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Your search keyword '"Chen, Xiaochun"' showing total 746 results
Start Over Author "Chen, Xiaochun" Publication Type Academic Journals
746 results on '"Chen, Xiaochun"'

9. China Alzheimer Report 2024

Author

WANG Gang, QI Jinlei, LIU Xinya, REN Rujing, LIN Shaohui, HU Yisong, LI Haixia, XIE Xinyi, WANG Jintao, LI Jianping, ZHU Yikang, GAO Mengyi, YANG Junjie, WANG Yiran, JING Yurong, GENG Jieli, ZHI Nan, CAO Wenwei, XU Qun, YU Xiaoping, ZHU Yuan, ZHOU Ying, WANG Lin, GAO Chao, LI Binyin, CHEN Shengdi, YUAN Fang, DOU Ronghua, LIU Xiaoyun, LI Xuena, YIN Yafu, CHANG Yan, XU Gang, XIN Jiawei, ZHONG Yanting, LI Chunbo, WANG Ying, ZHOU Maigeng, CHEN Xiaochun, representing the China Alzheimer's Disease Report Writing Group

Subjects
alzheimer's disease, epidemiology, diagnosis, treatment, public health resources, Medicine
Abstract

With the sustained growth of economy and significant changes in social demographics, the issue of elderly-related diseases has increasingly drawn attention particularly. Alzheimer's disease (AD),as a representative disease of neurodegenerative diseases has become a major challenge, affecting the health and quality of life among the elderly population severely. In recent years, the incidence, prevalence, and mortality rate of AD increase in China, imposing substantial economic burdens on families, society, and the entire healthcare system. To proactively address this challenge and respond to the national 'Healthy China Action' initiative, leading experts from Renji Hospital, Shanghai Jiao Tong University School of Medicine,and Chinese Center for Disease Control and Prevention Chronic Non-communicable Disease Control Center, Fudan University School of Public Health, Shanghai Mental Health Center, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, Fujian Medical University, and other authoritative institutions, have jointly authored the 'China Alzheimer Disease Report 2024'. Building upon previous editions of 2021, 2022, and 2023, this report updates epidemiological data on AD in China, thoroughly analyzes the latest economic burdens of the disease, and comprehensively evaluates the current status of AD diagnosis and treatment services, as well as the allocation of public health resources in our country. The release of the 'China Alzheimer Disease Report 2024' not only reflects China's progress and efforts in AD research and prevention, but also underscores the social heightened concern for elderly health issues. It aims to provide scientific and technical guidance and robust data support for the prevention, diagnosis, and treatment of AD, offering a professional basis for the government and relevant departments to formulate targeted health policies and intervention measures. Furthermore, it serves as a platform for promoting academic exchanges and cooperation in this field domestically and internationally. Through the dissemination and application of this report, we anticipate it will not only serve as a reference for professionals but also enhance public awareness of AD, promote active participation across various sectors of society, and jointly advance the development of elderly health care in China, empowering us towards achieving 'healthy aging'.

Published
2024
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12. The effect of developmental care on the length of hospital stay and family centered care practice for very low birth weight infants in neonatal intensive care units: A cluster randomized controlled trial

Author

Lyu, TianChan, Ye, Ruming, Li, Li Ling, Zhang, Li Li, Xiao, Juan, Ma, Yue Lan, Li, Fang, Rong, Hui, Liu, Dan, Wang, Hua, Wang, Yan, Gu, Wei Wei, Xuan, Yan, Chen, XiaoChun, Fan, Qiao Ling, Tang, Yun Fei, Huang, Xiang Hui, Qin, An, Zhang, Yi Li, Dou, Yalan, and Hu, Xiao Jing

Published
2024
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22. Efficacy and safety of GST-HG171 in adult patients with mild to moderate COVID-19: a randomised, double-blind, placebo-controlled phase 2/3 trial

Author

Lu, Hongzhou, Zhang, George, Mao, John, Chen, Xiaochun, Zhan, Yangqing, Lin, Ling, Zhang, Tianxiang, Tang, Yanan, Lin, Feng, Zhu, Feiyue, Lin, Yuanlong, Zeng, Yiming, Zhang, Kaiyu, Yuan, Wenfang, Liang, Zhenyu, Sun, Ruilin, Huo, Liya, Hu, Peng, Lin, Yihua, Zhuang, Xibin, Wei, Zhaohui, Chen, Xia, Yan, Wenhao, Yan, Xiuping, Mu, Lisa, Lin, Zhuhua, Tu, Xinyu, Tan, Hongshan, Huang, Fuhu, Hu, Zhiqiang, Li, Hongming, Li, Guoping, Fu, Haijun, Yang, Zifeng, Chen, Xinwen, Wang, Fu-Sheng, and Zhong, Nanshan

Published
2024
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34. Mapping of Spatial Distribution of Street Vendors Based on Street-View Images and Deep-Learning Technology

Author

Liu Yuchen, Chen Xiaochun, Liu Yilun, Wu Xiaofang, and Chen Feixiang

Subjects
informal economy, street vendor, street-view image, deep learning, yolo deep neural network, guangzhou, Geography (General), G1-922
Abstract

Street vendors are an indispensable part of the urban social ecosystem, but due to a lack of comprehensive understanding, many cities have adopted simple eviction policies, resulting in the gradual marginalization and stigmatization of the street economy. The efficient governance of street vendors requires the comprehensive investigation of their business scale and spatial distribution information. However, traditional methods have limitations in terms of automatically surveying large-scale street vendor information, particularly spatial distribution. This paper proposes a method for the automatic investigation spatial distribution of street vendors based on street-view images and a deep-learning object recognition model. Street-view images were collected at fixed intervals according to the urban road network, and 1,957 images containing one or more vendors were selected through human-machine interaction to establish street vendor label data. To achieve high recognition model accuracy, the category labels were subdivided into four categories: ground stalls, table stalls, tricycle stalls, and small truck stalls, based on the goods carriers used by street vendors. A deep neural-network-based image object detection model based on YOLO v4 was constructed to identify street vendors in the street-view image library, with an average F1 value of 0.77 and an mAP of 0.67. The accuracy of the model was satisfactory for investigating the number and location of street vendors covering the main roads in the city and then applying a kernel density distribution model to evaluate the spatial distribution pattern of street vendors. Using street vendors in Guangzhou as a case study, the proposed automatic investigation model identified 26,119 street vendors from 3,339,062 street-view images. The results showed that the street vendors were distributed in a multicenter aggregation pattern in the central urban area, mainly concentrated in areas with high pedestrian traffic, such as subway stations and urban villages; their numbers increased as road grades decreases. Street vendors were mainly distributed in areas with medium rents. The proposed method is helpful for performing the efficient, low-cost, and city-scale mapping of street vendors; the results obtained provide suggestions for formulating and implementing spatial governance policies for the informal economy and further provide suggestions for improving and implementing spatial governance policies for open and diverse urban street-view images. The results can be used as a reference for the location preference analysis of practitioners, the exploration of NIMBY syndrome, and the determination of the formalization zone. Although street-view images have an insufficient spatiotemporal coverage, using them to perform street vendor investigations is a low-cost and efficient method compared with the use of traditional investigation methods and data sources. In addition, the method proposed in this article can be coupled with multitask deep learning algorithms to investigate additional dimensions of street vendor information, such as the sex, age, and type of business of street vendors. Relevant research needs to be conducted in the future.

Published
2023
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