Liver-restricted Type I IFN Signature Precedes Liver Damage in Chronic Hepatitis B Patients Stopping Antiviral Therapy
Bibliographic record
Abstract
Immune-mediated liver damage is the driver of disease progression in patients with chronic hepatitis B virus (HBV) infection. Liver damage is an Ag-independent process caused by bystander activation of CD8 T cells and NK cells. How bystander lymphocyte activation is initiated in chronic hepatitis B patients remains unclear. Periods of liver damage, called hepatic flares, occur unpredictably, making early events difficult to capture. To address this obstacle, we longitudinally sampled the liver of chronic hepatitis B patients stopping antiviral therapy and analyzed immune composition and activation using flow cytometry and single-cell RNA sequencing. At 4 wk after stopping therapy, HBV replication rebounded but no liver damage was detectable. There were no changes in cell frequencies at viral rebound. Single-cell RNA sequencing revealed upregulation of IFN-stimulated genes (ISGs) and proinflammatory cytokine migration inhibitory factor (MIF) at viral rebound in patients that go on to develop hepatic flares 6-18 wk after stopping therapy. The type I IFN signature was only detectable within the liver, and neither IFN-α/β or ISG induction could be detected in the peripheral blood. In vitro experiments confirmed the type I IFN-dependent ISG profile whereas MIF was induced primarily by IL-12. MIF exposure further amplified inflammatory cytokine production by myeloid cells. Our data show that innate immune activation is detectable in the liver before clinically significant liver damage is evident. The combination of type I IFN and enhanced cytokine production upon MIF exposure represent the earliest immunological triggers of lymphocyte bystander activation observed in hepatic flares associated with chronic HBV 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".