| Title: | Exhaled Breath Markers for Nonimaging and Noninvasive Measures for Detection of Multiple Sclerosis |
| Author(s): | Broza YY; Har-Shai L; Jeries R; Cancilla JC; Glass-Marmor L; Lejbkowicz I; Torrecilla JS; Yao X; Feng X; Narita A; Mullen K; Miller A; Haick H; |
| Address: | "Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology , Haifa 32000003, Israel. Division of Neuroimmunology and Multiple Sclerosis Center, Carmel Medical Center , Haifa 34362, Israel. Rappaport Faculty of Medicine & Research Institute, Technion-Israel Institute of Technology , Haifa 31096, Israel. Departamento de Ingenieria Quimica, Facultad de Ciencias Quimicas, Universidad Complutense de Madrid , 28040 Madrid, Spain. Max Planck Institute for Polymer Research , Ackermannweg 10, D-55128 Mainz, Germany" |
| DOI: | 10.1021/acschemneuro.7b00181 |
| ISSN/ISBN: | 1948-7193 (Electronic) 1948-7193 (Linking) |
| Abstract: | "Multiple sclerosis (MS) is the most common chronic neurological disease affecting young adults. MS diagnosis is based on clinical characteristics and confirmed by examination of the cerebrospinal fluids (CSF) or by magnetic resonance imaging (MRI) of the brain or spinal cord or both. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease state. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on noninvasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 participants, 146 MS and 58 healthy control individuals. Analysis was performed by gas-chromatography mass-spectrometry (GC-MS) and nanomaterial-based sensor array. Predictive models were derived from the sensors, using artificial neural networks (ANNs). GC-MS analysis revealed significant differences in VOC abundance between MS patients and controls. Sensor data analysis on training sets was able to discriminate in binary comparisons between MS patients and controls with accuracies up to 90%. Blinded sets showed 95% positive predictive value (PPV) between MS-remission and control, 100% sensitivity with 100% negative predictive value (NPV) between MS not-treated (NT) and control, and 86% NPV between relapse and control. Possible links between VOC biomarkers and the MS pathogenesis were established. Preliminary results suggest the applicability of a new nanotechnology-based method for MS diagnostics" |
| Keywords: | "Adult Biomarkers/analysis Breath Tests/instrumentation/*methods Confounding Factors, Epidemiologic Electric Conductivity Equipment Design Female Gas Chromatography-Mass Spectrometry Gold Humans Ligands Male Metal Nanoparticles Middle Aged Multiple Scleros;" |
| Notes: | "MedlineBroza, Yoav Y Har-Shai, Lior Jeries, Raneen Cancilla, John C Glass-Marmor, Lea Lejbkowicz, Izabella Torrecilla, Jose S Yao, Xuelin Feng, Xinliang Narita, Akimitsu Mullen, Klaus Miller, Ariel Haick, Hossam eng Clinical Trial Comparative Study 2017/08/03 ACS Chem Neurosci. 2017 Nov 15; 8(11):2402-2413. doi: 10.1021/acschemneuro.7b00181. Epub 2017 Aug 16" |
