Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules
| Autor: | Eduard Koifman, Salam Khatib, Ilana Doweck, Manal Aboud, Samih Badarny, Raz Winer, Lea Glass-Marmor, Haitham Amal, Jennifer Fulton, Larry H. Wilf, Marc Humbert, Armands Sivins, Zaher Bahouth, Marcis Leja, Nir Peled, Gérald Simonneau, Tova Rainis, Frédéric Perros, John P.M. Finberg, Gilles Garcia, Ohad Ronen, Guntis Ancans, Qing-Ling Hua, Li Tao, Ieva Lasina, Barbara Girerd, Amir Karban, Sylvia Cohen-Kaminsky, Roberts Skapars, Shifaa' Badarneh, Hu Liu, Maayan Gruber, Ivars Tolmanis, Tal Marshak, Izabella Lejbkowicz, Wei Zhang, Ilze Kikuste, Raneen Jeries, A'laa Gharra, Douglas W. Johnson, Stuart Z. Millstone, Da-you Shi, Inta Liepniece-Karele, Marwan Hakim, Hossam Haick, Morad Nakhleh, Ariel Miller, Shira Baram, John W. Wells, David Montani, Farid Nakhoul, Raed Salim, Hodaya Ivgi, Ofer Nativ, Lior Har-Shai, Yue-Yin Pan, Yoav Y. Broza |
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| Rok vydání: | 2016 |
| Předmět: |
Adult
Male volatile organic compound diagnosis General Physics and Astronomy Pattern analysis Metal Nanoparticles Nanotechnology 02 engineering and technology Biosensing Techniques 01 natural sciences Article Pattern Recognition Automated Artificial Intelligence sensor noninvasive Medicine Humans General Materials Science carbon nanotube Volatile Organic Compounds disease breath business.industry Nanotubes Carbon nanoparticle 010401 analytical chemistry General Engineering Pattern recognition Middle Aged 021001 nanoscience & nanotechnology 0104 chemical sciences 3. Good health Breath Tests Case-Control Studies Female Artificial intelligence Gold 0210 nano-technology business |
| Zdroj: | ACS Nano |
| ISSN: | 1936-086X |
| Popis: | We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development. |
| Databáze: | OpenAIRE |
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