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 or by magnetic resonance imaging of the brain and/or spinal cord. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease progression. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on non-invasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 volunteers, 164 MS and 58 control individuals. Analysis was performed by: gas-chromatography mass-spectrometry (GC-MS) and nanomaterial-based sensors array. Predictive models were derived from the sensors, using Artificial Neural Networks. GC-MS analysis revealed significant differences in VOCs abundance between MS patients and Controls. Sensor data analysis on training sets were able to binary discriminate between MS patients and Controls with accuracies up-to 90%. Blinded sets showed 95% positive predictive value between MS-remission and control and 100% sensitivity with 100% negative predictive value 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.
Broza YY, Har-Shai L, Jeries R, Cancilla JC, Glass-Marmor L, Lejbkowicz I, Torrecilla JS, Yao X, Feng X, Narita A, Müllen K, Miller A, Haick H. Exhaled Breath Markers for Non-Imaging and Non-Invasive Measures for Detection of Multiple Sclerosis. ACS Chem Neurosci. 2017 Aug 2. doi: 10.1021/acschemneuro.7b00181. [Epub ahead of print]
We have all heard about disease sniffing dogs, which apparently can smell chemicals associated with things such as Parkinsons disease. So now they have made a mechanical dog that can smell multiple sclerosis.