Detection of Hepatitis B Virus (HBV) Genomes and HBV Drug Resistant Variants by Deep Sequencing Analysis of HBV Genomes in Immune Cell Subsets of HBV Mono-Infected and/or Human Immunodeficiency Virus Type-1 (HIV-1) and HBV Co-Infected Individuals
Bibliographic record
Abstract
UNLABELLED: The hepatitis B virus (HBV) and the human immunodeficiency virus type 1 (HIV-1) can infect cells of the lymphatic system. It is unknown whether HIV-1 co-infection impacts infection of peripheral blood mononuclear cell (PBMC) subsets by the HBV. AIMS: To compare the detection of HBV genomes and HBV sequences in unsorted PBMCs and subsets (i.e., CD4+ T, CD8+ T, CD14+ monocytes, CD19+ B, CD56+ NK cells) in HBV mono-infected vs. HBV/HIV-1 co-infected individuals. METHODS: Total PBMC and subsets isolated from 14 HBV mono-infected (4/14 before and after anti-HBV therapy) and 6 HBV/HIV-1 co-infected individuals (5/6 consistently on dual active anti-HBV/HIV therapy) were tested for HBV genomes, including replication indicative HBV covalently closed circular (ccc)-DNA, by nested PCR/nucleic hybridization and/or quantitative PCR. In CD4+, and/or CD56+ subsets from two HBV monoinfected cases, the HBV polymerase/overlapping surface region was analyzed by next generation sequencing. RESULTS: All analyzed whole PBMC from HBV monoinfected and HBV/HIV coinfected individuals were HBV genome positive. Similarly, HBV DNA was detected in all target PBMC subsets regardless of antiviral therapy, but was absent from the CD4+ T cell subset from all HBV/HIV-1 positive cases (P<0.04). In the CD4+ and CD56+ subset of 2 HBV monoinfected cases on tenofovir therapy, mutations at residues associated with drug resistance and/or immune escape (i.e., G145R) were detected in a minor percentage of the population. SUMMARY: HBV genomes and drug resistant variants were detectable in PBMC subsets from HBV mono-infected individuals. The HBV replicates in PBMC subsets of HBV/HIV-1 patients except the CD4+ T cell subpopulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| 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".