Bibliographic record
Abstract
HBsAg screening is carried out routinely to detect hepatitis B virus (HBV) infection. The immunoassays used employ capture antibodies often having specificity for epitopes present on the antigenic (a) determinant of the HBsAg. Loss of detection may occur due to mutations within and/or outside of the a determinant that affect conformational epitope recognition or HBsAg secretion or expression. Most of the mutations associated with immune escape occur within the second loop of the a determinant. In order to detect these HBsAg mutants, antibodies to subdominant regions within the a determinant or outside of the HBsAg may be required, and this has been the focus of many recent studies. Any changes to immunoassay formulations should also address the possible effect of HBV genotypic polymorphisms on assay specificity and sensitivity. HBsAg mutants may also be identified through nucleic acid detection of HBV in serum. Various molecular analysis methods have been developed to provide specific and sensitive detection of HBsAg mutants, including sequencing, limiting dilution cloning PCR (LDC-PCR), gap ligase chain reaction (gLCR), and real time PCR. Sequencing the HBsAg coding region provides specific information on the nucleotide sequence; however, it is relatively insensitive for the detection of minority quasispecies. Other nucleic acid methods offer greater sensitivity for the detection of point mutations. To improve immunoassays, further research will be required to increase detection sensitivity and specificity. Ultimately, a better understanding of the structure of antibody-bound HBsAg will help identify the immunological targets required for the accurate detection of HBsAg in blood.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.002 |
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".