Acute Lower Respiratory Tract Infection Increased the Risk of Cardiovascular Events and All-Cause Mortality in Elderly Patients With Stable Coronary Artery Disease.

Objective: To investigate the predictors of acute cardiovascular events within 90 days after an acute lower respiratory tract infection (ALRTI) in elderly patients with stable coronary artery disease (sCAD). Methods: Observational analyses were conducted in a prospective cohort of the elderly with sCAD, during 90 days after they were hospitalized for ALRTI. Multiple logistic regression analysis was performed to identify predictors for acute cardiovascular events and all-cause mortality. Results: The present study comprised 426 patients with sCAD (median age: 88 years; IQR: 84-91; range: 72-102). Among these patients, 257 suffering from ALRTI were enrolled in the infection group. Meanwhile, 169 patients who did not suffer from ALRTI were regarded as the non-infection group. Compared with the non-infection group, patients in the infection group had a higher incidence of acute cardiovascular events (31.9 vs. 13.6%, p < 0.001) and all-cause mortality (13.2 vs. 1.8%, p < 0.001) during the 90-day follow-up. In addition, in the infection group, the incidence of cardiovascular events was also higher than those in the non-infection group during the 7-day and 30-day follow-up (10.9 vs. 2.4%, p = 0.001; 20.6 vs. 6.5%, p < 0.001). The same difference in the incidence of all-cause mortality during 7 and 30 days (1.2 vs. 0%, p = 0.028; 3.9 vs. 0.6%, p = 0.021) was observed between the two groups. Furthermore, multiple regression analysis found that ALRTI was independently associated with increased risk of cardiovascular events and all-cause mortality in elderly patients with sCAD. Conclusion: In elderly patients with sCAD, ALRTI was an independent predictor for both cardiovascular events and all-cause mortality.

User abnormal behavior recommendation via multilayer network.

With the growing popularity of online services such as online banking and online shopping, one of the essential research topics is how to build a privacy-preserving user abnormal behavior recommendation system. However, a machine-learning based system may present a dilemma. On one aspect, such system requires large volume of features to pre-train the model, but on another aspect, it is challenging to design usable features without looking to plaintext private data. In this paper, we propose an unorthodox approach involving graph analysis to resolve this dilemma and build a novel private-preserving recommendation system under a multilayer network framework. In experiments, we use a large, state-of-the-art dataset (containing more than 40,000 nodes and 43 million encrypted features) to evaluate the recommendation ability of our system on abnormal user behavior, yielding an overall precision rate of around 0.9, a recall rate of 1.0, and an F1-score of around 0.94. Also, we have also reported a linear time complexity for our system. Last, we deploy our system on the "Wenjuanxing" crowd-sourced system and "Amazon Mechanical Turk" for other users to evaluate in all aspects. The result shows that almost all feedbacks have achieved up to 85% satisfaction.

Recent advances of adapter proteins in the regulation of heart diseases.

Heart diseases are major causes of mortality. Cardiac hypertrophy, myocardial infarction (MI), viral cardiomyopathy, ischemic and reperfusion (I/R) heart injury finally lead to heart failure and death. Insulin and IGF1 signal pathways play key roles in normal cardiomyocyte growth and physiological cardiac hypertrophy while inflammatory signal pathway is associated with pathological cardiac hypertrophy, MI, viral cardiomyopathy, I/R heart injury, and heart failure. Adapter proteins are the major family proteins, which transduce signals from insulin, IGF1, or cytokine receptors to the downstream pathways and have been shown to regulate variety of heart diseases. Here, we summarized the recent advances in understanding the physiological and pathological roles of adapter proteins in heart failure.