Chronic Hepatitis C Virus Infection Impairs M1 Macrophage Differentiation and Contributes to CD8+ T-Cell Dysfunction
Bibliographic record
Abstract
Chronic hepatitis C virus (HCV) infection causes generalized CD8+ T cell impairment, not limited to HCV-specific CD8+ T-cells. Liver-infiltrating monocyte-derived macrophages (MDMs) contribute to the local micro-environment and can interact with and influence cells routinely trafficking through the liver, including CD8+ T-cells. MDMs can be polarized into M1 (classically activated) and M2a, M2b, and M2c (alternatively activated) phenotypes that perform pro- and anti-inflammatory functions, respectively. The impact of chronic HCV infection on MDM subset functions is not known. Our results show that M1 cells generated from chronic HCV patients acquire M2 characteristics, such as increased CD86 expression and IL-10 secretion, compared to uninfected controls. In contrast, M2 subsets from HCV-infected individuals acquired M1-like features by secreting more IL-12 and IFN-γ. The severity of liver disease was also associated with altered macrophage subset differentiation. In co-cultures with autologous CD8+ T-cells from controls, M1 macrophages alone significantly increased CD8+ T cell IFN-γ expression in a cytokine-independent and cell-contact-dependent manner. However, M1 macrophages from HCV-infected individuals significantly decreased IFN-γ expression in CD8+ T-cells. Therefore, altered M1 macrophage differentiation in chronic HCV infection may contribute to observed CD8+ T-cell dysfunction. Understanding the immunological perturbations in chronic HCV infection will lead to the identification of therapeutic targets to restore immune function in HCV+ individuals, and aid in the mitigation of associated negative clinical outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".