• Journal of Life Science
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• v.34 no.5
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• pp.287-295
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• 2024

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide, necessitating novel therapeutic strategies. The chemotherapeutic agents used to treat HCC patients are toxic and have serious side effects. Therefore, we investigated the efficacy of anticancer drugs that reduce side effects by targeting tumor cells without causing cytotoxicity in healthy hepatocytes. Berberine, an isoquinoline alkaloid derived from plant compounds, has emerged as a potential candidate for cancer treatment due to its diverse pharmacological properties. The effect of berberine on HepG2 cell viability was determined using the 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide assay. HepG2 cell proliferation was determined through a colony-forming assay. The effects of berberine on HepG2 cell migration were evaluated using a wound-healing assay. Berberine inhibited the proliferation of HepG2 cells, as well as colony formation and migration. Berberine treatment increased the expression of autophagy-related genes and proteins, including Beclin-1 and LC3-II, and elevated the activities and mRNA expression of Caspase-9 and Caspase-3. Additionally, in experiments utilizing the Cell-Derived Xenograft animal model, berberine treatment reduced tumor size and weight in a concentration-dependent manner. These results demonstrate the potential of berberine as a versatile anticancer agent with efficacy in both cellular and animal models of hepatocellular carcinoma. The findings herein shed light on berberine's efficacy against HCC, presenting opportunities for targeted and personalized therapeutic interventions.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.21-41
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• 2024

This study explores the relationship between users' attachment styles and their interactions with ChatGPT (Chat Generative Pre-trained Transformer), an advanced language model developed by OpenAI. As artificial intelligence (AI) becomes increasingly integrated into everyday life, it is essential to understand how individuals with different attachment styles engage with AI chatbots in order to build a better user experience that meets specific user needs and interacts with users in the most ideal way. Grounded in attachment theory from psychology, we are exploring the influence of attachment style on users' interaction with ChatGPT, bridging a significant gap in understanding human-AI interaction. Contrary to expectations, attachment styles did not have a significant impact on ChatGPT usage or reasons for engagement. Regardless of their attachment styles, hesitated to fully trust ChatGPT with critical information, emphasizing the need to address trust issues in AI systems. Additionally, this study uncovers complex patterns of attachment styles, demonstrating their influence on interaction patterns between users and ChatGPT. By focusing on the distinctive dynamics between users and ChatGPT, our aim is to uncover how attachment styles influence these interactions, guiding the development of AI chatbots for personalized user experiences. The introduction of the Perceived Partner Responsiveness Scale serves as a valuable tool to evaluate users' perceptions of ChatGPT's role, shedding light on the anthropomorphism of AI. This study contributes to the wider discussion on human-AI relationships, emphasizing the significance of incorporating emotional intelligence into AI systems for a user-centered future.