ArticleMolecules (Basel, Switzerland)2021
Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Exhaled breath volatile organic compounds (VOCs) detection methods: GC-MS versus eNose in COPD diagnosis-a systematic review and meta-analysis.BMC pulmonary medicine · 2025Pooled it
- Volatile organic compounds in exhaled human breath for the diagnosis of malignant pleural mesothelioma: a meta-analysis.Frontiers in oncology · 2025Pooled it
- Pooled it
- Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study.Scientific reports · 2024Trial
- Exhaled breath profiling for early silicosis detection in workers at risk: a pilot study.ERJ open research · 2026Article
- Metabolomic Approaches to Lung Function in Pediatric Asthma: A Narrative Review.Children (Basel, Switzerland) · 2026Review
- Cross-Disease Breathomics by PTR-TOF-MS: Multiclass Machine Learning and Network Remodeling Across Asthma, COPD, Cystic Fibrosis, and Lymphangioleiomyomatosis.International journal of molecular sciences · 2026Article
- Article
- Collection methods of exhaled volatile organic compounds for lung cancer screening and diagnosis: a systematic review.Journal of thoracic disease · 2024Review
- Discovery and Validation of a Volatile Signature of Eosinophilic Airway Inflammation in Asthma.American journal of respiratory and critical care medicine · 2024Article
- Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Tuning phosphorene and MoSRSC advances · 2024Article
- A combined screening study for evaluating the potential of exhaled acetone, isoprene, and nitric oxide as biomarkers of lung cancer.RSC advances · 2023Article
- Review
- Methods to Detect Volatile Organic Compounds for Breath Biopsy Using Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry.Molecules (Basel, Switzerland) · 2023Article
- GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review.Frontiers in molecular biosciences · 2023Review
- Unraveling the potential of breath and sweat VOC capture devices for human disease detection: a systematic-like review of canine olfaction and GC-MS analysis.Frontiers in chemistry · 2023Review
- Review
- Analysis of volatile organic compounds from deep airway in the lung through intubation sampling.Analytical and bioanalytical chemistry · 2022Article
- Micro-Chamber/Thermal Extractor (µ-CTE) as a new sampling system for VOCs emitted by feces.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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, (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.