miR-21 is involved in skeletal deficiencies of zebrafish embryos exposed to polychlorinated biphenyls

2016 ◽  
Vol 24 (1) ◽  
pp. 886-891 ◽  
Author(s):  
Li Ju ◽  
Zhiwen Zhou ◽  
Bo Jiang ◽  
Yue Lou ◽  
Zhiqun Zhang
2019 ◽  
Vol 175 ◽  
pp. 164-172 ◽  
Author(s):  
Yue Jiang ◽  
Shuchun Zhang ◽  
Xin Zhang ◽  
Nan Li ◽  
Qingyu Zhang ◽  
...  

2008 ◽  
pp. 1-9 ◽  
Author(s):  
N. Loutfy ◽  
M. Fuerhacker ◽  
C. Lesueur ◽  
M. Gartner ◽  
M. Tawfic Ahmed ◽  
...  

2017 ◽  
Vol 12 (7) ◽  
pp. 497-499
Author(s):  
Kalimuthu Kalishwaralal ◽  
Subhaschandrabose Jeyabharathi ◽  
Krishnan Sundar ◽  
Azhaguchamy Muthukumaran

2020 ◽  
Author(s):  
Xueshu Li ◽  
Chun-Yun Zhang ◽  
Hans-Joachim Lehmler

Polychlorinated biphenyls (PCBs) are persistent organic pollutants that are linked to adverse health outcomes. PCB tissue levels are determinants of PCB toxicity; however, it is unclear how factors, such as an altered metabolism and/or a fatty liver, affect PCB distribution in vivo. We determined the congener-specific disposition of PCBs in mice with a liver specific deletion of cytochrome P450 reductase (KO), a model of fatty liver with impaired hepatic metabolism, and wildtype (WT) mice. Male and female KO and WT mice were exposed orally to Aroclor 1254, a technical PCB mixture. PCBs were quantified in adipose, blood, brain and liver tissues by gas chromatography-mass spectrometry. PCB profiles and levels in tissues were genotype and sex dependent. PCB levels were higher in the liver from KO compared to WT mice. PCB profiles showed clear differences between tissues from the same exposure group. While experimental tissue : blood partition coefficients in KO and WT mice did not follow the trends predicted using a composition-based model, the agreement between experimental and calculated partition coefficients was still reasonable. Thus, a fatty liver and/or an impaired hepatic metabolism alter the distribution of PCBs in mice and the magnitude of the partitioning of PCBs from blood into tissues can be approximated using composition-based models.<br>


2017 ◽  
Vol 83 (11) ◽  
pp. 15-20
Author(s):  
E. S. Brodskii ◽  
◽  
A. A. Shelepchikov ◽  
G. A. Kalinkevich ◽  
E. Ya. Mir-Kadyrova ◽  
...  

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