Artificially intelligent nanoarray analyzes 17 diseases from breaths

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Schematic representation of the concept and design of the study. It involved collection of breath samples from 1404 subjects in 14 departments in nine clinical centers in five different countries (Israel, France, USA, Latvia, and China). The population included 591 healthy controls and 813 patients diagnosed with one of 17 different diseases: lung cancer, colorectal cancer, head and neck cancer, ovarian cancer, bladder cancer, prostate cancer, kidney cancer, gastric cancer, Crohn’s disease, ulcerative colitis, irritable bowel syndrome, idiopathic Parkinson’s, atypical Parkinsonism, multiple sclerosis, pulmonary arterial hypertension, pre-eclampsia, and chronic kidney disease. One breath sample obtained from each subject was analyzed with the artificially intelligent nanoarray for disease diagnosis and classification, and a second was analyzed with GC-MS for exploring its chemical composition.

The present study reports on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and random network of single-wall carbon nanotubes for noninvasive diagnosis and classification of 17 different diseases from exhaled breath. The nanoarray was used for the practical evaluation of 1404 subjects in nine clinical settings worldwide. Blind experiments with the artificially intelligent nanoarray showed that 86% accuracy could be achieved, allowing discrimination between each pair of the diseases, and that each disease has its own unique volatile molecular print compared to both healthy controls and other diseases.

The artificially intelligent nanoarray had a low or no vulnerability to clinical and demographical confounding factors. The findings by nanoarray were examined by an independent analytical technique, GC-MS. This analysis found 13 exhaled VOCs associated with various diseases, and their composition differs from one disease to another, thereby validating the nanoarray results. While further and larger translational studies are required to validate these findings, this work provides a shuttling pad for in statu nascendi “volatolomics” field (the omics of volatile biomarkers), as well as a method for obtaining affordable, easy-to-use, inexpensive, and miniaturized tools for personalized screening, diagnosis, and follow-up of a range of diseases.

Control samples ruled out the possibility of coincidence and/or external biases. Of special importance, results from the artificially intelligent nanoarrays support the hypothesis that similarities in pathophysiological processes are expressed in quite similar breath patterns. The results also indicated that the adjustment for confounding factors was successful. The subgroups were not clustered according to similarities in demographic features or geographical location, which also stresses that the artificially intelligent nanoarray analysis is less sensitive to possible confounding factors since we have seen in some cases trends in the control groups that were like those seen among the diseases.

In some cases, two or more diseases shared the same control group, as in (1) Crohn’s disease, ulcerative colitis, and irritable bowel syndrome; (2) kidney and bladder cancer; and (3) idiopathic and atypical Parkinsonism. Therefore, the last analysis was not applicable in these cases (Figure 3, hatched boxes). In contrast to the high accuracy achieved among diseases (86%), the classification of the control samples resulted in random results with a total accuracy of 58%, ruling out the possibility of coincidence. In certain comparisons, the results were higher than the arbitrary classification of the control subjects.

In some cases, two or more diseases shared the same control group, as in (1) Crohn’s disease, ulcerative colitis, and irritable bowel syndrome; (2) kidney and bladder cancer; and (3) idiopathic and atypical Parkinsonism. Therefore, the last analysis was not applicable in these cases (Figure 3, hatched boxes). In contrast to the high accuracy achieved among diseases (86%), the classification of the control samples resulted in random results with a total accuracy of 58%, ruling out the possibility of coincidence. In certain comparisons, the results were higher than the arbitrary classification of the control subjects.

The artificially intelligent nanoarray analyzes the collective breath VOC patterns in a black-box approach. To identify and quantify the specific VOCs associated with each disease state, a second breath sample obtained from all participants was analyzed by GC-MS. This identified over 150 different VOCs in the different cohorts, but only 35 VOCs were selected for further investigation. The choice was made on the following criteria: (i) they were common to >70% of the total population (patients and controls); (ii) they were easily identified and verified by the analysis of pure standards; and (iii) they had concentrations in ambient air samples at least 10-fold lower (on average) than in the equivalent breath samples. Owing to the demographic differences between the groups, multiple linear regression for the abundance of each VOCs was first carried out to explore any possible correlation between abundance and the covariates (age, sex, location, and smoking status). The results indicate that the abundances of 15 VOCs were negatively correlated with age and/or smoking; three of them were also correlated with gender. However, there was no significant correlation between the abundance of those VOCs and the sampling site. Therefore, each VOC with significant correlations (p < 0.05) was adjusted according to the calculated coefficient corresponding to the confounding element (see SI, Table S16).

Regression models applied to the raw GC-MS data showed that the abundance of exhaled VOCs was affected by some common confounding factors. A number of the VOCs was affected by age and/or smoking habits (e.g., 2-ethylhexanol, 3-methylhexane, 5-ethyl-3-methyloctane, acetone, ethanol, ethyl acetate, ethylbenzene, isononane, isoprene, nonanal, styrene, toluene, and undecane), whereas three of them were also affected by the gender of the subject (isononane, nonanal, and undecane). This effect stemming from the first part of the VOCs could be explained by the relationship between the anatomical and physiological changes in the respiratory system and circulation associated with aging and/or smoking injury.(65) It includes stiffness and degeneration of the elastic fibers, fibrosis, aging-associated destruction of lung parenchyma, emphysema, and chronic bronchitis, mainly among smokers.(66) These alterations could easily affect the diffusion of VOCs through the blood–air barrier by altering the thickness or permeability of the epithelium (the so-called membrane conductance) or by reducing the total surface area of the membrane.(66) These factors could easily alter the flux, according to Fick’s first law, affecting the diffusion of gases in the exhaled air, eventually reducing/stressing the expression and/or concentrations of a wide range of the exhaled VOC components.(5) The effect stemming from the second part of the VOCs might be attributed to hormonal or structural gender-related differences.(67)

http://pubs.acs.org/doi/full/10.1021/acsnano.6b04930

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