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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".