B-Cell Activation Gene Signature in Blood and Liver of Hepatitis B e Antigen–Positive Patients With Immune Active Chronic Hepatitis B
Bibliographic record
Abstract
BACKGROUND: Studies on chronic hepatitis B virus (HBV) infection have shown immune dysfunction involving multiple cell types, including T cells. B cells have been evaluated more recently, but in contrast to T cells, more pronounced activation of circulating B cells has been reported. To gain more insight into the activation status of B cells, we investigated gene profiles of B cells in the blood and liver of patients with chronic HBV. METHODS: RNA-sequencing and flow cytometric analysis was performed on peripheral blood B cells of patients with immune active chronic HBV, comparing them with samples from healthy controls. In addition, gene expression profiles of B cells in the liver were analyzed by bulk and single-cell RNA-seq. RESULTS: Our data show a distinctive B-cell activation gene signature in the blood of patients with immune active chronic HBV, characterized by a significant upregulation of immune-related genes. This peripheral activation profile was also observed in B cells from the liver by single-cell RNA-seq, with naive and memory B-cell subsets being the primary carriers of the signature. CONCLUSIONS: Our findings suggest that B-cell gene profiles reflect responsiveness to HBV infection; these findings are relevant for clinical studies evaluating immunomodulatory treatment strategies for HBV.
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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 | 0.001 | 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".