CD8 T Cell Hyperfunction and Reduced Tumour Control in Murine Models of Advanced Liver Disease
Bibliographic record
Abstract
Immune dysfunction in liver disease contributes to significant morbidities, depending on liver damage severity and aetiology. We previously reported long-lasting generalized CD8 T cell hyperfunction in chronic HCV infection with advanced fibrosis, yet its separation from viral and fibrosis-driven effects, as well as clinical outcomes of advanced fibrosis, remains unclear. In a murine model of carbon tetrachloride-induced progressive liver fibrosis, advanced fibrosis was observed by 12 weeks, with pathologies similar to those of human chronic HCV infection. Blood-circulating CD8 T cells showed IFN-γ and granzyme B (GrB) hyperfunction in response to anti-CD3/28 stimulation, as well as impaired responses to ectopic tumour challenge and anti-PD-1/CTLA-4 immunotherapy. Hyperfunction and impaired tumour responses were retained despite liver insult cessation. In a 45% HFD model, which induced steatosis and minimal fibrosis, IFN-γ and GrB hyperfunction was also observed in blood-circulating CD8 T cells. This study highlights a prolonged systemic CD8 T cell dysfunction acquired during progressive liver disease, associated with impaired antitumour and immunotherapy responses. These mirror the bulk CD8 T cell dysfunction observed in advanced liver diseases in humans, suggesting that these models could be valuable for future mechanistic studies aimed at identifying targets to help improve clinical outcomes in chronic liver disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".