scholarly journals SERAPAN SENYAWA ORGANIK VOLATIL SEBAGAI BIOMARKER PENYAKIT KANKER PARU: SUATU MINI REVIEW

Biomedika ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 58-64
Author(s):  
Donni Kis Apriyanto ◽  
Mitrayana Mitrayana
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Lung Cancer Screening ◽  
Literature Study ◽  
Volatile Organic ◽  
Laser Sources ◽  
Lung Cancer Biomarker ◽  
General Collection

ABSTRAKUlasan ini merupakan hasil studi literatur yang memberikan tinjauan umum serapan senyawa-senyawa organik volatil yang dianggap sebagai biomarker kanker paru. Senyawa-senyawa ini dapat menyerap pada panjang gelombang tertentu. Senyawa-senyawa organik volatil yang teridentifikasi didaftar dan dijabarkan panjang gelombang yang dapat mereka serap. Studi literatur ini menyajikan kelompok senyawa-senyawa organik volatil dapat menyerap pada rentang panjang gelombang inframerah. Hasil ulasan ini mungkin dapat bermanfaat untuk pengembangan skrinning kanker paru dengan menggunakan alat spektroskopi fotoakustik yang menggunakan sumber laser pada rentang panjang gelombang inframerah atau ultraviolet dengan memanfaatkan serapan panjang gelombang oleh senyawa-senyawa tertentu.Keyword: Biomarker Kanker Paru,Senyawa Organik Volatil, Spektroskopi ABSTRACTThis review is the result of a literature study that provides a general collection of volatile organic compounds (VOC) which are considered as markers for lung cancer. These compounds can absorb certain long waves. The volatile organic compounds identified are listed and described in wavelengths that they can absorb. Literature studies that produce volatile organic compounds in the analysis wavelength range. The results of this review may be useful for the development of lung cancer screening by photoacoustic spectroscopic devices that use laser sources in the range of infrared or ultraviolet wavelengths by utilizing wavelength absorb by certain compounds.Keyword: Lung Cancer Biomarker, Volatile Organic Compounds, Spectroscopy

Calculated indices of volatile organic compounds (VOCs) in exhalation for lung cancer screening and early detection

Lung Cancer ◽  
2021 ◽  
Vol 154 ◽  
pp. 197-205
Author(s):  
Xing Chen ◽  
Kanhar Ghulam Muhammad ◽  
Channa Madeeha ◽  
Wei Fu ◽  
Linxin Xu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Volatile Organic Compounds ◽  
Early Detection ◽  
Organic Compounds ◽  
Lung Cancer Screening ◽  
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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