• Progress in Medical Physics
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• v.21 no.1
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• pp.35-41
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• 2010

The purpose of this study is to quantitate regional neurochemical profile of regional normal adult mice brain and assess regional metabolic differences by using ex vivo $^1H$ high-resolution magic angle spinning nuclear magnetic resonance spectroscopy ($^1H$ HR-MAS NMRS). The animals were matched in sex and age. The collected brain tissue included frontal cortex, temporal cortex, thalamus, and hippocampus. Quantitative 1D spectra were acquired on 40 samples with the CPMG pulse sequence (8 kHz spectral window, TR/TE = 5500/2.2 ms, NEX = 128, scan time: 17 min 20 sec). The mass of brain tissue and $D_2O$+TSP solvent were 8~14 mg and 7~13 mg. A total of 16 metabolites were quantified as follow: Acet, NAA, NAAG, tCr, Cr, tCho, Cho, GPC + PC, mIns, Lac, GABA, Glu, Gln, Tau and Ala. As a results, Acet, Cho, NAA, NAAG and mIns were showed significantly different aspects on frontal cortex, hippocampus, temporal cortex and thalamus respectively. The present study demonstrated that absolute metabolite concentrations were significantly different among four brain regions of adult mice. Our finding might be helpful to investigate brain metabolism of neuro-disease in animal model.

• Analytical Science and Technology
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• v.30 no.6
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• pp.355-378
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• 2017

Because neurochemicals are related to homeostasis and cognitive and behavioral functions in human body and because they enable the diagnosis of numerous neurodegenerative disorders, there has been increasing interest in the development of analytical platforms for neurochemical profiling in biological samples. In particular, mass spectrometry (MS)-based analytical methods combined with chromatographic separation have been widely used to profile neurochemicals in metabolic pathways. However, development of delicate sample preparation procedures and highly sensitive instrumental detection is necessary considering the trace levels and chemical instabilities of neurochemicals in biological samples. Therefore, in this review, analytical trends in MS-based metabolomics approaches to neurochemicals in multiple biological samples, such as urine, blood, CSF, and biological tissues, are discussed. This paper is expected to contribute to the development of an analytical platform to discover biomarkers that will aid diagnosis, prognosis, and treatment of neurodegenerative disorders.