HBV transcription and translation persist despite viral suppression in HBV‐HIV co‐infected patients on antiretroviral therapy
Bibliographic record
Abstract
BACKGROUND AND AIMS: Liver injury may persist in patients with HBV receiving antiviral therapy who have ongoing transcription and translation. We sought to assess ongoing HBV transcription by serum HBV RNA, translation by serum hepatitis B core related antigen (HBcrAg), and their associations with hepatic HBsAg and HBcAg staining in patients coinfected with HBV and HIV. METHODS: This is a cross-sectional study of 110 adults coinfected with HBV and HIV who underwent clinical assessment and liver biopsy. Immunohistochemistry (IHC) was performed for HBsAg and HBcAg. Viral biomarkers included quantitative HBsAg, HBV RNA, and HBcrAg. RESULTS: Participants' median age was 49 years (male, 93%; Black, 51%; HBeAg+, 65%), with suppressed HBV DNA (79%) and undetectable HIV RNA (77%) on dually active antiretroviral therapy. Overall, HBV RNA and HBcrAg were quantifiable in 81% and 83%, respectively (96% and 100% in HBeAg+, respectively). HBcAg staining was detected in 60% and HBsAg in 79%. Higher HBV RNA was associated with higher HBcAg and HBsAg IHC grades (both p < 0.0001). The HBsAg membranous staining pattern was significantly associated with higher HBV-RNA and HBcrAg levels. CONCLUSION: HBcAg and HBsAg IHC staining persisted despite viral suppression, and IHC grades and staining patterns correlated with markers of transcription (HBV RNA) and translation (HBcrAg). These data indicate that apparent HBV suppression is associated with residual transcription and translation that could contribute to liver pathology. Additional antiviral strategies directed to HBV protein expression may be useful to ameliorate liver injury.
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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 | 0.001 | 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".