Nanoarray sniffs out and distinguishes ‘breathprints’ of multiple diseases

December 23, 2016

Schematic representation of the concept and design of the study, which involved collection of breath samples from 1404 patients diagnosed with one of 17 different diseases. One breath sample obtained from each subject was analyzed with the artificially intelligent nanoarray for disease diagnosis and classification (represented by patterns in the illustration), and a second sample was analyzed with gas chromatography–mass spectrometry to explore its chemical composition. (credit: Morad K. Nakhleh et al./ACS Nano)


As far back as around 400 B.C., doctors diagnosed some diseases by smelling a patient’s exhaled breath, which contains nitrogen, carbon dioxide, oxygen, and a small amount of more than 100 other volatile chemical components. Relative amounts of these substances vary depending on the state of a person’s health. For example, diabetes creates a sweet breath smell. More recently, several teams of scientists have developed experimental breath analyzers, but most of these instruments focus on one disease, such as diabetes and melanoma, or a few diseases.

Detecting 17 diseases

The researchers developed an array of nanoscale sensors to detect the individual components in thousands of breath samples collected from 1404 patients who were either healthy or had one of 17 different diseases*, such as kidney cancer or Parkinson’s disease.

The team used mass spectrometry to identify the breath components associated with each disease. By analyzing the results with artificial intelligence techniques (binary classifiers), the team found that each disease produces a unique breathprint, based on differing amounts of 13 volatile organic chemical (VOC) components. They also showed that the presence of one disease would not prevent the detection of others — a prerequisite for developing a practical device to screen and diagnose various diseases.

Based on the research, the team designed an organic layer that functions as a sensing layer (recognition element) for adsorbed VOCs and an electrically conductive nanoarray based on resistive layers of molecularly modified gold nanoparticles and a random network of single-wall carbon nanotubes. The nanoparticles and nanotubes have different electrical conductivity patterns associated with different diseases.**

The authors received funding from the ERC and LCAOS of the European Union’s Seventh Framework Programme for Research and Technological Development, the EuroNanoMed Program under VOLGACORE, and the Latvian Council of Science.


** During exposure to breath samples, interaction between the VOC components and the organic sensing layer changes the electrical resistance of the sensors. The relative change of sensor’s resistance at the peak (beginning), middle, and end of the exposure, as well as the area under the curve of the whole measurement were measured. All breath samples identified by the AI nanoarray were also examined using an independent lab-based analytical technique: gas chromatography linked with mass spectrometry.

Abstract of Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules
