Genotypic and phenotypic spectrum of infantile liver failure due to pathogenic TRMU variants
Bibliographic record
Abstract
PURPOSE: This study aimed to define the genotypic and phenotypic spectrum of reversible acute liver failure (ALF) of infancy resulting from biallelic pathogenic TRMU variants and determine the role of cysteine supplementation in its treatment. METHODS: Individuals with biallelic (likely) pathogenic variants in TRMU were studied within an international retrospective collection of de-identified patient data. RESULTS: In 62 individuals, including 30 previously unreported cases, we described 47 (likely) pathogenic TRMU variants, of which 17 were novel, and 1 intragenic deletion. Of these 62 individuals, 42 were alive at a median age of 6.8 (0.6-22) years after a median follow-up of 3.6 (0.1-22) years. The most frequent finding, occurring in all but 2 individuals, was liver involvement. ALF occurred only in the first year of life and was reported in 43 of 62 individuals; 11 of whom received liver transplantation. Loss-of-function TRMU variants were associated with poor survival. Supplementation with at least 1 cysteine source, typically N-acetylcysteine, improved survival significantly. Neurodevelopmental delay was observed in 11 individuals and persisted in 4 of the survivors, but we were unable to determine whether this was a primary or a secondary consequence of TRMU deficiency. CONCLUSION: In most patients, TRMU-associated ALF was a transient, reversible disease and cysteine supplementation improved survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".