Molecular characterization of hepatitis B virus in Vietnam
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus (HBV) infection is a major public health problem globally. HBV genotypes and subgenotypes influence disease transmission, progression, and treatment outcome. A study was conducted among treatment naive chronic HBV patients in southern Vietnam to determine the genotypes and subgenotypes of HBV. METHODS: A prospective, exploratory study was conducted among treatment naïve chronic HBV patients attending at the Hospital for Tropical Diseases, in Ho Chi Minh City, Vietnam during 2012, 2014 and 2016. HBV DNA positive samples (systematically selected 2% of all treatment naïve chronic patients during 2012 and 2014, and 8% of all treatment naïve chronic patients during 2016) were subjected to whole genome sequencing (WGS) either by Sanger or Illumina sequencing. WGS was used to define genotype, sub-genotype, recombination, and the prevalence of drug resistance and virulence-associated mutations. RESULTS: One hundred thirty five treatment naïve chronic HBV patients including 18 from 2012, 24 from 2014, and 93 from 2016 were enrolled. Of 135 sequenced viruses, 72.6% and 27.4% were genotypes B and C respectively. Among genotype B isolates, 87.8% and 12.2% were subgenotypes B4 and B2 respectively. A G1896A mutation in the precore gene was present in 30.6% of genotype B isolates. The genotype C isolates were all subgenotype C1 and 78.4% (29/37) of them had at least one basal core promoter (BCP) mutation. A1762T and G1764 T mutations and a double mutation (A1762T and G1764 T) in the BCP region were significantly more frequent in genotype C1 isolates (p < 0.001). CONCLUSION: HBV genotype B including subgenotype B4 is predominant in southern Vietnam. However, one fourth of the chronic HBV infections were caused by subgenotype C1.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".