In the face of the new scientific and technological revolution and the transformation of medical paradigms, the field of rare disease medicine, as a cross-disciplinary emerging discipline integrating basic research, clinical practice, drug development, and policy support, is experiencing unprecedented development opportunities. This paper systematically sorts out the disciplinary connotation, construction paths and practical challenges of the Department of Rare Diseases, and puts forward countermeasures and suggestions for promoting the high-quality development of this emerging discipline in China.

The construction of a training system for visiting physicians in the department of rare diseases in China is an important measure to improve the overall diagnosis and treatment capacity for rare diseases and address the critical challenge of insufficient knowledge and skills among clinicians in practice. This article systematically describes the visiting physician training system established by the Department of Rare Diseases at Peking Union Medical College Hospital. It summarizes the training objectives and positioning, design logic, and learning modules of the system, aiming to provide a reference for the construction of the specialized talent team for rare diseases in China.

Homozygous familial hypercholesterolemia (HoFH) is an extremely rare and severe hereditary lipid metabolism disorder, characterized by markedly elevated levels of plasma low-density lipoprotein cholesterol (LDL-C), significantly increasing the risk of atherosclerotic cardiovascular diseases. Among traditional lipid-lowering therapies, the combination of statins and ezetimibe is the basic treatment approach, but its efficacy is limited. In recent years, notable progress has been made in lipid-lowering therapy for HoFH. New drugs such as proprotein convertase subtilisin/kexin type 9 inhibitors and angiopoietin-like protein 3 inhibitors have demonstrated favorable LDL-C-lowering effects and play an active role in treatment. Lipoprotein apheresis can rapidly reduce LDL-C levels and has become an important adjuvant treatment modality. Although approaches like gene therapy and liver transplantation face many challenges, they offer hope for radical cure. Further research is still needed to optimize treatment strategies and regimens for more effective blood lipid management in patients with HoFH.

The major obstacle to optimizing the design of rare disease coverage is the fragmented decision-making process among medical services, pharmaceuticals, and medical insurance departments. There is an urgent need to realize data sharing and digital empowerment, as well as to adopt top-level design and systematic decision-making. It is also crucial to establish mechanisms, facilitated by digital intelligence, for sharing power and responsibilities, and assessing rewards and punishments. Furthermore, there is an urgent need to incorporate the theories of collaborative governance, digital governance, and the full life cycle into the entire process, which includes patient classification, diagnosis and treatment, medical assistance, medication protection, and health insurance fund management for rare diseases. This integration aims to provide theoretical reference for the effective linkage of medical services, pharmaceuticals, and medical insurance, and to improve the efficiency and equity of resource allocation in the public sector.

To summarize the applications of data-intelligence technology in diagnosing lysosomal storage disease(LSD), analyze their opportunities and challenges in clinical practice as well as their development trends, and provide insights and recommendations for advancing digitally driven auxiliary diagnostic technologies.

To review the randomized controlled trials (RCTs) at home and abroad on digital intelligence (DI)-driven rehabilitation in patients of neuromuscular disease, compare the effects of DI-driven rehabilitation with traditional rehabilitation, summarize the special needs and challenges faced by patients in rehabilitation of rare neuromuscular diseases, and provide evidence for the development and quality improvement of rehabilitation for rare neuromuscular diseases.

To provide references to and give suggestions to the development and optimiza-tion of Digital Intelligence (DI) technology in management of non-hospitalized patients by systematical review the application of digital technology in non-hospital settings.

Since its establishment in 2016, the National Rare Diseases Registry System of China (NRDRS) has accumulated valuable case data and bio-specimen for basic and clinical research on rare diseases in China. However, the emerging challenges in clinical diagnosis and treatment of rare diseases make it unable for data and resource platform to fully meet the diversified needs. Under this backdrop, we have developed a protocol to optimize and upgrade the system based on the core functions of the NRDRS platform. The goal is to leverage intelligent digital technologies to transform NRDRS into a new platform integrating multimodal data and auxiliary diagnostic and treatment functions. It is specified as the development and construction of "one platform and four intelligent tools." Currently, we have upgraded and developed NRDRS platform, intelligent tool for genotype-phenotype analysis of rare diseases, AI-assisted diagnostic tool for rare diseases, remote multidisciplinary diagnosis and teaching tool for rare diseases, drug screening and validation tool for rare diseases. The next step will focus on the promotion of the application of these tools in clinical settings in order to address the issue of severe imbalance in the allocation of resources for the diagnosis and treatment of rare diseases. This article provides an overview of the digital and intelligent upgrades of the NRDRS, the trials in applications in clinical settings, and direction in the future.

Rare diseases are characterized by very low incidence and prevalence rates,complex genetic mechanisms,and diverse clinical phenotypes,posing significant diagnostic and therapeutic challenges in clinical research.In principle,the design of clinical research protocols for rare diseases does not differ significantly from general clinical research.However,the difficulties mainly stem from the unique characteristics of rare diseases,which amplify the challenges and limitations inherent in general clinical research.These challenges typically involve five aspects:data collection,data management,technical methods,ethical regulations,and patient engagement.However,with the rapid development of digital technologies such as information technology,artificial intelligence(AI),and blockchain,particularly in the innovative applications of data collection,storage,analysis,sharing,and management,new opportunities have emerged for the implementation and optimization of rare disease clinical research.Strategies for conducting rare disease clinical research using digital technologies are often applied to rare disease clinical research and patient management based on digitalized registration platforms,the development of AI-driven diagnostic aids to improve the accuracy of rare disease diagnosis,the use of digital technologies for decentralized rare disease clinical research,and the promotion of data fusion from multiple sources and modalities.However,during the application process,new challenges have gradually been identified.Despite of many challenges that still exist in terms of data privacy,algorithmic fairness,and ethical norms,with the continuous maturation of technology and the improvement of ethical frameworks,digitally-intelligent-driven clinical research on rare diseases remains promising.

Objective:To investigate the impact of receptor-interacting protein kinase 3 (RIPK3) on major adverse cardiovascular events (MACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI), as well as the predictive performance of RIPK3 combined with traditional cardiovascular risk factors.Methods:This study was a single-center prospective cohort study. It included patients with AMI who underwent PCI at Peking Union Medical College Hospital between September 2017 and November 2017. Baseline clinical data were collected, and plasma samples were obtained 6 hours after PCI to measure RIPK3 levels. Follow-up was conducted via outpatient visits or phone calls to record the occurrence of MACE, including cardiovascular death, hospitalization for heart failure, and vascular events (recurrent AMI or stroke). The predictive performance of RIPK3, traditional cardiovascular risk factors and their combination for MACE was compared using receiver operating characteristic (ROC) curves. Patients were divided into low- and high-RIPK3 level groups based on the optimal cutoff value of RIPK3. Multivariate Cox proportional hazards regression analysis was used to assess the impact of RIPK3 levels on MACE after PCI in AMI patients. Kaplan-Meier survival curves were plotted, and the log-rank test was used to compare MACE incidence between the low-and high-RIPK3 groups.Results:A total of 103 AMI patients who underwent PCI were included, aged 63.0 (56.0, 69.0) years, and 83 (80.6%) were male. The follow-up time was 5.17 (2.81, 5.17) years, during which 44 patients (42.7%) experienced MACE. The ROC curve analysis showed that the area under the curve ( AUC) for traditional cardiovascular risk factors was 0.68 (95% CI: 0.58-0.78), while the AUC for plasma RIPK3 was 0.72 (95% CI: 0.62-0.82). The combined AUC for traditional risk factors and RIPK3 was 0.75 (95% CI: 0.65-0.85). Multivariate Cox proportional hazards regression analysis indicated that plasma RIPK3 level is greater than or equal to the optimal cutoff value of 440.9 μg/L ( HR=3.31, 95% CI: 1.53-8.30, P=0.005) was an independent risk factor for MACE in AMI patients after PCI. Kaplan-Meier survival analysis demonstrated that the high-RIPK3 group had a significantly higher risk of MACE after PCI compared to the low-RIPK3 group (log-rank P=0.006). Conclusions:Elevated plasma RIPK3 level is an independent risk factor for MACE in AMI patients after PCI. Plasma RIPK3 combined with traditional cardiovascular risk factors can more effectively predict the occurrence of MACE in AMI patients after PCI. AMI patients with RIPK3≥440.9 μg/L have a higher risk of MACE after PCI.