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Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles

Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic ob...

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Autores principales: Monedeiro, Fernanda, Monedeiro-Milanowski, Maciej, Ratiu, Ileana-Andreea, Brożek, Beata, Ligor, Tomasz, Buszewski, Bogusław
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004837/
https://www.ncbi.nlm.nih.gov/pubmed/33810121
http://dx.doi.org/10.3390/molecules26061789
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author Monedeiro, Fernanda
Monedeiro-Milanowski, Maciej
Ratiu, Ileana-Andreea
Brożek, Beata
Ligor, Tomasz
Buszewski, Bogusław
author_facet Monedeiro, Fernanda
Monedeiro-Milanowski, Maciej
Ratiu, Ileana-Andreea
Brożek, Beata
Ligor, Tomasz
Buszewski, Bogusław
author_sort Monedeiro, Fernanda
collection PubMed
description Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic obstructive pulmonary disease and asthma. Besides that, global VOC profiles were investigated. A needle trap device (NTD) was used as pre-concentration technique, associated to gas chromatography-mass spectrometry (GC-MS) analysis. Univariate and multivariate approaches were applied to assess VOC distributions according to the studied diseases. Limits of quantitation ranged from 0.003 to 6.21 ppbv and calculated relative standard deviations did not exceed 10%. At least 15 of the quantified targets presented themselves as discriminating features. A random forest (RF) method was performed in order to classify enrolled conditions according to VOCs’ latent patterns, considering VOCs responses in global profiles. The developed model was based on 12 discriminating features and provided overall balanced accuracy of 85.7%. Ultimately, multinomial logistic regression (MLR) analysis was conducted using the concentration of the nine most discriminative targets (2-propanol, 3-methylpentane, (E)-ocimene, limonene, m-cymene, benzonitrile, undecane, terpineol, phenol) as input and provided an average overall accuracy of 95.5% for multiclass prediction.
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spelling pubmed-80048372021-03-29 Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles Monedeiro, Fernanda Monedeiro-Milanowski, Maciej Ratiu, Ileana-Andreea Brożek, Beata Ligor, Tomasz Buszewski, Bogusław Molecules Article Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic obstructive pulmonary disease and asthma. Besides that, global VOC profiles were investigated. A needle trap device (NTD) was used as pre-concentration technique, associated to gas chromatography-mass spectrometry (GC-MS) analysis. Univariate and multivariate approaches were applied to assess VOC distributions according to the studied diseases. Limits of quantitation ranged from 0.003 to 6.21 ppbv and calculated relative standard deviations did not exceed 10%. At least 15 of the quantified targets presented themselves as discriminating features. A random forest (RF) method was performed in order to classify enrolled conditions according to VOCs’ latent patterns, considering VOCs responses in global profiles. The developed model was based on 12 discriminating features and provided overall balanced accuracy of 85.7%. Ultimately, multinomial logistic regression (MLR) analysis was conducted using the concentration of the nine most discriminative targets (2-propanol, 3-methylpentane, (E)-ocimene, limonene, m-cymene, benzonitrile, undecane, terpineol, phenol) as input and provided an average overall accuracy of 95.5% for multiclass prediction. MDPI 2021-03-22 /pmc/articles/PMC8004837/ /pubmed/33810121 http://dx.doi.org/10.3390/molecules26061789 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Monedeiro, Fernanda
Monedeiro-Milanowski, Maciej
Ratiu, Ileana-Andreea
Brożek, Beata
Ligor, Tomasz
Buszewski, Bogusław
Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles
title Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles
title_full Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles
title_fullStr Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles
title_full_unstemmed Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles
title_short Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles
title_sort needle trap device-gc-ms for characterization of lung diseases based on breath voc profiles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004837/
https://www.ncbi.nlm.nih.gov/pubmed/33810121
http://dx.doi.org/10.3390/molecules26061789
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