کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7616985 | 1494053 | 2015 | 43 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Application of GC/MS-based metabonomic profiling in studying the therapeutic effects of Huangbai-Zhimu herb-pair (HZ) extract on streptozotocin-induced type 2 diabetes in mice
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
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چکیده انگلیسی
A protocol for metabolic profiling of mice urine was developed based on gas chromatograph-mass spectrometer (GC-MS) to explore metabolic state directly. The intra-day, inter-day, repeatability, and stability RSD for most endogenous compounds were less than 3%. Type 2 diabetic mellitus (T2DM) mice model was induced by high calorie diet combined with streptozocin. Urine from the control, T2DM and Huangbai-Zhimu herb-pair (HZ) treatment mice were enrolled in the subsequent study to show the usefulness of the method. OPLS-DA scores plots demonstrate that the cluster of T2DM mice is separated from that of control mice, while HZ-T2DM mice are located close to control mice, indicating that metabolic profiles of these HZ-T2DM mice are placed toward those of control group. The results illustrate that HZ treatment could lower the level of d-glucose, hexadecanoic acid, octadecanoic acid, propanoic acid, 3-hydroxybutyric acid, and 2,3-dihydroxybutanoic acid in urine of DM mice, meanwhile the results show that HZ treatment could ameliorate T2DM symptoms by intervening the fatty acid metabolism, starch and sucrose metabolism, and glyoxylate and dicarboxylate metabolism. This preliminary application indicated that the method is suitable and reliable for urine metabolic profiling. This study might explain the metabolic effects of T2DM and the mechanisms of action of HZ against T2DM.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Chromatography B - Volume 997, 1 August 2015, Pages 96-104
Journal: Journal of Chromatography B - Volume 997, 1 August 2015, Pages 96-104
نویسندگان
Lili Song, Hongyue Liu, Yan Wang, Yuming Wang, Jinbiao Liu, Zhensheng Zhou, Huilun Chu, Pengwei Zhuang, Yanjun Zhang,