Biomarkers of transfusion transmitted occult hepatitis B virus infection: Where are we and what next?
Bibliographic record
Abstract
Blood transfusion is a vital procedure, where transfusion-transmitted infection of hepatitis B virus (HBV) remains an important issue, especially from blood donors with occult hepatitis B virus infection (OBI). Occult hepatitis B virus infection is a complex entity to detect using surrogate blood biomarkers for intrahepatic viral transcriptional activity, requiring a continually refined battery of tests utilised for screening. This review aims to critically evaluate the latest advances in the current blood biomarkers to guide the identification of OBI donors and discuss novel HBV markers that could be introduced in future diagnostic practice. Challenges in detecting low HBV surface antigen levels, mutants, and complexes necessitate ultrasensitive multivalent dissociation assays, whilst HBV DNA testing requires improved sensitivity but worsens inaccessibility. Anti-core antibody assays defer almost all potentially infectious donations but have low specificity, and titres of anti-surface antibodies that prevent infectivity are poorly defined with suboptimal sensitivity. The challenges associated with these traditional blood HBV markers create an urgent need for alternative biomarkers that would help us better understand the OBI. Emerging viral biomarkers, such as pre-genomic RNA and HBV core-related antigen, immunological HBV biomarkers of T-cell reactivity and cytokine levels, and host biomarkers of microRNA and human leucocyte antigen molecules, present potential advances to gauge intrahepatic activity more accurately. Further studies on these markers may uncover an optimal diagnostic algorithm for OBI using quantification of various novel and traditional blood HBV markers. Addressing critical knowledge gaps identified in this review would decrease the residual risk of transfusion-transmitted HBV infection without compromising the sustainability of blood supplies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".