Differences between exhausted CD8<sup>+</sup> T cells in hepatocellular carcinoma patients with and without uremia
Bibliographic record
Abstract
The purpose of this study was to explore the differences between exhausted CD8+ T cells in hepatocellular carcinoma (HCC) patients with and without uremia. We enrolled 45 uremic patients who were recently diagnosed with HCC into the HCC + uremia cohort and similar patients with HCC but without uremia into the HCC-only cohort. Lymphocytes were obtained from the two cohorts, and exhausted CD8+ T cells, comprising PD-1+CD8+, TIM-3+CD8+, and LAG-3+CD8+ T cells, were sorted and expanded in vitro. After expansion, the proportions of PD-1+CD8+, TIM-3+CD8+, and LAG-3+CD8+ T cells were significantly higher in the HCC-only cohort than in the HCC + uremia cohort. CD8+ T cells expressing PD-1, TIM-3, or LAG-3 showed increased tumor reactivity and release of interferon-γ in vitro; however, these cells demonstrated weaker anti-tumor activity in HCC + uremia patients than in HCC-only patients. Among the expanded lymphocytes, only the decreased proportion of PD-1+CD8+ T cells significantly correlated with the HCC + uremia cohort (odds ratio of 2.731, p = 0.009). We concluded that peripheral CD8+ T cells expressing PD-1, TIM-3, or LAG-3 from the HCC + uremia cohort were dysfunctional in vitro. Among these populations, PD-1+CD8+ T cells were most evident in HCC patients with uremia.
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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 | 0.001 |
| 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".