Systemic cytokine and viral antigen-specific responses in hepatitis D virus RNA positive versus HDV RNA negative patients
Bibliographic record
Abstract
Background Hepatitis B virus (HBV)/Hepatitis D Virus (HDV) co-infection increases the risk of severe liver disease compared to HBV mono-infection. Adaptive immune responses to HDV are weakly detectable, and the involvement of innate immunity in the progression of HDV-related liver fibrosis is suggested. We hypothesize that an overall innate immune activation in HBV/HDV co-infection plays a role in liver disease progression and also impacts virus specific T cell response. Methods Sixteen HBV/HDV-co-infected-patients (median age 42y/7F/6 Asian/4 White/6 Black/15 HBeAg-) and 8 HBV monoinfected-patients (median age 39y/4F/4 Asian/3 Black/1 White/HBeAg-) with median follow-up of 5 years were enrolled. Liver fibrosis was assessed by liver stiffness measurement (LSM, FibroScan®). Proliferation of CD3 + CD4+ T cells in response to viral antigens using CFSE assays and cytokine secreting monocytes was analyzed by flow cytometry. Results Of 16 HBV/HDV, 11 were HDV-RNA+ (HBV-DNA 0–1,040 IU/mL), 5/11 Interferon (IFN) + Nucleos/tide Analog (NA), 3/11 NA monotherapy, median ALT 77 U/L at the time of sample collection, median LSM of 9.8. In 5 HDV RNA−, median HBV DNA 65 IU/mL, 4/5 prior IFN and/or NA, ALT 31 U/L, and median LSM 8.5 kPa. In 8 HBV controls, median HBV-DNA, ALT, LSM was 69 IU/mL, 33 U/L,5 kPa, respectively. PBMC stimulation with HBV core antigen (HBcAg) and HDV antigen (HDAg) showed weaker CD3 + CD4 + T-cell proliferation in HDV-RNA+ vs. HDV RNA− and HBV-mono-infected patients (p < 0.05). In HDV-RNA+ patients, a correlation between ALT and TNF-α (r = 0.76, p = 0.008), higher IL-10 levels and increased proportion of CD14 + TNF-α+ cells were found. Conclusion In summary, during HBV/HDV coinfection, HDV RNA+ patients had weaker HBV and HDV specific responses, associated with increased TNF-α + monocytes irrespective of IFN treatment.
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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 | 0.001 |
| 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 | 0.000 |
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".