کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8294931 | 1536754 | 2018 | 30 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
NMR-based serum metabolomics study reveals a innovative diagnostic model for missed abortion
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
زیست شیمی
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چکیده انگلیسی
A missed abortion (MA) is an in-utero death of the embryo or fetus before the 20th week of gestation with retained products of conception. In order to discover novel biomarkers for MA, a 1H NMR spectroscopy-based metabolomics approach was applied to detect human MA serum metabolic profiles. Serum samples were obtained from patients with MA (nâ¯=â¯15) and healthy controls (nâ¯=â¯9) for study. The NOESYPR1D spectrum combined with multi-variate pattern recognition analysis was used to cluster the groups and establish a disease-specific metabolites phenotype. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models were capable of distinguishing MA patients from healthy subjects. The results revealed that 24 metabolites altered in MA patients compared with the control population. Metabolomic pathway analysis demonstrated that alanine, aspartate and glutamate metabolism, citrate cycle (TCA cycle), taurine and hypotaurine metabolism were significantly altered in MA. The results indicated that serum NMR-based metabolomic profiling method is sensitive and specific enough to distinguish MA and from healthy controls, this method could be developed as a clinically useful diagnostic tool for MA. The finding from the MA serum metabolic profiling shed a new light on further understanding of MA disease mechanisms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biochemical and Biophysical Research Communications - Volume 496, Issue 2, 5 February 2018, Pages 679-685
Journal: Biochemical and Biophysical Research Communications - Volume 496, Issue 2, 5 February 2018, Pages 679-685
نویسندگان
Zhenghong Wu, Lei Jin, Wenyi Zheng, Chenxi Zhang, Liwen Zhang, Yaping Chen, Junhua Guan, He Fei,