| Auteur | Rechercher : Nicolescu, A.1; Rechercher : Dolenko, B.1; Rechercher : Bezabeh, T.1; Rechercher : Stefan, L.-I.; Rechercher : Ciurtin, C.; Rechercher : Kovacs, E.; Rechercher : Smith, I. C. P.; Rechercher : Simionescu, B. C.; Rechercher : Deleanu, C. |
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| Affiliation | - Conseil national de recherches du Canada. Institut du biodiagnostic du CNRC
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| Format | Texte, Article |
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| Sujet | chemometry; diabetes; NMR spectroscopy; statistical classification; urinary metabolites |
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| Résumé | 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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| Date de publication | 2011-12 |
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| Maison d’édition | SYSCOM |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 23004715 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 5deb6b80-54e6-47c1-a49f-d248cf3f25ae |
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| Enregistrement créé | 2018-12-12 |
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| Enregistrement modifié | 2020-04-21 |
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