Association of interferon regulatory factor ‐7 gene polymorphism with liver cirrhosis in chronic hepatitis C patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Interferon (IFN) regulatory factor 7 (IRF-7) has been shown to play an essential role in the transcriptional activation of virus-inducible cellular genes, especially IFN genes. Polymorphisms of the IRF-7 gene may probably affect both the quality and the quantity of IRF-7. We investigated the role of IRF-7 polymorphisms in Japanese patients with chronic hepatitis C virus (HCV) infection. METHODS: We studied a total of nine polymorphisms of the IRF-7 gene including SNP1047A/G (Lys/Glu) and SNP2157A/G (Gln/Arg) using the Taqman allelic discrimination and sequencing techniques in 406 Japanese patients with chronic HCV infection. We further performed functional analysis of SNP1047 and SNP2157 by transcriptional activation of the IFNA promoter. RESULTS: We found that SNP1047AG and SNP2157AG genotypes were in complete linkage disequilibrium and were present in a significantly higher proportion in HCV-infected patients with cirrhosis (5.6%) than in those without cirrhosis (1.7%) (P=0.03). Multivariate analysis also revealed that SNP1047 and SNP2157 were independently associated with cirrhosis at an odds ratio of 2.5. Functional analysis revealed that SNP1047G and SNP2157G alleles increased IFNA expression. CONCLUSION: SNP1047AG and SNP2157AG genotypes were strongly associated with cirrhosis. SNP1047G and SNP2157G alleles might be used as markers of host factors associated with a higher risk of cirrhosis in Japanese patients with chronic HCV infection.
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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 | 0.001 | 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) | 0.002 | 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".