Virus-associated Inflammation Imprints an Inflammatory Profile on Long-lived Monocyte-derived Macrophages in the Human Liver
Bibliographic record
Abstract
Abstract Chronic liver injury triggers the activation and recruitment of immune cells, causing antigen-independent tissue damage and liver disease progression. Tissue inflammation can reshape macrophage composition through monocyte replacement. Replacement of tissue macrophages with monocytes differentiating in an inflammatory environment can potentially imprint a phenotype that switches the liver from an immunotolerant organ to one predisposed to tissue damage. We longitudinally sampled the liver of chronic hepatitis B (CHB) patients with active liver inflammation starting antiviral therapy. Antiviral therapy suppressed viral replication and liver inflammation, which coincided with decreased myeloid activation markers. Single-cell RNA sequencing mapped peripheral inflammatory markers to a monocyte-derived macrophage population, distinct from Kupffer cells, with an inflammatory transcriptional profile. The inflammatory macrophages (iMacs) differentiated from blood monocytes and established a long-lived population. The iMacs retained their core transcriptional signature, consistent with trained immunity, resulting in a population of macrophages primed for inflammation potentially driving progressive liver disease.
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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 | 0.001 |
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".