagnosis: All patients improved the oculocutaneous lesions after dietary treatment but neurological symptoms prevailed. The discovery of founder mutations in isolated populations, and the benefits of early intervention, should increase diagnostic awareness in newborns.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".