Activation of endogenous type I IFN signaling contributes to persistent HCV infection
Bibliographic record
Abstract
HCV infection is a major world health problem, leading to both end-stage liver disease and primary liver cancer. Great efforts have been made in developing new therapies for HCV infection; however, combination therapy with pegylated IFN-α and ribavirin (pegIFN-RBV) remains the first choice of treatment for chronic HCV infection in most countries. The treatment response to pegIFN-RBV remains relatively low. Understanding the molecular mechanisms of persistent HCV infection and pegIFN-RBV resistance will suggest ways of improving the current standard of care and offers new antiviral therapies for both HCV and other viral infections. Recent data suggest that increased expression of hepatic IFN-stimulated genes (ISGs) before treatment is associated with treatment nonresponse in patients chronically infected with HCV. Although ISGs are generally antiviral in nature, in the case of HCV, the virus may exploit some of them to its benefit. This is not unique to HCV: Blockade of type I IFN signaling has been shown to control persistent LCMV infection. Thus, in certain viral infections, preactivation or overactivation of type I IFN signaling may contribute to viral persistence. In this review, we briefly summarize the findings from high-throughput gene expression profiling from patients chronically infected with HCV, then focus on a novel ubiquitin-like signaling pathway (ISG15/USP18) and its potential role in HCV persistence. Finally, the role of activation of endogenous type I IFN signaling in persistent HCV infection will be discussed in the context of recent studies indicating that blocking IFN signaling controls persistent LCMV 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 | 0.000 |
| 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".