Assessment of HBV variants and novel viral and immune biomarkers in chronic hepatitis B patients with metabolic dysfunction associated steatotic liver disease
Bibliographic record
Abstract
Abstract Co‐existing chronic hepatitis B virus (CHB) infection and metabolic dysfunction associated steatotic liver disease (MASLD) can exert complex effects on hepatic metabolism, requiring mechanistic study. CHB participants were assessed for MASLD and the impact of hepatic steatosis/metabolic syndrome (MetS) on novel viral and immunological markers. In this prospective, cohort study, untreated CHB subjects were assessed for liver disease by non‐invasive tests (i.e. FibroScan, controlled attenuation parameter, CAP). Subjects were tested for cytokines and IFN‐γ ELISPOT assay to HBV Surface (S) and Core (C) proteins. Standard HBV serological, exploratory biomarkers and deep sequencing of HBV S and C genes were performed. In 53 subjects (median age 45 years [SD = 10.6], 35% F, 56% Asian, 20% Black, 3% White), 94% (50) HBeAg negative, 63% genotype B/C, mean HBV DNA 3.2 log10 IU/mL (SD = 1.8), quantitative HBsAg 2.9 log10 IU/mL (SD = 1.2) and HBV pgRNA 2.1 log10 copies/mL (SD = 1.3). In enrolled subjects, the mean ALT was 41.9 U/L (SD = 24.0), FibroScan was 5.7 kPa (SD = 1.9) and CAP was 306.4 dB/m (SD = 49.0). The mean BMI was 28.2 kg/m2 (SD = 4.2), 20% (11/53) had diabetes, 35% (19/53) dyslipidaemia and 24% (13/53) hypertension. Subjects with MetS and steatosis showed lower HBV markers (p < .01), higher HBV S diversity (p = .02) and greater frequency of HBV variants associated with host‐anti‐viral immune escape. Pro‐inflammatory cytokine levels and HBV‐specific cellular responses were higher in participants with hepatic steatosis. In CHB, MASLD/hepatic steatosis was associated with HBV variants and systemic immune responses potentially impacting liver disease progression despite low‐level viraemia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".