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

ABSTRACT: 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.

SUBMITTER: Nakhleh MK

PROVIDER: S-EPMC5269643 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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

Nakhleh Morad K MK Amal Haitham H Jeries Raneen R Broza Yoav Y YY Aboud Manal M Gharra Alaa A Ivgi Hodaya H Khatib Salam S Badarneh Shifaa S Har-Shai Lior L Glass-Marmor Lea L Lejbkowicz Izabella I Miller Ariel A Badarny Samih S Winer Raz R Finberg John J Cohen-Kaminsky Sylvia S Perros Frédéric F Montani David D Girerd Barbara B Garcia Gilles G Simonneau Gérald G Nakhoul Farid F Baram Shira S Salim Raed R Hakim Marwan M Gruber Maayan M Ronen Ohad O Marshak Tal T Doweck Ilana I Nativ Ofer O Bahouth Zaher Z Shi Da-You DY Zhang Wei W Hua Qing-Ling QL Pan Yue-Yin YY Tao Li L Liu Hu H Karban Amir A Koifman Eduard E Rainis Tova T Skapars Roberts R Sivins Armands A Ancans Guntis G Liepniece-Karele Inta I Kikuste Ilze I Lasina Ieva I Tolmanis Ivars I Johnson Douglas D Millstone Stuart Z SZ Fulton Jennifer J Wells John W JW Wilf Larry H LH Humbert Marc M Leja Marcis M Peled Nir N Haick Hossam H

ACS nano 20161221 1

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). ...[more]

PMID: 28000444

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Project description:BackgroundMagnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA).MethodsSeventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus.ResultsThe primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification.ConclusionsThe results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

Project description:The interaction between genetic susceptibility, epigenetic, endogenous, and environmental factors play a key role in the initiation and progression of autoimmune thyroid diseases (AITDs). Studies have shown that gut microbiota alterations take part in the development of autoimmune diseases. We have investigated the possible relationship between gut microbiota composition and the most frequent AITDs. A total of nine Hashimoto's thyroiditis (HT), nine Graves-Basedow's disease (GD), and 11 otherwise healthy donors (HDs) were evaluated. 16S rRNA pyrosequencing and bioinformatics analysis by Quantitative Insights into Microbial Ecology and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) were used to analyze the gut microbiota. Beta diversity analysis showed that gut microbiota from our groups was different. We observed an increase in bacterial richness in HT and a lower evenness in GD in comparison to the HDs. GD showed a significant increase of Fusobacteriaceae, Fusobacterium and Sutterella compared to HDs and the core microbiome features showed that Prevotellaceae and Prevotella characterized this group. Victivallaceae was increased in HT and was part of their core microbiome. Streptococcaceae, Streptococcus and Rikenellaceae were greater in HT compared to GD. Core microbiome features of HT were represented by Streptococcus, Alistipes, Anaerostipes, Dorea and Haemophilus. Faecalibacterium decreased in both AITDs compared to HDs. PICRUSt analysis demonstrated enrichment in the xenobiotics degradation, metabolism, and the metabolism of cofactors and vitamins in GD patients compared to HDs. Moreover, correlation studies showed that some bacteria were widely correlated with autoimmunity parameters. A prediction model evaluated a possible relationship between predominant concrete bacteria such as an unclassified genus of Ruminococcaceae, Sutterella and Faecalibacterium in AITDs. AITD patients present altered gut microbiota compared to HDs. These alterations could be related to the immune system development in AITD patients and the loss of tolerance to self-antigens.

Dataset Information

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

Publications

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

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