| Author | Search for: Nicolescu, A.1; Search for: Dolenko, B.1; Search for: Bezabeh, T.1; Search for: Stefan, L.-I.; Search for: Ciurtin, C.; Search for: Kovacs, E.; Search for: Smith, I. C. P.; Search for: Simionescu, B. C.; Search for: Deleanu, C. |
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| Affiliation | - National Research Council of Canada. NRC Institute for Biodiagnostics
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| Format | Text, Article |
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| Subject | chemometry; diabetes; NMR spectroscopy; statistical classification; urinary metabolites |
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| Abstract | A NMR dataset with non-buffered urine samples consisting of 73 controls and 94 type II diabetes was suojeci to an in-house statistical classifier. A model was developed based on two glucose-free regions of the spectrum and those maximally discriminatory subregions selected most often by the algorithm were noted. The final classifier achieved 83.0% sensitivity and 83.6% specificity, with 83.2% overall accuracy. There were five spectral subregions selected by the algorithm as most relevant for discrimination. The protocol works well with non-buffered samples and has the potential for an automated clinical diagnosis of diabetes. |
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| Publication date | 2011-12 |
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| Publisher | SYSCOM |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23004715 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 5deb6b80-54e6-47c1-a49f-d248cf3f25ae |
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| Record created | 2018-12-12 |
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| Record modified | 2020-04-21 |
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