Rapid detection of hepatitis B virus mutations using real-time PCR and melting curve analysis
Bibliographic record
Abstract
Current methods of detecting hepatitis B virus (HBV) mutations are time consuming, labor intensive, and not suitable for screening large numbers of samples. In the present study, we documented the advantages of a system that exploits differences in thermal stability between perfect match and mismatch hybrids, and thereby distinguishes between wild-type and mutants. Hybridization probes were designed complementary to specific wild-type HBV sequences in surface (S), precore, and basal core promoter (BCP) regions of the HBV genome (nt 587, 1896, and 1762/1764, respectively). Two probes were designed for each mutation: anchor probes were 3' labeled with fluorescein and sensor probes, 5' labeled with LC-Red 640, and 3' phosphorylated. Temperatures for each probe melted from amplification products were then determined in a melting program. Sera from 12 patients, each containing identified HBV mutants (6 S-escape, 1 precore, 1 BCP, and 4 mixed precore and BCP), and 5 control sera from patients with wild-type virus were analyzed. Genomic sequences of mutant and wild-type viruses were confirmed by direct sequencing. Real-time polymerase chain reaction (PCR) with fluorescent hybridization probes accurately identified each mutant and wild-type genome. Melting temperatures obtained from probe-product duplexes for the 3 mutants were distinguished from wild-type (>4.0 degrees C, minimal) within 45 minutes. The sensitivity of the system was 100 copies/mL and as few as 5% of mutant among wild-type virus were detected. In conclusion, real-time PCR with fluorescent hybridization probes is a specific, sensitive, quantitative, and rapid means of detecting clinically relevant HBV mutants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".