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 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) | 0.001 | 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".