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In the process of digitisation of medical data,choosing the right technology for efficient processing and accurate analysis of medical data is a common problem faced by the medical field today.The use of knowledge graph technology with the excellent association and reasoning capabilities to process and analyse medical data can better enable applications such as wise information technology of medicine and aided diagnoses.The complete process of constructing a medical knowledge graph includes know-ledge extraction,knowledge fusion and knowledge reasoning.Knowledge extraction can be subdivided into entity extraction,relationship extraction and attribute extraction,while knowledge fusion mainly includes entity alignment and entity disambiguation.Firstly,the constructiontechnologies and practical applications of medical knowledge graphs are summarised,and the development of the technologies is clarified for each specific construction process.On this basis,the relevant techniques are introduced,and their advantages and limitations are explained.Secondly,introducing several medical knowledge graphs that are being successfully applied.Finally,based on the current state of technology and applications of knowledge graphs in the medical field,future research directions for knowledge graphs in technology and applications are given.

Intelligent substation is an important fulcrum of the smart grid strategy, with the increasing of primary equipment in smart substation, the amount of equipment information and data increases exponentially, traditional data retrieval methods have been unable to adapt to requirement of the massive data processing, how accurate and efficient retrieval substation equipment information become the focus of the research problem. Therefore, a method for the primary information retrieval of smart substation equipment based on knowledge graphs was proposed. First, the primary equipment information of the smart substation was classified to obtain relevant equipment information corpus. then an unstructured equipment information extraction framework was defined to achieve efficient equipment information corpus Extraction. then, hidden Markov model was used to segment equipment information so as to construct a knowledge map of substation primary equipment information, realize the visual display of equipment information, and break through the limitations of two-dimensional semantic tables. Finally, the knowledge map technology was utilized to design the information management system of the smart substation primary equipment. Experiments show that the smart substation primary equipment information record retrieval system based on the knowledge graph can provide accurate equipment information and realize the efficient retrieval of smart substation equipment information. [ABSTRACT FROM AUTHOR]

Acrolein is a highly toxic unsaturated aldehyde which is abundant in many circumstances. People exposed to acrolein may have significant clinical relevance in human cardiotoxicity situations. Paeoniflorin (PEF) is a bioactive glucoside isolated from the roots of Paeonia lactiflora Pall. It is reported that PEF performs a beneficial role in cardiovascular system. The aim of the current research was to evaluate the potential protective effect of PEF against acrolein-induced apoptotic damage in H9c2 cardiomyocytes. This study revealed that PEF exerted a protective effect on acrolein-induced cardiomyocyte apoptosis. Furthermore, treatment with acrolein could markedly increase the levels of reactive oxygen species (ROS) and expression of cleavage of caspase-9 and caspase-3 in H9c2 cardiomyocytes, which were significantly reversed by co-treatment with PEF (100uM). These results demonstrated that PEF protects H9c2 cardiomyocytes against acrolein-induced cardiomyocyte injury via decreasing ROS production and down regulating caspases cascade reaction, indicating that PEF is a potential therapeutic agent for cardiac toxic environmental pollutant injury.