Intrinsic Viral Factors Are the Dominant Determinants of the Hepatitis C Virus Response to Interferon Alpha Treatment in Chimeric Mice
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus infection is a global health problem. New direct-acting antiviral agents have been recently approved. However, due to their high cost and some genotypes remaining difficult to treat, interferon-based therapy with pegylated interferon and ribavirin likely may remain a component of hepatitis C virus treatment for some patients. Unfortunately, pegylated interferon / ribavirin treatment achieved favorable outcomes in less than 50% of patients. Factors determining the outcome to pegylated interferon/ribavirin include both host and viral factors. It has been a major challenge to separate the host and viral factors in most in vivo systems. AIMS & METHODS: We used two hepatitis C virus strains from patients with different interferon-sensitivities and three hepatocyte donors, each with distinct interleukin 28B and interferon lambda 4 single nucleotide polymorphisms to investigate the contributions of viral and host factors to the response of hepatitis C virus to interferon treatment in chimeric mice. RESULTS AND CONCLUSIONS: We found that viral factors were the dominant factors in determining the interferon treatment outcomes in chimeric mice. Host factors, such as pre-treatment liver interferon-stimulated gene expression and single nucleotide polymorphisms near interleukin 28B and interferon lambda 4 coding regions, were less important determinants of the response to interferon in the chimeric mice than they were in patients. Our results also suggest that a complete immune system as seen in patients may be required for host factors such as single nucleotide polymorphisms near interleukin 28B/interferon lambda 4 and pre-treatment liver interferon-stimulated gene upregulation to have an effect on the interferon response.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".