High-throughput powder X-ray diffraction, IR-spectroscopy and ion chromatography analysis of urinary stones: A comparative study |
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Authors: | Elena V Yusenko Kirill V Yusenko Ilya V Korolkov Alexandr A Shubin Fedor P Kapsargin Alexandr A Efremov Maria V Yusenko |
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Institution: | 1. Department of Analytical and Organic Chemistry, Siberian Federal University, 660041, Krasnoyarsk, Russia 2. Department of Chemistry, Center for Materials Science and Nanotechnology, University of Oslo, PO Box 1033, Blindern, N-0315, Oslo, Norway 3. Department of Crystal Chemistry, Nikolaev Institute of Inorganic Chemistry, 630090, Novosibirsk, Russia 4. Department of Physical and Inorganic Chemistry, Siberian Federal University, 660041, Krasnoyarsk, Russia 5. Department of Urology, Andrology and Sexology, Vojno-Yasenetsky Krasnoyarsk Medical State University, 660022, Krasnoyarsk, Russia 6. Institute of Molecular Tumor Biology (IMTB), Medical Faculty of the University of Münster (Westf?lische Wilhelms-Universit?t), D-48149, Münster, Germany
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Abstract: | The instrumental qualitative analysis of urinary stones is a critical step in clinical practice and urological research. A powder X-ray diffraction, IR-spectroscopy and ion chromatography have been applied for the qualitative analysis of 20 urinary stones. Suggestions for a sample preparation and an optimal measurement strategy were formulated. The main difficulties for the powder X-ray diffraction qualitative analysis are a limiting amount of the sample and a preferential orientation of crystals, both issues should be minimized by the special sample preparation. Urinary stones samples have been clustered into four groups using different sets of numerical input data (cation and anion content, phase composition). At the same time a high-throughput multivariate clustering has been applied for powder X-ray diffraction and IR-spectroscopy data. The multivariate whole-profile approach can be used as a tool for a high-throughput time reducing technique for clinical practice, when a quick and stable classification of samples is required. All three sets of the data can be automatically separated into three clusters: oxalate-reach, oxalate-pure and non-oxalate samples. Uricite-pure and uricite-rich samples can be easily clustered. |
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