Distinct and overlapping genomic profiles and antiviral effects of Interferon‐λ and ‐α on HCV‐infected and noninfected hepatoma cells
Bibliographic record
Abstract
Recently, several SNPs in the region of the IL28B (IFN-λ) gene have been associated with spontaneous clearance of hepatitis C virus (HCV) and enhanced cure rates for IFN-alfa-based therapies, suggesting a potential correlation between IFN-λ and the ability to clear HCV. To understand the mechanism of IFN-λ's as compared to IFN-α's antiviral activity, we performed a comprehensive analysis of their anti-HCV effects, whole genome transcriptome profiling with validation, and signalling of IFN-α and IFN-λ using J6/JFH-1 and Huh7.5 cells in vitro. IFN-λ and IFN-α exhibited comparable anti-HCV activity and gene expression profiles in Huh7.5 cells. While the majority of genes induced by IFN-α and IFN-λ were similar, IFN-λ exhibits profound, but delayed kinetics of IFN-stimulated genes (ISG) induction, while IFN-α induced more rapid induction of ISGs. Furthermore, the increased induction of ISG expression by IFN-λ correlated with up-regulation of IFN-λ receptor (IL-28RA) expression and more prolonged activation of the Jak-STAT signalling pathway. The findings from our comparative analysis of IFN-α and IFN-λ in HCV-infected and noninfected cells support the clinical use of IFN-λ as a potential alternative to IFN-α in the treatment of chronic hepatitis C.
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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 | 0.000 |
| 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) | 0.001 | 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".