Radiofrequency ablation (RFA) represents a convenient, minimally invasive, and cost-effective approach for the treatment of small liver cancers measuring less than 3 cm in diameter. Nonetheless, the existing RFA techniques encounter challenges in precisely controlling the extent of ablation and the risk of overheating, which can lead to damage on the surrounding tissues. The ability to control the ablation area also plays a crucial role in the success of RFA procedures. To address these issues, we introduce a novel method that utilizes tumor boundary temperature monitoring to achieve precise control over the RFA process. Through the utilization of COMSOL Multiphysics simulation software, the proposed method was verified by comprehensive simulation and modeling and its efficacy in achieving regional control was testified. Subsequently, ex vivo experiments were conducted employing a custom-designed cooling RFA instrument. The experimental results demonstrated that the RFA controlled by boundary temperature yielded an ablation area with a diameter approaching 30 mm. In comparison to the standard spherical solidification zone (characterized by both the ellipticity and regularity indices of 1), the cooling RFA discoloration zone, under boundary temperature control, exhibited a maximum deviation of 7% in the ellipticity index and 17% in the regularity index. The average ellipticity index was determined to be 0.96, while the average regularity index was 0.86. Collectively, these findings underscore the capability of the proposed temperature-controlled cooling RFA method to attain precise control over the ablation area, contributing to comprehensive ablation of tumor tissue within the target region.

Specialty education in neurology faces structural bottlenecks including insufficient training in low-frequency, high-risk clinical scenarios, unequal opportunities for performing key procedures, difficulty in replicating ethically sensitive communication situations, and a weak cross-stage evaluation system. Based on the “Huisheng Intelligent Education” metaverse platform of Zhongshan Hospital, Fudan University, this paper proposes a conceptual framework for building a neurology-specific metaverse platform targeting full-cycle medical education, training, and management. Using extended reality (XR) and artificial intelligence (AI) as the technological foundation, and focusing on core teaching scenarios such as neurological examination, emergency decision-making in stroke, status epilepticus management, long-term care of Parkinson‘s disease, and breaking bad news, the platform constructs four major modules: a neurology knowledge ecosystem, immersive virtual teaching scenarios, a specialized teaching intelligence system, and a full-cycle teaching management hub. The platform supports cross-campus synchronous teaching and process tracking, aiming to establish a new paradigm of specialty education characterized by “scenario-based training — formative evaluation — personalized guidance — governance closed loop”. Taking the “acute ischemic stroke thrombolysis decision-making metaverse teaching unit” as an example, this paper demonstrates the feasible approach of the platform in scenario design, task chain organization, and technical implementation. This paper provides a replicable engineering framework and design reference for the systematic construction of metaverse teaching in neurology, with future effectiveness validation to be conducted based on actual operational data.

Key Words: neurology; metaverse in medicine; full-cycle medical education; immersive training; thrombolysis decision-making; competency evaluation

Objective: To design and validate a portable multi-degree-of-freedom electric needle holder to improve operational flexibility and precision in laparoscopic surgery, while reducing the operator's workload. The design optimizes the transmission system to enable precise control of surgical instruments, addressing the challenges of complex surgical environments. Methods: A multi-degree-of-freedom transmission structure, driven by steel wires, was designed. It integrates motors and angle sensors to achieve multi-directional rotation and force control for the grasping forceps. The experimental section includes tests for maximum gripping force, maximum extraction force, and a porcine suturing experiment. The performance of the electric needle holder was compared with traditional needle holders in simulated laparoscopic surgery to verify its effectiveness. Results: Experimental results show that the maximum gripping force of the electric needle holder at various angles reached 29.6 N, significantly higher than that of traditional needle holders. The maximum extraction force for needles of different sizes is approximately 12.9 N, effectively preventing needle slippage. Additionally, the porcine suturing experiment demonstrated that the electric needle holder significantly reduced operation time and improved suturing precision in complex surgical settings. Conclusion: The portable multi-degree-of-freedom electric needle holder enhances operational flexibility and safety in laparoscopic surgery by increasing the rotational degrees of freedom and precise motor control. Its superior gripping force and operational efficiency suggest strong clinical potential, offering significant improvements over traditional surgical instruments.

Objective: This study integrated network pharmacology and molecular docking to identify the active constituents, core targets, and potential mechanisms of Dunhuang Mofeng ointment in treating rheumatoid arthritis (RA). The therapeutic efficacy was validated using a collagen-induced arthritis (CIA) rat model. Methods: Bioactive compounds and target molecules of Dunhuang Mofeng ointment were identified using R language-based network pharmacological analysis. These were integrated with RA-related differential genes from the GEO database to construct regulatory and protein-protein interaction networks. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to elucidate intervention mechanisms. Molecular docking simulations validated the conformational stability of core targets. A seven-week CIA rat model was established to assess therapeutic effects, including general health, joint swelling, limb deformities, serum inflammatory markers, and mRNA expression of key targets in joint tissues, thereby validating the network pharmacology predictions. Results: The core active ingredients were quercetin, apigenin, luteolin, aloe-emodin,and emodin. Primary therapeutic targets included STAT1, CDK4, CCND1, MCL1, FOS, CDKN1A and MYC, with anti-inflammatory effects mediated through pathways such as JAK-STAT, PI3K-Akt, and Toll-like receptor pathways. After 7 weeks of treatment, CIA rats showed significant improvements, including reduced joint swelling and deformity, decreased pro-inflammatory cytokines, and increased anti-inflammatory cytokine IL-10. MRNA expression of STAT1, CDK4, CCND1, MCL1, FOS, and MYC in joint tissues was significantly downregulated (P<0.05), while CDKN1A expression was upregulated (P<0.001). The medium-dose and Western medicine groups exhibited pronounced therapeutic effects. Conclusion: Dunhuang Mofeng ointment may exert therapeutic effects in RA by modulating pro-inflammatory cytokines and regulating key genes. These findings support its potential as an effective intervention for RA.

The pandemic virus COVID-19 has caused hundreds of millions of infections and deaths, resulting in enormous social and economic losses worldwide. As the virus strains continue to evolve, their ability to spread increases. The detection by reverse transcription polymerase chain reaction is time-consuming and less sensitive. As a result, X-ray images and computed tomography images started to be used in the diagnosis of COVID-19. Since the global outbreak, medical image processing researchers have proposed several automated diagnostic models in the hope of helping radiologists and improving diagnostic accuracy. This paper provides a systematic review of these diagnostic models from three aspects: image preprocessing, image segmentation, and classification, including the common problems and feasible solutions that encountered in each category. Furthermore, commonly used public COVID-19 datasets are reviewed. Finally, future research directions for medical image processing in managing COVID-19 are proposed.

The construction of collaborative “Flagship” hospitals integrating Chinese and Western medicine has placed higher demands on talent cultivation in integrated medicine, inheritance of the experience of renowned veteran TCM experts, innovation in collaborative diagnostic and therapeutic models, and information-based support. As one of the first batch of national pilot institutions for collaborative “Flagship” hospitals integrating Chinese and Western medicine, Zhongshan Hospital, Fudan University, has already established a full-cycle framework spanning undergraduate medical education, postgraduate medical education, and continuing medical education based on the “Huisheng Intelligent Education” metaverse platform. On this basis, an innovative teaching module for Traditional Chinese Medicine (TCM) inheritance is proposed to address the current gap whereby the existing platform primarily serves modern medical education and provides insufficient support for Western medicine physicians receiving TCM training and senior TCM physicians in the cultivation of TCM inheritance and innovation as well as collaborative Chinese-Western medicine competencies. Relying on the four existing platform foundations—namely the metaverse medical knowledge ecosystem, metaverse virtual teaching scenarios, comprehensive teaching intelligent agents, and the full-cycle teaching management hub—the module is designed around four core components: a metaverse teaching module for renowned veteran TCM experts’ studios, a virtual outpatient module for collaborative Chinese-Western medicine practice, a collaborative ward-round/MDT module integrating Chinese and Western medicine, and a TCM appropriate techniques and situational training module. Together, these form a continuous teaching chain of “inheritance resource accumulation—outpatient collaborative training—complex case collaborative decision-making—application of appropriate techniques.” Furthermore, taking the metaverse teaching module for the studio of Shanghai Famous TCM Expert CAI Dingfang as an example, this paper illustrates the specific design for knowledge resource integration, reconstruction of teaching scenarios, intelligent-agent support, and process-based management and evaluation. This module is expected to provide a new practical pathway for the digital transformation of TCM inheritance and innovative collaborative training models in the context of collaborative “Flagship” hospitals integrating Chinese and Western medicine.

Key Words: huisheng intelligent education; metaverse; collaborative “Flagship” hospital integrating Chinese and western medicine; TCM inheritance and innovation; teaching module