IL-10–Producing Regulatory B Cells in the Pathogenesis of Chronic Hepatitis B Virus Infection
Bibliographic record
Abstract
A regulatory subset of B cells has been found to modulate immune responses in autoimmunity, infection, and cancer, but it has not been investigated in the setting of human persistent viral infection. IL-10 is elevated in patients with chronic hepatitis B virus infection (CHB), but its cellular sources and impact on antiviral T cells have not been addressed. We investigated the role of IL-10 and regulatory B cells in the pathogenesis of CHB. Serum IL-10 levels were studied longitudinally in patients with CHB undergoing spontaneous disease flares. There was a close temporal correlation between IL-10 levels and fluctuations in viral load or liver inflammation. Blockade of IL-10 in vitro rescued polyfunctional virus-specific CD8 T cell responses. To investigate the potential contribution of regulatory B cells, their frequency was measured directly ex vivo and after exposure to stimuli relevant to hepatitis B virus (HBV) (CpG or HBV Ags). IL-10-producing B cells were enriched in patients, and their frequency correlated temporally with hepatic flares, both after stimulation and directly ex vivo. Phenotypically, these cells were predominantly immature (CD19(+)CD24(hi)CD38(hi)) ex vivo; sorted CD19(+)CD24(hi)CD38(hi) cells suppressed HBV-specific CD8 T cell responses in an IL-10-dependent manner. In summary, these data reveal a novel IL-10-producing subset of B cells able to regulate T cell immunity in CHB.
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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) | 0.000 | 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".