Liaoning University of Traditional Chinese Medicine

About: Liaoning University of Traditional Chinese Medicine is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Randomized controlled trial & Acupuncture. The organization has 2040 authors who have published 1326 publications receiving 14664 citations.

Teasaponin improves leptin sensitivity in the prefrontal cortex of obese mice

Abstract: Scope Obesity impairs cognition, and the leptin-induced increase of brain-derived neurotrophic factor (BDNF) and neurogenesis. Tea consumption improves cognition and increases brain activation in the prefrontal cortex. Methods and results This study examined whether teasaponin, an active ingredient in tea, could improve memory and central leptin effects on neurogenesis in the prefrontal cortex of obese mice, and in vitro in cultured prefrontal cortical neurons. Teasaponin (10 mg/kg, intraperitoneal) for 21 days improved downstream leptin signaling (JAK2 and STAT3), and leptin's effect on BDNF, in the prefrontal cortex of high-fat diet (HFD) fed mice. Prefrontal cortical neurons were cultured with teasaponin and palmitic acid (the most abundant dietary saturated fatty acid) to examine their effects on neurogenesis and BDNF expression in response to leptin. Palmitic acid decreased leptin's effect on neurite outgrowth, postsynaptic density protein 95, and BDNF expression in cultured cortical neurons, which was reversed by teasaponin. Conclusion Teasaponin improved the leptin sensitivity of prefrontal cortical neurons in obese mice or when treated by palmitic acid. This in turn increased BDNF expression and neurite growth. Therefore, teasaponin supplementation may be used to prevent obesity-associated neurodegeneration and improve cognitive function.

MR-Forest: A Deep Decision Framework for False Positive Reduction in Pulmonary Nodule Detection

Abstract: With the development of deep learning methods such as convolutional neural network (CNN), the accuracy of automated pulmonary nodule detection has been greatly improved. However, the high computational and storage costs of the large-scale network have been a potential concern for the future widespread clinical application. In this paper, an alternative Multi-ringed (MR)-Forest framework, against the resource-consuming neural networks (NN)-based architectures, has been proposed for false positive reduction in pulmonary nodule detection, which consists of three steps. First, a novel multi-ringed scanning method is used to extract the order ring facets (ORFs) from the surface voxels of the volumetric nodule models; Second, Mesh-LBP and mapping deformation are employed to estimate the texture and shape features. By sliding and resampling the multi-ringed ORFs, feature volumes with different lengths are generated. Finally, the outputs of multi-level are cascaded to predict the candidate class. On 1034 scans merging the dataset from the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine (AH-LUTCM) and the LUNA16 Challenge dataset, our framework performs enough competitiveness than state-of-the-art in false positive reduction task (CPM score of 0.865). Experimental results demonstrate that MR-Forest is a successful solution to satisfy both resource-consuming and effectiveness for automated pulmonary nodule detection. The proposed MR-forest is a general architecture for 3D target detection, it can be easily extended in many other medical imaging analysis tasks, where the growth trend of the targeting object is approximated as a spheroidal expansion.