Additive effect of sirolimus and anti‐death receptor 5 agonistic antibody against hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND & AIMS: Despite careful patient selection, hepatocellular carcinoma (HCC) recurs in 10-20% of cases after liver transplantation, and the use of potent adjuvant anticancer drugs would be welcome. The aim of this study was to evaluate the efficiency of a combined therapy of rapamycin (sirolimus) and anti-death receptor (DR)5 monoclonal antibody (mAb) on HCC. METHODS: We first assessed the side effects of anti-DR5 mAb administration in vivo by giving various doses of anti-DR5 mAb. Cell proliferation assays were then performed using mouse Hepa1-6 cells or human Huh7 cells to quantify the relative cell viability under various concentrations of sirolimus, anti-DR5 mAb or a combination. Next, one million Hepa1-6 cells were transplanted into C.B17-SCID-beige mice subcutaneously, and four groups were created: (1) untreated, (2) anti-DR5 mAb alone, (3) sirolimus alone and (4) anti-DR5 mAb + sirolimus. RESULTS: Anti-DR5 mAb (200 and 300 μg/day) induced liver dysfunction with partial necrosis of the liver, but 100 μg/day was well tolerated with transaminitis, but normal bilirubin and only minor histological liver damage. In vitro, anti-DR5 mAb lysed Hepa1-6 and Huh7 cells in a dose-dependent manner, and combinations of sirolimus and anti-DR5 mAb demonstrated an additive effect. In vivo studies demonstrated that tumour sizes were significantly smaller in the combined therapy group than in the monotherapy groups. CONCLUSIONS: Combining sirolimus and low-dose anti-DR5 mAb has a significant effect against HCC. This strategy represents a potential novel approach for the management of HCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".