Combined Administration of Escitalopram Oxalate and Nivolumab Exhibits Synergistic Growth-Inhibitory Effects on Liver Cancer Cells through Inducing Apoptosis
Bibliographic record
Abstract
Liver cancer is one of the most lethal malignant cancers worldwide. However, the therapeutic options for advanced liver cancers are limited and reveal scant efficacy. The current study investigated the effects of nivolumab (Niv) and escitalopram oxalate (Esc) in combination on proliferation of liver cancer cells both in vitro and in vivo. Significantly decreased viability of HepG2 cells that were treated with Esc or Niv was observed in a dose-dependent manner at 24 h, 48 h, and 72 h. Administration of Esc (50 μM) + Niv (20 μM), Esc (75 μM) + Niv (5 μM), and Esc (75 μM) + Niv (20 μM) over 24 h exhibited synergistic effects, inhibiting the survival of HepG2 cells. Additionally, treatment with Esc (50 μM) + Niv (1 μM), Esc (50 μM) + Niv (20 μM), and Esc (75 μM) + Niv (20 μM) over 48 h exhibited synergistic effects, inhibiting the survival of HepG2 cells. Finally, treatment with Esc (50 μM) + Niv (1 μM), Esc (50 μM) + Niv (20 μM), and Esc (75 μM) + Niv (20 μM) for 72 h exhibited synergistic effects, inhibiting HepG2 survival. Com-pared with controls, HepG2 cells treated with Esc (50 μM) + Niv (20 μM) exhibited significantly increased sub-G1 portion and annexin-V signals. In a xenograft animal study, Niv (6.66 mg/kg) + Esc (2.5 mg/kg) significantly suppressed the growth of xenograft HepG2 tumors in nude mice. This study reports for the first time the synergistic effects of combined administration of Niv and Esc for inhibiting HepG2 cell proliferation, which may provide an alternative option for liver cancer treatment.
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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 | 0.001 |
| 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".