GB Virus C (GBV‐C) Infection in Hepatitis C Virus (HCV)/HIV–Coinfected Patients Receiving HCV Treatment: Importance of the GBV‐C Genotype
Bibliographic record
Abstract
BACKGROUND: Persistent GB virus C (GBV-C) coinfection leads to slower human immunodeficiency virus (HIV) progression. Despite the existence of multiple GBV-C genotypes, their relevance to the progression of HIV disease is unknown. We therefore investigated (1) the prevalence and genotype of GBV-C in hepatitis C virus (HCV)/HIV-coinfected patients and (2) the impact of HCV treatment on GBV-C RNA clearance. METHODS: We retrospectively studied 130 HCV/HIV-coinfected patients initiating HCV therapy. Anti-E2 enzyme-linked immunosorbent assay, reverse-transcription polymerase chain reaction (PCR), and real-time PCR were used to detect and quantify GBV-C infection. GBV-C genotype was determined by sequencing the 5' untranslated region. RESULTS: GBV-C infection (past or current) was identified in 111 (85%) of the patients. Ongoing GBV-C replication was detected in 40 patients. Coinfection with GBV-C genotype 2 was associated with significantly higher CD4(+) cell counts. After 24 weeks of HCV therapy, GBV-C RNA clearance was observed in 50% of patients, although this was not associated with changes in HIV load or with CD4(+) cell counts. Sustained GBV-C RNA clearance was observed in 31% of patients with GBV-C RNA detected at baseline. CONCLUSIONS: GBV-C coinfection was extremely common. GBV-C RNA clearance with HCV therapy was associated with neither short-term loss of HIV control nor impaired immune status. The association of GBV-C genotype 2 with higher CD4(+) cell counts merits further study.
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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 | 0.001 |
| 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) | 0.000 | 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".