A Tale of Two Viruses: Immunological Insights Into HCV/HIV Coinfection
Bibliographic record
Abstract
Nearly 2.3 million individuals worldwide are coinfected with human immunodeficiency virus (HIV) and hepatitis C virus (HCV). Odds of HCV infection are six times higher in people living with HIV (PLWH) compared to their HIV-negative counterparts, with the highest prevalence among people who inject drugs (PWID) and men who have sex with men (MSM). HIV coinfection has a detrimental impact on the natural history of HCV, including higher rates of HCV persistence following acute infection, higher viral loads, and accelerated progression of liver fibrosis and development of end-stage liver disease compared to HCV monoinfection. Similarly, it has been reported that HCV coinfection impacts HIV disease progression in PLWH receiving anti-retroviral therapies (ART) where HCV coinfection negatively affects the homeostasis of CD4+ T cell counts and facilitates HIV replication and viral reservoir persistence. While ART does not cure HIV, direct acting antivirals (DAA) can now achieve HCV cure in nearly 95% of coinfected individuals. However, little is known about how HCV cure and the subsequent resolution of liver inflammation influence systemic immune activation, immune reconstitution and the latent HIV reservoir. In this review, we will summarize the current knowledge regarding the pathogenesis of HIV/HCV coinfection, the effects of HCV coinfection on HIV disease progression in the context of ART, the impact of HIV on HCV-associated liver morbidity, and the consequences of DAA-mediated HCV cure on immune reconstitution and HIV reservoir persistence in coinfected patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".