Early Interferon Therapy for Hepatitis C Virus Infection Rescues Polyfunctional, Long-Lived CD8 <sup>+</sup> Memory T Cells
Bibliographic record
Abstract
The majority of acute hepatitis C virus (HCV) infections progress to chronicity and progressive liver damage. Alpha interferon (IFN-alpha) antiviral therapy achieves the highest rate of success when IFN-alpha is administered early during the acute phase, but the underlying mechanisms are unknown. We used a panel of major histocompatibility complex class I tetramers to monitor the phenotypic and functional signatures of HCV-specific T cells during acute HCV infection with different infection outcomes and during early IFN therapy. We demonstrate that spontaneous resolution correlates with the early development of polyfunctional (IFN-gamma- and IL-2-producing and CD107a(+)) virus-specific CD8(+) T cells. These polyfunctional T cells are distinguished by the expression of CD127 and Bcl-2 and represent a transitional memory T-cell subset that exhibits the phenotypic and functional signatures of both central and effector memory T cells. In contrast, HCV-specific CD8(+) T cells in acute infections evolving to chronicity expressed low levels of CD127 and Bcl-2, exhibited diminished proliferation and cytokine production, and eventually disappeared from the periphery. Early therapeutic intervention with pegylated IFN-alpha rescued polyfunctional memory T cells expressing high levels of CD127 and Bcl-2. These cells were detectable for up to 1 year following discontinuation of therapy. Our results suggest that the polyfunctionality of HCV-specific T cells can be predictive of the outcome of acute HCV infection and that early therapeutic intervention can reconstitute the pool of long-lived polyfunctional memory T cells.
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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".