Liver-specific Inflammatory Signatures Predict Clinically Significant Liver Damage
Bibliographic record
Abstract
ABSTRACT Background and Aims Inflammation drives progression of chronic liver disease. However, the triggers of inflammation remain undefined during chronic hepatitis B (CHB) because hepatic flares are spontaneous and difficult to capture. We used nucleoside analogue (NA) withdrawal to investigate early inflammatory events because liver damage after stopping therapy occurs in a predictable time frame. 11 CHB patients underwent 192 weeks of NA therapy before a protocol defined stop. Liver fine-needle aspirates (FNAs) were collected at baseline and 4-weeks post-withdrawal and analyzed using flow cytometry and single-cell RNA sequencing (scRNA-seq). Intrahepatic mononuclear cells (IHMCs) from uninfected livers were used to validate transcriptomic findings. At 4 weeks post NA-withdrawal, HBV DNA rebounded but alanine aminotransferase (ALT) levels remained normal, 7/11 patients developed ALT elevations (>2xULN) at later timepoints. There were no changes in cell frequencies between baseline and viral rebound. ScRNA-seq revealed upregulation of IFN stimulated genes (ISGs) and pro-inflammatory cytokine MIF upon viral rebound. 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 detectable in the serum.
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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) | 0.003 | 0.001 |
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".