OBJECTIVETo investigate the risk of acute myocardial infarction (AMI) following stress hyperglycemia after hip fracture.RESEARCH DESIGN AND METHODSFrom February 2007 to February 2012, we carried out a prospective observational analysis of 1,257 consecutive patients with no history of diabetes who suffered hip fractures. Fasting blood glucose (FBG) and glycosylated hemoglobin tests as well as electrocardiography, ultrasonic cardiography, and chest X-ray examinations were performed after admission. All selected hip fracture patients were divided into stress hyperglycemia and non-hyperglycemia groups according to their FBG, and the incidence of AMI was monitored.RESULTSAmong the patients enrolled, the frequency of stress hyperglycemia was 47.89% (602/1,257) and that of AMI was 9.31% (117/1,257), and the occurrence of AMI in the stress hyperglycemia group was higher than in the non-hyperglycemia group (12.46 vs. 6.41%, P < 0.05). In the stress hyperglycemia patients, FBG reached maximum levels at 2–3 days after hip fractures and then decreased gradually. The AMI incidence (62.67% [47/75]) of the stress hyperglycemia group was highest in the initial 3 days after hip fracture, significantly coinciding with the FBG peak time (P < 0.05). In all patients with AMI, non–ST-segment elevation myocardial infarction occurred more often than ST-segment elevation myocardial infarction (62.39% [73/117] vs. 37.61% [44/117]).CONCLUSIONSStress-induced hyperglycemia after hip fracture increased the risk of AMI.
The existing network security situation prediction algorithm cannot deal with the subjective attitude deviation of multi-experts, and the traditional sequential machine learning model cannot deal with the problem of deviation accumulation in time period. In this paper, an algorithm of network security situation prediction combining entropy correlation and deep time series network model is proposed. First, the expert fuzzy evaluation index is constructed by trigonometric fuzzy function, and the modified weighted dempster-shafer (DS) evidence reasoning correction index is used, then the loss and possibility matrix features are created, and finally the information security is evaluated by deep time series network. The simulation experiments are carried out on MIT dataset. The experiments analyze whether the features can cope with multi-expert conflicts, and evaluate the accuracy, robustness, and time efficiency of the algorithm.The experimental results show that the algorithm proposed in this paper has stronger fuzzy evaluation ability, stronger ability to deal with conflict opinions among experts, more accurate prediction of network security situation in time sequence, and higher robustness, but the efficiency of the algorithm has been maintained.
The expression of RhoA/Rho kinase mRNA and protein and function in the RA were significantly stronger than in the IMA, suggesting that RhoA/Rho kinase pathway may be one mechanism by which RA is more susceptible to spasm than IMA. Rho kinase inhibitors can be effective drug candidates to prevent and treat vasospasm.
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