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.

The exploration and pilot studies of applying blockchain to drug supply chain show great potential in promoting information sharing, collaboration competence among the actors, regulatory efficiency, and etc. In the future, with the help of blockchain, the optimization of the entire supply chain for orphan drugs is expected to be realized. However, there is no such exploration in China at present. This paper systematically sorts out the whole process of supply chain for orphan drugs and the existing problems of the chain. The article concludes that at present, blockchain is mainly used in the " circulation" and " use" of the drug supply chain. It helps to improve the traceability of drugs, to cope with the problem of counterfeit drugs, to enable actors of the drug supply chain to form a collaborative network in optimizing resource allocation, and to improve the operation and supervision efficiency of the supply chain. In the future, the application faces challenges such as high costs in system conversion, lack of personnel awareness, and incomplete supporting systems. Based on the three dimensions of technology, practice, and research, this paper also looks into the future and suggests for the future use of blockchain in the supply chain of orphan drugs by constructing a practice model, the so called DI-GIVE (Digital, Intelligence, Government′s supervision, Innovation, Views of variety, Evaluation-based) hoping to innovate the supply chain of orphan drugs and to ensure the drug use for the patients with rare diseases in China.

To summarize the current status in the application of digital and intelligent technologies in the field of pharmacovigilance and to provide reference to the selection and development of methods for pharmacovigilance of rare diseases.

Our study aims at systematically summarizing and evaluating the applications of digital intelligence technologies in the field of rare disease medical care insurance now and in the future and at constructing a conceptual framework for the digital powered mechanism for the medical care insurance for rare diseases. By using Chinese keywords of " rare disease" " medical insurance"" artificial intelligence"" prediction model"" machine learning"" big data"" algorithm" and their English equivalents, we searched the databases of PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, collected relevant literature, and decided the criteria of inclusion and exclusion. The finding of our study shows that medical care insurance mechanism of rare disease in China faces significant challenges in drug accessbility and the funding sustainability. Meanwhile, our study shows that the digital intelligence technologies have broad potential in applications-in financing, accessbility, payment, and supervision. Specifically, dynamic simulation models and big data analysis can make precise prediction of the demand for funding of medical care insurance. The machine learning algorithms improve the dynamic evaluation of drug safety and cost-effectiveness. The personalized payment models enhance the efficiency in identifying the cohort with high expenditure so as to alleviate fund expenditure pressures. The intelligent monitoring technologies can accurately detect the abnormal behaviors in funds of medical care insurance. These technologies provide systematic and scientific solutions for improving the medical care mechanism for rare diseases. Even though further investigation is needed, the digital intelligence technologies have shown remarkable potential in enhancing the flexibility, efficiency, and sustainability of the medical care insurance system and a promising future in meeting the needs of patients with rare diseases.

In recent years, the rapid development of digital intelligence has provided a new path for rare disease data sharing and injected new power into the progress of research of rare diseases. This research is aimed at summarizing and consolidating relevant literatures on data sharing driven by digital intelligence (DI) in China and abroad, and constructing a local theoretical framework of DI-driven data sharing for rare diseases based on the status of rare diseases in China. Searching PubMed, EMbase, Cochrane, CNKI, Wanfang, and VIP database, we obtain a total of 214 representative literatures. Through literature review, we find that DI technologies have played important roles in different aspects of rare disease data sharing. China, the United States, and Europe have formed their own DI-driven data sharing systems for rare disease. From the theory of " Information Commons", we analyze the gap between China′s current situation and the goal of a " Rare Disease Data Commons". Based on the analysis, we put forward the idea of framework of " DI-STARS". China should develop the Data Sharing system making DI as the core of the system. Meanwhile, China should strengthen the data standardization system, create an innovation-encouraging environment, and build a bridge between different platforms. Using the DI-STARS theory, China will be able to build the " Rare Disease Data Commons" so that the diagnosis and treatment of rare diseases will be enhanced in China to meet the patients′ needs.

Objective:To investigate the value of diffusion kurtosis imaging (DKI) combined with quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting axillary lymph node metastasis in breast cancer.Methods:A total of 150 cases of breast cancer confirmed by pathology in the Affiliated Hospital of Jining Medical University were retrospectively analyzed. 68 cases had axillary lymph node (ALN) metastasis and 82 cases had no ALN metastasis. All breast lesions were examined by DKI and DCE-MRI before operation. We analyzed clinical case data, routine MRI features, DKI, and DCE-MRI parameters between two groups, including diffusion kurtosis (MK), mean diffusion rate (MD), volume transfer constant (K trans), extravascular volume fraction (Ve), and rate constant (Kep); The receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy of quantitative parameters for ALN metastasis of breast cancer. Results:The proportion of lesions with blurred edges in the metastatic group was higher than that in the non ALN metastatic group ( P=0.032); The proportion of uneven and circular enhancement within the ALN metastasis group was relatively high ( P=0.018). The MD value of the ALN transfer group was lower than that of the group without ALN transfer ( P=0.021); The MK value, K trans value, and Kep value were higher than those in the group without ALN metastasis (all P<0.01). The K trans value of DCE-MRI model was the most effective in diagnosing ALN metastasis of breast cancer, and the area under the ROC curve (AUC) was 0.831; The AUC of DCE-MRI model was 0.833, which was higher than that of DKI model (AUC=0.733), and the difference was statistically significant ( Z=2.208; P=0.027). The AUC of DCE-MRI and DKI models were higher than that of conventional MRI models ( Z=3.184, P=0.002; Z=1.917, P=0.046). The sensitivity and accuracy of combined DKI and DCE-MRI models in the diagnosis of ALN metastasis in breast cancer were higher than those of single model. Conclusions:DKI and DCE-MRI models can be used to predict axillary lymph node metastasis in breast cancer. Among them, the K trans value of DCE-MRI model is the most effective in diagnosing axillary lymph node metastasis in breast cancer.

Multiple sclerosis (MS) is a neuroinflammatory demyelinating disease, mediated by pathogenic T helper 17 (Th17) cells. However, the therapeutic effect is accompanied by the fluctuation of the proportion and function of Th17 cells, which prompted us to find the key regulator of Th17 differentiation in MS. Here, we demonstrated that the triggering receptor expressed on myeloid cells 2 (TREM-2), a modulator of pattern recognition receptors on innate immune cells, was highly expressed on pathogenic CD4-positive T lymphocyte (CD4⁺ T) cells in both patients with MS and experimental autoimmune encephalomyelitis (EAE) mouse models. Conditional knockout of Trem-2 in CD4⁺ T cells significantly alleviated the disease activity and reduced Th17 cell infiltration, activation, differentiation, and inflammatory cytokine production and secretion in EAE mice. Furthermore, with Trem-2 knockout in vivo experiments and in vitro inhibitor assays, the TREM-2/zeta-chain associated protein kinase 70 (ZAP70)/signal transducer and activator of transcription 3 (STAT3) signal axis was essential for Th17 activation and differentiation in EAE progression. In conclusion, TREM-2 is a key regulator of pathogenic Th17 in EAE mice, and this sheds new light on the potential of this therapeutic target for MS.
Animals ; Humans ; Mice ; CD4-Positive T-Lymphocytes/pathology* ; Cell Differentiation ; Encephalomyelitis, Autoimmune, Experimental/metabolism* ; Mice, Inbred C57BL ; Multiple Sclerosis ; Th1 Cells/pathology*