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Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The weighted link prediction algorithm based on local traditional architecture only uses the topological attribute of the one-layer neighbor node. They are unable to reflect the contribution of common neighbors’ neighbor node on the potential node pair, and calculate the influence of the degree of interconnection of neighbors. This paper analyzed the contribution of the neighboring nodes on the potential pairs from the dense level of the local structure, and proposed a similarity index called( WCCLP) of the integrated weighted clustering coefficients, which could effectively enlarge the influence of the local structure on the prediction performance. Besides, WCCLP could easily be extended to the weighted local naive Bayesian link prediction model( WLNB). The experiments with unsupervised learning show that, compared with the existing local similarity algorithm, WCCLP achieves better and predictive results in many real data sets. The experimental effect of extending to WLNB also proves that weighted clustering coefficients can be effectively extended to other models. At the same time, under the classifier with supervised learning for link prediction, the feature construction with WCCLP are more discriminative than the features which derived from the existing local similarity algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To investigate the risk factors of malignant ventricular arrhythmias in elderly patients with ST segment elevation myocardial infarction (STEMI). Methods: 150 elderly patients with acute STEMI were analyzed retrospectively. Based on the presence or absence of malignant ventricular arrhythmia (MVA), the patients were divided into the control group (non-MVA) and observation group (MVA), the clinical data and indicators were compared between two groups. Results: Gender, age, smoking history, history of hypertension, diabetes mellitus, history of angina, blood glucose, glycosylated hemoglobin (HbA1c), serum potassium, creatinine (Cr), BUN, triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL), low density lipoprotein (LDL) and lesion type of MVA had no relationship with the occurrence of MVA in elderly patients with STEMI, while Killip classification, Cr, and LDL were correlated with the occurrence of MVA, and Killip class, creatinine levels were independent risk factors for the occurrence of MVA. Conclusions: Killip classification and Cr levels were independent risk factors of the occurrence of MVA in elderly patients with STEMI. [ABSTRACT FROM AUTHOR]