[A new model for disease control and prevention driven by big data in healthcare].

With the rapid development of Internet technology and the continuous advancement of medical informatization, big data in healthcare has gradually become an important resource to innovate health management and meet the growing health needs of people and the application of big data in healthcare has been one of the indispensable parts of national big data strategy in China. Based on the established healthcare big data platform and the application of big data technology, Yinzhou district has made innovative efforts to explore a new model driven by big data for the prevention and control of communicable and non-communicable diseases and the management of vaccination programs. It is expected that the "Internet plus healthcare" model will strengthen the disease prevention and control and public health management in local area, create a new business form and provide strong support for Healthy China 2030. This article introduces this new model driven by big data in Yinzhou and discusses the preliminary efficiency of this model in public health practice.

[Epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform].

Objective: To understand the epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform to provide evidence for the construction of COVID-19 monitoring system. Methods: Data on Yinzhou COVID-19 daily surveillance were collected. Information on patients' population classification, epidemiological history, COVID-19 nucleic acid detection rate, positive detection rate and confirmed cases monitoring detection rate were analyzed. Results: Among the 1 595 COVID-19 monitoring cases, 79.94% were community population and 20.06% were key population. The verification rate of monitoring cases was 100.00%. The total percentage of epidemiological history related to Wuhan city or Hubei province was 6.27% in total, and was 2.12% in community population and 22.81% in key population (P<0.001). The total COVID-19 nucleic acid detection rate was 18.24% (291/1 595), and 53.00% in those with epidemiological history and 15.92% in those without (P<0.001).The total positive detection rate was 1.72% (5/291) and the confirmed cases monitoring detection rate was 0.31% (5/1 595). The time interval from the first visit to the first nucleic acid detection of the confirmed monitoring cases and other confirmed cases was statistically insignificant (P>0.05). Conclusions: The monitoring system of COVID-19 based on the health big data platform was working well but the confirmed cases monitoring detection rate need to be improved.

[Application of healthcare big data in active case finding of COVID-19 in Yinzhou district of Ningbo].

During the prevention and control of the COVID-19 epidemic, identifying and controlling the source of infection has become one of the most important prevention and control measures to curb the epidemic in the absence of vaccines and specific therapeutic drugs. While actively taking traditional and comprehensive "early detection" measures, Yinzhou district implemented inter-departmental data sharing through the joint prevention and control mechanism. Relying on a healthcare big data platform that integrates the data from medical, disease control and non-health sectors, Yinzhou district innovatively explored the big data-driven COVID-19 case finding pattern with online suspected case screening and offline verification and disposal. Such effort has laid a solid foundation and gathered experience to conduct the dynamic and continuous surveillance and early warning for infectious disease outbreaks more effectively and efficiently in the future. This article introduces the exploration of this pattern in Yinzhou district and discusses the role of big data-driven disease surveillance in the prevention and control of infectious diseases.

[A quantitative study on the relationship between the specific binding of glucocorticoid receptor and glucocorticoid biopotency in rat thymocytes].

The inhibitory effect of dexamethasone (Dex) on the incorporation of [3H] Urd and specific binding of [3H] Dex with glucocorticoid receptor (GR) were measured in order to quantitatively study the relationship between these two indices in rat thymocytes. The results showed that there existed a close correlation between the two indices in the range of 10(-9)-10(-5) mol/L Dex. When thymocytes were treated with RU486 in the concentration range of 10(-10)-10(-6) mol/L, both [3H] Dex specific binding and inhibitory effect of Dex were blocked with different fractions. The specific binding was linearly related to Dex biopotency. When extrapolated 100% of specific binding corresponded to 100% of biopotency while 20% of specific binding corresponded approximately to 0% of biopotency. It is likely that GR exhibited an occupancy threshold of about 20% total receptors, with only few spare ones in rat thymocytes.