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Wei Wang
Researcher at Central South University
Publications - 880
Citations - 15984
Wei Wang is an academic researcher from Central South University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 50, co-authored 794 publications receiving 10555 citations. Previous affiliations of Wei Wang include Hefei Institutes of Physical Science & University of Kansas.
Papers
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The presence of SARS-CoV-2 RNA in the feces of COVID-19 patients.
Yifei Chen,Liangjun Chen,Qiaoling Deng,Guqin Zhang,Kaisong Wu,Lan Ni,Yibin Yang,Bing Liu,Wei Wang,Chaojie Wei,Jiong Yang,Guangming Ye,Zhen-shun Cheng +12 more
TL;DR: The results demonstrated the presence of Sars‐CoV‐2 RNA in the feces of COVID‐19 patients and suggested the possibility of SARS‐Cov‐2 transmission via the fecal‐oral route.
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Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives.
Xiaoqi Mao,Jin Xu,Wei Wang,Chen Liang,Jie Hua,Jiang Liu,Bo Zhang,Qingcai Meng,Xianjun Yu,Si Shi +9 more
TL;DR: Cancer-associated fibroblasts (CAFs), a stromal cell population with cell-of-origin, phenotypic and functional heterogeneity, are the most essential components of the tumor microenvironment (TME). Through multiple pathways, activated CAFs can promote tumor growth, angiogenesis, invasion and metastasis, along with extracellular matrix remodeling and even chemoresistance.
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Spatio-temporal variation trends of satellite-based aerosol optical depth in China during 1980-2008
Jianping Guo,Xiaoye Zhang,Yerong Wu,Yerong Wu,Yangzong Zhaxi,Huizheng Che,Ba La,Wei Wang,Xiaowen Li,Xiaowen Li +9 more
TL;DR: In this article, the authors analyzed TOMS AOD at 500 nm (1980e2001), along with MODIS data (2000e2008) at 550 nm to investigate variations at one-degree grid over eight typical regions in China and the trends in AODs, temporally and spatially.
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Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations
TL;DR: In this article, five remotely sensed soil moisture products, namely, the Soil Moisture Active Passive (SMAP), two SoilMoisture and Ocean Salinity (SMOS) products, the Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), and the European Space Agency (ESA) Climate Change Initiative (CCI), were systematically investigated by utilizing in-situ soil moisture observations from global dense and sparse networks.
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Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.
TL;DR: Although conventional CT is useful for diagnosis of SRMs, it has limitations, and machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC.