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Capuano R, Ciotti M, Catini A, Bernardini S, Di Natale C. Clinical applications of volatilomic assays. Crit Rev Clin Lab Sci 2024:1-20. [PMID: 39129534 DOI: 10.1080/10408363.2024.2387038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/23/2024] [Accepted: 07/29/2024] [Indexed: 08/13/2024]
Abstract
The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.
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Affiliation(s)
- Rosamaria Capuano
- Department of Electronic Engineering, University of Rome Tor Vergata, Roma, Italy
- Interdepartmental Center for Volatilomics, "A. D'Amico", University of Rome Tor Vergata, Rome, Italy
| | - Marco Ciotti
- Department of Laboratory Medicine, University Hospital Tor Vergata, Rome, Italy
| | - Alexandro Catini
- Department of Electronic Engineering, University of Rome Tor Vergata, Roma, Italy
- Interdepartmental Center for Volatilomics, "A. D'Amico", University of Rome Tor Vergata, Rome, Italy
| | - Sergio Bernardini
- Interdepartmental Center for Volatilomics, "A. D'Amico", University of Rome Tor Vergata, Rome, Italy
- Department of Laboratory Medicine, University Hospital Tor Vergata, Rome, Italy
- Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Corrado Di Natale
- Department of Electronic Engineering, University of Rome Tor Vergata, Roma, Italy
- Interdepartmental Center for Volatilomics, "A. D'Amico", University of Rome Tor Vergata, Rome, Italy
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2
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Zhai Z, Liu Y, Li C, Wang D, Wu H. Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing. SENSORS (BASEL, SWITZERLAND) 2024; 24:4806. [PMID: 39123852 PMCID: PMC11314697 DOI: 10.3390/s24154806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 06/14/2024] [Accepted: 06/16/2024] [Indexed: 08/12/2024]
Abstract
Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field.
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Affiliation(s)
- Zhenyu Zhai
- National Institute of Metrology of China, Beijing 100029, China; (Z.Z.); (D.W.)
| | - Yaqian Liu
- Inner Mongolia Institute of Metrology Testing and Research, Hohhot 010020, China
| | - Congju Li
- College of Textiles, Donghua University, Shanghai 201620, China;
| | - Defa Wang
- National Institute of Metrology of China, Beijing 100029, China; (Z.Z.); (D.W.)
| | - Hai Wu
- National Institute of Metrology of China, Beijing 100029, China; (Z.Z.); (D.W.)
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3
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Jeong TI, Nguyen TM, Choi E, Gliserin A, Nguyen TMT, Kim S, Kim S, Kim H, Bak GH, Kim NY, Devaraj V, Choi E, Oh JW, Kim S. Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose. ACS Sens 2024; 9:2869-2876. [PMID: 38548672 DOI: 10.1021/acssensors.3c02663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB sensors, which cannot capture the complete spectral response of the system. This limitation can degrade the performance of machine learning analysis, leading to inaccurate identification of chemicals with similar functional groups. Here, we propose a novel time-resolved hyperspectral (TRH) data set from colorimetric array sensors consisting of 1D spatial, 1D spectral, and 1D temporal axes, which enables hierarchical analysis of multichannel 2D spectrograms via a convolution neural network (CNN). We assessed the outstanding classification performance of the TRH data set compared to an RGB data set by conducting a relative humidity (RH) concentration classification. The time-dependent spectral response of the colorimetric sensor was measured and trained as a CNN model using TRH and RGB sensor systems at different RH levels. While the TRH model shows a high classification accuracy of 97.5% for the RH concentration, the RGB model yields 72.5% under identical conditions. Furthermore, we demonstrated the detection of various functional volatile gases with the TRH system by using experimental and simulation approaches. The results reveal distinct spectral features from the TRH system, corresponding to changes in the concentration of each substance.
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Affiliation(s)
- Tae-In Jeong
- Department of Cogno-mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Thanh Mien Nguyen
- Bio-IT Fusion Technology Research Institute, Pusan National University, Busan 46241, Republic of Korea
| | - Eunji Choi
- Department of Optics and Mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Alexander Gliserin
- Department of Cogno-mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
- Department of Optics and Mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Thu M T Nguyen
- Department of Nano Fusion Technology, Pusan National University, Busan 46241, Republic of Korea
| | - San Kim
- Department of Cogno-mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Sehyeon Kim
- Department of Cogno-mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Hyunseo Kim
- Department of Optics and Mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
| | - Gyeong-Ha Bak
- Bio-IT Fusion Technology Research Institute, Pusan National University, Busan 46241, Republic of Korea
| | - Na-Yeong Kim
- Department of Nano Fusion Technology, Pusan National University, Busan 46241, Republic of Korea
| | - Vasanthan Devaraj
- Bio-IT Fusion Technology Research Institute, Pusan National University, Busan 46241, Republic of Korea
| | - Eunjung Choi
- Bio-IT Fusion Technology Research Institute, Pusan National University, Busan 46241, Republic of Korea
| | - Jin-Woo Oh
- Bio-IT Fusion Technology Research Institute, Pusan National University, Busan 46241, Republic of Korea
- Department of Nano Fusion Technology, Pusan National University, Busan 46241, Republic of Korea
| | - Seungchul Kim
- Department of Cogno-mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
- Department of Optics and Mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
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4
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Mashhadbani M, Faizabadi E. Investigating the enhancement of lung cancer sensing: the effect of edge halogenation in armchair stanene nanoribbons. Phys Chem Chem Phys 2024; 26:13335-13349. [PMID: 38639922 DOI: 10.1039/d3cp06343g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
In this research, we explore the impact of edge passivation using halogen atoms on armchair stanene nanoribbon (ASNR) for the early detection of lung cancer biomarkers. We employ non-equilibrium green function (NEGF) and density functional theory (DFT) methods to evaluate sensing characteristics. The edges of ASNR are passivated with fluorine, chlorine, bromine, and iodine atoms. Our findings indicate a significant enhancement in sensing performance upon halogenation of ASNR. Notable changes in adsorption energy and current for edge-halogenated ASNR configurations demonstrate improved sensing behavior. Moreover, current curves exhibit greater distinctiveness of halogenated ASNR in comparison to hydrogenated ASNR. The calculations indicate a change in adsorption energy (Eads) of -7.59 eV, -7.6 eV, -8.3 eV, and -8.6 eV for the adsorption by styrene on I-ASnNR, Br-ASnNR, toluene on Cl-ASnNR, and styrene on F-ASnNR, respectively. The corresponding sensitivity improves up to 37.33%, 38.09%, 38.35%, and 45.5%, respectively. These findings highlight that the most significant change occurs with the edge fluorination of ASnNR. Our findings underscore the effectiveness of halogen atom edge passivation in ASNR for heightened sensing performance, making it a promising choice for the development of early-detection lung cancer sensors.
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Affiliation(s)
| | - Edris Faizabadi
- Iran University of Science and Technology, Islamic Republic of Iran.
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5
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Saeki Y, Maki N, Nemoto T, Inada K, Minami K, Tamura R, Imamura G, Cho-Isoda Y, Kitazawa S, Kojima H, Yoshikawa G, Sato Y. Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array. Lung Cancer 2024; 190:107514. [PMID: 38447302 DOI: 10.1016/j.lungcan.2024.107514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/12/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
INTRODUCTION Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. METHODS Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. RESULTS Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. CONCLUSION Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.
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Affiliation(s)
- Yusuke Saeki
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Naoki Maki
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Takahiro Nemoto
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Katsushige Inada
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan
| | - Kosuke Minami
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; International Center for Young Scientists (ICYS), National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Ryo Tamura
- World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Ibaraki, Japan; Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan; Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Ibaraki, Japan; Center for Basic Research on Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Gaku Imamura
- Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Ibaraki, Japan; Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Yukiko Cho-Isoda
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan
| | - Shinsuke Kitazawa
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Hiroshi Kojima
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan; Ibaraki Clinical Education and Training Center, University of Tsukuba Hospital, Ibaraki, Japan
| | - Genki Yoshikawa
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Materials Science and Engineering, Graduate School of Pure and Applied Science, University of Tsukuba, Ibaraki, Japan
| | - Yukio Sato
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan.
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Martin JDM, Claudia F, Romain AC. How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment. J Breath Res 2024; 18:026002. [PMID: 38211310 DOI: 10.1088/1752-7163/ad1d64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between 'healthy' samples (breath without additives) and 'cancer' samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.
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Affiliation(s)
- Justin D M Martin
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| | - Falzone Claudia
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| | - Anne-Claude Romain
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
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7
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Lan K, Liu S, Wang Z, Long L, Qin G. High-performance pyramid-SiNWs biosensor for NH 3gas detection. NANOTECHNOLOGY 2023; 35:105501. [PMID: 38055986 DOI: 10.1088/1361-6528/ad12eb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/05/2023] [Indexed: 12/08/2023]
Abstract
NH3is widely existed in the environment and is closely associated with various health issues. Additionally, detecting the small amounts of NH3exhaled by patients with liver and kidney diseases offers potential opportunities for painless early disease diagnosis. Therefore, there is an urgent need for a convenient, rapid, and highly sensitive real-time NH3monitoring method. This work presents a high-performance NH3sensor based on olfactory receptor-derived peptides (ORPs) on a pyramid silicon nanowires (SiNWs) structure substrate. First, we successfully fabricated the pyramid-SiNWs structure on a silicon substrate using a chemical etching method. Subsequently, by dehydrative condensation reaction between the amino groups on APTES and the carboxyl groups of ORPs, ORPs were successfully immobilized onto the pyramid-SiNWs structure. This methodology allows the ORPs sensor on the pyramid-SiNWs substrate to detect NH3as low as 1 ppb, which was the reported lowest limit of detection, with a higher response rate compared to ORPs sensors on flat SiNWs substrates. The sensors also exhibit good sensitivity and stability for NH3gas detection. The results show the feasibility and potential applications of ORPs-pyramid-SiNWs structure sensors, in the fields of food safety, disease monitoring, and environmental protection, etc.
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Affiliation(s)
- Kuibo Lan
- School of Microelectronics, Tianjin University, Tianjin, 300072, People's Republic of China
- Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin, 300072, People's Republic of China
| | - Shuaiyan Liu
- School of Microelectronics, Tianjin University, Tianjin, 300072, People's Republic of China
| | - Zhi Wang
- School of Microelectronics, Tianjin University, Tianjin, 300072, People's Republic of China
- Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin, 300072, People's Republic of China
| | - Lixia Long
- School of Materials Science and Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Guoxuan Qin
- School of Microelectronics, Tianjin University, Tianjin, 300072, People's Republic of China
- Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin, 300072, People's Republic of China
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8
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Kok R, van Schaijik B, Johnson NW, Malki MI, Frydrych A, Kujan O. Breath biopsy, a novel technology to identify head and neck squamous cell carcinoma: A systematic review. Oral Dis 2023; 29:3034-3048. [PMID: 35801385 DOI: 10.1111/odi.14305] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/22/2022] [Accepted: 07/05/2022] [Indexed: 11/27/2022]
Abstract
Head and neck cancers are a heterogeneous group of neoplasms, which together comprise the sixth most common cancer globally. Breath biopsies are a non-invasive clinical investigation that detect volatile organic compounds (VOCs) in exhaled breath. This systematic review examines current applications of breath biopsy for the diagnosis of head and neck squamous cell carcinoma (HNSCC), including data on efficacy and utility, and speculates on the future uses of this non-invasive detection method. Medline, PubMed, Web of Science, Cochrane and Scopus, as well as the grey literature were searched using a search strategy developed to identify relevant studies on the role of breath biopsy in the diagnosis of HNSCC. All included studies were subject to a thorough methodological quality assessment. The initial search generated a total of 1443 articles, 20 of which were eligible for review. A total of 660 HNSCC samples were investigated across the included studies. 3,7-dimethylundecane and benzaldehyde were among several VOCs to be significantly correlated with the presence of HNSCC compared to healthy controls. We show that current breath biopsy methods have high accuracy, specificity and sensitivity for identifying HNSCC. However, further studies are needed given the reported poor quality of the included studies.
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Affiliation(s)
- Rachel Kok
- UWA Dental School, The University of Western Australia, Perth, Western Australia, Australia
| | - Bede van Schaijik
- UWA Dental School, The University of Western Australia, Perth, Western Australia, Australia
| | - Newell W Johnson
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
- Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | | | - Agnieszka Frydrych
- UWA Dental School, The University of Western Australia, Perth, Western Australia, Australia
| | - Omar Kujan
- UWA Dental School, The University of Western Australia, Perth, Western Australia, Australia
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9
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Vanstraelen S, Jones DR, Rocco G. Breathprinting analysis and biomimetic sensor technology to detect lung cancer. J Thorac Cardiovasc Surg 2023; 166:357-361.e1. [PMID: 36997463 DOI: 10.1016/j.jtcvs.2023.02.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/15/2023] [Accepted: 02/28/2023] [Indexed: 03/11/2023]
Affiliation(s)
- Stijn Vanstraelen
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - David R Jones
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Fiona and Stanley Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gaetano Rocco
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Fiona and Stanley Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY.
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10
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Ketchanji Mougang YC, Endale Mangamba LM, Capuano R, Ciccacci F, Catini A, Paolesse R, Mbatchou Ngahane HB, Palombi L, Di Natale C. On-Field Test of Tuberculosis Diagnosis through Exhaled Breath Analysis with a Gas Sensor Array. BIOSENSORS 2023; 13:bios13050570. [PMID: 37232931 DOI: 10.3390/bios13050570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/21/2023] [Accepted: 05/18/2023] [Indexed: 05/27/2023]
Abstract
Tuberculosis (TB) is among the more frequent causes of death in many countries. For pulmonary TB, early diagnosis greatly increases the efficiency of therapies. Although highly sensitive tests based on nucleic acid amplification tests (NAATs) and loop-mediated isothermal amplification (TB-LAMP) are available, smear microscopy is still the most widespread diagnostics method in most low-middle-income countries, and the true positive rate of smear microscopy is lower than 65%. Thus, there is a need to increase the performance of low-cost diagnosis. For many years, the use of sensors to analyze the exhaled volatile organic compounds (VOCs) has been proposed as a promising alternative for the diagnosis of several diseases, including tuberculosis. In this paper, the diagnostic properties of an electronic nose (EN) based on sensor technology previously used to identify tuberculosis have been tested on-field in a Cameroon hospital. The EN analyzed the breath of a cohort of subjects including pulmonary TB patients (46), healthy controls (38), and TB suspects (16). Machine learning analysis of the sensor array data allows for the identification of the pulmonary TB group with respect to healthy controls with 88% accuracy, 90.8% sensitivity, 85.7% specificity, and 0.88 AUC. The model trained with TB and healthy controls maintains its performance when it is applied to symptomatic TB suspects with a negative TB-LAMP. These results encourage the investigation of electronic noses as an effective diagnostic method for future inclusion in clinical practice.
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Affiliation(s)
| | - Laurent-Mireille Endale Mangamba
- Faculty of Medicine and Pharmaceutical Sciences, University of Douala, Carrefour Ange Raphael, Douala P.O. Box 4035, Cameroon
- Center for Respiratory Diseases, Douala Laquintinie Hospital, Avenue du Jamot, Douala P.O. Box 4035, Cameroon
| | - Rosamaria Capuano
- Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133 Roma, Italy
- Interdepartmental Centre for Volatilomics "A D'Amico", University of Rome Tor Vergata, via del Politecnico 1, 00133 Roma, Italy
| | - Fausto Ciccacci
- UniCamillus, Saint Camillus International University of Health and Medical Sciences, 00131 Rome, Italy
| | - Alexandro Catini
- Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133 Roma, Italy
- Interdepartmental Centre for Volatilomics "A D'Amico", University of Rome Tor Vergata, via del Politecnico 1, 00133 Roma, Italy
| | - Roberto Paolesse
- Interdepartmental Centre for Volatilomics "A D'Amico", University of Rome Tor Vergata, via del Politecnico 1, 00133 Roma, Italy
- Department of Chemical Science and Technology, University of Rome Tor Vergata, via della Ricerca Scientifica, 00133 Rome, Italy
| | - Hugo Bertrand Mbatchou Ngahane
- Faculty of Medicine and Pharmaceutical Sciences, University of Douala, Carrefour Ange Raphael, Douala P.O. Box 4035, Cameroon
- Internal Medicine Service, Douala General Hospital, Douala P.O. Box 4856, Cameroon
| | - Leonardo Palombi
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Viale Montpellier 1, 00133 Roma, Italy
| | - Corrado Di Natale
- Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133 Roma, Italy
- Interdepartmental Centre for Volatilomics "A D'Amico", University of Rome Tor Vergata, via del Politecnico 1, 00133 Roma, Italy
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11
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Osmólska E, Stoma M, Starek-Wójcicka A. Juice Quality Evaluation with Multisensor Systems-A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4824. [PMID: 37430738 DOI: 10.3390/s23104824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
E-nose and e-tongue are advanced technologies that allow for the fast and precise analysis of smells and flavours using special sensors. Both technologies are widely used, especially in the food industry, where they are implemented, e.g., for identifying ingredients and product quality, detecting contamination, and assessing their stability and shelf life. Therefore, the aim of this article is to provide a comprehensive review of the application of e-nose and e-tongue in various industries, focusing in particular on the use of these technologies in the fruit and vegetable juice industry. For this purpose, an analysis of research carried out worldwide over the last five years, concerning the possibility of using the considered multisensory systems to test the quality and taste and aroma profiles of juices is included. In addition, the review contains a brief characterization of these innovative devices through information such as their origin, mode of operation, types, advantages and disadvantages, challenges and perspectives, as well as the possibility of their applications in other industries besides the juice industry.
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Affiliation(s)
- Emilia Osmólska
- Department of Power Engineering and Transportation, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
| | - Monika Stoma
- Department of Power Engineering and Transportation, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
| | - Agnieszka Starek-Wójcicka
- Department of Biological Bases of Food and Feed Technologies, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
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Sharma A, Kumar R, Varadwaj P. Smelling the Disease: Diagnostic Potential of Breath Analysis. Mol Diagn Ther 2023; 27:321-347. [PMID: 36729362 PMCID: PMC9893210 DOI: 10.1007/s40291-023-00640-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2023] [Indexed: 02/03/2023]
Abstract
Breath analysis is a relatively recent field of research with much promise in scientific and clinical studies. Breath contains endogenously produced volatile organic components (VOCs) resulting from metabolites of ingested precursors, gut and air-passage bacteria, environmental contacts, etc. Numerous recent studies have suggested changes in breath composition during the course of many diseases, and breath analysis may lead to the diagnosis of such diseases. Therefore, it is important to identify the disease-specific variations in the concentration of breath to diagnose the diseases. In this review, we explore methods that are used to detect VOCs in laboratory settings, VOC constituents in exhaled air and other body fluids (e.g., sweat, saliva, skin, urine, blood, fecal matter, vaginal secretions, etc.), VOC identification in various diseases, and recently developed electronic (E)-nose-based sensors to detect VOCs. Identifying such VOCs and applying them as disease-specific biomarkers to obtain accurate, reproducible, and fast disease diagnosis could serve as an alternative to traditional invasive diagnosis methods. However, the success of VOC-based identification of diseases is limited to laboratory settings. Large-scale clinical data are warranted for establishing the robustness of disease diagnosis. Also, to identify specific VOCs associated with illness states, extensive clinical trials must be performed using both analytical instruments and electronic noses equipped with stable and precise sensors.
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Affiliation(s)
- Anju Sharma
- Systems Biology Lab, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Uttar Pradesh, Lucknow Campus, Lucknow, India
| | - Pritish Varadwaj
- Systems Biology Lab, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India.
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13
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Kort S, Brusse-Keizer M, Schouwink H, Citgez E, de Jongh FH, van Putten JWG, van den Borne B, Kastelijn EA, Stolz D, Schuurbiers M, van den Heuvel MM, van Geffen WH, van der Palen J. Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study. Chest 2023; 163:697-706. [PMID: 36243060 DOI: 10.1016/j.chest.2022.09.042] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 09/02/2022] [Accepted: 09/23/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. RESEARCH QUESTION This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? STUDY DESIGN AND METHODS In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. RESULTS A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. INTERPRETATION Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer. CLINICAL TRIAL REGISTRATION The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.
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Affiliation(s)
- Sharina Kort
- Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands.
| | - Marjolein Brusse-Keizer
- Medical School Twente, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands
| | - Hugo Schouwink
- Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands
| | - Emanuel Citgez
- Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands
| | - Frans H de Jongh
- Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands
| | - Jan W G van Putten
- Department of Respiratory Medicine, Martini Ziekenhuis, Groningen, The Netherlands
| | - Ben van den Borne
- Department of Respiratory Medicine, Catharina Ziekenhuis, Eindhoven, The Netherlands
| | - Elisabeth A Kastelijn
- Department of Respiratory Medicine, Sint Antonius Ziekenhuis, Utrecht, The Netherlands
| | - Daiana Stolz
- Clinic for Pulmonary Medicine and Respiratory Cell Research, Universitätspital Basel, Basel, Switzerland; Clinic for Respiratory Medicine, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Milou Schuurbiers
- Department of Respiratory Medicine, Radboud UMC, Nijmegen, The Netherlands
| | | | - Wouter H van Geffen
- Department of Respiratory Medicine, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Job van der Palen
- Medical School Twente, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands
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14
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Kiss H, Örlős Z, Gellért Á, Megyesfalvi Z, Mikáczó A, Sárközi A, Vaskó A, Miklós Z, Horváth I. Exhaled Biomarkers for Point-of-Care Diagnosis: Recent Advances and New Challenges in Breathomics. MICROMACHINES 2023; 14:391. [PMID: 36838091 PMCID: PMC9964519 DOI: 10.3390/mi14020391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
Cancers, chronic diseases and respiratory infections are major causes of mortality and present diagnostic and therapeutic challenges for health care. There is an unmet medical need for non-invasive, easy-to-use biomarkers for the early diagnosis, phenotyping, predicting and monitoring of the therapeutic responses of these disorders. Exhaled breath sampling is an attractive choice that has gained attention in recent years. Exhaled nitric oxide measurement used as a predictive biomarker of the response to anti-eosinophil therapy in severe asthma has paved the way for other exhaled breath biomarkers. Advances in laser and nanosensor technologies and spectrometry together with widespread use of algorithms and artificial intelligence have facilitated research on volatile organic compounds and artificial olfaction systems to develop new exhaled biomarkers. We aim to provide an overview of the recent advances in and challenges of exhaled biomarker measurements with an emphasis on the applicability of their measurement as a non-invasive, point-of-care diagnostic and monitoring tool.
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Affiliation(s)
- Helga Kiss
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Zoltán Örlős
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Áron Gellért
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Zsolt Megyesfalvi
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Angéla Mikáczó
- Department of Pulmonology, University of Debrecen, Nagyerdei krt 98, 4032 Debrecen, Hungary
| | - Anna Sárközi
- Department of Pulmonology, University of Debrecen, Nagyerdei krt 98, 4032 Debrecen, Hungary
| | - Attila Vaskó
- Department of Pulmonology, University of Debrecen, Nagyerdei krt 98, 4032 Debrecen, Hungary
| | - Zsuzsanna Miklós
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Ildikó Horváth
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
- Department of Pulmonology, University of Debrecen, Nagyerdei krt 98, 4032 Debrecen, Hungary
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15
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Mashhadbani M, Faizabadi E. Early detection of lung cancer biomarkers in exhaled breath by modified armchair stanene nanoribbons. Phys Chem Chem Phys 2023; 25:3875-3889. [PMID: 36647633 DOI: 10.1039/d2cp04940f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
In this study, we analyze armchair stanene nanoribbons as excellent sensing substances for the early diagnosis of lung cancer using density functional theory and the non-equilibrium Green function. Four modified configurations of surface- and edge-defected armchair stanene nanoribbons were studied to improve the sensing performance. Our probes indicated that the adsorption energy of armchair stanene nanoribbons is at least five times greater than that of other previously reported substances, such as single-wall carbon nanotubes, phosphorene, and silicene. A noticeable reduction in the current was observed, implying the high sensitivity of our sensing configurations. The adsorption energy and current results suggest that configurations with a single vacancy and edge defects improve the sensitivity and selectivity of the system because of their free dangling bonds. The calculated results demonstrate that the both-side edge defected armchair stanene nanoribbons reduce the adsorption energy to -8.35 eV and increase the sensitivity up to 45% for toluene detection. This reduction in adsorption energy and the surge of sensitivity shows ultra-high sensing performance, yielding a more efficient structure for the future design of early-diagnosis lung cancer sensing applications, thus improving lung cancer patients' survival and life expectancy.
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16
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Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities. Metabolites 2023; 13:metabo13020203. [PMID: 36837822 PMCID: PMC9960124 DOI: 10.3390/metabo13020203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/21/2023] [Accepted: 01/28/2023] [Indexed: 01/31/2023] Open
Abstract
Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82-88% sensitivity and 80-86% specificity on the test data.
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17
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Gashimova EM, Temerdashev AZ, Porkhanov VA, Polyakov IS, Perunov DV. Comparative Analysis of Pre- and Post-Surgery Exhaled Breath Profiles of Volatile Organic Compounds of Patients with Lung Cancer and Benign Tumors. JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1134/s1061934822120036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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18
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Gashimova E, Temerdashev A, Porkhanov V, Polyakov I, Perunov D, Dmitrieva E. Non-invasive Exhaled Breath and Skin Analysis to Diagnose Lung Cancer: Study of Age Effect on Diagnostic Accuracy. ACS OMEGA 2022; 7:42613-42628. [PMID: 36440120 PMCID: PMC9685768 DOI: 10.1021/acsomega.2c06132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Development of simple, fast, and non-invasive tests for lung cancer diagnostics is essential for clinical practice. In this paper, exhaled breath and skin were studied as potential objects to diagnose lung cancer. The influence of age on the performance of diagnostic models was studied. Gas chromatography in combination with mass spectrometry (MS) was used to analyze the exhaled breath of 110 lung cancer patients and 212 healthy individuals of various ages. Peak area ratios of volatile organic compounds (VOCs) were used for data analysis instead of VOC peak areas. Various machine learning algorithms were applied to create diagnostic models, and their performance was compared. The best results on the test data set were achieved using artificial neural networks (ANNs): classification of patients with lung cancer and young healthy volunteers: 88 ± 4% sensitivity and 83 ± 3% specificity; classification of patients with lung cancer and old healthy individuals: 81 ± 3% sensitivity and 85 ± 1% specificity. The difference between performance of models based on young and old healthy groups was minor. The results obtained have shown that metabolic dysregulation driven by the disease biology is too high, which significantly overlaps the age effect. The influence of tumor localization and histological type on exhaled breath samples of lung cancer patients was studied. Statistically significant differences between some parameters in these samples were observed. A possibility of assessing the disease status by skin analysis in the Zakharyin-Ged zones using an electronic nose based on the quartz crystal microbalance sensor system was evaluated. Diagnostic models created using ANNs allow us to classify the skin composition of patients with lung cancer and healthy subjects of different ages with a sensitivity of 69 ± 2% and a specificity of 68 ± 8% for the young healthy group and a sensitivity of 74 ± 7% and a specificity of 66 ± 6% for the old healthy group. Primary results of skin analysis in the Zakharyin-Ged zones for the lung cancer diagnosis have shown its utility, but further investigation is required to confirm the results obtained.
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Affiliation(s)
- Elina Gashimova
- Department
of Analytical Chemistry, Kuban State University, Krasnodar350040, Russia
| | - Azamat Temerdashev
- Department
of Analytical Chemistry, Kuban State University, Krasnodar350040, Russia
| | - Vladimir Porkhanov
- Research
Institute—Regional Clinical Hospital No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar350086, Russia
| | - Igor Polyakov
- Research
Institute—Regional Clinical Hospital No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar350086, Russia
| | - Dmitry Perunov
- Research
Institute—Regional Clinical Hospital No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar350086, Russia
| | - Ekaterina Dmitrieva
- Department
of Analytical Chemistry, Kuban State University, Krasnodar350040, Russia
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19
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Development of an Algorithm for Cervical High-Grade Squamous Intraepithelial Lesion Based on Breath Print Analysis. J Low Genit Tract Dis 2022; 27:7-11. [PMID: 36196881 DOI: 10.1097/lgt.0000000000000707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES This study was designed to develop an algorithm for the diagnosis of cervical high-grade squamous intraepithelial lesions (HSIL), based on patterns of volatile organic compounds, evaluated using an e-nose. METHODS For this pilot study, the study population consisted of a group of 25 patients with histologically confirmed HSIL and a group of 26 controls. Controls consisted of women visiting the outpatient department for gynecological complaints unrelated to cancer. Women had a negative high-risk human papillomavirus and/or normal cytology (negative for intraepithelial lesions of malignancy) of their most recent test performed in the context of participation in routine cervical cancer screening. Breath tests were performed and labeled with the correct diagnosis. Machine-learning techniques were used to develop a model for predicting HSIL. Based on the receiver operating characteristics curve, both sensitivity and specificity were calculated. RESULTS Individual classifications of all patients with HSIL and controls, as calculated by the model, showed a sensitivity of 0.88 (95% CI = 0.68-0.97) and specificity of 0.92 (95% CI = 0.73-0.99). The positive predictive value and the negative predictive value were 0.92 (95% CI = 0.72-0.99) and 0.89 (95% CI = 0.70-0.97), respectively. The Cohen κ coefficient was 0.80. CONCLUSIONS E-nose can detect distinctive patterns of volatile organic compounds between cervical HSIL patients and controls. Validation of the algorithm in further studies is necessary before possible implementation into daily practice.
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20
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Keogh RJ, Riches JC. The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime? Curr Oncol 2022; 29:7355-7378. [PMID: 36290855 PMCID: PMC9600994 DOI: 10.3390/curroncol29100578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/22/2022] [Accepted: 09/28/2022] [Indexed: 11/07/2022] Open
Abstract
Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.
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21
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Li J, Zhang Y, Chen Q, Pan Z, Chen J, Sun M, Wang J, Li Y, Ye Q. Development and validation of a screening model for lung cancer using machine learning: A large-scale, multi-center study of biomarkers in breath. Front Oncol 2022; 12:975563. [PMID: 36203414 PMCID: PMC9531270 DOI: 10.3389/fonc.2022.975563] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/18/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives Lung cancer (LC) is the largest single cause of death from cancer worldwide, and the lack of effective screening methods for early detection currently results in unsatisfactory curative treatments. We herein aimed to use breath analysis, a noninvasive and very simple method, to identify and validate biomarkers in breath for the screening of lung cancer. Materials and methods We enrolled a total of 2308 participants from two centers for online breath analyses using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). The derivation cohort included 1007 patients with primary LC and 1036 healthy controls, and the external validation cohort included 158 LC patients and 107 healthy controls. We used eXtreme Gradient Boosting (XGBoost) to create a panel of predictive features and derived a prediction model to identify LC. The optimal number of features was determined by the greatest area under the receiver‐operating characteristic (ROC) curve (AUC). Results Six features were defined as a breath-biomarkers panel for the detection of LC. In the training dataset, the model had an AUC of 0.963 (95% CI, 0.941–0.982), and a sensitivity of 87.1% and specificity of 93.5% at a positivity threshold of 0.5. Our model was tested on the independent validation dataset and achieved an AUC of 0.771 (0.718–0.823), and sensitivity of 67.7% and specificity of 73.0%. Conclusion Our results suggested that breath analysis may serve as a valid method in screening lung cancer in a borderline population prior to hospital visits. Although our breath-biomarker panel is noninvasive, quick, and simple to use, it will require further calibration and validation in a prospective study within a primary care setting.
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Affiliation(s)
- Jing Li
- Laser Medicine Laboratory, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, China
| | - Yuwei Zhang
- Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics, Nankai University, Tianjin, China
| | - Qing Chen
- Departmentof Cardio-Pulmonary Function, National Clinical Research Center for Cancer, Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Zhenhua Pan
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Jun Chen
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Meixiu Sun
- Laser Medicine Laboratory, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, China
- *Correspondence: Meixiu Sun, ; Junfeng Wang,
| | - Junfeng Wang
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- *Correspondence: Meixiu Sun, ; Junfeng Wang,
| | - Yingxin Li
- Laser Medicine Laboratory, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, China
| | - Qing Ye
- Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics and TEDA Applied Physics, Nankai University, Tianjin, China
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22
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Giró Benet J, Seo M, Khine M, Gumà Padró J, Pardo Martnez A, Kurdahi F. Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine. Sci Rep 2022; 12:14873. [PMID: 36050339 PMCID: PMC9435419 DOI: 10.1038/s41598-022-17795-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 07/31/2022] [Indexed: 11/12/2022] Open
Abstract
A rising number of authors are drawing evidence on the diagnostic capacity of specific volatile organic compounds (VOCs) resulting from some body fluids. While cancer incidence in society is on the rise, it becomes clear that the analysis of these VOCs can yield new strategies to mitigate advanced cancer incidence rates. This paper presents the methodology implemented to test whether a device consisting of an electronic nose inspired by a dog's olfactory system and olfactory neurons is significantly informative to detect breast cancer (BC). To test this device, 90 human urine samples were collected from control subjects and BC patients at a hospital. To test this system, an artificial intelligence-based classification algorithm was developed. The algorithm was firstly trained and tested with data resulting from gas chromatography-mass spectrometry (GC-MS) urine readings, leading to a classification rate of 92.31%, sensitivity of 100.00%, and specificity of 85.71% (N = 90). Secondly, the same algorithm was trained and tested with data obtained with our eNose prototype hardware, and class prediction was achieved with a classification rate of 75%, sensitivity of 100%, and specificity of 50%.
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Affiliation(s)
- Judit Giró Benet
- Center for Embedded Cyber-Physical Systems (CEPS), University of California Irvine (UCI), Irvine, 92697, USA.
| | - Minjun Seo
- Center for Embedded Cyber-Physical Systems (CEPS), University of California Irvine (UCI), Irvine, 92697, USA
| | - Michelle Khine
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, 92697, USA
| | - Josep Gumà Padró
- South Catalonia Oncology Institute (IOCS), Sant Joan de Reus University Hospital, IISPV, Rovira i Virgili University, 43204, Reus, Spain
| | - Antonio Pardo Martnez
- Department of Electronic and Biomedical Engineering, Universitat de Barcelona (UB), 08028, Barcelona, Spain
| | - Fadi Kurdahi
- Center for Embedded Cyber-Physical Systems (CEPS), University of California Irvine (UCI), Irvine, 92697, USA
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23
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Kim C, Lee KK, Kang MS, Shin DM, Oh JW, Lee CS, Han DW. Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review. Biomater Res 2022; 26:40. [PMID: 35986395 PMCID: PMC9392354 DOI: 10.1186/s40824-022-00287-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/13/2022] [Indexed: 11/19/2022] Open
Abstract
Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.
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24
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Gasparri R, Capuano R, Guaglio A, Caminiti V, Canini F, Catini A, Sedda G, Paolesse R, Di Natale C, Spaggiari L. Volatolomic urinary profile analysis for diagnosis of the early stage of lung cancer. J Breath Res 2022; 16. [PMID: 35952625 DOI: 10.1088/1752-7163/ac88ec] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 08/11/2022] [Indexed: 12/24/2022]
Abstract
Nowadays in clinical practice there is a pressing need for potential biomarkers that can identify lung cancer at early stage before becoming symptomatic or detectable by conventional means. Several researchers have independently pointed out that the volatile organic compounds (VOCs) profile can be considered as a lung cancer fingerprint useful for diagnosis. In particular, 16% of volatiles contributing to the human volatilome are found in urine, which is therefore an ideal sample medium. Its analysis through non-invasive, relatively low-cost and straightforward techniques could offer great potential for the early diagnosis of lung cancer. In this study, urinary VOCs were analysed with a gas chromatography-ion mobility spectrometer (GC-IMS) and an electronic nose (e-nose) made by a matrix of twelve quartz microbalances (QMBs) complemented by a photoionization detector (PID). This clinical prospective study involved 127 individuals, divided into two groups: 46 with lung cancer stage I-II-III confirmed by computerized tomography (CT) or positron emission tomography-(PET) imaging techniques and histology (biopsy), and 81 healthy controls. Both instruments provided a multivariate signal which, after being analysed by a machine learning algorithm, identified eight VOCs that could distinguish lung cancer patients from healthy ones. The eight VOCs are 2-pentanone, 2-hexenal, 2-hexen-1-ol, hept-4-en-2-ol, 2-heptanone, 3-octen-2-one, 4-methylpentanol, 4-methyl-octane. Results show that GC-IMS identifies lung cancer with respect to the control group with a diagnostic accuracy of 88%. Sensitivity resulted as being 85%, and specificity was 90% - Area Under the Receiver Operating Characteristics (AUROC): 0.91. The contribution made by the e-nose was also important, even though the results were slightly less sensitive with an accuracy of 71.6%. Moreover, of the eight VOCs identified as potential biomarkers, five VOCs had a high sensitivity (p≤ 0.06) for early stage (stage I) lung cancer.
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Affiliation(s)
- Roberto Gasparri
- Department of Thoracic Surgery, Istituto Europeo di Oncologia, Via Giuseppe Ripamonti, 435, Milan, Milan, 20141, ITALY
| | - Rosamaria Capuano
- Department of Electronic Engineering, Universita di Roma 'Tor Vergata', via di tor Vergata 133, 00133 Roma, Roma, 00133, ITALY
| | - Alessandra Guaglio
- General toracic surgery, European Institute of Oncology, Via Ripamonti 435, 20141 Milan, Milano, Lombardia, 20141, ITALY
| | - Valentina Caminiti
- Department of Thoracic Surgery, European Institute of Oncology, Via Giuseppe Ripamonti, 435, Milan, Milan, 20141, ITALY
| | - Federico Canini
- Department of Electronic Engineering, Universita di Roma 'Tor Vergata', via di tor Vergata 133, 00133 Roma, Roma, 00133, ITALY
| | - Alexandro Catini
- Department of Electronic Engineering, Universita di Roma 'Tor Vergata', via di tor Vergata 133, 00133 Roma, Roma, 00133, ITALY
| | - Giulia Sedda
- Department of Thoracic Surgery, European Institute of Oncology, Via Giuseppe Ripamonti, 435, Milan, Milan, 20141, ITALY
| | - Roberto Paolesse
- Department of Chemical Science and Technology, Via della Ricerca Scientifica, University of Rome 'Tor Vergata', Rome, Rome, 00133, ITALY
| | - Corrado Di Natale
- Department of Electronic Engineering, Universita di Roma 'Tor Vergata', via di tor Vergata 133, 00133 Roma, Roma, 00133, ITALY
| | - Lorenzo Spaggiari
- Division of Thoracic Surgery, European Institute of Oncology, Via Ripamonti 435, Milano, Lombardia, 20141, ITALY
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Gashimova EM, Temerdashev AZ, Porkhanov VA, Polyakov IS, Perunov DV. Volatile Organic Compounds in Exhaled Breath as Biomarkers of Lung Cancer: Advances and Potential Problems. JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1134/s106193482207005x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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26
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Kaloumenou M, Skotadis E, Lagopati N, Efstathopoulos E, Tsoukalas D. Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:1238. [PMID: 35161984 PMCID: PMC8840008 DOI: 10.3390/s22031238] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/30/2022] [Accepted: 02/01/2022] [Indexed: 05/07/2023]
Abstract
Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.
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Affiliation(s)
- Maria Kaloumenou
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
| | - Evangelos Skotadis
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
| | - Nefeli Lagopati
- Medical School, National and Kapodistrian University of Athens, 75, Mikras Asias Str., Goudi, 11527 Athens, Greece; (N.L.); (E.E.)
| | - Efstathios Efstathopoulos
- Medical School, National and Kapodistrian University of Athens, 75, Mikras Asias Str., Goudi, 11527 Athens, Greece; (N.L.); (E.E.)
| | - Dimitris Tsoukalas
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
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27
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Hu W, Wu W, Jian Y, Haick H, Zhang G, Qian Y, Yuan M, Yao M. Volatolomics in healthcare and its advanced detection technology. NANO RESEARCH 2022; 15:8185-8213. [PMID: 35789633 PMCID: PMC9243817 DOI: 10.1007/s12274-022-4459-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 05/21/2023]
Abstract
Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.
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Affiliation(s)
- Wenwen Hu
- School of Aerospace Science and Technology, Xidian University, Xi’an, 730107 China
| | - Weiwei Wu
- Interdisciplinary Research Center of Smart Sensors, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an, 730107 China
| | - Yingying Jian
- Interdisciplinary Research Center of Smart Sensors, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an, 730107 China
| | - Hossam Haick
- Faculty of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200002 Israel
| | - Guangjian Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 China
| | - Yun Qian
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006 China
| | - Miaomiao Yuan
- The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033 China
| | - Mingshui Yao
- State Key Laboratory of Multi-phase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 310006 China
- Institute for Integrated Cell-Material Sciences, Kyoto University Institute for Advanced Study, Kyoto University, Kyoto, 606-8501 Japan
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28
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Annerino A, Gouma PI(P. Future Trends in Semiconducting Gas-Selective Sensing Probes for Skin Diagnostics. SENSORS 2021; 21:s21227554. [PMID: 34833630 PMCID: PMC8618486 DOI: 10.3390/s21227554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/09/2021] [Accepted: 11/11/2021] [Indexed: 11/16/2022]
Abstract
This paper presents sensor nanotechnologies that can be used for the skin-based gas “smelling” of disease. Skin testing may provide rapid and reliable results, using specific “fingerprints” or unique patterns for a variety of diseases and conditions. These can include metabolic diseases, such as diabetes and cholesterol-induced heart disease; neurological diseases, such as Alzheimer’s and Parkinson’s; quality of life conditions, such as obesity and sleep apnea; pulmonary diseases, such as cystic fibrosis, asthma, and chronic obstructive pulmonary disease; gastrointestinal tract diseases, such as irritable bowel syndrome and colitis; cancers, such as breast, lung, pancreatic, and colon cancers; infectious diseases, such as the flu and COVID-19; as well as diseases commonly found in ICU patients, such as urinary tract infections, pneumonia, and infections of the blood stream. Focusing on the most common gaseous biomarkers in breath and skin, which is nitric oxide and carbon monoxide, and certain abundant volatile organic compounds (acetone, isoprene, ammonia, alcohols, sulfides), it is argued here that effective discrimination between the diseases mentioned above is possible, by capturing the relative sensor output signals from the detection of each of these biomarkers and identifying the distinct breath print for each disease.
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29
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Park B, Park C. Kernel variable selection for multicategory support vector machines. J MULTIVARIATE ANAL 2021. [DOI: 10.1016/j.jmva.2021.104800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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30
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Gouzerh F, Bessière JM, Ujvari B, Thomas F, Dujon AM, Dormont L. Odors and cancer: Current status and future directions. Biochim Biophys Acta Rev Cancer 2021; 1877:188644. [PMID: 34737023 DOI: 10.1016/j.bbcan.2021.188644] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/22/2021] [Accepted: 10/23/2021] [Indexed: 02/07/2023]
Abstract
Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.
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Affiliation(s)
- Flora Gouzerh
- CREEC/CANECEV (CREES), Montpellier, France; MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France; CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France.
| | - Jean-Marie Bessière
- Ecole Nationale de Chimie de Montpellier, Laboratoire de Chimie Appliquée, Montpellier, France
| | - Beata Ujvari
- Deakin University, School of Life and Environmental Sciences, Centre for Integrative Ecology, Waurn Ponds, Vic 3216, Australia
| | - Frédéric Thomas
- CREEC/CANECEV (CREES), Montpellier, France; MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France
| | - Antoine M Dujon
- CREEC/CANECEV (CREES), Montpellier, France; MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France; Deakin University, School of Life and Environmental Sciences, Centre for Integrative Ecology, Waurn Ponds, Vic 3216, Australia
| | - Laurent Dormont
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
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31
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Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis. Sci Rep 2021; 11:20898. [PMID: 34686703 PMCID: PMC8536694 DOI: 10.1038/s41598-021-00033-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 09/27/2021] [Indexed: 01/03/2023] Open
Abstract
More than one million new cases of prostate cancer (PCa) were reported worldwide in 2020, and a significant increase of PCa incidence up to 2040 is estimated. Despite potential treatability in early stages, PCa diagnosis is challenging because of late symptoms' onset and limits of current screening procedures. It has been now accepted that cell transformation leads to release of volatile organic compounds in biologic fluids, including urine. Thus, several studies proposed the possibility to develop new diagnostic tools based on urine analysis. Among these, electronic noses (eNoses) represent one of the most promising devices, because of their potential to provide a non-invasive diagnosis. Here we describe the approach aimed at defining the experimental protocol for eNose application for PCa diagnosis. Our research investigates effects of sample preparation and analysis on eNose responses and repeatability. The dependence of eNose diagnostic performance on urine portion analysed, techniques involved for extracting urine volatiles and conditioning temperature were analysed. 192 subjects (132 PCa patients and 60 controls) were involved. The developed experimental protocol has resulted in accuracy, sensitivity and specificity of 83% (CI95% 77-89), 82% (CI95% 73-88) and 87% (CI95% 75-94), respectively. Our findings define eNoses as valuable diagnostic tool allowing rapid and non-invasive PCa diagnosis.
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32
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Gashimova E, Osipova A, Temerdashev A, Porkhanov V, Polyakov I, Perunov D, Dmitrieva E. Exhaled breath analysis using GC-MS and an electronic nose for lung cancer diagnostics. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:4793-4804. [PMID: 34581316 DOI: 10.1039/d1ay01163d] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Exhaled breath analysis is an interesting and promising approach for the diagnostics of various diseases. Being non-invasive, convenient and simple, this approach has tremendous potential utility for further translation into clinical practice. In this study, gas chromatography-mass spectrometry (GC-MS) and quartz microbalance sensor-based "electronic nose" were applied for analysis of the exhaled breath of 40 lung cancer patients and 40 healthy individuals. It was found that the electronic nose was unable to distinguish the samples of different groups. However, the application of GC-MS allowed identifying statistically significant differences in compound peak areas and their ratios for investigated groups. Diagnostic models were created using random forest classifier based on peak areas and their ratios with the sensitivity and specificity of peak areas (ratios) of 85.7-96.5% (75.0-93.1%) and 73.3-85.1% (90.0-92.5%) on training data and 63.6-75.0% (72.7-100.0%) and 50.0-69.2% (76.9-84.6%) on test data, respectively. The exhaled breath samples of lung cancer patients and healthy volunteers could be distinguished by GC-MS with the use of individual compounds, but application of their ratios could help to determine specific differences between investigated groups and the level the influence of individual metabolism features alternating from one person to another as well as daily instrument reproducibility deviations. The electronic nose has to be significantly improved to apply it to lung cancer diagnostics of exhaled breath analysis and the influence of water vapour has to be lowered to increase the sensitivity of the sensors to detect lung cancer biomarkers.
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Affiliation(s)
- Elina Gashimova
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia.
| | - Anna Osipova
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia.
| | - Azamat Temerdashev
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia.
| | - Vladimir Porkhanov
- Research Institute - Regional Clinical Hospital, No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar, Russia
| | - Igor Polyakov
- Research Institute - Regional Clinical Hospital, No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar, Russia
| | - Dmitry Perunov
- Research Institute - Regional Clinical Hospital, No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar, Russia
| | - Ekaterina Dmitrieva
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia.
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A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer. Sci Rep 2021; 11:19801. [PMID: 34611278 PMCID: PMC8492640 DOI: 10.1038/s41598-021-99396-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/20/2021] [Indexed: 01/05/2023] Open
Abstract
Safe and noninvasive methods for breast cancer screening with improved accuracy are urgently needed. Volatile organic compounds (VOCs) in biological samples such as breath and blood have been investigated as noninvasive novel markers of cancer. We investigated volatile organic compounds in urine to assess their potential for the detection of breast cancer. One hundred and ten women with biopsy-proven breast cancer and 177 healthy volunteers were enrolled. The subjects were divided into two groups: a training set and an external validation set. Urine samples were collected and analyzed by gas chromatography and mass spectrometry. A predictive model was constructed by multivariate analysis, and the sensitivity and specificity of the model were confirmed using both a training set and an external set with reproducibility tests. The training set included 60 breast cancer patients (age 34–88 years, mean 60.3) and 60 healthy controls (age 34–81 years, mean 58.7). The external validation set included 50 breast cancer patients (age 35–85 years, mean 58.8) and 117 healthy controls (age 18–84 years, mean 51.2). One hundred and ninety-one compounds detected in at least 80% of the samples from the training set were used for further analysis. The predictive model that best-detected breast cancer at various clinical stages was constructed using a combination of two of the compounds, 2-propanol and 2-butanone. The sensitivity and specificity in the training set were 93.3% and 83.3%, respectively. Triplicated reproducibility tests were performed by randomly choosing ten samples from each group, and the results showed a matching rate of 100% for the breast cancer patient group and 90% for the healthy control group. Our prediction model using two VOCs is a useful complement to the current diagnostic tools. Further studies inclusive of benign tumors and non-breast malignancies are warranted.
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34
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Liver Impairment-The Potential Application of Volatile Organic Compounds in Hepatology. Metabolites 2021; 11:metabo11090618. [PMID: 34564434 PMCID: PMC8471934 DOI: 10.3390/metabo11090618] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/08/2021] [Accepted: 09/09/2021] [Indexed: 12/20/2022] Open
Abstract
Liver diseases are currently diagnosed through liver biopsy. Its invasiveness, costs, and relatively low diagnostic accuracy require new techniques to be sought. Analysis of volatile organic compounds (VOCs) in human bio-matrices has received a lot of attention. It is known that a musty odour characterises liver impairment, resulting in the elucidation of volatile chemicals in the breath and other body fluids such as urine and stool, which may serve as biomarkers of a disease. Aims: This study aims to review all the studies found in the literature regarding VOCs in liver diseases, and to summarise all the identified compounds that could be used as diagnostic or prognostic biomarkers. The literature search was conducted on ScienceDirect and PubMed, and each eligible publication was qualitatively assessed by two independent evaluators using the SANRA critical appraisal tool. Results: In the search, 58 publications were found, and 28 were kept for inclusion: 23 were about VOCs in the breath, one in the bile, three in urine, and one in faeces. Each publication was graded from zero to ten. A graphical summary of the metabolic pathways showcasing the known liver disease-related VOCs and suggestions on how VOC analysis on liver impairment could be applied in clinical practice are given.
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35
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Tyagi H, Daulton E, Bannaga AS, Arasaradnam RP, Covington JA. Non-Invasive Detection and Staging of Colorectal Cancer Using a Portable Electronic Nose. SENSORS (BASEL, SWITZERLAND) 2021; 21:5440. [PMID: 34450881 PMCID: PMC8398649 DOI: 10.3390/s21165440] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/30/2021] [Accepted: 07/30/2021] [Indexed: 12/24/2022]
Abstract
Electronic noses (e-nose) offer potential for the detection of cancer in its early stages. The ability to analyse samples in real time, at a low cost, applying easy-to-use and portable equipment, gives e-noses advantages over other technologies, such as Gas Chromatography-Mass Spectrometry (GC-MS). For diseases such as cancer with a high mortality, a technology that can provide fast results for use in routine clinical applications is important. Colorectal cancer (CRC) is among the highest occurring cancers and has high mortality rates, if diagnosed late. In our study, we investigated the use of portable electronic nose (PEN3), with further analysis using GC-TOF-MS, for the analysis of gases and volatile organic compounds (VOCs) to profile the urinary metabolome of colorectal cancer. We also compared the different cancer stages with non-cancers using the PEN3 and GC-TOF-MS. Results obtained from PEN3, and GC-TOF-MS demonstrated high accuracy for the separation of CRC and non-cancer. PEN3 separated CRC from non-cancerous group with 0.81 AUC (Area Under the Curve). We used data from GC-TOF-MS to obtain a VOC profile for CRC, which identified 23 potential biomarker VOCs for CRC. Thus, the PEN3 and GC-TOF-MS were found to successfully separate the cancer group from the non-cancer group.
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Affiliation(s)
- Heena Tyagi
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (H.T.); (E.D.)
| | - Emma Daulton
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (H.T.); (E.D.)
| | - Ayman S. Bannaga
- Department of Gastroenterology, University Hospital Coventry & Warwickshire, Coventry CV2 2DX, UK; (A.S.B.); (R.P.A.)
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Ramesh P. Arasaradnam
- Department of Gastroenterology, University Hospital Coventry & Warwickshire, Coventry CV2 2DX, UK; (A.S.B.); (R.P.A.)
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- School of Health Sciences, Coventry University, Coventry CV1 5FB, UK
- Leicester Cancer Centre, University of Leicester, Leicester LE1 7RH, UK
| | - James A. Covington
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (H.T.); (E.D.)
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36
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Jaeschke C, Padilla M, Glöckler J, Polaka I, Leja M, Veliks V, Mitrovics J, Leja M, Mizaikoff B. Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies. Molecules 2021; 26:3776. [PMID: 34205805 PMCID: PMC8235513 DOI: 10.3390/molecules26123776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/11/2021] [Accepted: 06/14/2021] [Indexed: 11/17/2022] Open
Abstract
Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.
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Affiliation(s)
- Carsten Jaeschke
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, 89081 Ulm, Germany; (C.J.); (J.G.)
| | - Marta Padilla
- JLM Innovation GmbH, Vor dem Kreuzberg 17, 72070 Tuebingen, Germany; (M.P.); (J.M.)
| | - Johannes Glöckler
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, 89081 Ulm, Germany; (C.J.); (J.G.)
| | - Inese Polaka
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1079 Riga, Latvia; (I.P.); (M.L.); (V.V.); (M.L.)
| | - Martins Leja
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1079 Riga, Latvia; (I.P.); (M.L.); (V.V.); (M.L.)
| | - Viktors Veliks
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1079 Riga, Latvia; (I.P.); (M.L.); (V.V.); (M.L.)
| | - Jan Mitrovics
- JLM Innovation GmbH, Vor dem Kreuzberg 17, 72070 Tuebingen, Germany; (M.P.); (J.M.)
| | - Marcis Leja
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1079 Riga, Latvia; (I.P.); (M.L.); (V.V.); (M.L.)
| | - Boris Mizaikoff
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, 89081 Ulm, Germany; (C.J.); (J.G.)
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37
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Fang W, Liu K, Wang G, Liang Y, Huang R, Liu T, Ding L, Peng J, Peng H, Fang Y. Dual-Phase Emission AIEgen with ICT Properties for VOC Chromic Sensing. Anal Chem 2021; 93:8501-8507. [DOI: 10.1021/acs.analchem.1c00980] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Wan Fang
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Ke Liu
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Gang Wang
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Yuzhe Liang
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Rongrong Huang
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Taihong Liu
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Liping Ding
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Junxia Peng
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Haonan Peng
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
| | - Yu Fang
- Key Laboratory of Applied Surface and Colloid Chemistry (Ministry of Education), School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an 710062, P. R. China
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Kaiser A, Torres Ceja E, Liu Y, Huber F, Müller R, Herr U, Thonke K. H 2S sensing for breath analysis with Au functionalized ZnO nanowires. NANOTECHNOLOGY 2021; 32:205505. [PMID: 33498025 DOI: 10.1088/1361-6528/abe004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
This work presents a H2S selective resistive gas sensor design based on a chemical field effect transistor (ChemFET) with open gate formed by hundreds of high temperature chemical vapour deposition (CVD) grown zinc oxide nanowires (ZnO NW). The sensing ability of pristine ZnO NWs and surface functionalized ZnO NWs for H2S is analysed systematically. ZnO NWs are functionalized by deposition of discontinuous gold (Au) nanoparticle films of different thicknesses of catalyst layer ranging from 1 to 10 nm and are compared in their gas sensing properties. All experiments were performed in a temperature stabilized small volume compartment with adjustable gas mixture at room temperature. The results allow for a well-founded understanding of signal-to-noise ratio, enhanced response, and improved limit of detection due to the Au functionalisation. Comprehension and controlled application of the beneficial effects of Au catalyst on ZnO NWs allow for the detection of very low H2S concentrations down to 10 ppb, and a theoretically estimated 500 ppt in synthetic air at room temperature.
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Affiliation(s)
- Angelika Kaiser
- Institute of Quantum Matter/Semiconductor Physics Group, Ulm University, D-89069 Ulm, Germany
| | - Erick Torres Ceja
- Institute of Quantum Matter/Semiconductor Physics Group, Ulm University, D-89069 Ulm, Germany
| | - Yujia Liu
- Institute of Quantum Matter/Semiconductor Physics Group, Ulm University, D-89069 Ulm, Germany
| | - Florian Huber
- Institute of Quantum Matter/Semiconductor Physics Group, Ulm University, D-89069 Ulm, Germany
| | - Raphael Müller
- Institute of Quantum Matter/Semiconductor Physics Group, Ulm University, D-89069 Ulm, Germany
| | - Ulrich Herr
- Institute of Functional Nanosystems, Ulm University, D-89069 Ulm, Germany
| | - Klaus Thonke
- Institute of Quantum Matter/Semiconductor Physics Group, Ulm University, D-89069 Ulm, Germany
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Thiol-Amine Functionalized Decorated Carbon Nanotubes for Biomarker Gases Detection. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9050087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Thousands of gas molecules are expelled in exhaled breath, and some of them can reveal diseases and metabolomic disorders. For that reason, the development of fast, inexpensive, and reliable sensing devices has been attracting growing interest. Here, we present the development of different chemoresistors based on multi-walled carbon nanotubes (MWCNTs) decorated with platinum (MWCNT/Pt) and palladium (MWCNT/Pt) nanoparticles and also functionalized with a self-assembled monolayer (SAM) of 11-amino-1-undecanethiol (Thiol-amine). The nanocomposites developed are a proof-of-concept to detect some biomarker molecules. Specifically, the capability to identify and measure different concentrations of volatile organic compounds (VOCs), either aromatic (toluene and benzene) and non-aromatic (ethanol and methanol) was assessed. As a result, this paper reports the significant differences in sensing performance achieved according to the metal nanoparticle used, and the high sensitivity obtained when SAMs are grown on the sensitive film, acting as a receptor for biomarker vapours.
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Wu M, Guo C, Guo N, Zhang T, Wang Y, Wang Y, Lin X, Wu F, Feng Y. Similarity Evaluation on the Compound TCM Formulation "Huoling Shengji Granule" and Its Placebo by Intelligent Sensory Evaluation Technologies and the Human Sensory Evaluation Method Based on Critical Quality Attributes. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2021; 2021:6637326. [PMID: 33936240 PMCID: PMC8062196 DOI: 10.1155/2021/6637326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 03/16/2021] [Accepted: 04/02/2021] [Indexed: 11/17/2022]
Abstract
To evaluate the similarity of Huoling Shengji granule (HLG) and its placebo at both granules and solution status, the innovative methods that consist of intelligent sensory evaluation technologies and human sensory evaluation methods were developed based on critical quality attributes (CQAs) of granule. The CQAs for traditional Chinese medicine (TCM) placebo granule were mainly divided into three categories: formulation attributes, visual attributes, and attributes of taste and smell. In this investigation, the novel intelligent sensory evaluation technologies including the physical property testing apparatus, computer vision system, color card, and electronic tongue (E-tongue) were employed for characterization of CQAs of HLG and its placebo. Meanwhile, human sensory evaluation by test panels was used to description the HLG and its placebo in terms of appearance, color, taste, and smell. On that basis, the similarity of placebo to CQAs of HLG was assessed by calculating the angle cosine values. The intelligent and human sensory evaluation results showed that the similarity values of HLG and its placebo about the CQAs at granule and solution status were all close to 1, which means that the two preparations have high similarities. In this study, the established similarity evaluation methods based on the CQAs were convenient and reliable, which can be utilized to evaluate the similarity of TCM granule and their placebo at granule and solution status, and demonstrated to be well applied in placebo-controlled trials.
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Affiliation(s)
- Mei Wu
- Engineering Research Center of Modern Preparation Technology of Traditional Chinese Medicine, Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chengjie Guo
- Engineering Research Center of Modern Preparation Technology of Traditional Chinese Medicine, Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ning Guo
- College of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tianyi Zhang
- Engineering Research Center of Modern Preparation Technology of Traditional Chinese Medicine, Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Youjie Wang
- Engineering Research Center of Modern Preparation Technology of Traditional Chinese Medicine, Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yuan Wang
- Engineering Research Center of Modern Preparation Technology of Traditional Chinese Medicine, Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiao Lin
- College of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fei Wu
- Engineering Research Center of Modern Preparation Technology of Traditional Chinese Medicine, Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- College of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yi Feng
- Engineering Research Center of Modern Preparation Technology of Traditional Chinese Medicine, Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Alam A, Ansari MA, Badrealam KF, Pathak S. Molecular approaches to lung cancer prevention. Future Oncol 2021; 17:1793-1810. [PMID: 33653087 DOI: 10.2217/fon-2020-0789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Lung cancer is generally diagnosed at advanced stages when surgical resection is not possible. Late diagnosis, along with development of chemoresistance, results in high mortality. Preventive approaches, including smoking cessation, chemoprevention and early detection are needed to improve survival. Smoking cessation combined with low-dose computed tomography screening has modestly improved survival. Chemoprevention has also shown some promise. Despite these successes, most lung cancer cases remain undetected until advanced stages. Additional early detection strategies may further improve survival and treatment outcome. Molecular alterations taking place during lung carcinogenesis have the potential to be used in early detection via noninvasive methods and may also serve as biomarkers for success of chemopreventive approaches. This review focuses on the utilization of molecular biomarkers to increase the efficacy of various preventive approaches.
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Affiliation(s)
- Asrar Alam
- Department of Preventive Oncology, Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Mohammad A Ansari
- Department of Epidemic Disease Research, Institute of Research & Medical Consultation, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
| | - Khan F Badrealam
- Cardiovascular & Mitochondrial Related Disease Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970, Taiwan
| | - Sujata Pathak
- Department of Preventive Oncology, Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
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Recognizing lung cancer and stages using a self-developed electronic nose system. Comput Biol Med 2021; 131:104294. [PMID: 33647830 DOI: 10.1016/j.compbiomed.2021.104294] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/17/2021] [Accepted: 02/17/2021] [Indexed: 12/25/2022]
Abstract
Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that could be used as non-invasive biomarkers of lung cancer. Breath-based lung cancer screening has attracted wide attention on account of its convenience, low cost and easy popularization. In this paper, the research of lung cancer detection and staging is conducted by the self-developed electronic nose (e-nose) system. In order to investigate the performance of the device in distinguishing lung cancer patients from healthy controls, two feature extraction methods and two different classification models were adopted. Among all the models, kernel principal component analysis (KPCA) combined with extreme gradient boosting (XGBoost) achieved the best results among 235 breath samples. The accuracy, sensitivity and specificity of e-nose system were 93.59%, 95.60% and 91.09%, respectively. Meanwhile, the device could innovatively classify stages of 90 lung cancer patients (i.e., 44 stage III and 46 stage IV). Experimental results indicated that the recognition accuracy of lung cancer stages was more than 80%. Further experiments of this research also showed that the combination of sensor array and pattern recognition algorithms could identify and distinguish the expiratory characteristics of lung cancer, smoking and other respiratory diseases.
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Rodríguez KBM, Gómez LMR, Yáñez LC, Ramírez RF, Ornelas-Rebolledo O, Borjas-García JA, Pérez-Vázquez F, Aguilar MR. Application of the Electronic Nose in Predicting Preeclampsia in High-risk Pregnancies. Pilot Study. Arch Med Res 2021; 52:561-568. [PMID: 33597111 DOI: 10.1016/j.arcmed.2021.01.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 01/16/2021] [Accepted: 01/28/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND Preeclampsia is a syndrome that affects 2-8 % of pregnancies worldwide and is the leading cause of maternal death. Therefore, early detection is crucial to identify women who require clinical monitoring during pregnancy and to evaluate new preventive therapies before clinical symptoms occur. METHODS The chemical fingerprints of the urine from three study groups pregnant with Preeclampsia, Healthy Pregnant (HP) and pregnant at High Risk of Preeclampsia (HRP) were evaluated using an electronic nose and the data obtained were subjected to principal component analysis (PCA), Canonical Analysis of Principal Coordinates (CAP), Partial Least Squares - Discriminant Analysis (PLS-DA) and ROC curves to determine the diagnostic power of the test. RESULTS A separation was found between the patients with preeclampsia and HP explaining 99% of the variability of the data. Subsequently, a CAP was obtained with a correct classification of 100%, and the PLS-DA was obtained an accuracy of 88%. With the results of axis CAP1, a ROC curve was performed resulting in a sensitivity of 100% and a specificity of 95.5%. Based on the CAP model it was found that 36% (n=9) of the HRP patients would develop preeclampsia based on the metabolites found in urine. CONCLUSION metabolomics can be used as a tool for early detection of preeclampsia in high-risk pregnant women, using portable olfactory technology.
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Affiliation(s)
- Karen Beatriz Méndez Rodríguez
- Centro de Investigación Aplicada en Ambiente y Salud, CIACYT, Facultad de Medicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México
| | - Luis Manuel Ramírez Gómez
- Departamento de Nefrología, Hospital Central, Dr. Ignacio Morones Prieto, San Luis Potosí, San Luis Potosí, México
| | - Leticia Carrizales Yáñez
- Centro de Investigación Aplicada en Ambiente y Salud, CIACYT, Facultad de Medicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México
| | - Rogelio Flores Ramírez
- CONACYT, Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México
| | - Omar Ornelas-Rebolledo
- Centro Labinnova de Investigación en Respiración para la detección temprana de enfermedades, Guadalajara, México
| | - Jaime Antonio Borjas-García
- Departamento de Nefrología, Hospital Central, Dr. Ignacio Morones Prieto, San Luis Potosí, San Luis Potosí, México
| | - Francisco Pérez-Vázquez
- CONACYT, Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México
| | - Maribel Rodríguez Aguilar
- Centro de Investigación Aplicada en Ambiente y Salud, CIACYT, Facultad de Medicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México; Centro Labinnova de Investigación en Respiración para la detección temprana de enfermedades, Guadalajara, México.
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Matsumoto K, Murakami Y, Shimizu Y, Hirayama T, Ishikawa W, Iwamura M. Electronic nose to distinguish bladder cancer by urinary odour feature: A pilot study. Cancer Biomark 2021; 28:33-39. [PMID: 32176623 DOI: 10.3233/cbm-190466] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND No study has yet investigated the use of electronic nose (eNose) technology to reveal pattern recognition of urological diseases, including bladder cancer. OBJECTIVE We sought to determine the diagnostic performance of the eNose in recognizing urinary odour in patients with bladder cancer. METHODS The eNose is a commercially available model equipped with two sensors. The angle of the two sensors (θ) depends on the kinds of chemical substances, thus defining θ as the feature of odour. Quantity of odour is the number of θ detected during a measurement. Urine samples were from 36 untreated patients with bladder cancer, 29 with urolithiasis, 10 with urinary tract infection (UTI), and 27 healthy volunteers. RESULTS Based on ROC analysis of the quantity in patients with bladder cancer, an optimal cut-off value for θ of 49, 48, and 55 was applied to compare with samples from the healthy volunteer, urolithiasis and UTI groups, respectively. There were significantly differences between bladder cancer and the other conditions using these specific points (p< 0.0001, respectively). The resulting diagnostic sensitivity was 61.4%, 45.6%, and 60.8%, and specificity was 52.8%, 68.4%, and 90.2%, respectively. The AUC for bladder cancer was 0.565, 0.548, and 0.909, respectively. CONCLUSION The eNose is a small, portable, rapid, low cost, and noninvasive instrument for distinguishing bladder cancer from other benign conditions.
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Tozlu BH, Şimşek C, Aydemir O, Karavelioglu Y. A High performance electronic nose system for the recognition of myocardial infarction and coronary artery diseases. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102247] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Volatile Organic Compounds in Exhaled Breath as Fingerprints of Lung Cancer, Asthma and COPD. J Clin Med 2020; 10:jcm10010032. [PMID: 33374433 PMCID: PMC7796324 DOI: 10.3390/jcm10010032] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/14/2020] [Accepted: 12/22/2020] [Indexed: 12/11/2022] Open
Abstract
Lung cancer, chronic obstructive pulmonary disease (COPD) and asthma are inflammatory diseases that have risen worldwide, posing a major public health issue, encompassing not only physical and psychological morbidity and mortality, but also incurring significant societal costs. The leading cause of death worldwide by cancer is that of the lung, which, in large part, is a result of the disease often not being detected until a late stage. Although COPD and asthma are conditions with considerably lower mortality, they are extremely distressful to people and involve high healthcare overheads. Moreover, for these diseases, diagnostic methods are not only costly but are also invasive, thereby adding to people’s stress. It has been appreciated for many decades that the analysis of trace volatile organic compounds (VOCs) in exhaled breath could potentially provide cheaper, rapid, and non-invasive screening procedures to diagnose and monitor the above diseases of the lung. However, after decades of research associated with breath biomarker discovery, no breath VOC tests are clinically available. Reasons for this include the little consensus as to which breath volatiles (or pattern of volatiles) can be used to discriminate people with lung diseases, and our limited understanding of the biological origin of the identified VOCs. Lung disease diagnosis using breath VOCs is challenging. Nevertheless, the numerous studies of breath volatiles and lung disease provide guidance as to what volatiles need further investigation for use in differential diagnosis, highlight the urgent need for non-invasive clinical breath tests, illustrate the way forward for future studies, and provide significant guidance to achieve the goal of developing non-invasive diagnostic tests for lung disease. This review provides an overview of these issues from evaluating key studies that have been undertaken in the years 2010–2019, in order to present objective and comprehensive updated information that presents the progress that has been made in this field. The potential of this approach is highlighted, while strengths, weaknesses, opportunities, and threats are discussed. This review will be of interest to chemists, biologists, medical doctors and researchers involved in the development of analytical instruments for breath diagnosis.
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Licht JC, Grasemann H. Potential of the Electronic Nose for the Detection of Respiratory Diseases with and without Infection. Int J Mol Sci 2020; 21:E9416. [PMID: 33321951 PMCID: PMC7763696 DOI: 10.3390/ijms21249416] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/16/2020] [Accepted: 11/23/2020] [Indexed: 02/06/2023] Open
Abstract
Respiratory tract infections are common, and when affecting the lower airways and lungs, can result in significant morbidity and mortality. There is an unfilled need for simple, non-invasive tools that can be used to screen for such infections at the clinical point of care. The electronic nose (eNose) is a novel technology that detects volatile organic compounds (VOCs). Early studies have shown that certain diseases and infections can result in characteristic changes in VOC profiles in the exhaled breath. This review summarizes current knowledge on breath analysis by the electronic nose and its potential for the detection of respiratory diseases with and without infection.
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Affiliation(s)
- Johann-Christoph Licht
- Division of Respiratory Medicine, Department of Pediatrics, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada;
- Translational Medicine Research Program, Hospital for Sick Children Research Institute, Toronto, ON M5G 1X8, Canada
- Department of Immunology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Hartmut Grasemann
- Division of Respiratory Medicine, Department of Pediatrics, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada;
- Translational Medicine Research Program, Hospital for Sick Children Research Institute, Toronto, ON M5G 1X8, Canada
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Chernov VI, Choynzonov EL, Kulbakin DE, Obkhodskaya EV, Obkhodskiy AV, Popov AS, Sachkov VI, Sachkova AS. Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors. Diagnostics (Basel) 2020; 10:E677. [PMID: 32899544 PMCID: PMC7555125 DOI: 10.3390/diagnostics10090677] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 08/19/2020] [Accepted: 09/04/2020] [Indexed: 01/27/2023] Open
Abstract
"Electronic nose" technology, including technical and software tools to analyze gas mixtures, is promising regarding the diagnosis of malignant neoplasms. This paper presents the research results of breath samples analysis from 59 people, including patients with a confirmed diagnosis of respiratory tract cancer. The research was carried out using a gas analytical system including a sampling device with 14 metal oxide sensors and a computer for data analysis. After digitization and preprocessing, the data were analyzed by a neural network with perceptron architecture. As a result, the accuracy of determining oncological disease was 81.85%, the sensitivity was 90.73%, and the specificity was 61.39%.
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Affiliation(s)
- Vladimir I. Chernov
- Tomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, Russia; (V.I.C.); (E.L.C.); (D.E.K.)
| | - Evgeniy L. Choynzonov
- Tomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, Russia; (V.I.C.); (E.L.C.); (D.E.K.)
| | - Denis E. Kulbakin
- Tomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, Russia; (V.I.C.); (E.L.C.); (D.E.K.)
| | - Elena V. Obkhodskaya
- Laboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (E.V.O.); (A.S.P.)
| | - Artem V. Obkhodskiy
- Laboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (E.V.O.); (A.S.P.)
- School of Nuclear Science & Engineering, National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634050 Tomsk, Russia;
| | - Aleksandr S. Popov
- Laboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (E.V.O.); (A.S.P.)
- School of Nuclear Science & Engineering, National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634050 Tomsk, Russia;
| | - Victor I. Sachkov
- Laboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia; (E.V.O.); (A.S.P.)
| | - Anna S. Sachkova
- School of Nuclear Science & Engineering, National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634050 Tomsk, Russia;
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Can mice be trained to discriminate urine odor of conspecifics with melanoma before clinical symptoms appear? J Vet Behav 2020. [DOI: 10.1016/j.jveb.2020.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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50
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Baldini C, Billeci L, Sansone F, Conte R, Domenici C, Tonacci A. Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review. BIOSENSORS-BASEL 2020; 10:bios10080084. [PMID: 32722438 PMCID: PMC7459473 DOI: 10.3390/bios10080084] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/13/2020] [Accepted: 07/21/2020] [Indexed: 12/13/2022]
Abstract
Cancer is fast becoming the most important cause of death worldwide, its mortality being mostly caused by late or wrong diagnosis. Novel strategies have been developed to identify early signs of cancer in a minimally obtrusive way, including the Electronic Nose (E-Nose) technology, user-friendly, cost- and time-saving alternative to classical approaches. This systematic review, conducted under the PRISMA guidelines, identified 60 articles directly dealing with the E-Nose application in cancer research published up to 31 January 2020. Among these works, the vast majority reported successful E-Nose use for diagnosing Lung Cancer, showing promising results especially when employing the Aeonose tool, discriminating subjects with Lung Cancer from controls in more than 80% of individuals, in most studies. In order to tailor the main limitations of the proposed approach, including the application of the protocol to advanced stage of cancer, sample heterogeneity and massive confounders, future studies should be conducted on early stage patients, and on larger cohorts, as to better characterize the specific breathprint associated with the various subtypes of cancer. This would ultimately lead to a better and faster diagnosis and to earlier treatment, possibly reducing the burden associated to such conditions.
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Affiliation(s)
- Chiara Baldini
- School of Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy;
| | - Lucia Billeci
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Francesco Sansone
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Raffaele Conte
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Claudio Domenici
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Alessandro Tonacci
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
- Correspondence:
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