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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".