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

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

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.

Keywords: breath; carbon nanotube; diagnosis; disease; nanoparticle; noninvasive; sensor; volatile organic compound.

Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
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.
Figure 2
Figure 2
Heat map of 59 stable sensing features, extracted from 20 different nanomaterial-based sensors on the artificially intelligent nanoarray. Each raw datum in the heat map represents the mean responses for each of the 17 diseases tested in this way. Some sensing features (SFs) were more sensitive than others to differences in the breath VOCs. No individual sensing feature was sufficiently informative to discriminate among all the diseases, but the overall response patterns had discriminative potential (columns in the heat map). For details regarding each of the measured sensing features, see SI, Table S13.
Figure 3
Figure 3
Graphical presentation of the accuracy of the binary DFA classifiers. Each box represents the accuracy achieved in a blind validation of each pair of subject groups. The left heat map gives the results of comparisons between groups of patients, whereas the graph on the right gives the results of the same classifiers applied to the corresponding control groups. The average accuracy was 86% for all disease classifiers (left graph) and 58% for the corresponding control groups (right graph). The letter “C” beside each disease named in the right figure means the “control” group relates to that specific disease.
Figure 4
Figure 4
Clustering analysis of the responses of the sensors. Each cluster represents a similar response profile, suggesting considerable resemblance between samples (subjects) in a specific cluster. It is clear that the clustering is not based on any of the potential confounding factors, but there are strong resemblances between subgroups with common pathophysiologies.
Figure 5
Figure 5
GC-MS analysis of the breath samples. The area under the peak (abundance) measured in the different diseases of three representative VOCs: (a) nonanal, (b) undecane, and (c) isononane. The whisker boxes present first quartile, third quartile, median (line), and average (square); the bars represent the 10% and 90% points, whereas the dots represent minimal and maximal readings.
Figure 6
Figure 6
Heat map of the GC-MS analysis of patients’ breath samples. The average of each of the 13 VOCs is given on the color scale. Hatched boxes are cases in which the VOC was found in

References

    1. Phillips M. Breath Tests in Medicine. Sci. Am. 1992, 267, 74–79. 10.1038/scientificamerican0792-74.
    1. Buszewski B.; Kesy M.; Ligor T.; Amann A. Human Exhaled Air Analytics: Biomarkers of Diseases. Biomed. Chromatogr. 2007, 21, 553–566. 10.1002/bmc.835.
    1. Haick H.; Broza Y. Y.; Mochalski P.; Ruzsanyi V.; Amann A. Assessment, Origin, and Implementation of Breath Volatile Cancer Markers. Chem. Soc. Rev. 2014, 43, 1423–1449. 10.1039/C3CS60329F.
    1. Broza Y. Y.; Mochalski P.; Ruzsanyi V.; Amann A.; Haick H. Hybrid Volatolomics and Disease Detection. Angew. Chem., Int. Ed. 2015, 54, 11036–11048. 10.1002/anie.201500153.
    1. Amann A.; Mochalski P.; Ruzsanyi V.; Broza Y. Y.; Haick H. Assessment of the Exhalation Kinetics of Volatile Cancer Biomarkers Based on Their Physicochemical Properties. J. Breath Res. 2014, 8, 016003.10.1088/1752-7155/8/1/016003.
    1. Nakhleh M.; Broza Y. Y.; Haick H. Monolayer-Capped Gold Nanoparticles for Disease Detection from Breath. Nanomedicine (London, U. K.) 2014, 9, 1991–2002. 10.2217/nnm.14.121.
    1. Broza Y. Y.; Haick H. Nanomaterial-Based Sensors for Detection of Disease by Volatile Organic Compounds. Nanomedicine (London, U. K.) 2013, 8, 785–806. 10.2217/nnm.13.64.
    1. Hakim M.; Broza Y. Y.; Barash O.; Peled N.; Phillips M.; Amann A.; Haick H. Volatile Organic Compounds of Lung Cancer and Possible Biochemical Pathways. Chem. Rev. 2012, 112, 5949–5966. 10.1021/cr300174a.
    1. de Lacy Costello B.; Amann A.; Al-Kateb H.; Flynn C.; Filipiak W.; Khalid T.; Osborne D.; Ratcliffe N. M. A Review of the Volatiles from the Healthy Human Body. J. Breath Res. 2014, 8, 014001.10.1088/1752-7155/8/1/014001.
    1. Haick H. Chemical Sensors Based on Molecularly Modified Metallic Nanoparticles. J. Phys. D: Appl. Phys. 2007, 40, 7173–7186. 10.1088/0022-3727/40/23/S01.
    1. Phillips M.; Basa-Dalay V.; Blais J.; Bothamley G.; Chaturvedi A.; Modi K. D.; Pandya M.; Natividad M. P.; Patel U.; Ramraje N. N.; Schmitt P.; Udwadia Z. F. Point-of-Care Breath Test for Biomarkers of Active Pulmonary Tuberculosis. Tuberculosis 2012, 92, 314–320. 10.1016/j.tube.2012.04.002.
    1. Phillips M.; Basa-Dalay V.; Bothamley G.; Cataneo R. N.; Lam P. K.; Natividad M. P.; Schmitt P.; Wai J. Breath Biomarkers of Active Pulmonary Tuberculosis. Tuberculosis 2010, 90, 145–151. 10.1016/j.tube.2010.01.003.
    1. Bean H. D.; Jimenez-Diaz J.; Zhu J.; Hill J. E. Breathprints of Model Murine Bacterial Lung Infections are Linked with Immune Response. Eur. Respir. J. 2015, 45, 181–190. 10.1183/09031936.00015814.
    1. Cohen-Kaminsky S.; Nakhleh M.; Perros F.; Montani D.; Girerd B.; Garcia G.; Simonneau G.; Haick H.; Humbert M. A Proof of Concept for the Detection and Classification of Pulmonary Arterial Hypertension Through Breath Analysis with a Sensor Array. Am. J. Respir. Crit. Care Med. 2013, 188, 756–759. 10.1164/rccm.201303-0467LE.
    1. Allers M.; Langejuergen J.; Gaida A.; Holz O.; Schuchardt S.; Hohlfeld J. M.; Zimmermann S. Measurement of Exhaled Volatile Organic Compounds from Patients with Chronic Obstructive Pulmonary Disease (COPD) using Closed Gas Loop GC-IMS and GC-APCI-MS. J. Breath Res. 2016, 10, 026004.10.1088/1752-7155/10/2/026004.
    1. Baumbach J. I.; Maddula S.; Sommerwerck U.; Besa V.; Kurth I.; Boedeker B.; Teschler H.; Freitag L.; Darwiche K. Significant Different Volatile Biomarker During Bronchoscopic Ion Mobility Spectrometry Investigation of Patients Suffering Lung Carcinoma. Int. J. Ion Mobility Spectrom. 2011, 14, 159–166. 10.1007/s12127-011-0078-5.
    1. Bos L. D.; Weda H.; Wang Y.; Knobel H. H.; Nijsen T. M.; Vink T. J.; Zwinderman A. H.; Sterk P. J.; Schultz M. J. Exhaled Breath Metabolomics as a Noninvasive Diagnostic Tool for Acute Respiratory Distress Syndrome. Eur. Respir. J. 2014, 44, 188–197. 10.1183/09031936.00005614.
    1. Mansoor J. K.; Schelegle E. S.; Davis C. E.; Walby W. F.; Zhao W.; Aksenov A. A.; Pasamontes A.; Figueroa J.; Allen R. Analysis of Volatile Compounds in Exhaled Breath Condensate in Patients with Severe Pulmonary Arterial Hypertension. PLoS One 2014, 9, e95331.10.1371/journal.pone.0095331.
    1. Smith D.; Sovova K.; Dryahina K.; Dousova T.; Drevinek P.; Spanel P. Breath Concentration of Acetic Acid Vapour is Elevated in Patients with Cystic Fibrosis. J. Breath Res. 2016, 10, 021002.10.1088/1752-7155/10/2/021002.
    1. Amann A.; Corradi M.; Mazzone P.; Mutti A. Lung Cancer Biomarkers in Exhaled Breath. Expert Rev. Mol. Diagn. 2011, 11, 207–217. 10.1586/erm.10.112.
    1. Phillips M.; Gleeson K.; Hughes J. M. B.; Greenberg J.; Cataneo R. N.; Baker L.; McVay W. P. Volatile Organic Compounds in Breath as Markers of Lung Cancer: A Cross-Sectional Study. Lancet 1999, 353, 1930–1933. 10.1016/S0140-6736(98)07552-7.
    1. Zhang Y.; Gao G.; Liu H.; Fu H.; Fan J.; Wang K.; Chen Y.; Li B.; Zhang C.; Zhi X.; He L.; Cui D. Identification of Volatile Biomarkers of Gastric Cancer Cells and Ultrasensitive Electrochemical Detection Based on Sensing Interface of Au-Ag Alloy Coated MWCNTs. Theranostics 2014, 4, 154–162. 10.7150/thno.7560.
    1. Amal H.; Leja M.; Funka K.; Lasina I.; Skapars R.; Sivins A.; Ancans G.; Kikuste I.; Vanags A.; Tolmanis I.; Kirsners A.; Kupcinskas L.; Haick H. Breath Testing as Potential Colorectal Cancer Screening Tool. Int. J. Cancer 2016, 138, 229–236. 10.1002/ijc.29701.
    1. Amal H.; Leja M.; Funka K.; Skapars R.; Sivins A.; Ancans G.; Liepniece-Karele I.; Kikuste I.; Lasina I.; Haick H. Detection of Precancerous Gastric Lesions and Gastric Cancer Through Exhaled Breath. Gut 2016, 65, 400–407. 10.1136/gutjnl-2014-308536.
    1. Amal H.; Shi D.-Y.; Ionescu R.; Zhang W.; Hua Q.-L.; Pan Y.-Y.; Tao L.; Liu H.; Haick H. Assessment of Ovarian Cancer Conditions from Exhaled Breath. Int. J. Cancer 2015, 136, E614–E622. 10.1002/ijc.29166.
    1. Barash O.; Peled N.; Tisch U.; Bunn P. A.; Hirsch F. R.; Haick H. Classification of the Lung Cancer Histology by Gold Nanoparticle Sensors. Nanomedicine (N. Y., NY, U. S.) 2012, 8, 580–589. 10.1016/j.nano.2011.10.001.
    1. Barash O.; Zhang W.; Halpern J. M.; Hua Q. L.; Pan Y. Y.; Kayal H.; Khoury K.; Liu H.; Davies M. P.; Haick H. Differentiation between Genetic Mutations of Breast Cancer by Breath Volatolomics. Oncotarget 2015, 6, 44864–44876. 10.18632/oncotarget.6269.
    1. Davies M. P.; Barash O.; Jeries R.; Peled N.; Ilouze M.; Hyde R.; Marcus M. W.; Field J. K.; Haick H. Unique Volatolomic Signatures of TP53 and KRAS in Lung Cells. Br. J. Cancer 2014, 111, 1213–1221. 10.1038/bjc.2014.411.
    1. Hakim M.; Billan S.; Tisch U.; Peng G.; Dvrokind I.; Marom O.; Abdah-Bortnyak R.; Kuten A.; Haick H. Diagnosis of Head and Neck Cancer from Exhaled Breath. Br. J. Cancer 2011, 104, 1649–1655. 10.1038/bjc.2011.128.
    1. Peled N.; Hakim M.; Bunn P. A. Jr; Miller Y. E.; Kennedy T. C.; Mattei J.; Mitchell J. D.; Hirsch F. R.; Haick H. Non-Invasive Breath Analysis of Pulmonary Nodules. J. Thorac. Oncol. 2012, 7, 1528–1533. 10.1097/JTO.0b013e3182637d5f.
    1. Peng G.; Hakim M.; Broza Y. Y.; Billan S.; Abdah-Bortnyak R.; Kuten A.; Tisch U.; Haick H. Detection of Lung, Breast, Colorectal, and Prostate Cancers from Exhaled Breath using a Single Array of Nanosensors. Br. J. Cancer 2010, 103, 542–551. 10.1038/sj.bjc.6605810.
    1. Peng G.; Tisch U.; Adams O.; Hakim M.; Shehada N.; Broza Y. Y.; Billan S.; Abdah-Bortnyak R.; Kuten A.; Haick H. Diagnosing Lung Cancer in Exhaled Breath Using Gold Nanoparticles. Nat. Nanotechnol. 2009, 4, 669–673. 10.1038/nnano.2009.235.
    1. McKeown T. A Basis for Health Strategies. A Classification of Disease. Br. Med. J. 1983, 287, 594–596. 10.1136/bmj.287.6392.594.
    1. Patterson M. W. Classification in General Practice. Family Practice 1985, 2, 1–3. 10.1093/fampra/2.1.1.
    1. Savaliya R.; Shah D.; Singh R.; Kumar A.; Shanker R.; Dhawan A.; Singh S. Nanotechnology in Disease Diagnostic Techniques. Curr. Drug Metab. 2015, 16, 645–661. 10.2174/1389200216666150625121546.
    1. Vishinkin R.; Haick H. Nanoscale Sensor Technologies for Disease Detection via Volatolomics. Small 2015, 11, 6142–6164. 10.1002/smll.201501904.
    1. Konvalina G.; Haick H. Sensors for Breath Testing: From Nanomaterials to Comprehensive Disease Detection. Acc. Chem. Res. 2014, 47, 66–76. 10.1021/ar400070m.
    1. Chen Y.; Zhang Y.; Pan F.; Liu J.; Wang K.; Zhang C.; Cheng S.; Lu L.; Zhang W.; Zhang Z.; Zhi X.; Zhang Q.; Alfranca G.; de la Fuente J. M.; Chen D.; Cui D. Breath Analysis Based on Surface-Enhanced Raman Scattering Sensors Distinguishes Early and Advanced Gastric Cancer Patients from Healthy Persons. ACS Nano 2016, 10, 8169–8179. 10.1021/acsnano.6b01441.
    1. Gouma P.; Stanacevic M. Selective Nanosensor Array Microsystem For Exhaled Breath Analysis. Procedia Eng. 2011, 25, 1557–1560. 10.1016/j.proeng.2011.12.385.
    1. Gouma P. I.; Kalyanasundaram K. A Selective Nanosensing Probe for Nitric Oxide. Appl. Phys. Lett. 2008, 93, 244102.10.1063/1.3050524.
    1. Zhou Y.; Yu G.; Chang F.; Hu B.; Zhong C. J. Gold-Platinum Alloy Nanowires as Highly Sensitive Materials for Electrochemical Detection of Hydrogen Peroxide. Anal. Chim. Acta 2012, 757, 56–62. 10.1016/j.aca.2012.10.036.
    1. Göpel W. Chemical Sensing, Molecular Electronics and Nanotechnology: Interface Technologies Down to the Molecular Scale. Sens. Actuators, B 1991, 4, 7–21. 10.1016/0925-4005(91)80172-G.
    1. Shehada N.; Cancilla J. C.; Torrecilla J. S.; Pariente E. S.; Bronstrup G.; Christiansen S.; Johnson D. W.; Leja M.; Davies M. P.; Liran O.; Peled N.; Haick H. Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath. ACS Nano 2016, 10, 7047–7057. 10.1021/acsnano.6b03127.
    1. Wang B.; Cancilla J. C.; Torrecilla J. S.; Haick H. Artificial Sensing Intelligence with Silicon Nanowires for Ultraselective Detection in the Gas Phase. Nano Lett. 2014, 14, 933–938. 10.1021/nl404335p.
    1. Nakhleh M. K.; Baram S.; Jeries R.; Salim R.; Haick H.; Hakim M. Artificially Intelligent Nanoarray for the Detection of Preeclampsia under Real-World Clinical Conditions. Adv. Healthcare Mater. 2016, 1600132.10.1002/admt.201600132.
    1. Homede E.; Abo Jabal M.; Ionescu R.; Haick H. Printing Ultrasensitive Artificially Intelligent Sensors Array with a Single Self-Propelled Droplet Containing Nanoparticles. Adv. Funct. Mater. 2016, 26, 6359–6370. 10.1002/adfm.201602326.
    1. Shehada N.; Brönstrup G.; Funka K.; Christiansen S.; Leja M.; Haick H. Ultrasensitive Silicon Nanowire for Real-World Gas Sensing: Noninvasive Diagnosis of Cancer from Breath Volatolome. Nano Lett. 2015, 15, 1288–1295. 10.1021/nl504482t.
    1. Wang B.; Huynh T.-P.; Wu W.; Hayek N.; Do T. T.; Cancilla J. C.; Torrecilla J. S.; Nahid M. M.; Colwell J. M.; Gazit O. M.; Puniredd S. R.; McNeill C. R.; Sonar P.; Haick H. Highly Sensitive Ambipolar Field Effect Transistor-Based Diketopyrrolopyrrole Copolymer for Selective Detection and Discrimination of Xylene Isomers. Adv. Mater. 2016, 28, 4012–4018. 10.1002/adma.201505641.
    1. Ionescu R.; Broza Y.; Shaltieli H.; Sadeh D.; Zilberman Y.; Feng X.; Glass-Marmor L.; Lejbkowicz I.; Mullen K.; Miller A.; Haick H. Detection of Multiple Sclerosis from Exhaled Breath using Bilayers of Polycyclic Aromatic Hydrocarbons and Single-Wall Carbon Nanotubes. ACS Chem. Neurosci. 2011, 2, 687–693. 10.1021/cn2000603.
    1. Karban A.; Nakhleh M. K.; Cancilla J. C.; Vishinkin R.; Rainis T.; Koifman E.; Jeries R.; Ivgi H.; Torrecilla J. S.; Haick H. Programmed Nanoparticles for Tailoring the Detection of Inflammatory Bowel Diseases and Irritable Bowel Syndrome Disease via Breathprint. Adv. Healthcare Mater. 2016, 5, 2339–2344. 10.1002/adhm.201600588.
    1. Marom O.; Nakhoul F.; Tisch U.; Shiban A.; Abassi Z.; Haick H. Gold Nanoparticle Sensors for Detecting Chronic Kidney Disease and Disease Progression. Nanomedicine (London, U. K.) 2012, 7, 639–650. 10.2217/nnm.11.135.
    1. Nakhleh M. K.; Badarny S.; Winer R.; Jeries R.; Finberg J.; Haick H. Distinguishing Idiopathic Parkinson’s Disease from Other Parkinsonian Syndromes by Breath Test. Parkinsonism Relat. Disord. 2015, 21, 150–153. 10.1016/j.parkreldis.2014.11.023.
    1. Nakhleh M. K.; Jeries R.; Gharra A.; Binder A.; Broza Y. Y.; Pascoe M.; Dheda K.; Haick H. Detecting Active Pulmonary Tuberculosis by Breath Test using Nanomaterial-Based Sensors. Eur. Respir. J. 2014, 43, 1522–1525. 10.1183/09031936.00019114.
    1. Nardi-Agmon I.; Abud-Hawa M.; Liran O.; Gai-Mor N.; Ilouze M.; Onn A.; Bar J.; Shlomi D.; Haick H.; Peled N. Exhaled Breath Analysis for Monitoring Response to Treatment in Advanced Lung Cancer. J. Thorac. Oncol. 2016, 11, 827–837. 10.1016/j.jtho.2016.02.017.
    1. Peled N.; Barash O.; Tisch U.; Ionescu R.; Broza Y. Y.; Ilouze M.; Mattei J.; Bunn P. A. Jr.; Hirsch F. R.; Haick H. Volatile Fingerprints of Cancer Specific Genetic Mutations. Nanomedicine (N. Y., NY, U. S.) 2013, 9, 758–766. 10.1016/j.nano.2013.01.008.
    1. Shuster G.; Gallimidi Z.; Reiss A. H.; Dovgolevsky E.; Billan S.; Abdah-Bortnyak R.; Kuten A.; Engel A.; Shiban A.; Tisch U.; Haick H. Classification of Breast Cancer Precursors Through Exhaled Breath. Breast Cancer Res. Treat. 2011, 126, 791–796. 10.1007/s10549-010-1317-x.
    1. Xu Z. Q.; Broza Y. Y.; Ionsecu R.; Tisch U.; Ding L.; Liu H.; Song Q.; Pan Y. Y.; Xiong F. X.; Gu K. S.; Sun G. P.; Chen Z. D.; Leja M.; Haick H. A Nanomaterial-Based Breath Test for Distinguishing Gastric Cancer from Benign Gastric Conditions. Br. J. Cancer 2013, 108, 941–950. 10.1038/bjc.2013.44.
    1. Tisch U.; Haick H. Nanomaterials for cross-reactive sensor arrays. MRS Bull. 2010, 35, 797–803. 10.1557/mrs2010.509.
    1. Konvalina G.; Haick H. The Effect of Humidity on Nanoparticle-Based Chemiresistors: A Comparison Between Synthetic and Real-World Samples. ACS Appl. Mater. Interfaces 2012, 4, 317–325. 10.1021/am2013695.
    1. Amann A.; Miekisch W.; Pleil J.; Risby T.; Schubert J. Chapter 7: Methodological Issues of Sample Collection and Analysis of Exhaled Breath. Europ. Resp. Soc. Monog. 2010, 49, 96–114. 10.1183/1025448x.00018509.
    1. Li J.; Peng Y.; Liu Y.; Li W.; Jin Y.; Tang Z.; Duan Y. Investigation of Potential Breath biomarkers for the Early Diagnosis of Breast Cancer Using Gas Chromatography - Mass Spectrometry. Clin. Chim. Acta 2014, 436, 59–67. 10.1016/j.cca.2014.04.030.
    1. Kumar S.; Huang J.; Abbassi-Ghadi N.; Mackenzie H. A.; Veselkov K. A.; Hoare J. M.; Lovat L. B.; Spanel P.; Smith D.; Hanna G. B. Mass Spectrometric Analysis of Exhaled Breath for the Identification of Volatile Organic Compound Biomarkers in Esophageal and Gastric Adenocarcinoma. Ann. Surg. 2015, 262, 981–990. 10.1097/SLA.0000000000001101.
    1. Alkhouri N.; Singh T.; Alsabbagh E.; Guirguis J.; Chami T.; Hanouneh I.; Grove D.; Lopez R.; Dweik R. Isoprene in the Exhaled Breath is a Novel Biomarker for Advanced Fibrosis in Patients with Chronic Liver Disease: A Pilot Study. Clin. Transl. Gastroenterol. 2015, 6, e112.10.1038/ctg.2015.40.
    1. Smith D.; Spanel P.; Fryer A. A.; Hanna F.; Ferns G. A. Can Volatile Compounds in Exhaled Breath be Used to Monitor Control in Diabetes Mellitus?. J. Breath Res. 2011, 5, 022001.10.1088/1752-7155/5/2/022001.
    1. Matera M. G.; Calzetta L.; Rogliani P.; Cesario A.; Cazzola M. New Treatments for COPD in the Elderly. Curr. Pharm. Des. 2014, 20, 5968–5982. 10.2174/1381612820666140314154331.
    1. Sharma G.; Goodwin J. Effect of Aging on Respiratory System Physiology and Immunology. Clin. Interv. Aging 2006, 1, 253–260. 10.2147/ciia.2006.1.3.253.
    1. Jain R. B. Distributions of Selected Urinary Metabolites of Volatile Organic Compounds by Age, Gender, Race/Ethnicity, and Smoking Status in a Representative Sample of U.S. Adults. Environ. Toxicol. Pharmacol. 2015, 40, 471–479. 10.1016/j.etap.2015.07.018.
    1. Lechner M.; Moser B.; Niederseer D.; Karlseder A.; Holzknecht B.; Fuchs M.; Colvin S.; Tilg H.; Rieder J. Gender and Age Specific Differences in Exhaled Isoprene Levels. Respir. Physiol. Neurobiol. 2006, 154, 478–483. 10.1016/j.resp.2006.01.007.
    1. Phillips M.; Cataneo R. N.; Greenberg J.; Gunawardena R.; Naidu A.; Rahbari-Oskoui F. Effect of Age on the Breath Methylated Alkane Contour - A Display of Apparent New Markers of Oxidative Stress. J. Lab. Clin. Med. 2000, 136, 243–249. 10.1067/mlc.2000.108943.
    1. Schwarz K.; Filipiak W.; Amann A. Determining Concentration Patterns of Volatile Compounds in Exhaled Breath by PTR-MS. J. Breath Res. 2009, 3, 027002.10.1088/1752-7155/3/2/027002.
    1. Dovgolevsky E.; Konvalina G.; Tisch U.; Haick H. Monolayer-Capped Cubic Platinum Nanoparticles for Sensing Nonpolar Analytes in Highly Humid Atmospheres. J. Phys. Chem. C 2010, 114, 14042–14049. 10.1021/jp105810w.
    1. Dovgolevsky E.; Tisch U.; Haick H. Chemically Sensitive Resistors Based on Monolayer-Capped Cubic Nanoparticles: Towards Configurable Nanoporous Sensors. Small 2009, 5, 1158–1161. 10.1002/smll.200801831.

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