Objective:To construct a management model of data mining for medical imaging equipment,so as to improve the quality of managing equipment.Methods:A management model of mining data was constructed to manage medical imaging equipment.A total of twenty imaging equipment that were using at Hainan Hospital of the General Hospital of the People's Liberation Army of China from April 2022 to March 2024 were selected.According to different management methods,the conventional management was adopted to manage them during April 2022 to March 2023,and the management model of mining data(model management)was adopted to manage them during April 2023 to March 2024.A self-developed questionnaire was used to conduct a satisfaction survey for imaging physicians,staffs of operating and maintaining equipment,and technicians who using and managing equipment,and patients who received diagnosis and treatment by using equipment.The failure rate and the imaging effect of equipment,the satisfaction scores of the relative staffs who used equipment,and the growth amplitude of operational benefits of equipment between two management methods were compared.Results:A total of 20 failures occurred in imaging equipment that adopted model management.In them,the failure rates of the self-equipment,improper operation and insufficient professional level were respectively 15%,5%and 5%,all of which were lower than those of the conventional management method.The predicted failure rate of model management was 75%,which was higher than that of the conventional management method,and the differences of the above indicators between two methods were statistically significant(x2=6.547,4.392,5.124,6.701,P<0.05).The scores of image clarity,qualification rate,excellent rate,qualification rate of body position,and the total score of imaging effect of adopting model management method were respectively(22.36±2.01),(23.21±1.54),(22.65±1.87),(23.21±1.52)and(91.43±6.77)points,all of which were higher than those of the conventional management method,and the differences were statistically significant(t=10.662,12.727,15.324,16.333,13.742,P<0.05).The satisfaction scores of imaging physicians,staffs of operation and maintenance,technicians and patients who uses management for adopting the model management method were all higher than those of the conventional management method,and the differences were statistically significant(t=13.586,14.249,17.021,11.006,P<0.05).The average values of growth amplitude of cost and benefit of equipment operation of adopting model management method were higher than those of the conventional management method,and the average value of the growth amplitude of the cost of the troubleshooting of model management method was lower than that of the conventional management method,and the differences of them were statistically significant(t=15.057,19.310,18.336,P<0.05).Conclusion:The application of the management model of data mining for medical imaging equipment can provide warning of equipment failures in advance,and reduce the failure rate of equipment,and improve the quality of management and operation of equipment,and enhance the service level of equipment.

Objective:To construct a management model of data mining for medical imaging equipment,so as to improve the quality of managing equipment.Methods:A management model of mining data was constructed to manage medical imaging equipment.A total of twenty imaging equipment that were using at Hainan Hospital of the General Hospital of the People's Liberation Army of China from April 2022 to March 2024 were selected.According to different management methods,the conventional management was adopted to manage them during April 2022 to March 2023,and the management model of mining data(model management)was adopted to manage them during April 2023 to March 2024.A self-developed questionnaire was used to conduct a satisfaction survey for imaging physicians,staffs of operating and maintaining equipment,and technicians who using and managing equipment,and patients who received diagnosis and treatment by using equipment.The failure rate and the imaging effect of equipment,the satisfaction scores of the relative staffs who used equipment,and the growth amplitude of operational benefits of equipment between two management methods were compared.Results:A total of 20 failures occurred in imaging equipment that adopted model management.In them,the failure rates of the self-equipment,improper operation and insufficient professional level were respectively 15%,5%and 5%,all of which were lower than those of the conventional management method.The predicted failure rate of model management was 75%,which was higher than that of the conventional management method,and the differences of the above indicators between two methods were statistically significant(x2=6.547,4.392,5.124,6.701,P<0.05).The scores of image clarity,qualification rate,excellent rate,qualification rate of body position,and the total score of imaging effect of adopting model management method were respectively(22.36±2.01),(23.21±1.54),(22.65±1.87),(23.21±1.52)and(91.43±6.77)points,all of which were higher than those of the conventional management method,and the differences were statistically significant(t=10.662,12.727,15.324,16.333,13.742,P<0.05).The satisfaction scores of imaging physicians,staffs of operation and maintenance,technicians and patients who uses management for adopting the model management method were all higher than those of the conventional management method,and the differences were statistically significant(t=13.586,14.249,17.021,11.006,P<0.05).The average values of growth amplitude of cost and benefit of equipment operation of adopting model management method were higher than those of the conventional management method,and the average value of the growth amplitude of the cost of the troubleshooting of model management method was lower than that of the conventional management method,and the differences of them were statistically significant(t=15.057,19.310,18.336,P<0.05).Conclusion:The application of the management model of data mining for medical imaging equipment can provide warning of equipment failures in advance,and reduce the failure rate of equipment,and improve the quality of management and operation of equipment,and enhance the service level of equipment.

Objective:To explore the effects of intelligently preventive maintenance of magnetic resonance imaging(MRI) equipment based on a predictive model in the intelligently preventive maintenance of medical equipment. Methods:An autoregressive integrated moving average (ARIMA) predictive model of intelligently preventive maintenance of MRI equipment was designed to perform detection for performance of MRI equipment,so as to do well for the intelligently preventive maintenance from three dimensions:preventive maintenance,fault repair and quality detection. A total of 20 MRI equipment in Hainan Hospital of Chinese PLA General Hospital from 2022 to 2023 were selected,and conventional maintenance method was adopted for equipment maintenance from January to December 2022,and intelligently preventive maintenance method (predictive model method) based on predictive model was adopted for MRI equipment based on predictive model for equipment maintenance from January to December 2023. The quality scores of preventive maintenance,the standardization level of operation and management of equipment and the satisfaction scores of operators for service quality of equipment were compared between the two maintenance methods. Results:The maintenance efficiency score,maintenance timeliness score,qualification rate of quality inspection and average score of maintenance regularity of the equipment of using maintenance method of predictive model were respectively (90.36±6.33) points,(89.14±4.36) points,(88.62±3.36) points and (91.58±3.47) points,all of which were significantly higher than those of conventional maintenance method,and the differences were statistically significant (t=10.876,11.360,12.283,12.226,P<0.05). The average operation rate,operational rate and allocation rate of equipment of using the maintenance method of predictive model were (91.58±4.12)％,(90.69±5.14)％,and (89.25±6.01)％,respectively,which were significantly higher than those of conventional maintenance method,while the fault frequency of the former was (1.02±0.25) times/year,which was significantly lower than that of conventional maintenance method,the differences were statistically significant (t=5.298,6.557,10.572,27.867,P<0.05). The average scores of the satisfaction of preventive maintenance,preventive repair,fault maintenance,post-maintenance and quality survey of 10 operators were respectively (90.54±2.36) points,(91.59±3.14) points,(92.54±4.69) points,(91.89±3.25) points and (92.54±2.45) points by using maintenance method of predictive model,which were all higher than those of conventional maintenance methods,and the differences were statistically significant (t=12.807,12.290,8.764,12.146,15.612,P<0.05). Conclusion:The predictive model-based intelligently preventive maintenance method of MRI equipment can improve the maintenance quality and operational quality of equipment,and relevant operators have a higher satisfaction for the service of equipment,which can effectively enhance the management effect for equipment and ensure the stable and efficient application of the equipment.

Objective:To explore the effects of intelligently preventive maintenance of magnetic resonance imaging(MRI) equipment based on a predictive model in the intelligently preventive maintenance of medical equipment. Methods:An autoregressive integrated moving average (ARIMA) predictive model of intelligently preventive maintenance of MRI equipment was designed to perform detection for performance of MRI equipment,so as to do well for the intelligently preventive maintenance from three dimensions:preventive maintenance,fault repair and quality detection. A total of 20 MRI equipment in Hainan Hospital of Chinese PLA General Hospital from 2022 to 2023 were selected,and conventional maintenance method was adopted for equipment maintenance from January to December 2022,and intelligently preventive maintenance method (predictive model method) based on predictive model was adopted for MRI equipment based on predictive model for equipment maintenance from January to December 2023. The quality scores of preventive maintenance,the standardization level of operation and management of equipment and the satisfaction scores of operators for service quality of equipment were compared between the two maintenance methods. Results:The maintenance efficiency score,maintenance timeliness score,qualification rate of quality inspection and average score of maintenance regularity of the equipment of using maintenance method of predictive model were respectively (90.36±6.33) points,(89.14±4.36) points,(88.62±3.36) points and (91.58±3.47) points,all of which were significantly higher than those of conventional maintenance method,and the differences were statistically significant (t=10.876,11.360,12.283,12.226,P<0.05). The average operation rate,operational rate and allocation rate of equipment of using the maintenance method of predictive model were (91.58±4.12)％,(90.69±5.14)％,and (89.25±6.01)％,respectively,which were significantly higher than those of conventional maintenance method,while the fault frequency of the former was (1.02±0.25) times/year,which was significantly lower than that of conventional maintenance method,the differences were statistically significant (t=5.298,6.557,10.572,27.867,P<0.05). The average scores of the satisfaction of preventive maintenance,preventive repair,fault maintenance,post-maintenance and quality survey of 10 operators were respectively (90.54±2.36) points,(91.59±3.14) points,(92.54±4.69) points,(91.89±3.25) points and (92.54±2.45) points by using maintenance method of predictive model,which were all higher than those of conventional maintenance methods,and the differences were statistically significant (t=12.807,12.290,8.764,12.146,15.612,P<0.05). Conclusion:The predictive model-based intelligently preventive maintenance method of MRI equipment can improve the maintenance quality and operational quality of equipment,and relevant operators have a higher satisfaction for the service of equipment,which can effectively enhance the management effect for equipment and ensure the stable and efficient application of the equipment.

Genome-wide physical protein-protein interaction(PPI)mapping remains a major chal-lenge for current technologies.Here,we reported a high-efficiency BiFC-seq method,yeast-enhanced green fluorescent protein-based bimolecular fluorescence complementation(yEGFP-BiFC)coupled with next-generation DNA sequencing,for interactome mapping.We first applied yEGFP-BiFC method to systematically investigate an intraviral network of the Ebola virus.Two-thirds(9/14)of known interactions of EBOV were recaptured,and five novel interactions were discovered.Next,we used the BiFC-seq method to map the interactome of the tumor protein p53.We identified 97 interactors of p53,more than three-quarters of which were novel.Furthermore,in a more complex background,we screened potential interactors by pooling two BiFC libraries together and revealed a network of 229 interactions among 205 proteins.These results show that BiFC-seq is a highly sensitive,rapid,and economical method for genome-wide interactome map-ping.

Objective To evaluate the effect of propofol infusion on hepatic blood flow (HBF) and oxygen delivery and consumption in rabbit. Methods Thirty adult male rabbits weighing 1.6-2.4 kg were randomly allocated into 3 groups: group I high dose propofol (HP) ( n = 11); group II low dose propofol (LP) (n = 10) and group III control group (C) ( n = 9). The rabbits were anesthetized with intravenous 3 % pentobarbital 45 mg ?kg-1 and mechanically ventilated (VT = 10 ml?kg-1 RR = 40 bpm, I:E= 1:2) after tracheal intubation. ECG, urinary output and rectal temperature were continuously monitored. Portal vein and hepatic artery were dissected and exposed for measurement of blood flow using electromagnetic flowmeter. Catheters were inserted into carotid artery, portal vein and hepatic vein via the mesenteric vein and right femoral vein for collection of blood samples. After the circulation was stabilized for 30 min, propofol infusion was started at a rate of 1.2 mg ? kg-1 ? min-1 ( HP) or 0.4 mg?kg-1 ?min-1(LP). In control group normal saline was infused instead of propofol. Portal venous and hepatic arterial blood flow were continuously measured. Blood samples were obtained from carotid artery, portal vein and hepatic vein before ( baseline) and at 30, 50, 70 and 90 min of propofol infusion for determination of Hb, SO2, PO2 and PCO2. The hepatic O2 delivery (DO2 ) and consumption (VO2 ) were calculated. Results The three groups were comparable with respect to body weight, duration of operation, the volume of fluid infused and blood loss and urinary output. HBF was significantly higher at 30-90 min of propofol infusion in HP group than in C group, meanwhile DO2 and VO2 in HP group were significantly higher during propofol infusion than the baseline value before infusion and those in C group. However, there was no significant difference in DO2/VO2 ratio between HP and C group. Conclusion High dose propofol infusion improves liver blood flow and O2 delivery but it also increases hepatic O2 consumption. However the balance between hepatic O2 supply/demand remains unchanged.

Aim To investigate the effect of Tetramethylpyrazine (TMP) on the proliferation of airway smooth muscle cells (ASMCs). Methods Primary ASMCs of rats were cultured. The absorbance (A490) value of ASMCs treatment with platelet-derived growth factor (PDGF) in the presence of TMP was detected by MTTto observe the anti-proliferation of TMP. The levels of ERK1/2 and p-ERK1/2 proteins were determined by Western blot.Results In presence of the TMP with different concentrations (12.5,25,50,100 and 200 ?mol?L-1) at 6,12,24,36 and 48 hours,compared with control groups,the average inhibitory rates of cell proliferation in all groups were increased significantly (P