H
Hongqiang Ren
Researcher at Nanjing University
Publications - 339
Citations - 11621
Hongqiang Ren is an academic researcher from Nanjing University. The author has contributed to research in topics: Wastewater & Chemistry. The author has an hindex of 41, co-authored 281 publications receiving 6797 citations.
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Journal ArticleDOI
Uptake and Accumulation of Polystyrene Microplastics in Zebrafish (Danio rerio) and Toxic Effects in Liver.
TL;DR: The uptake and tissue accumulation of polystyrene microplastics in zebrafish were detected, and the toxic effects in liver were investigated, showing that both 5 μm and 70 nm PS-MPs caused inflammation and lipid accumulation in fish liver.
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Tissue accumulation of microplastics in mice and biomarker responses suggest widespread health risks of exposure.
TL;DR: Investigation of tissue distribution, accumulation, and tissue-specific health risk of MPs in mice revealed significant alteration in several biomarkers that indicate potential toxicity from MPs exposure, and provided new evidence for the adverse consequences of MPs.
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Microplastics induce intestinal inflammation, oxidative stress, and disorders of metabolome and microbiome in zebrafish.
TL;DR: Evidence is provided that MPs exposure causes gut damage as well as alterations in gut metabolome and microbiome, yielding novel insights into the consequences of MPs exposure.
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Accumulation of different shapes of microplastics initiates intestinal injury and gut microbiota dysbiosis in the gut of zebrafish.
Ruxia Qiao,Yongfeng Deng,Shenghu Zhang,Marina Borri Wolosker,Qiande Zhu,Hongqiang Ren,Yan Zhang +6 more
TL;DR: Microplastics caused multiple toxic effects in fish intestine, including mucosal damage, and increased permeability, inflammation and metabolism disruption, and it is suggested that shape-depended effects should not be ignored in the health risk assessment of microplastics.
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Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data.
Kangyang Chen,Hexia Chen,Chuanlong Zhou,Yichao Huang,Xiangyang Qi,Ruqin Shen,Fengrui Liu,Min Zuo,Xinyi Zou,Jinfeng Wang,Yan Zhang,Da Chen,Xingguo Chen,Xingguo Chen,Yongfeng Deng,Yongfeng Deng,Hongqiang Ren +16 more
TL;DR: Compared to other 7 models, decision tree (DT), random forest (RF) and deep cascade forest (DCF) trained by data sets of pH, DO, CODMn, and NH3-N had significantly better performance in prediction of all 6 Levels of water quality recommended by Chinese government.