Type 2 autoimmune hepatitis: Genetic susceptibility
Bibliographic record
Abstract
Two types of autoimmune hepatitis (AIH) are recognized; AIH-1 is characterized by the presence of anti-nuclear and/or anti-smooth muscle autoantibodies, while AIH-2 is associated with the presence of anti-Liver kidney microsome and/or anti-Liver Cytosol antibodies. The autoantigens targeted by AIH-2 autoantibodies are the cytochrome P450 2D6 and Formiminotransferase-cyclodeaminase for anti-LKM1 and anti-LC1 respectively. Both autoantigens are expressed in hepatocytes at higher levels than in any other cell type. Therefore, compared to AIH-1, the autoantigens targeted in AIH-2 are predominantly tissue-specific. Distinct clinical features are specific to AIH-2 compared to AIH-1, including diagnosis in younger patients (mean age 6.6 years), onset as fulminant hepatitis in very young patients (3 years of age or less), higher frequency in children than in adults and is frequently associated with extrahepatic T cell-mediated autoimmune diseases. AIH-2 is also often diagnosed in patients with primary immunodeficiency. AIH-2 is associated with specific HLA class II susceptibility alleles; DQB1*0201 is considered the main determinant of susceptibility while DRB1*07/DRB1*03 is associated with the type of autoantibody present. HLA DQB1*0201 is in strong linkage disequilibrium with both HLA DRB1*03 and DRB1*07. Interestingly, as in humans, MHC and non-MHC genes strongly influence the development of the disease in an animal model of AIH-2. Altogether, these findings suggest that AIH-2 incidence is likely dependent on specific genetic susceptibility factors combined with distinct environmental triggers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".