VR technology can generate virtual,immersive,and interactive environments,allowing users to immerse themselves in these environments and interact with objects within them.AR technology,on the other hand,can accurately overlay virtual information onto real-world scenes,achieving a seamless integration of the virtual and the real.These two emerging technologies each possess unique advantages and exhibit broad development prospects.They have already begun to be applied in various aspects of medical education,such as basic theoretical teaching and skills training,with promising results.They can compensate for the shortcomings of traditional medical education,enhance students'learning enthusiasm and safety,and improve teaching effectiveness.However,limitations remain,such as the need for improved hardware infrastructure and a scarcity of teaching resources.Based on this,this paper systematically introduces the concepts of AR and VR technologies,reviews their application prospects,current status,advantages,and limitations in medical education,aiming to provide evidence-based support and feasible approaches for medical schools to develop digital teaching plans,promote educational reform,and drive research innovation.

VR technology can generate virtual,immersive,and interactive environments,allowing users to immerse themselves in these environments and interact with objects within them.AR technology,on the other hand,can accurately overlay virtual information onto real-world scenes,achieving a seamless integration of the virtual and the real.These two emerging technologies each possess unique advantages and exhibit broad development prospects.They have already begun to be applied in various aspects of medical education,such as basic theoretical teaching and skills training,with promising results.They can compensate for the shortcomings of traditional medical education,enhance students'learning enthusiasm and safety,and improve teaching effectiveness.However,limitations remain,such as the need for improved hardware infrastructure and a scarcity of teaching resources.Based on this,this paper systematically introduces the concepts of AR and VR technologies,reviews their application prospects,current status,advantages,and limitations in medical education,aiming to provide evidence-based support and feasible approaches for medical schools to develop digital teaching plans,promote educational reform,and drive research innovation.

BACKGROUND: Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. METHODS: In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). RESULTS: A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. CONCLUSIONS A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.
Humans ; Female ; Breast Neoplasms/genetics* ; East Asian People ; Neoplasm Recurrence, Local/genetics* ; Breast ; Algorithms ; Chronic Disease ; Prognosis ; Tumor Microenvironment