Objective To examine effects of hydrogen peroxide on intracellular free calcium concentration(i) in cardiac myocytes and its antagonism by carvedilol. Methods A cell culture of neonatal rat cardiac myocytes was used for experimentation. They were divided into four groups, i.e. control group, hydrogen peroxide (H 2O 2) group, carvedilol group,and H 2O 2 + carvedilol group. Confocal microscope was used with Fluo-3/AM as calcium indicator to detect changes in i immediately and 15 minutes after H 2O 2 intervention, respectively. Results The intracellular fluoresence intensity of a single cardiae myocyts in the control group and carvedilol group was low. The intracellular fluoresence intensity of a single cardiac myocyte in H 2O 2 group was significantly higher than in the control group 15 minutes after intervention (P

AIM: To examine the effects of hydrogen peroxide on intracellular free calcium concentration([Ca~(2+)]i) in cardiomyocytes and its antagonism by taurine. METHODS: A cell culture model of neonatal rat cardiac myocytes was used. There were four groups, control group, hydrogen peroxide (H_2O_2) group, H_2O_2+taurine (simultaneously) group,and H_2O_2+taurine (in sequence) group. Confocal microscope was used with Fluo-3/AM as calcium indicator to detect changes of [Ca~(2+)]i immediately and 15 minutes after H_2O_2 intervention, respectively. RESULTS:The intracellular fluoresence intensity of singular cardiomyocyte in H_2O_2 group was significantly higher than the control group 15 minutes after intervention (P

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan′s health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan′s health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

To explore the effectiveness of "near peer learning" (NPL) in the electromyography(EMG)teaching module for neurology residents.