| کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن | 
|---|---|---|---|---|
| 10533271 | 961861 | 2005 | 5 صفحه PDF | دانلود رایگان | 
عنوان انگلیسی مقاله ISI
												Fast-track DRESSA: a bioassay for fast, sensitive, and selective detection of halogenated and polycyclic aromatic hydrocarbons
												
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																																												کلمات کلیدی
												
											موضوعات مرتبط
												
													مهندسی و علوم پایه
													شیمی
													شیمی آنالیزی یا شیمی تجزیه
												
											پیش نمایش صفحه اول مقاله
												 
												چکیده انگلیسی
												Dioxin and related chemicals cause a variety of toxic and biological effects via the aryl hydrocarbon receptor (AhR). We recently reported a mammalian cell-based bioassay system (dioxin-responsive-element-based sensing via secreted alkaline phosphatase; DRESSA) that can detect dioxin and dioxin-like chemicals with high sensitivity. In this report, we describe an advanced method (designated “fast-track DRESSA”) that achieves fast, selective, and sensitive detection of dioxin and other toxic compounds. By optimization of assay conditions on cell number and serum concentration, the fast-track DRESSA enabled detection of 0.5 pM 2,3,7,8-tetrachlorodibenzo-p-dioxin within 6 h. It also enabled detection of 10 pM 3-methylcholanthrene, 100 pM benzo[a]pyrene, and 100 pM β-naphthoflavone within 6-16 h. By combination with the AhR antagonist α-naphthoflavone, nonspecific, false-positive responses could be eliminated. Because of its time-saving property and easiness, sensitiveness, and specificity, the fast-track DRESSA would be advantageous for high-throughput screening of dioxin and dioxin-like compounds in environmental samples.
											ناشر
												Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Analytical Biochemistry - Volume 337, Issue 1, 1 February 2005, Pages 84-88
											Journal: Analytical Biochemistry - Volume 337, Issue 1, 1 February 2005, Pages 84-88
نویسندگان
												Ayumi Kasai, Nobuhiko Hiramatsu, Yiman Meng, Jian Yao, Shuichiro Maeda, Masanori Kitamura,