| Title: | Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath |
| Author(s): | Shehada N; Cancilla JC; Torrecilla JS; Pariente ES; Bronstrup G; Christiansen S; Johnson DW; Leja M; Davies MP; Liran O; Peled N; Haick H; |
| Address: | "Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology , Haifa 3200003, Israel. Department of Chemical Engineering, Complutense University of Madrid , Madrid 28040, Spain. Max-Planck-Institute for the Science of Light , Gunther-Scharowsky-Strasse 1, Erlangen 91058, Germany. Helmholtz-Zentrum Berlin fur Materialien und Energie GmbH , Hahn-Meitner-Platz 1, 14109 Berlin, Germany. Florida Radiation Oncology Group, Department of Radiation Oncology, Baptist Cancer Institute (BCI) , 1235 San Marco Boulevard., Suite 100, Jacksonville, Florida 32207, United States. Faculty of Medicine, University of Latvia , 19 Raina boulv., LV1586 Riga, Latvia. Department of Research, Riga East University Hospital , 6 Linezera iela, LV1006 Riga, Latvia. Digestive Diseases Centre GASTRO , Riga, Latvia. 6 Linezera iela, LV1006 Riga, Latvia. Molecular & Clinical Cancer Medicine, University of Liverpool , William Duncan Building, 6 West Derby Street, Liverpool L7 8TX, United Kingdom. Thoracic Cancer Unit, Davidoff cancer center, Petah Tiqwa and the Tel Aviv University , Tel Aviv, Israel" |
| ISSN/ISBN: | 1936-086X (Electronic) 1936-0851 (Linking) |
| Abstract: | "Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations" |
| Keywords: | Asthma/diagnosis *Breath Tests Humans Lung Diseases/*diagnosis *Nanowires *Silicon Volatile Organic Compounds/*analysis breath cancer diagnosis disease nanowire sensor volatile organic compound; |
| Notes: | "MedlineShehada, Nisreen Cancilla, John C Torrecilla, Jose S Pariente, Enrique S Bronstrup, Gerald Christiansen, Silke Johnson, Douglas W Leja, Marcis Davies, Michael P A Liran, Ori Peled, Nir Haick, Hossam eng Research Support, Non-U.S. Gov't 2016/07/08 ACS Nano. 2016 Jul 26; 10(7):7047-57. doi: 10.1021/acsnano.6b03127. Epub 2016 Jul 14" |
