Haplotype Analysis of Hemochromatosis Gene Polymorphisms in Chronic Hepatitis C Virus Infection: A Case Control Study
Bibliographic record
Abstract
BACKGROUND: Chronic hepatitis C virus (HCV) infection is frequently associated with elevated serum iron markers. Polymorphisms in the hemochromatosis (HFE) genes are responsible for iron accumulation in most cases of hemochromatosis, and may play a role in HCV infection. OBJECTIVES: We aimed to assess the prevalence of HFE gene polymorphisms in a group of Iranian HCV-infected patients, and to explore the association of these polymorphisms with HCV infection. PATIENTS AND METHODS: HFE gene polymorphisms were examined in a total of 69 HCV patients and 69 healthy controls using polymerase chain reaction and restriction fragment length polymorphism techniques. Haplotype and diplotype analyses were performed using PHASE software. RESULTS: In a recessive analysis model of the His63Asp (H63D) locus (HH vs. HD + DD), the HH genotype was more common in patients compared to controls (adjusted P = 0.012; OR = 6.42 [95% CI: 1.51 - 27.33]). Also, in a recessive analysis model of the Cys282Tyr (C282Y) locus (CC vs. CY + YY), the CC genotype was more frequent in patients compared to controls (adjusted P = 0.03; OR = 5.06 [95% CI: 1.13 - 22.06]). In addition, there was a significant association between the HC haplotype and the HCDC diplotype and HCV infection. CONCLUSIONS: Polymorphism in the hemochromatosis gene may confer some degree of risk for HCV infection, and individuals carrying the H and C alleles may be susceptible to this disease; however, a larger sample of HCV patients and healthy individuals may be necessary to further illustrate the role of these polymorphisms in HCV.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".