Getting to HBV cure—Will new biomarkers help?
Bibliographic record
Abstract
The natural history and response to therapy in chronic hepatitis B (CHB) infection have been defined by a combination of serological and virological biomarkers along with liver biochemistry and/or histology. A number of novel biomarkers, including HBV RNA, hepatitis B core-related antigen, hepatitis B core antigen, and quantitative HBsAg, have been developed and evaluated in different clinical settings. Novel immunological biomarkers have also been studied but have been less well characterized. In addition to providing insights into HBV biology, these novel biomarkers may significantly aid in the design, development, and assessment of novel antiviral strategies aiming for the cure of chronic hepatitis B. Biomarkers can be used to confirm the mechanism of action or target engagement of a novel agent but also may be used for patient selection for trials and clinical use. Ideally, biomarkers can be used to more accurately define stages of chronic hepatitis B, particularly degrees of virological control. In this review, the serological, virological, and immunological biomarkers are described with a focus on how they can be used to guide the development of HBV cure strategies. New terminology is proposed for clinical endpoints, including sustained control to replace the concept of partial cure and resolved chronic infection to replace functional cure, reserving the term cure for clearance or silencing of all covalently closed circular DNA and integrated HBV DNA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".