Standardized rehabilitation training for patients with coronary artery disease following coronary revascularization surgery is a core therapeutic strategy to improve postoperative cardiac function, boost exercise tolerance and optimize long-term prognosis. However, mainstream cardiac rehabilitation methods currently suffer from three prominent limitations: monotonous training scenarios, poorly individualized regimen titration, and poor adherence to home-based exercise regimens. In recent years, alongside iterative advances and technical maturation in artificial intelligence (AI), the Zhongshan-specific metaverse platform featuring virtual-physical integration has offered novel insights into overcoming bottlenecks in cardiac rehabilitation training. Leveraging metaverse-based immersive interactive technology and wearable dynamic electrocardiogram (ECG) monitoring technology, this study aims to develop an integrated intervention system tailored to standardized cardiac rehabilitation for coronary artery disease patients post coronary revascularization. It further defines the system’s implementation workflow, risk control protocols and clinical eligibility criteria. The research adopts four methodological approaches: system architecture development, scenario-based modular design, alignment with evidence-based clinical guidelines, and hierarchical competency-based teaching modeling. The postoperative cardiac rehabilitation framework consists of three core functional modules: virtual reality (VR) immersive exercise training, wearable real-time dynamic ECG data acquisition, and AI-powered ECG risk early warning. Rehabilitation phases are stratified according to patients’ postoperative functional capacity. On this basis, we established individualized immersive rehabilitation scenarios, graded exercise intervention thresholds, a tiered judgment system for ECG abnormalities, and standardized emergency response workflows. Meanwhile, to meet clinical teaching demands in cardiac rehabilitation departments, this study explores a sustainable specialty teaching transformation model supported by the Fudan Zhongshan Huisheng Intelligent Education platform. The findings will provide a comprehensive theoretical framework, technical roadmap and empirical evidence for the large-scale clinical translation of this innovative cardiac rehabilitation protocol.

Key Words: metaverse; immersive exercise training; cardiac rehabilitation; medical teaching transformation

Pain is a subjective and complex symptom, making its prediction, management, and treatment a significant chal lenge in clinical research. To address these challenges, the search for reliable and objective pain biomarkers has become a focal point in pain studies. Electroencephalography (EEG), a non-invasive clinical tool, has emerged as the most widely used method for assessing brain regions associated with pain due to its temporal resolution, ac curacy, and comprehensive nature. Multichannel EEG is now a primary technique in the study of pain biomarkers. This review discusses the current status and future prospects of EEG biomarkers in pain research, synthesizing evidence on the potential of EEG recordings as reliable biomarkers for pain perception. This will contribute to es tablishing a more solid foundation for the prediction, diagnosis, and intervention of pain in future research and management.

Medical ultrasound imaging is an integral part of preoperative diagnosis, lesion screening and ultrasound-guided interventional surgeries. Image segmentation techniques can enhance the identification of lesions and separate them from complex backgrounds, aiding physicians in both quantitative and qualitative analyses. Ultrasound image segmentation algorithms are primarily categorized into two types: traditional non-semantic segmentation and deep learning-based semantic segmentation, each with distinct advantages and drawbacks. This paper delves into these segmentation principles, elucidating their relevance in the realm of ultrasound image segmentation, and offers an overview of current research trends. Our goal is to provide guidance for physicians and researchers in selecting the most suitable segmentation algorithm that tailors to their specific requirements.

Neuraxial anesthesia is often challenging in patients with obesity, spinal deformity, prior spinal surgery, ankylosing spondylitis, or of advanced age, as conventional landmark-based techniques frequently fail in this population. Ultrasound guidance has improved puncture accuracy; however, commonly used approaches may still be limited by patient positioning and the side from which the operator prefers to work. We report five cases of extremely difficult neuraxial anesthesia in which the procedure was successfully performed using an ultrasound-guided paramedian sagittal long-axis out-of-plane approach. This technique allows continuous visualization of the lamina and the anterior–posterior complex, providing a short and direct puncture path with real-time guidance. All procedures were completed successfully on the first attempt, with short puncture times and no anesthesia-related complications. These cases suggest that the ultrasound-guided long-axis out-of-plane approach is a safe, effective, and flexible option for managing difficult neuraxial anesthesia.

Purpose: To explore the effect of electrode structure on welding quality by modifying the structure of magnesium alloy electrode. Methods: Two novel electrodes were designed in this study, including one with a circle (C) surface and the other with a square and arched (SA) surface. The designed electrodes were compared to a control electrode with a smooth surface in terms of temperature distribution, thermal damage to tissue, and effective welding area. Finite element analysis was used to analyze the stress and strain of all electrodes and thermal damage to the tissue. Results: Pressure applied to the designed electrodes was within the elastic limit, and the deformation was less than 1%. The highest temperature of SA electrode (99.6 ℃) was similar to that of the control (100 ℃), while that of C electrode (106 ℃) was higher than the control. The mean temperature at the welding site in intestine of the control electrode was significantly higher than that of the other two electrodes. Besides, the mean temperature of C electrode was also slightly higher than that of SA electrode. The tissues welded by the control electrode, C electrode and SA electrode were completely necrotic within an axial distance of 2.546 mm, 2.079 mm, and 1.835 mm from the edge of the welding area, respectively. Conclusion: SA electrode has the lowest thermal damage compared with the other two electrodes due to smaller effective welding area. Therefore, SA electrode is better than the other two electrodes.