Abstract Propranolol (PROP) is a nonselective β-adrenergic receptor antagonist used to treat hypertension and cardiac arrhythmias. Oral administration of PROP has recently emerged as a new treatment modality for hemangiomas. However, the side effects of PROP at the cellular level have not been adequately described. The present study investigates and highlights the mechanisms of coupling of the drugs cyclosporin-A (CyA) and PROP on cell proliferation and the occurrence of apoptosis. It also relays the antioxidant effect of PROP on human umbilical vein endothelial cells (HUVECs). HUVECs were treated with CyA and PROP. At 24 hours after treatment, the levels of reactive oxygen species (ROS), cell proliferation, and apoptosis were determined using the ROS kit, MTT assay, and Annexin V staining. In addition, the related proteins of phospho-p38 mitogen-activated protein kinase were determined by western blotting. Subsequently, HUVECs pretreated with CyA or PROP were treated with the p38 inhibitor (SB203580). Finally, the ROS level, cell proliferation, and apoptosis were measured again in both active HUVECs and HUVECs, in which the p38 proteins were inhibited. The combination of CyA and PROP reversed the effect of CyA on cell viability, reduced the ROS level and the cell apoptosis induced by PROP. Moreover, inhibition of p38 protein catalase activity immediately stopped the effect of CyA–propranolol in HUVECs. The effect of the CyA–propranolol combination on HUVECs is associated with the p38 pathway changes, which is proven to be a potential chemotherapeutic agent that minimizes the side effects of PROP in hemangioma therapy. <<<

Xidong Feng, Vivek Veeriah, Marcus Chiam, Michael Dennis, Ryan Pachauri, Thomas Tumiel, Federico Barbero, Johan Obando-Ceron, Jiaxin Shi, Satinder Singh, Shaobo Hou, Nenad Tomašev, Tom Zahavy <<<

While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to tackle these difficulties in the domain of chess puzzles. We start by benchmarking Generative AI architectures, and then introduce an RL framework with novel rewards based on chess engine search statistics to overcome some of those shortcomings. The rewards are designed to enhance a puzzle's uniqueness, counter-intuitiveness, diversity, and realism. Our RL approach dramatically increases counter-intuitive puzzle generation by 10x, from 0.22\% (supervised) to 2.5\%, surpassing existing dataset rates (2.1\%) and the best Lichess-trained model (0.4\%). Our puzzles meet novelty and diversity benchmarks, retain aesthetic themes, and are rated by human experts as more creative, enjoyable, and counter-intuitive than composed book puzzles, even approaching classic compositions. Our final outcome is a curated booklet of these AI-generated puzzles, which is acknowledged for creativity by three world-renowned experts. <<<