Protease inhibitors partially overcome the interferon nonresponse phenotype in patients with chronic hepatitis C
Bibliographic record
Abstract
The outcome of triple therapy with protease inhibitors (PI) depends on the intrinsic response to interferon. Interferon-stimulated gene (ISG) expression differs by cell type in the liver and is a strong predictor of interferon responsiveness. Patients who respond well to interferon have low/absent ISG expression in hepatocytes but significant ISG expression in macrophages. Nonresponders (NRs) show the opposite pattern. We aimed to determine the association between cell-type-specific ISG staining and treatment outcome with PI-based triple therapy. Liver biopsy tissue from consecutive patients treated with boceprevir or telaprevir with peginterferon and ribavirin was stained for myxovirus A (MxA). Staining was scored 0-3 in macrophages (M-MxA) and hepatocytes (H-MxA), and IL28B genotyping was performed. Of 56 patients included 41 achieved SVR (73%) (sustained virological response), 2 (4%) relapsed, 10 (18%) were NRs, and 3 (5%) were lost to follow-up. Median M-MxA staining was stronger and H-MxA staining was weaker in patients who achieved SVR. MxA staining correlated with IL28B genotype and with the HCV RNA decline during lead-in phase. However, unlike with dual therapy, the negative predictive value (NPV) of absent or weak M-MxA staining was poor (42%), while the positive predictive value improved (93%). Although by multivariable logistic regression M-MxA staining was significantly associated with SVR (OR 4.35, 1.32-14.28, P = 0.012), the predictive ability was inadequate to withhold therapy. The interaction between macrophages and hepatocytes plays a critical role in interferon responsiveness; however, the addition of a PI at least partially overcomes the interferon nonresponse phenotype making the predictive ability of ISG staining less clinically useful.
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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 | 0.001 |
| 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".