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1. Silibinin Ameliorates Formaldehyde-Induced Cognitive Impairment by Inhibiting Oxidative Stress

Author

Pengsheng Wei, Xue Li, Shuai Wang, Yanxin Dong, Haoran Yin, Zikun Gu, Xiaoting Na, Xi Wei, Jiayu Yuan, Jiahui Cao, Haotian Gao, Yebo Su, Yong Xu Chen, and Ge Jin

Subjects
Memory Disorders, Aging, Glycogen Synthase Kinase 3 beta, Article Subject, NF-E2-Related Factor 2, Cell Biology, General Medicine, Hippocampus, Biochemistry, Antioxidants, Mice, Oxidative Stress, Alzheimer Disease, Formaldehyde, Silybin, Animals, Milk Thistle, Cognitive Dysfunction, Silymarin
Abstract

Silibinin is a flavonoid extracted from the medicinal plant Silybum marianum (milk thistle), traditionally used to treat liver disease. Recent studies have shown that the antioxidative stress and anti-inflammatory effects of milk thistle are used in the treatment of neurological diseases. Silibinin has antioxidative stress and antiapoptotic effects and reduces cognitive impairment in models of Alzheimer’s disease (AD). However, the underlying mechanism of silibinin related to improvement of cognition remains poorly understood. In this study, we used the model of lateral ventricle injection of formaldehyde to examine the related mechanism of silibinin in improving cognitive impairment disorders. Oral administration of silibinin for three weeks significantly attenuated the cognitive deficits of formaldehyde-induced mice in a Y -maze test and Morris water maze test. Y -maze results show that silibinin increases the rate of spontaneous response alternation in FA-induced mice. Silibinin increases the target quadrant spending time and decreases escape latency in the Morris water maze test. We examined the effect of silibinin on the NRF2 signaling pathway, and silibinin promoted the nuclear transfer of NRF2 and increased the expression of HO-1 but did not significantly increase the protein expression of NRF2 in the hippocampus. Well, silibinin reduces the content of DHE and decreases the levels of apoptosis of mature neuron cells. We investigated the effect of silibinin on the content of formaldehyde degrading enzymes; biochemical analyses revealed that silibinin increased GSH and ALDH2 in formaldehyde-induced mice. In addition, as one of the pathological changes of AD, TAU protein is also hyperphosphorylated in FA model mice. Silibinin inhibits the expression of GSK-3β in model mice, thereby reducing the phosphorylation of TAU proteins ser396 and ser404 mediated by GSK3β. Based on our findings, we verified that the mechanism of silibinin improving cognitive impairment may be antioxidative stress, and silibinin is one of the potentially promising drugs to prevent formaldehyde-induced cognitive impairment.

Published
2022
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2. An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation

Author

Junjie Xu, Bin Song, Xi Yang, and Xiaoting Nan

Subjects
space target, pose estimation, keypoint detection, online hard keypoint mining, dilated convolution, Science
Abstract

The on-board pose estimation of uncooperative target is an essential ability for close-proximity formation flying missions, on-orbit servicing, active debris removal and space exploration. However, the main issues of this research are: first, traditional pose determination algorithms result in a semantic gap and poor generalization abilities. Second, specific pose information cannot be accurately known in a complicated space target imaging environment. Deep learning methods can effectively solve these problems; thus, we propose a pose estimation algorithm that is based on deep learning. We use keypoints detection method to estimate the pose of space targets. For complicated space target imaging environment, we combined the high-resolution network with dilated convolution and online hard keypoint mining strategy. The improved network pays more attention to the obscured keypoints, has a larger receptive field, and improves the detection accuracy. Extensive experiments have been conducted and the results demonstrate that the proposed algorithms can effectively reduce the error rate of pose estimation and, compared with the related pose estimation methods, our proposed model has a higher detection accuracy and a lower pose determination error rate in the speed dataset.

Published
2020
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