A Comprehensive Analysis of the Impact of HIV on HCV Immune Responses and Its Association with Liver Disease Progression in a Unique Plasma Donor Cohort
Bibliographic record
Abstract
OBJECTIVE: Human Immunodeficiency Virus (HIV) and Hepatitis C virus (HCV) co-infection is recognized as a major cause of morbidity and mortality among HIV-1 infected patients. Our understanding of the impact of HIV infection on HCV specific immune responses and liver disease outcome is limited by the heterogeneous study populations with genetically diverse infecting viruses, varying duration of infection and anti-viral treatment. METHODS: Viral-specific immune responses in a cohort of 151 HCV mono- and HIV co-infected former plasma donors infected with a narrow source of virus were studied. HCV and HIV specific T cell responses were correlated with clinical data. RESULTS: HIV-1 accelerated liver disease progression and decreased HCV specific T cell immunity. The magnitude of HCV specific T cell responses inversely correlated with lower HCV RNA load and reduced liver injury as assessed by non-invasive markers of liver fibrosis. HIV co-infection reduced the frequency of HCV specific CD4+ T cells with no detectable effect on CD8+ T cells or neutralizing antibody levels. CONCLUSION: Our study highlights the impact of HIV co-infection on HCV specific CD4+ T cell responses in a unique cohort of patients for both HCV and HIV and suggests a crucial role for these cells in controlling chronic HCV replication and liver disease progression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".