Analysis of volatile organic compounds (VOCs) in the headspace of NCI-H1666 lung cancer cells

2011 ◽  
Vol 7 (3) ◽  
pp. 153-161 ◽  
Author(s):  
Andreas Sponring ◽  
Wojciech Filipiak ◽  
Clemens Ager ◽  
Jochen Schubert ◽  
Wolfram Miekisch ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Cancer Cells ◽  
Organic Compounds ◽  
Lung Cancer Cells ◽  
Volatile Organic

A PRELIMINARY STUDY ON DETECTION OF LUNG CANCER CELLS BASED ON VOLATILE ORGANIC COMPOUNDS SENSING USING ELECTRONIC NOSE

2015 ◽  
Vol 77 (7) ◽  
Author(s):  
Reena Thriumani ◽  
Amanina Iymia Jeffreea ◽  
Ammar Zakaria ◽  
Yumi Zuhanis Has-Yun Hasyim ◽  
Khaled Mohamed Helmy ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Cancer Cells ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Preliminary Investigation ◽  
Lung Cancer Cells ◽  
Polymer Sensors ◽  
Volatile Organic

 This paper proposes a preliminary investigation on the volatile production patterns generated from three sets of in-vitro cancerous cell samples of headspace that contains volatile organic compounds using the electronic nose system.  A commercialized electronic nose consisting of 32 conducting polymer sensors (Cyranose 320) is used to analyze the three classes of signals which are lung cancer cells grown in media, breast cancer cells grown in media and the blank media (without cells). Neural Network (PNN) based classification technique is applied to investigate the performance of an electronic nose (E-nose) system for cancerous lung cell classification.  

A study of the volatile organic compounds exhaled by lung cancer cells in vitro for breath diagnosis

Cancer ◽  
2007 ◽  
Vol 110 (4) ◽  
pp. 835-844 ◽  
Author(s):  
Xing Chen ◽  
Fengjuan Xu ◽  
Yue Wang ◽  
Yuefeng Pan ◽  
Deji Lu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Cancer Cells ◽  
Organic Compounds ◽  
Lung Cancer Cells ◽  
Volatile Organic

Influence of Chronic Obstructive Pulmonary Disease on Volatile Organic Compounds in Patients with Non-Small Cell Lung Cancer

2020 ◽  
Vol 56 (12) ◽  
pp. 801-805
Author(s):  
Maria Ángeles Muñoz-Lucas ◽  
Javier Jareño-Esteban ◽  
Carlos Gutiérrez-Ortega ◽  
Pablo López-Guijarro ◽  
Luis Collado-Yurrita ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Chronic Obstructive Pulmonary Disease ◽  
Volatile Organic Compounds ◽  
Small Cell Lung Cancer ◽  
Organic Compounds ◽  
Small Cell ◽  
Chronic Obstructive ◽  
Small Cell Lung ◽  
Obstructive Pulmonary Disease ◽  
Volatile Organic

scholarly journals Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

Metabolites ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 317
Author(s):  
Michalis Koureas ◽  
Paraskevi Kirgou ◽  
Grigoris Amoutzias ◽  
Christos Hadjichristodoulou ◽  
Konstantinos Gourgoulianis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Ambient Air ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Healthy Controls ◽  
Volatile Organic

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal–Wallis and Mann–Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001–0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve—AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.

scholarly journals Multi-layer graphene as a selective detector for future lung cancer biosensing platforms

Nanoscale ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 2476-2483 ◽  
Author(s):  
E. Kovalska ◽  
P. Lesongeur ◽  
B. T. Hogan ◽  
A. Baldycheva
Keyword(s):  
Lung Cancer ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Surface Patterning ◽  
Multilayer Graphene ◽  
Layer Graphene ◽  
Selective Detector ◽  
Volatile Organic ◽  
Enhanced Selectivity

Multilayer graphene can be used to detect volatile organic compounds, with enhanced selectivity and sensitivity through surface patterning.

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