Assessment of the Relation Between SNP in MxA Gene and the Responsiveness of Egyptian HCV Genotype 4 Patients to Pegylated Interferon and Ribavirin Treatment
Bibliographic record
Abstract
BACKGROUND: Pegylated interferon (PegIFN) is used in the treatment of chronic hepatitis C virus (HCV) patients especially in resource limited countries. Treatment with PegIFN stimulates the expression of a number of host genes encoding enzymes with antiviral activities, including myxovirus resistance gene-A (MxA gene). MxA gene was found to have a single nucleotide polymorphism (SNP) at position -88 in the promoter region that affects the expression of MxA gene protein and was suggested to affect the treatment outcome. The aim of the work was to assess the relation between the SNP in the MxA gene and its impact on treatment of chronic HCV patients with PegIFN and ribavirin. METHODS: We therefore genotyped the biallelic G/T SNP in the promoter region of MxA gene at position -88 from the transcription start site by restriction fragment length polymorphism (RFLP) in 70 chronic HCV genotype 4 interferon naive Egyptians and 40 healthy controls. RESULTS: G allele was the prevalent one in both HCV patients group (105, 74.5%) and control group (66, 82.5%), while T allele was less expressed in patients group (36, 25.5%) and control group (14, 17.5%). There is no correlation between genotypes and response to IFN-alpha therapy: GG (OR: 0.958, 95% CI: 0.541 - 1.698, P = 0.884), GT (OR: 0.667, 95% CI: 0.188 - 2.362, P = 0.530), and TT (OR: 0.300, 95% CI: 0.083 - 1.090, P = 0.067). CONCLUSION: MxA nt-88 SNP did not affect the sustained virological response (SVR) rates after PegIFN and ribavirin combined treatment and did not act as a biological marker to potentially identify responders and non-responders to treatment. Our results call for additional large studies and/or meta-analysis of all currently available data to examine the role of MxA nt-88 SNP in predicting response to PegIFN and ribavirin in patients with IFN-alpha naive HCV genotype 4.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".