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Author

Min Zhao

Bio: Min Zhao is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Medicine & Cell migration. The author has an hindex of 71, co-authored 547 publications receiving 24549 citations. Previous affiliations of Min Zhao include Third Military Medical University & Vanderbilt University Medical Center.


Papers
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Journal ArticleDOI
Jianyin Qiu1, Bin Shen, Min Zhao1, Zhen Wang1, Bin Xie1, Yifeng Xu1 
06 Mar 2020
TL;DR: This study is the first nationwide large-scale survey of psychological distress in the general population of China during the COVID-19 epidemic, which triggered a wide variety of psychological problems, including panic disorder, anxiety and depression.
Abstract: The Coronavirus Disease 2019 (COVID-19) epidemic emerged in Wuhan, China, spread nationwide and then onto half a dozen other countries between December 2019 and early 2020. The implementation of unprecedented strict quarantine measures in China has kept a large number of people in isolation and affected many aspects of people's lives. It has also triggered a wide variety of psychological problems, such as panic disorder, anxiety and depression. This study is the first nationwide large-scale survey of psychological distress in the general population of China during the COVID-19 epidemic.

2,938 citations

Journal ArticleDOI
TL;DR: This Article contains typographical errors in Table 2 where ‘Week 2 (N = 32)’ was incorrectly given as ‘week (n’=‬2’.
Abstract: Scientific Reports 5: Article number: 10942; published online: 01 June 2015; updated: 23 February 2016 This Article contains typographical errors in Table 2 where ‘Week 2 (N = 32)’ was incorrectly given as ‘Week (N = 2)’.

2,328 citations

Journal ArticleDOI
TL;DR: This review aims to resolve issues by describing the historical context of bioelectricity, the fundamental principles of physics and physiology responsible for DC electric fields within cells and tissues, the cellular mechanisms for the effects of small electric fields on cell behavior, and the clinical potential for electric field treatment of damaged tissues.
Abstract: Direct-current (DC) electric fields are present in all developing and regenerating animal tissues, yet their existence and potential impact on tissue repair and development are largely ignored. This is primarily due to ignorance of the phenomenon by most researchers, some technically poor early studies of the effects of applied fields on cells, and widespread misunderstanding of the fundamental concepts that underlie bioelectricity. This review aims to resolve these issues by describing: 1) the historical context of bioelectricity, 2) the fundamental principles of physics and physiology responsible for DC electric fields within cells and tissues, 3) the cellular mechanisms for the effects of small electric fields on cell behavior, and 4) the clinical potential for electric field treatment of damaged tissues such as epithelia and the nervous system.

896 citations

Journal ArticleDOI
27 Jul 2006-Nature
TL;DR: It is shown that electric fields, of a strength equal to those detected endogenously, direct cell migration during wound healing as a prime directional cue.
Abstract: Wound healing is essential for maintaining the integrity of multicellular organisms. In every species studied, disruption of an epithelial layer instantaneously generates endogenous electric fields, which have been proposed to be important in wound healing. The identity of signalling pathways that guide both cell migration to electric cues and electric-field-induced wound healing have not been elucidated at a genetic level. Here we show that electric fields, of a strength equal to those detected endogenously, direct cell migration during wound healing as a prime directional cue. Manipulation of endogenous wound electric fields affects wound healing in vivo. Electric stimulation triggers activation of Src and inositol-phospholipid signalling, which polarizes in the direction of cell migration. Notably, genetic disruption of phosphatidylinositol-3-OH kinase-gamma (PI(3)Kgamma) decreases electric-field-induced signalling and abolishes directed movements of healing epithelium in response to electric signals. Deletion of the tumour suppressor phosphatase and tensin homolog (PTEN) enhances signalling and electrotactic responses. These data identify genes essential for electrical-signal-induced wound healing and show that PI(3)Kgamma and PTEN control electrotaxis.

871 citations

Journal ArticleDOI
TL;DR: The aim of this review is to recapitulate the clinical understanding of CSCR, with an emphasis on the most recent findings on epidemiology, risk factors, clinical and imaging diagnosis, and treatments options, and the novel mineralocorticoid pathway hypothesis.

690 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: Characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia, and further investigation is needed to explore the applicability of the Mu LBSTA scores in predicting the risk of mortality in 2019-nCoV infection.

16,282 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations