کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5137699 | 1494587 | 2018 | 28 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Accurate quantification of PGE2 in the polyposis in rat colon (Pirc) model by surrogate analyte-based UPLC-MS/MS
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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
An accurate and reliable UPLC-MS/MS method is reported for the quantification of endogenous Prostaglandin E2 (PGE2) in rat colonic mucosa and polyps. This method adopted the “surrogate analyte plus authentic bio-matrix” approach, using two different stable isotopic labeled analogs - PGE2-d9 as the surrogate analyte and PGE2-d4 as the internal standard. A quantitative standard curve was constructed with the surrogate analyte in colonic mucosa homogenate, and the method was successfully validated with the authentic bio-matrix. Concentrations of endogenous PGE2 in both normal and inflammatory tissue homogenates were back-calculated based on the regression equation. Because of no endogenous interference on the surrogate analyte determination, the specificity was particularly good. By using authentic bio-matrix for validation, the matrix effect and exaction recovery are identically same for the quantitative standard curve and actual samples - this notably increased the assay accuracy. The method is easy, fast, robust and reliable for colon PGE2 determination. This “surrogate analyte” approach was applied to measure the Pirc (an Apc-mutant rat kindred that models human FAP) mucosa and polyps PGE2, one of the strong biomarkers of colorectal cancer. A similar concept could be applied to endogenous biomarkers in other tissues.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Pharmaceutical and Biomedical Analysis - Volume 148, 30 January 2018, Pages 42-50
Journal: Journal of Pharmaceutical and Biomedical Analysis - Volume 148, 30 January 2018, Pages 42-50
نویسندگان
Changhong Yun, Wan-Mohaiza Dashwood, Lawrence N. Kwong, Song Gao, Taijun Yin, Qinglan Ling, Rashim Singh, Roderick H. Dashwood, Ming Hu,