AIM: To study the feasibility of a novel method in nanomedicine that is based on breath testing for identifying Alzheimer's disease (AD) and Parkinson's disease (PD), as representative examples of neurodegenerative conditions. PATIENTS & METHODS: Alveolar breath was collected from 57 volunteers (AD patients, PD patients and healthy controls) and analyzed using combinations of nanomaterial-based sensors (organically functionalized carbon nanotubes and gold nanoparticles). Discriminant factor analysis was applied to detect statistically significant differences between study groups and classification success was estimated using cross-validation. The pattern identification was supported by chemical analysis of the breath samples using gas chromatography combined with mass spectrometry. RESULTS: The combinations of sensors could clearly distinguish AD from healthy states, PD from healthy states, and AD from PD states, with a classification accuracy of 85, 78 and 84%, respectively. Gas chromatography combined with mass spectrometry analysis showed statistically significant differences in the average abundance of several volatile organic compounds in the breath of AD, PD and healthy subjects, thus supporting the breath prints observed with the sensors. CONCLUSION: The breath prints that were identified with combinations of nanomaterial-based sensors have future potential as cost-effective, fast and reliable biomarkers for AD and PD.
AIM: To study the feasibility of a novel method in nanomedicine that is based on breath testing for identifying Alzheimer's disease (AD) and Parkinson's disease (PD), as representative examples of neurodegenerative conditions. PATIENTS & METHODS: Alveolar breath was collected from 57 volunteers (ADpatients, PDpatients and healthy controls) and analyzed using combinations of nanomaterial-based sensors (organically functionalized carbon nanotubes and gold nanoparticles). Discriminant factor analysis was applied to detect statistically significant differences between study groups and classification success was estimated using cross-validation. The pattern identification was supported by chemical analysis of the breath samples using gas chromatography combined with mass spectrometry. RESULTS: The combinations of sensors could clearly distinguish AD from healthy states, PD from healthy states, and AD from PD states, with a classification accuracy of 85, 78 and 84%, respectively. Gas chromatography combined with mass spectrometry analysis showed statistically significant differences in the average abundance of several volatile organic compounds in the breath of AD, PD and healthy subjects, thus supporting the breath prints observed with the sensors. CONCLUSION: The breath prints that were identified with combinations of nanomaterial-based sensors have future potential as cost-effective, fast and reliable biomarkers for AD and PD.
Authors: Eunkyoung Kim; Yi Liu; Hadar Ben-Yoav; Thomas E Winkler; Kun Yan; Xiaowen Shi; Jana Shen; Deanna L Kelly; Reza Ghodssi; William E Bentley; Gregory F Payne Journal: Adv Healthc Mater Date: 2016-09-12 Impact factor: 9.933