Application of AI-empowered scenario-based simulation teaching mode in cardiovascular disease education.

BACKGROUND: Cardiovascular diseases present a significant challenge in clinical practice due to their sudden onset and rapid progression. The management of these conditions necessitates cardiologists to possess strong clinical reasoning and individual competencies. The internship phase is crucial for medical students to transition from theory to practical application, with an emphasis on developing clinical thinking and skills. Despite the critical need for education on cardiovascular diseases, there is a noticeable gap in research regarding the utilization of artificial intelligence in clinical simulation teaching. OBJECTIVE: This study aims to evaluate the effect and influence of AI-empowered scenario-based simulation teaching mode in the teaching of cardiovascular diseases. METHODS: The study utilized a quasi-experimental research design and mixed-methods. The control group comprised 32 students using traditional teaching mode, while the experimental group included 34 students who were instructed on cardiovascular diseases using the AI-empowered scenario-based simulation teaching mode. Data collection included post-class tests, "Mini-CEX" assessments, Clinical critical thinking scale from both groups, and satisfaction surveys from experimental group. Qualitative data were gathered through semi-structured interviews. RESULTS: Research shows that compared with traditional teaching models, AI-empowered scenario-based simulation teaching mode significantly improve students' performance in many aspects. The theoretical knowledge scores(P < 0.001), clinical operation skills(P = 0.0416) and clinical critical thinking abilities of students(P < 0.001) in the experimental group were significantly improved. The satisfaction survey showed that students in the experimental group were more satisfied with the teaching scene(P = 0.008), Individual participation(P = 0.006) and teaching content(P = 0.009). There is no significant difference in course discussion, group cooperation and teaching style of teachers(P > 0.05). Additionally, the qualitative data from the interviews highlighted three themes: (1) Positive new learning experience, (2) Improved clinical critical thinking skills, and (3) Valuable suggestions and concerns for further improvement. CONCLUSION: The AI-empowered scenario simulation teaching Mode plays an important role in the improvement of clinical thinking and skills of medical undergraduates. This study believes that the AI-empowered scenario simulation teaching mode is an effective and feasible teaching model, which is worthy of promotion in other courses.

Construction and analysis of competitive endogenous RNA networks and prognostic models associated with ovarian cancer based on the exoRBase database.

OBJECTIVE: To construct a competitive endogenous RNA (ceRNA) regulatory network in blood exosomes of patients with ovarian cancer (OC) using bioinformatics and explore its pathogenesis. METHODS: The exoRbase2.0 database was used to download blood exosome gene sequencing data from patients OC and normal controls and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were detected independently using R language for differential expression analysis. TargetScan and miRanda databases were combined for the prediction and differential expression of mRNA-binding microRNAs (miRNA). The miRcode and starBase databases were used to predict miRNAs that bind to differentially expressed lncRNAs and circRNAs repectively. The relevant mRNA, circRNA, lncRNA and their corresponding miRNA prediction data were imported into Cytoscape software for visualization of the ceRNA network. The R language and KEGG Orthology-based Annotation System (KOBAS) were used to execute and illustrate the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using The CytoHubba plugin. RESULTS: Thirty-one differentially expressed mRNAs, 17 differentially expressed lncRNAs, and 24 differentially expressed circRNAs were screened. Cytoscape software was used to construct the ceRNA network with nine mRNA nodes, two lncRNA nodes, eight circRNA nodes, and 51 miRNA nodes. Both GO and KEGG were focused on the Spliceosome pathway, indicating that spliceosomes are closely linked with the development of OC, while heterogenous nuclear ribonucleoprotein K and RNA binding motif protein X-linked genes were the top 10 score Hub genes screened by Cytoscape software, including two lncRNAs, four mRNAs, and four circRNAs. In patients with OC, the expression of eukaryotic translation initiation factor 4 gamma 2 (EIF4G2), SERPINE 1 mRNA binding protein 1 (SERBP1), ribosomal protein L15 (RPL15) and human leukocyte antigen complex P5 (HCP5) was significantly higher whereas that of testis expressed transcript, Y-linked 15 and DEAD-box helicase 3 Y-linked genes was lower compared to normal controls Immunocorrelation scores revealed that SERBP1 was significantly and negatively correlated with endothelial cells and CD4+ T cells and positively correlated with natural killer (NK) cells and macrophages, respectively; RPL15 was significantly positively correlated with macrophages and endothelial cells and negatively correlated with CD8+ T cells and uncharacterized cells, respectively. EIF4G2 was significantly and negatively correlated with endothelial cells and CD4+ T cells, and positively correlated with uncharacterized cells, respectively. Based on the survival data and the significant correlation characteristics derived from the multifactorial Cox analysis (P < 0.05), the survival prediction curves demonstrated that the prognostic factors associated with 3-year survival in patients with OC were The prognostic factors associated with survival were Macrophage, RPL15. CONCLUSION: This study successfully constructs a ceRNA regulatory network in blood exosomes of OV patients, which provides the specific targets for diagnosis and treatment of OC.