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Sensors for detecting pulmonary diseases from exhaled breath

Dina Hashoul, Hossam Haick
European Respiratory Review 2019 28: 190011; DOI: 10.1183/16000617.0011-2019
Dina Hashoul
Dept of Chemical Engineering, Russell Berrie Nanotechnology Institute, and the Technion Integrated Cancer Center, Haifa, Israel
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  • For correspondence: hhossam@technion.ac.il
Hossam Haick
Dept of Chemical Engineering, Russell Berrie Nanotechnology Institute, and the Technion Integrated Cancer Center, Haifa, Israel
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  • FIGURE 1
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    FIGURE 1

    a) Overview of the working principal of nanomaterial-based sensors array. b) Different nanomaterial-based sensors. 1) Chemiresistor based on monolayer-capped nanoparticles; 2) chemiresistors based on single-wall carbon nanotubes; 3) chemiresistor based on conducting polymers; 4) chemiresistor based on metal oxide film; 5) quartz microbalance with selective coating; 6) colorimetric sensor; and 7) surface acoustic wave sensor. Reproduced from [11] with permission from the publisher.

  • FIGURE 2
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    FIGURE 2

    Discriminant factor analysis plots calculated from the responses of the nanoarray sensors for a) early lung cancer and benign pulmonary nodules and b) patients with lung cancer with and without the epidermal growth factor receptor (EGFR) mutation. Each point represents one patient. The positions of the mean values are marked with an unfilled square; the boxes correspond to the first and third quartiles, and the error bars correspond to the sd. CV1: first canonical variable. Reproduced from [36] with permission from the publisher.

  • FIGURE 3
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    FIGURE 3

    a) Dot plots response of a single molecularly modified gold nanoparticle. Each symbol represents a single sample. The dashed line represents Youden's cut-off point, and the dotted lines represent the cut-off points to rule in and rule out tuberculosis. Samples from the validation set with responses lower than the threshold were classified as tuberculosis positive (open stars) or nontuberculosis positive (closed stars) according to Youden's cut-off point. b) Receiver operating characteristic curve of the sensor. c) Receiver operating characteristic curves of the sensor when comparing smoking, HIV and treatment status. AUC: area under curve. Reproduced from [56] with permission from the publisher.

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  • TABLE 1

    Sensor arrays in the diagnosis of respiratory diseases

    DiseaseSensor typeReferences
    Lung cancerCBPC, MO, SWNTs, SiNW FET, MCMNPs, QMB, colourimetric[27, 28, 29, 30, 31, 32, 33, 34, 35, 36]
    COPDCBPC, QMB, MO[37, 38, 39, 40, 41, 42]
    AsthmaCBPC, MO[37, 43, 44, 45, 46, 47, 48]
    PAHMCMNPs, colourimetric[49]
    OSASCBPC[50, 51, 52]
    CFCBPC[53, 54, 55]
    TBMO, SWNTs, MCMNPs,[56, 57, 58, 59, 60, 61]
    PneumoconiosisCBPC[62]

    COPD: chronic obstructive pulmonary disease; PAH: pulmonary arterial hypertension; OSAS: obstructive sleep apnoea syndrome; CF: cystic fibrosis; TB: tuberculosis. CBPC: carbon black polymer composite; MO: metal oxide; SWNTs: single-walled carbon nantotubes; SiNW FET: silicon nanowire field effect transistors; MCMNPs: monolayer-capped metal-coated nanoparticles; QMB: quartz microbalance.

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      ERR-0011-2019_Supplementary tables ERR-0011-2019_Supplementary_tables

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    Sensors for detecting pulmonary diseases from exhaled breath
    Dina Hashoul, Hossam Haick
    European Respiratory Review Jun 2019, 28 (152) 190011; DOI: 10.1183/16000617.0011-2019

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    Sensors for detecting pulmonary diseases from exhaled breath
    Dina Hashoul, Hossam Haick
    European Respiratory Review Jun 2019, 28 (152) 190011; DOI: 10.1183/16000617.0011-2019
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    • Article
      • Abstract
      • Abstract
      • Introduction
      • Volatile organic compounds as biomarkers for respiratory diseases
      • Sensor arrays for disease detection in exhaled breath
      • Future perspectives and concluding remarks
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    • Pulmonary pharmacology and therapeutics
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