Výsledky vyhledávání

Long-Term Exposure to Environmentally Relevant Doses of Large Polystyrene Microplastics Disturbs Lipid Homeostasis via Bowel Function Interference

Autor: Yongfeng Deng, Hexia Chen, Yichao Huang, Yan Zhang, Hongqiang Ren, Mingliang Fang, Qing Wang, Wen Chen, Robert C. Hale, Tamara S. Galloway, Da Chen

Publikováno v: Environmental Science & Technology. 56:15805-15817

The question of whether long-term chronic exposure to microplastics (MPs) could induce dose- and size-dependent adverse effects in mammals remains controversial and poorly understood. Our study explored potential health risks from dietary exposure to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55341275885d8037f6afefcfb94aa102
https://doi.org/10.1021/acs.est.1c07933

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Lipidomic Changes in Banana (Musa cavendish) during Ripening and Comparison of Extraction by Folch and Bligh–Dyer Methods

Autor: Hongli Tan, Daniel Cozzolino, Hexia Chen, Fengjiang Sun, Da Chen, Yichao Huang

Publikováno v: Journal of Agricultural and Food Chemistry. 68:11309-11316

Banana (Musa cavendish) is one of the most popular fruits globally and is an important foodstuff in many regions, attributed to its high nutritional value. Contrast to its high consumption volume, relatively little research has been conducted on bana

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::10a981e13eb92a9556ea986fe2c3b3d1
https://doi.org/10.1021/acs.jafc.0c04236

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Prenatal exposure to organophosphate esters is associated with decreased anogenital distance in offspring

Autor: Min, Luan, Hong, Liang, Yafei, Chen, Da, Chen, Honglei, Ji, Hexia, Chen, Maohua, Miao, Wei, Yuan

Publikováno v: Science of The Total Environment. 856:159050

Evidence from in vitro and rodent studies suggests that organophosphate esters (OPEs) may disrupt sex steroid hormone homeostasis, but no human studies, to date, have examined the effects of in utero exposure to OPEs on offspring reproductive develop

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ccc85e7a20404fb0927c8aed3160f98d
https://doi.org/10.1016/j.scitotenv.2022.159050

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DEHP and DINP Induce Tissue- and Gender-Specific Disturbances in Fatty Acid and Lipidomic Profiles in Neonatal Mice: A Comparative Study

Autor: Yichao Huang, Hexia Chen, Fengjiang Sun, Hongli Tan, Yongfeng Deng, Da Chen, Zhiqiang Sun, Jing Li

Publikováno v: Environmental Science & Technology. 53:12812-12822

Di-isononyl phthalate (DINP) is considered one of the main industrial alternatives to di(2-ethylhexyl)phthalate (DEHP), a well-known chemical with various toxic effects including the disruption with lipid metabolism. However, the potential effects of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3ef47cf4b1c33f56389eb17cd858923
https://doi.org/10.1021/acs.est.9b04369

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Exposure to bisphenol analogues interrupts growth, proliferation, and fatty acid compositions of protozoa Tetrahymena thermophila

Autor: Jing Li, Hexia Chen, Yichao Huang, Sen Hou, Jie Wang, Zhiqiang Sun, Da Chen

Publikováno v: Journal of Hazardous Materials. 395:122643

A number of bisphenol A (BPA) analogues are increasingly used as its industrial alternatives. However, their effects on aquatic organisms at both individual and population levels have not been well understood. In this study, effects of five bisphenol

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7769f85c32a10bbb7f0ababb5dcac43
https://doi.org/10.1016/j.jhazmat.2020.122643

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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

Autor: Ruqin Shen, Jinfeng Wang, Xingguo Chen, Fengrui Liu, Xiangyang Qi, Hexia Chen, Chuanlong Zhou, Yichao Huang, Yan Zhang, Yongfeng Deng, Kangyang Chen, Min Zuo, Xinyi Zou, Da Chen, Hongqiang Ren

Publikováno v: Water Research. 171:115454

The water quality prediction performance of machine learning models may be not only dependent on the models, but also dependent on the parameters in data set chosen for training the learning models. Moreover, the key water parameters should also be i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53f9f7ce93feae13e2c1f9302c34d86e
https://doi.org/10.1016/j.watres.2019.115454

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