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Objective With the focus on emerging infectious diseases and diseases of unknown cause,the study aims to realize multi-point trigger monitoring of infectious diseases through key monitoring sites and key populations.Methods Using ar-tificial intelligence,deep learning,big data and other information technologies to build an intelligent information center for infec-tious diseases with patients'disease files as the core,construct a core capacity of infectious disease surveillance,early warning and situation prediction,and predict and evaluate the importance of infectious disease warning signals.Results The system cov-ered 1 425 primary-level medical institutions,18 hospitals,2 580+schools,4 134 pharmacies,4 laboratories and civil affairs departments,detected 55 kinds of infectious diseases and 6 kinds of syndrome monitoring signals.Since its launch,121 000 ac-tive notification cards have been issued,more than 54 000 new notification cards have been added,35.256 million times of multi-source monitoring and 14.4 million disease files have been recorded.Conclusion By expanding monitoring content and chan-nels,we realized early monitoring,auxiliary investigation and multi-mode visual early warning of infectious diseases,built a multi-point trigger mechanism,and moved forward the infectious disease surveillance.
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0.05).The percentage of apoptotic lymphocytes in those with more than two kinds of autoantibodies was markedly higher than those with only one kind of autoantibody(P
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0. 05). This paper surggests that the activity of Na+-K+ ATPase on erythrocyte membrane may be a indicator in the diagnosis of stroke and human senility. The mechanism of changes of Na+-K+ATPase was also discussed in this paper.