Clinical spectrum and genetic causes of mitochondrial hepatopathy phenotype in children
Bibliographic record
Abstract
BACKGROUND: Alterations in both mitochondrial DNA (mtDNA) and nuclear DNA genes affect mitochondria function, causing a range of liver-based conditions termed mitochondrial hepatopathies (MH), which are subcategorized as mtDNA depletion, RNA translation, mtDNA deletion, and enzymatic disorders. We aim to enhance the understanding of pathogenesis and natural history of MH. METHODS: We analyzed data from patients with MH phenotypes to identify genetic causes, characterize the spectrum of clinical presentation, and determine outcomes. RESULTS: Three enrollment phenotypes, that is, acute liver failure (ALF, n = 37), chronic liver disease (Chronic, n = 40), and post-liver transplant (n = 9), were analyzed. Patients with ALF were younger [median 0.8 y (range, 0.0, 9.4) vs 3.4 y (0.2, 18.6), p < 0.001] with fewer neurodevelopmental delays (40.0% vs 81.3%, p < 0.001) versus Chronic. Comprehensive testing was performed more often in Chronic than ALF (90.0% vs 43.2%); however, etiology was identified more often in ALF (81.3% vs 61.1%) with mtDNA depletion being most common (ALF: 77% vs Chronic: 41%). Of the sequenced cohort (n = 60), 63% had an identified mitochondrial disorder. Cluster analysis identified a subset without an underlying genetic etiology, despite comprehensive testing. Liver transplant-free survival was 40% at 2 years (ALF vs Chronic, 16% vs 65%, p < 0.001). Eighteen (21%) underwent transplantation. With 33 patient-years of follow-up after the transplant, 3 deaths were reported. CONCLUSIONS: Differences between ALF and Chronic MH phenotypes included age at diagnosis, systemic involvement, transplant-free survival, and genetic etiology, underscoring the need for ultra-rapid sequencing in the appropriate clinical setting. Cluster analysis revealed a group meeting enrollment criteria but without an identified genetic or enzymatic diagnosis, highlighting the need to identify other etiologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".