Cuong Ly To

Alzheimer’s disease (AD) is increasing in prevalence among aging populations and presents challenges in finding effective treatments. Tian-Wang-Bu-Xin-Dan (TWBXD) is a classical remedy used to treat anxiety, insomnia, dementia, and memory disorders, with neuroprotective, vasorelaxant, hypotensive, and anti-Alzheimer’s effects reported in experimental studies. This study aimed to elucidate TWBXD mechanisms in AD using network pharmacology. Methods: Targets of TWBXD and AD were retrieved from multiple databases. Pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and topological analysis identified core targets. Molecular docking was used to examine the binding affinity between core targets and active compounds. The TWBXD-AD network comprised 174 bioactive compounds and 92 overlapping targets. Enrichment analysis revealed these targets were involved in AD regulation through apoptosis (neurotrophins, PI3K-Akt, and MAPK signaling pathways), inflammation (IL-17, tumor necrosis factor [TNF], and C-type lectin receptor signaling pathways), and neurotransmission (serotonergic synapse and calcium signaling pathways). The most significant core targets identified were INS, AKT1, IL6, IL1B, TNF, BCL2, CASP3, and BDNF, indicating TWBXD primarily targets inflammation, apoptosis, neurotrophic factors, and neuronal growth. Molecular docking yielded favorable predicted docking scores for multiple compound–target pairs. Tanshinone IIA showed relatively low predicted docking scores with several core targets, suggesting potential binding propensity in silico. Active compounds and targets in TWBXD may modulate AD-related pathways associated with apoptosis, inflammation, and neurotransmission. These predictions provide valuable data for further comprehensive studies on the effects, mechanisms, and safety of TWBXD in experimental and clinical settings.

The integration of artificial intelligence (AI) in medical education has emerged as a transformative shift, particularly within competency-based medical education (CBME). AI technologies, including natural language processing (NLP) and machine learning (ML), offer opportunities to enhance personalized learning and competency assessment. A scoping review was conducted following the framework by Arksey and O’Malley to examine the current integration of AI in CBME. Empirical studies were included, focusing on AI applications in medical education, competency assessments, and skill development. The 50 studies, published from 2010 to 2025, were included in the scoping review and the synthesized evidence demonstrated that AI has shown potential in automating assessments, providing real-time feedback, and supporting personalized learning paths. Common AI technologies such as generative AI, NLP, and ML were applied across diverse medical education settings. However, challenges regarding ethical concerns, faculty training, and limited integration within established curricula were identified. The integration of AI into CBME offers significant potential in medical education; however, several challenges remain. There is a need for more empirical research, longitudinal studies, and AI literacy programs such as training in prompt engineering, AI ethics, and responsible data use for both educators and students. Addressing these gaps will ensure AI’s effective, ethical, and equitable integration in medical training.