Leadless pacemakers have emerged as a mainstream clinical solution, and their communication capabilities, crucial for reliable pacing and device monitoring, continue to evolve. This review systematically examines the fundamental principles of leadless pacemaker communication systems, current design requirements, existing challenges, and future development trends. We outline the bidirectional communication mechanism between leadless pacemakers and external programmers through wireless technologies, focusing on radio-frequency field communication coupled with load modulation techniques to optimize energy efficiency and transmission reliability. Additionally, we analyze the role of artificial intelligence in adaptive communication protocols and explore the clini cal potential of remote monitoring and control systems. This comprehensive analysis aims to serve as a reference for the development of communication architectures for leadless pacemakers.

Finite element analysis (FEA) is a computer-aided tool widely employed in the field of prosthodontics, offering a comprehensive understanding of biomechanical behavior and assisting in the design and evaluation of dental prostheses. By dividing a model into finite elements, FEA enables accurate predictions of stress, strain, and displacement of structures. This review summarizes recent research developments in the application of FEA across various aspects of prosthodontics, including dental implant, removable partial denture, fixed partial denture and their combinations. FEA plays a significant role in selecting restoration materials, optimizing prosthetic designs, and examining the dynamic interactions between prostheses and natural teeth. Its computational efficiency and accuracy have expanded its application potentials for preoperative planning in custom-made prosthodontics. Upon the physician’s assessment of the repair requirements tailored to the individual patient’s condition, FEA can be employed to evaluate the stress distribution, displacement, and other relevant outcomes associated with the proposed restoration. When integrated with clinical expertise, it facilitates assessing design feasibility, identifying necessary adjustments, and optimizing prosthetic solutions to mitigate the risk of failure. Additionally, FEA helps identify potential complications arising from long-term prosthetics use, allowing for the implementation of preventive strategies. Presenting FEA results to patients enhances their understanding of the scientific basis and rationale behind the design, thereby bolstering patient confidence in the proposed intervention. Despite its ongoing limitations, FEA underscores the importance of integrating computational findings with clinical judgment and supplementary diagnostic tools. This review emphasizes the growing role of FEA in advancing prosthodontics by offering computational analysis and design optimization, ultimately improving treatment outcomes and patient satisfaction.

Traditional methods of tissue closure, such as sutures and staples, have long been the gold standard in surgery. However, they have major drawbacks, such as the body's reaction to foreign materials and the difficulty of the technical aspects of minimally invasive procedures. Energy-based tissue fusion (EBTF) technology is a revolutionary alternative that uses energy to produce autologous tissue sealing. This review aims to provide a comprehensive analysis of the biophysical principles, technological evolution, and clinical applications of current EBTF technologies and related devices. The fundamental mechanisms of EBTF technologies are investigated, with a focus on collagen denaturation and cross-linking induced by different energy modalities such as radiofrequency (RF) current, ultrasound, and laser. Three representative systems are critically evaluated: the impedance-controlled bipolar system (LigaSure^TM), the ultrasonic coagulating shears (Harmonic^TM), and the hybrid ultrasonic-bipolar device (Thunderbeat^TM). Their performance is compared in terms of vessel sealing efficacy, thermal spread, operative time, and complication rates across various surgical specialties. The clinical evidence indicates that the primary advantage of RF device lies in safety, whereas the ultrasonic devices offer reduced lateral thermal damage, and the hybrid device demonstrate superior versatility and procedural speed. The review concludes by identifying future trends, including the integration of artificial intelligence and robotic platforms, which promise to further enhance the safety and precision of surgical energy devices.

Orthodontics is a discipline that heavily relies on spatial perception and fine motor skills, placing extremely high demands on the practitioner’s experience, technique, and judgment. However, traditional teaching models face numerous challenges in orthodontic training. The metaverse, an emerging technology integrating virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and other technologies, offers an immersive and highly interactive solution for orthodontic education. This article reviews the application and development of metaverse technology in orthodontic education.

Key Words: metaverse; orthodontics; education

Objective: With the ongoing digital transformation of higher education in recent years, virtual simulation experiment teaching has emerged as a key direction for instructional reform in universities. Taking the course Pain Diagnostics and Therapeutics offered by the School of Anesthesiology at Wannan Medical University as an example, this study aimed to evaluate the effectiveness of a virtual simulation experiment system in the experimental teaching of this course. Methods: A total of 236 anesthesiology students from the class of 2021 were enrolled as research participants and randomly divided into a control group and an experimental group. The control group received traditional teaching combining theoretical lectures with bedside clinical observation. The experimental group underwent a blended teaching model consisting of virtual simulation teaching plus bedside clinical observation, which included three stages: online virtual pre-training, offline inclass hands-on practice, and clinical observation with virtual-real mutual verification. Results: The experimental group demonstrated significantly better performance than the control group across all evaluation indicators (all P<0.001). Regarding course scores, the experimental group achieved a mean score of 90.98±6.23, which was significantly higher than that of the control group (85.28±4.42). The experimental group also showed marked superiority in regular assessment scores (92.58±5.42 vs. 84.56±5.44) and final theoretical examination scores (90.30±6.58 vs. 85.59±3.98). In terms of clinical reasoning ability, the mean score of the experimental group reached 27.55±1.59, far exceeding that of the control group (20.42±2.87). Regarding teaching satisfaction, the experimental group had significantly higher proportions of students reporting enhanced learning interest (91.53% vs. 55.08%), in-depth understanding of learning content (80.51% vs. 63.56%), and a comfortable clinical teaching atmosphere (69.49% vs. 49.15%) compared with the control group (all P<0.05). Conclusion: The blended teaching model integrating virtual and real practice improves teaching effectiveness, helps consolidate students' professional theoretical foundation, and enhances their clinical reasoning ability. Therefore, this approach holds positive reference value for the teaching reform of Pain Diagnostics and Therapeutics.

The new quality productivity empowers chronic respiratory disease health tourism, that is, the use of modern technology and innovative means to enhance the travel experience and quality of life of patients. Specifically, it includes: improving the telemedicine system, monitoring and diagnosing patients’ health in real time, and ensuring timely medical assistance; applying smart devices to track physiological indicators and adjust treatment plans; developing personalized health plans, designing travel programs that are suitable for patients, and reduce the risk of disease; providing psychological support to help cope with travel challenges; the government guides the integration of resources and encourages the participation of all sectors of society to form a win-win situation; promote interdisciplinary collaboration and research and development of comprehensive service solutions; strengthen the training of tourism personnel and improve the care capacity; regular monitoring and evaluation to optimize the service content to ensure the best travel experience. These measures will provide a dual guarantee for the travel and health of patients.

Key Words: artificial intelligence; Internet of Things in medicine; metaverse in medicine; medical GPT; new quality productive forces

Objective This study aims to explore the prevalence of AI platform usage among student groups. Methods　Sixty undergraduate students enrolled in the general elective course "Metaverse and Future Medicine" at Fudan University were divided into 10 groups, each consisting of 6 members. After class, a questionnaire was administered to collect feedback on the logos generated by the groups. Results　A total of 45 valid questionnaires were received. Among them, 8 groups of students used AI platforms to design logos. The keywords focused on "metaverse," "medicine," and "logo." The most popular logos were from Group 4, Group 7, and Group 3. Among the 29 students who filled out their MBTI types, there were 4 INFPs and 5 INTJs among those who used the AI platform, while the non-AI users included 3 ENFPs and 2 INTJs. Conclusion  The application of AI platforms in student learning and creation is increasingly common, though challenges remain. Future teaching could consider enhancing training on AI tools to improve students' usage skills and creative outcomes.

Key Words：metaverse; artificial intelligence; education

The rapid evolution of healthcare services toward virtual–physical integration, continuous interaction, and platform-based organization is fundamentally challenging the premises of traditional healthcare payment systems, which are grounded in episodic, in-person care. Unlike the mere digitization of payment channels in early digital health, metaverse medicine reconstitutes service delivery, value creation, and accountability networks, thereby exposing deep-seated structural inadequacies of existing payment systems in process measurement, real-time coordination, and multi-actor value distribution. The conventional theoretical presumption of payments as a “neutral settlement infrastructure” has become insufficient to explain and support the development of metaverse medicine. Moving beyond the “tool-neutrality” perspective, this paper repositions the payment system as an endogenous institutional arrangement within healthcare governance. Employing institutional analysis and comparative institutional analysis, it systematically examines the applicability limits of current payment instruments (e.g., CBDCs, stablecoins) in virtual–physical, process-oriented service contexts. It constructs a three-dimensional analytical framework centered on “institutional compliance – technical flexibility – depth of governance embeddedness.” The study reveals that metaverse medicine drives three fundamental shifts in the payment system: in function, from an ex-post settlement tool to a process coordination mechanism; in design logic, from uniform rules to dynamic, programmable protocols; and institutionally, from a supporting ancillary to a core governance infrastructure. Consequently, this paper argues that restructuring the payment system for metaverse medicine constitutes a cross-layer, systemic endeavor. It requires, at the institutional level, affirming its governance role; at the mechanism level, developing dynamic models for payment based on process and outcomes; and at the technical level, prudently leveraging programmable and protocol-based capabilities as enablers. The conclusion underscores that resolving payment issues is a prerequisite for the sustainable development of metaverse medicine. Its evolution is not a matter of simple technological substitution but a complex process of co-construction among technological possibilities, medical value rationality, and institutional constraints. This analysis provides a novel theoretical lens for understanding healthcare’s organizational and institutional transformations in the digital age.

Key Words: metaverse medicine; healthcare payment systems; programmable payments; payment protocols; institutional restructuring

Named Entity Recognition, as a core task in Natural Language Processing, plays a crucial role in identifying medical entities such as diseases and symptoms in Electronic Medical Records, which is of great significance for clinical decision support and the construction of medical knowledge bases. However, traditional methods rely heavily on large amounts of annotated data and complex models, resulting in high training and inference costs. This paper proposes a generative medical NER method that integrates semantic retrieval and prompt learning with large language models. First, a sentence-level vector database is constructed to semantically encode EMRs for retrievable representations. Then, based on the input sentence, semantic similarity retrieval is performed, and similar examples are dynamically injected into a prompt template to guide the model in entity extraction. Finally, entity type annotation results are generated through structured special markers, enabling direct decoding output. Experimental results demonstrate that the proposed method performs well on both a self-constructed EMR dataset and the Ruijin Hospital diabetes dataset, and exhibits strong robustness and transferability, especially in low-resource scenarios.

Key Words: named entity recognition; electronic medical records; large language models

In the era of digital economy, the new quality productive forces, supported by digital, networked and intelligent new technologies, with scientific and technological innovation as the core driving force, has a wide range of penetration and integration, and is profoundly changing the development mode of all walks of life. Nebulizer therapy is an effective way to treat respiratory diseases such as asthma and chronic obstructive pulmonary disease by converting liquid drugs into tiny particles and delivering them directly to the lungs. The application of new quality productive forces, such as the Internet of Things, artificial intelligence, and the metaverse, has revolutionized nebulizer therapy. These technologies not only enable real-time monitoring and accurate analysis of patient physiological data to support the development of personalized treatment plans, but also improve the convenience of treatment and patient compliance. Through IoT technology, healthcare professionals can remotely monitor the treatment process to ensure the safety and effectiveness of treatment. At the same time, the introduction of AI technology has improved the efficiency of data-driven decision-making, making treatment plans more precise and scientific. However, new quality productive forces enabling nebulizing therapy also face challenges such as technology acceptance, data security and privacy protection, and economic cost. In the future, with the continuous development and improvement of technology, new quality productive forces will play a greater role in the field of nebulizer therapy and promote the high-quality development of medical service system.

Key Words: medical new quality productive forces; nebulizer therapy; metaverse in medicine