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531 results on '"Qian, Zhiyong"'

5. Comparisons of long-term clinical outcomes with left bundle branch pacing, left ventricular septal pacing, and biventricular pacing for cardiac resynchronization therapy

Author

Zhu, Haojie, Qin, Chaotong, Du, Anjie, Wang, Qian, He, Chen, Zou, Fengwei, Li, Xiaofei, Tao, Jin, Wang, Chuangshi, Liu, Zhimin, Xue, Siyuan, Zeng, Jiaxin, Qian, Zhiyong, Wang, Yao, Hou, Xiaofeng, Ellenbogen, Kenneth A., Gold, Michael R., Yao, Yan, Zou, Jiangang, and Fan, Xiaohan

Published
2024
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15. A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI

Author

He, Zhuo, Zhang, Xinwei, Zhao, Chen, Qian, Zhiyong, Wang, Yao, Hou, Xiaofeng, Zou, Jiangang, and Zhou, Weihua

Subjects
Physics - Medical Physics, Computer Science - Artificial Intelligence
Abstract

Background. Studies have shown that the conventional left ventricular mechanical dyssynchrony (LVMD) parameters have their own statistical limitations. The purpose of this study is to extract new LVMD parameters from the phase analysis of gated SPECT MPI by deep learning to help CRT patient selection. Methods. One hundred and three patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as a decrease in left ventricular end-systolic volume (LVESV) >= 15% at 6 +- 1 month follow up. Autoencoder (AE), an unsupervised deep learning method, was trained by the raw LV systolic phase polar maps to extract new LVMD parameters, called AE-based LVMD parameters. Correlation analysis was used to explain the relationships between new parameters with conventional LVMD parameters. Univariate and multivariate analyses were used to establish a multivariate model for predicting CRT response. Results. Complete data were obtained in 102 patients, 44.1% of them were classified as CRT responders. AE-based LVMD parameter was significant in the univariate (OR 1.24, 95% CI 1.07 - 1.44, P = 0.006) and multivariate analyses (OR 1.03, 95% CI 1.01 - 1.06, P = 0.006). Moreover, it had incremental value over PSD (AUC 0.72 vs. 0.63, LH 8.06, P = 0.005) and PBW (AUC 0.72 vs. 0.64, LH 7.87, P = 0.005), combined with significant clinic characteristics, including LVEF and gender. Conclusions. The new LVMD parameters extracted by autoencoder from the baseline gated SPECT MPI has the potential to improve the prediction of CRT response., Comment: 10 pages, 3 figures, will submit to journal of nuclear cardiology

Published
2021
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43. Histones released by NETosis enhance the infectivity of SARS-CoV-2 by bridging the spike protein subunit 2 and sialic acid on host cells

Author

Hong, Weiqi, Yang, Jingyun, Zou, Jun, Bi, Zhenfei, He, Cai, Lei, Hong, He, Xuemei, Li, Xue, Alu, Aqu, Ren, Wenyan, Wang, Zeng, Jiang, Xiaohua, Zhong, Kunhong, Jia, Guowen, Yang, Yun, Yu, Wenhai, Huang, Qing, Yang, Mengli, Zhou, Yanan, Zhao, Yuan, Kuang, Dexuan, Wang, Junbin, Wang, Haixuan, Chen, Siyuan, Luo, Min, Zhang, Ziqi, Lu, Tianqi, Chen, Li, Que, Haiying, He, Zhiyao, Sun, Qiu, Wang, Wei, Shen, Guobo, Lu, Guangwen, Zhao, Zhiwei, Yang, Li, Yang, Jinliang, Wang, Zhenling, Li, Jiong, Song, Xiangrong, Dai, Lunzhi, Chen, Chong, Geng, Jia, Gou, Maling, Chen, Lu, Dong, Haohao, Peng, Yong, Huang, Canhua, Qian, Zhiyong, Cheng, Wei, Fan, Changfa, Wei, Yuquan, Su, Zhaoming, Tong, Aiping, Lu, Shuaiyao, Peng, Xiaozhong, and Wei, Xiawei

Published
2022
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49. Research Progress on Mechanism and Intervention of Renal Function Injury in Hyperlipidemia Animal Model

Author

ZHOU Xiaoli, ZHANG Qian, and QIAN Zhiyong

Subjects
hyperlipidemia, kidney injury, animal model, Medicine
Abstract

Hyperlipidemia is closely related to renal function damage, and they influence each other. Lipid deposition in the kidney affects the function and structure of glomerulus and renal tubules directly and indirectly, resulting in renal function injury. On the contrary, renal function damage further affects lipid metabolism and aggravates the progress of hyperlipidemia. At present, besides synthetic drugs, there are many non-synthetic drugs which have been well verified in the intervention of kidney injury in hyperlipidemia animal model. In this paper, the research progress in the mechanism of renal function damage and the related intervention using hyperlipidemia animal model at home and abroad in recent years were summarized.

Published
2022
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