Spontaneous adverse event reporting systems are widely used to identify adverse reactions to drugs following their introduction into the marketplace. In this article, a James-Stein type shrinkage estimation strategy was developed in a Bayesian logistic regression model to analyze pharmacovigilance data. This method is effective in detecting signals as it combines information and borrows strength across medically related adverse events. Computer simulation demonstrated that the shrinkage estimator is uniformly better than the maximum likelihood estimator in terms of mean squared error. This method was used to investigate the possible association of a series of diabetic drugs and the risk of cardiovascular events using data from the Canada Vigilance Online Database.
Objective:Glycoprotein acetylation (GlycA), an emerging inflammatory biomarker, has been used as an indicator of cardiovascular disease. Our research aimed to evaluate the correlation between GlycA and coronary artery disease (CAD) using coronary computed tomography angiography (CCTA).Methods:In the present study, a total of 342 patients were enrolled, and each of them underwent CCTA. The correlation between GlycA and major adverse cardiac events (MACE) was detected via Cox’s proportional hazards models. Based on differences in the GlycA level, patients were categorized into three groups (T1, T2, and T3).Results:Compared with the group with the lowest GlycA level (T1), the group with the highest GlycA level (T3) exhibited stronger atherosclerotic pressure involving the extent of atherosclerotic plaque and risk of obstructive CAD. In addition, the patients in the T3 group had a greater chance of experiencing MACE and higher all-cause mortality than those in the T1 group. Among patients without CAD who underwent CCTA, those with high GlycA levels experienced elevated atherosclerotic stress and heightened risk of MACE compared with those with low GlycA levels.Conclusion:These results suggest that serum GlycA is significantly associated with the long-term clinical results of patients with no known CAD undergoing CCTA. The risks of death and experiencing MACE increase among patients with high GlycA levels.
Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks. Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions for learning multiple-spatial-frequency features, thus can better capture tumors with varying sizes and shapes. The proposed network takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. Finally, we integrate octave convolutions into the encoder-decoder architecture of UNet, which can generate high resolution tumor segmentation in one single forward feeding without post-processing steps. Both architectures are trained on a subset of the LiTS (Liver Tumor Segmentation) Challenge. The proposed approach is shown to significantly outperform other networks in terms of various accuracy measures and processing speed.
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