Progression to fibrosis and hepatocellular carcinoma in DEN CCl4 liver mice, is associated with macrophage and striking regulatory T cells infiltration
Bibliographic record
Abstract
Background and aim: Hepatocellular carcinoma (HCC) is a classic inflammation related cancer with most cases arising from chronic liver disease (CLD). This study investigates immune dysregulation that occurs during the progression of CLD to HCC by delineating changes in immune cell composition and distribution within the liver microenvironment. Methods: ) for 6 and 21 weeks to induce liver fibrosis and HCC. Naïve and Phosphate-buffered saline (PBS) corn oil treated mice were used as controls. Immune cell profiling was performed using multiplex immunofluorescence and flow cytometry analyses. Results: The spatial analysis of immune cell populations in HCC reveals stable leukocytes overall, with notable increases in myeloid cells, particularly infiltrating macrophages (Inf mph). Indeed, Inf mph show a progressive enrichment from control to tumor, reaching a 5-fold and 10-fold increase in the invasive margin (IM) and surrounding non-tumor tissue (NTT) regions, respectively. T lymphocytes, especially CD4+ T cells but not CD8+ T cells, significantly expand, with CD4+ cells increasing up to 10-fold in the IM and NTT regions of HCC livers. Regulatory T cells (Tregs) population exhibits an extraordinary 125-fold and 80-fold surge in the IM and NTT regions, respectively. Conclusions: induced HCC mouse model replicates key immunosuppressive features of human HCC, notably increased Tregs and macrophages, which provides a robust platform for testing immunotherapies. The prominence of immune cells in the IM region underscores its importance as a critical interface modulating tumor-immune interactions, while the elevated immune presence in the NTT region reflects broader immune dysregulation associated with advanced CLD, and potentially facilitating tumor 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".