High Frequency Occult Hepatitis B Virus Infection Detected in Non-Resolved Donations Suggests the Requirement of Anti-HBc Test in Blood Donors in Southern China
Bibliographic record
Abstract
Background: Most Chinese Blood Centers adopted mini pool (MP) nucleic acid testing (NAT) for HBV screening due to high cost of Individual donation (ID) NAT, and different proportions of MP-reactive but ID-non-reactive donations (MP+/ID-, defined as non-resolved donations) have been observed during daily donor screening process. Some of these non-resolved donations are occult HBV infections (OBIs), which pose potential risk of HBV transmission if they are not deferred. This study is aimed to further analyze these non-resolved donations. Methods: The non-resolved plasma samples were further analyzed by serological tests and various HBV DNA amplification assays including quantitative PCR (qPCR) and nested PCR amplifying the basic core and pre-core promoter regions (BCP/PC; 295 base pairs) and HBsAg (S) region (496 base pairs). Molecular characterizations of HBV DNA+ non-resolved samples were determined by sequencing analysis. Results: Of 17,226 MPs from 103,356 seronegative blood donations, 98 MPs were detected reactive for HBV. Fifty-six out of these 98 (57.1%) reactive MPs were resolved as HBV DNA+, but the remaining 42 pools (42.9%, 252 donations) were left non-resolved with a high rate (53.2%) of anti-HBc+. Surprisingly, among 42 non-resolved MPs, 17 contained one donation identified as OBIs by alternative NAT assays. Sequence analysis on HBV DNAs extracted from these OBI donations showed some key mutations in the S region that may lead to failure in HBsAg detection and vaccine escape. Conclusion: A total of 53.2% of the non-resolved donations were anti-HBc+, and OBIs were identified in 40.5% of these non-resolved pools. Therefore, non-resolved donations with anti-HBc+ might pose potential risk for HBV transmission. Our present analysis indicates that anti-HBc testing in non-resolved donations should be used to identify OBIs in order to further increase blood safety in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".