| Title: | Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles |
| Author(s): | Monedeiro F; Monedeiro-Milanowski M; Ratiu IA; Brozek B; Ligor T; Buszewski B; |
| Address: | "Interdisciplinary Centre of Modern Technologies, Nicolaus Copernicus University in Torun, 4 Wilenska St., 87-100 Torun, Poland. 'Raluca Ripan' Institute for Research in Chemistry, Babes-Bolyai University, 30 Fantanele St., RO-400294 Cluj-Napoca, Romania. Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Torun, 7 Gagarina St., 87-100 Torun, Poland. Department of Lung Diseases, Provincial Polyclinic Hospital in Torun, 4 Krasinskiego St., 87-100 Torun, Poland" |
| DOI: | 10.3390/molecules26061789 |
| ISSN/ISBN: | 1420-3049 (Electronic) 1420-3049 (Linking) |
| Abstract: | "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" |
| Keywords: | "Adenocarcinoma of Lung/*metabolism Adult Asthma/*metabolism Breath Tests Female *Gas Chromatography-Mass Spectrometry Humans Lung Neoplasms/*metabolism Male Pulmonary Disease, Chronic Obstructive/*metabolism Volatile Organic Compounds/*metabolism Copd Ntd;" |
| Notes: | "MedlineMonedeiro, Fernanda Monedeiro-Milanowski, Maciej Ratiu, Ileana-Andreea Brozek, Beata Ligor, Tomasz Buszewski, Boguslaw eng POLTUR2/4/2018/Narodowe Centrum Badan i Rozwoju/ Switzerland 2021/04/04 Molecules. 2021 Mar 22; 26(6):1789. doi: 10.3390/molecules26061789" |
