European Society of Cardiology guidelines on diagnosis and treatment of heart failure are among the mostly authorized international directing documents,applied widely in clinical practice,epidemiological researches and randomized clinical trials.The guidelines published in 2008 combined chronic and acute hear failure and made significant modifications in the definition,classification,diagnosis,pharmacological and non-pharmacological treatment of heart failure.Simplification of the recommendations and enhancement of the guidelines implication are also point of focusing.This article represents a brief review of the new guidelines,together with comparison with the previous edition and analysis of recommendations based on local clinical practice,to enhance the standardized management of heart failure in China. Abstract:Summ ary:European Soc iety of Card iology gu idelines on d iagnosis and treatm ent of heart failure are among the mostly au-thorized international d irecting docum ents,app lied w idely in c lin ical practice,ep idem iological researches and random ized c lin ical trials.The gu idelines pub lished in 2008 comb ined chron ic and acute hear failure and m ade sign ificant mod ifica-tions in the defin ition,c lassification,d iagnosis,pharm acological and non-pharm acological treatm ent of heart failure.S imp li-fication of the recomm endations and enhancem ent of the gu idelines imp lication are also point of focusing.Th is artic le re-presents a brief review of the new gu idelines,togetherw ith comparison w ith the previous ed ition and analysis of recomm en-dations based on local c lin ical practice,to enhance the standard ized m anagem ent of heart failure in Ch ina.

[Summary] Rare diseases are one of the major challenges we face today in the era of precision medicine, because of the low incidence and prevalence, difficulty in diagnosis, lack of sufficient therapeutic methods, as well as their significant impacts on affected individuals, families and the society. Integration of clinical phenotypic and biological omics data and the further analysis are providing a way to illustrate the mechanisms of rare diseases, discovering novel diagnostic and prognostic biomarkers, developing orphan drugs and other therapeutics, and improving clinical outcomes and quality of life for the patients. A nation-wide registry system and the cohorts studies based on the registry are vital to the research of rare diseases. National Rare Diseases Registry System ( NRDRS) of China will provide this essential platform to promote the rare diseases research in China. With the collaboration of 20 leading medical institutes and innovation in medical informatics technologies, this system will, for the first time in China, collect the epidemiological, clinical, socio-economical, genomics and metabolomics data of more than 50 rare diseases and not less than 50 000 cases. As a national strategy for enhancing the development of medical sciences and the improvement of population health in China, NRDRS and its cohort studies will provide the pivotal support to policy making, clinical care, novel drug discovery, patient advocacy, and finally scientific progress in the field of rare diseases.

ObjectiveTo investigate the impacts of different serum creatinine detection methods,including Jaffe and enzymatic methods,on the efficacy of different GFR estimation equations in CKD patients in China.MethodsrGFR of 176 patients with CKD were determined by dual plasma sample method 99mTc-diethylenetriamine pentaacetic acid (99mTc-DTPA) plasma clearance rate.Serum creatinine was detected with four kinds of creatinine reagents from different manufacturers.Cockcroft-Gault Equation corrected for body surface area (CG/BSA),simplified Modification of Diet in Renal Disease (MDRD) Study equation,IDMS-traceable MDRD equation,CKD epidemiology collaborative research (CKD-EPI) equation and two Chinese simplified MDRD equation (project group equation 1,2) were applied to calculate estimated GFR (eGFR)respectively.eGFRwerecomparedwithrGFRforthecorrelation, deviation, precisionand30％ accuracy.ResultsThe mean rGFR of 176 patients with CKD,was [ 40.70 ( 19.41 -84.35 ) ] ml · min- 1 ·( 1.73 m2 ) -1.For all GFR estimation equations,there were significant differences in eGFR results between enzymatic method and Jaffe method,when analyzed by the Wilcoxon signed-rank test.eGFR results assessed by two enzymatic creatinine detection systems showed no significant difference,while eGFR results analyzed by two Jaffe detection system were significantly different.The intraclass correlation coefficient (ICC) of eGFR and rGFR ranged from 0.879 to 0.923 by Jaffe method,while from 0.925 to 0.946 by enzymatic creatinine method.ICC and Pearson correlation analysis revealed a significant correlation between eGFR and rGFR,and the correlation was better when using enzymatic method.Bland-Altman plots indicated that large deviation occurred in the high value area of GFR using various equations.However,deviation with the enzymatic creatinine method was smaller than that with the Jaffe method. When rGFR ≥ 60 ml · min- 1 ·(1.73 m2) -1,the 30％ accuracy of eGFR using enzymatic creatinine method for all six equations was between 68.3％ and 90.0％,while it was between 41％ and 75％ when using Jaffe method. The 30％ accuracy of eGFR using enzymatic creatinine method was significantly higher than that using picric acid method for these equations except for the project group equation 1.When rGFR ＜60 ml · min -1 · ( 1.73 m2 ) -1,the 30％accuracy of eGFR using both methods was between 39.7％ -49.1％,40.5％ -52.6％respectively,and the difference of data showed no statistical significance.For the same equation,there was a significant differernce in 30％ accuracy of eGFR between two enzymatic creatinine detection systems,while there was no significant differernce between two Jaffe creatinine detection systems.ConclusionsA significant difference was demonstrated in the same GFR evaluation equation using two different creatinine detection methods (Jaffe method and enzymatic method).The correlation between rGFR and eGFR,the degree of deviation,and accuracy of eGFR results assessed by enzymatic creatinine method were better than those by Jaffe method.The eGFR results assessed by different enzymatic detection systems revealed no significant difference.

Purpose/Significance To introduce the knowledge graph technology in the field of electronic information into the study of traditional Chinese medicine(TCM)terminology,and to demonstrate the dialectical relationship between the therapeutic effects of TCM and its nature,flavor,meridians and TCM diseases in a vivid way.Method/Process The top-down ontology construction method is adopted to build the top-level structure of TCM ontology on the Protégé platform.Taking the 2020 edition of the Chinese Pharmacopoeia as the data source,the TCM information in the pharmacopoeia is split and extracted,and the information of each axis is sorted,disambiguated and nor-malized.With the help of the Protégé platform,entities and relationships are created,and the TCM triple data is output in RDF data for-mat.Finally,the Neo4j graph database is used to store and display the RDF data to form a systematic TCM knowledge graph.Result/Con-clusion It mainly realizes the construction of the knowledge graph of TCM in the pharmacopoeia,and fully reveals the complex knowledge system structure in the field of TCM through data statistical analysis,knowledge measurement and drawing of visual graphics.

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.

It is noteworthy that only 5% of more than 7000 described rare diseases are treated. In the era of big data, there is ever-increasing data for understanding biomedicine. The need for efficient and rapid data collection, analyses and characterization methods is pressing. Rare diseases can particularly benefit from artificial intelligence (AI) application. AI, with an emphasis on machine learning, creates a path for such efforts and is being applied to diagnosis and treatment. AI has demonstrated its potential to learn and analyze data from different sources with results in prediction。Presently, there are AI-driven technologies applied for rare diseases and this review aims to summarize these advances. Moreover, this review scrutinizes the limitation and identifies the pitfalls of AI applications in the diagnosis and treatment of rare diseases.

The diagnosis and treatment resources for rare diseases in China are highly imbalanced. The basic diagnosis and treatment capabilities are weak, the diagnosis period for patients is long, and the rates of missed diagnosis and misdiagnosis are relatively high. The establishment of a hierarchical diagnosis and treatment system is the inevitable approach to enhancing the diagnosis and treatment standards of rare diseases. Currently, the implementation of the domestic hierarchical diagnosis and treatment system for rare diseases still confronts numerous challenges, such as ambiguous referral standards and processes of primary medical institutions, and ineffective information interaction among institutions at all levels. Thus, it is essential to facilitate high-level information construction for the hierarchical diagnosis and treatment of rare diseases. This paper explores the process of constructing a multidisciplinary joint remote diagnosis and treatment platform and a health management platform through informatization, with the hope of establishing two closed loops of digital diagnosis and treatment services and health follow-up management for patients with rare diseases, as well as achieving timely diagnosis and lifelong health management for patients. It integrates and optimizes auxiliary diagnostic tools, promotes the rapid dissemination of rare disease diagnosis and treatment experiences to the grassroots, enhances the information construction level of the hierarchical diagnosis and treatment system, and endeavors to address the practical predicament of weak diagnosis and treatment capabilities of rare diseases in grassroots medical institutions. Additionally, this paper proposes an essential approach for multi-dimensional independent innovation to guide the popularization of efficient and high-quality rare disease diagnosis and treatment services. By encompassing innovating the rare disease diagnosis and treatment collaboration network and multidisciplinary diagnosis and treatment model, facilitating the application of the latest biomedical and informatics technologies to the grassroots, and constructing a national intelligent data platform for rare disease innovation, a new model for rare disease services with Chinese characteristics will be established. This will significantly enhance the medical treatment level of rare diseases in China and strive for more benefits for patients.

Humans ; Infant, Newborn ; Intensive Care Units, Neonatal ; Logistic Models ; Phenotype ; Risk Factors