Thiopurine S‐methyltransferase gene polymorphism in Japanese patients with autoimmune liver diseases
Bibliographic record
Abstract
BACKGROUND AND AIM: Thiopurine S-methyltransferase (TPMT) genotypes or phenotypes may be a predictive factor for azathioprine-induced toxicities. We investigated the genotypic status of TPMT to evaluate the risk of azathioprine-related adverse effects in Japanese patients with different liver diseases, including autoimmune hepatitis (AIH). METHODS: 49 patients with AIH, 67 with primary biliary cirrhosis (PBC), and 120 with hepatitis C virus (HCV) were examined. TPMT genotypes were determined by PCR-restriction fragment length polymorphism-based assays. RESULTS: The distribution of TPMT genotypes was 90% TPMT*1/TPMT*1, 8% TPMT*1/TPMT*3C, and 2% TPMT*3C/TPMT*3C in AIH, and 94% TPMT*1/TPMT*1, 4.5% TPMT*1/TPMT*3C, and 1.5% TPMT*3C/TPMT*3C in PBC. All except 1 patient with HCV had the TPMT*1/TPMT*1 genotype. Severe myelosuppression occurred in two of nine patients with AIH who received azathioprine, one of whom was homozygous for TPMT*3C. CONCLUSIONS: TPMT*3C variants are more frequent in patients with AIH or PBC than in patients with viral hepatitis or healthy volunteers in Japan. Pharmacogenetic screening for TPMT polymorphisms before commencing azathioprine therapy may help to prevent severe hematotoxicity in patients with TPMT deficiency.
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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 | 0.000 | 0.001 |
| 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".