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Lei Wang

Researcher at Changsha University

Publications -  171
Citations -  2224

Lei Wang is an academic researcher from Changsha University. The author has contributed to research in topics: Wireless sensor network & Linear network coding. The author has an hindex of 19, co-authored 158 publications receiving 1466 citations. Previous affiliations of Lei Wang include Beijing Normal University & Lakehead University.

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Graphene Oxide Induces Toll-like Receptor 4 (TLR4)-Dependent Necrosis in Macrophages

TL;DR: The combined data reveal that interaction of GO with TLR4 is the predominant molecular mechanism underlying GO-induced macrophagic necrosis; also, cytoskeletal damage and oxidative stress contribute to decreased viability and function of macrophages upon GO treatment.
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Estrogen regulates iron homeostasis through governing hepatic hepcidin expression via an estrogen response element

TL;DR: Estrogen greatly contributes to iron homeostasis by regulating hepatic hepcidin expression directly through a functional ERE in the promoter region of hePCidin gene, which might help build a better understanding towards the etiology of postmenopausal osteoporosis accompanied by excess tissue iron.
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Significant reduction of PM 2.5 in eastern China due to regional-scale emission control: evidence from SORPES in 2011–2018

TL;DR: In this article, the authors reported long-term continuous measurements of PM 2.5, chemical components, and their precursors at a regional background station, the Station for Observing Regional Processes of the Earth System (SORPES), in Beijing, eastern China, since 2011.
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A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

TL;DR: A bipartite network based on known lncRNA-disease associations is constructed and a novel model for inferring potential lncRNAs associations is proposed, which significantly outperformed previous state-of-the-art models.
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A Novel Probability Model for LncRNA–Disease Association Prediction Based on the Naïve Bayesian Classifier

TL;DR: A novel approach was proposed based on the naïve Bayesian classifier to predict potential lncRNA–disease associations (NBCLDA) that can achieve a reliable performance with effective area under ROC curve (AUCs)in leave-one-out cross validation and demonstrated that NBCLDA can be an excellent tool for biomedical research in the future.