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V A B, Mathew P, Thomas S, Mathew L. Detection of lung cancer and stages via breath analysis using a self-made electronic nose device. Expert Rev Mol Diagn 2024; 24:341-353. [PMID: 38369930 DOI: 10.1080/14737159.2024.2316755] [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: 04/20/2023] [Accepted: 01/25/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND Breathomics is an emerging area focusing on monitoring and diagnosing pulmonary diseases, especially lung cancer. This research aims to employ metabolomic methods to create a breathprint in human-expelled air to rapidly identify lung cancer and its stages. RESEARCH DESIGN AND METHODS An electronic nose (e-nose) system with five metal oxide semiconductor (MOS) gas sensors, a microcontroller, and machine learning algorithms was designed and developed for this application. The volunteers in this study include 114 patients with lung cancer and 147 healthy controls to understand the clinical potential of the e-nose system to detect lung cancer and its stages. RESULTS In the training phase, in discriminating lung cancer from controls, the XGBoost classifier model with 10-fold cross-validation gave an accuracy of 91.67%. In the validation phase, the XGBoost classifier model correctly identified 35 out of 42 patients with lung cancer samples and 44 out of 51 healthy control samples providing an overall sensitivity of 83.33% and specificity of 86.27%. CONCLUSIONS These results indicate that the exhaled breath VOC analysis method may be developed as a new diagnostic tool for lung cancer detection. The advantages of e-nose based diagnostics, such as an easy and painless method of sampling, and low-cost procedures, will make it an excellent diagnostic method in the future.
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Affiliation(s)
- Binson V A
- Saintgits College of Engineering, Kottayam, Kerala, India
| | - Philip Mathew
- Department of Critical Care Medicine, Believers Church Medical College Hospital, Thiruvalla, Kerala, India
| | - Sania Thomas
- Saintgits College of Engineering, Kottayam, Kerala, India
| | - Luke Mathew
- Department of Pulmonology, Believers Church Medical College Hospital, Thiruvalla, Kerala, India
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van der Sar IG, Wijsenbeek MS, Braunstahl GJ, Loekabino JO, Dingemans AMC, In 't Veen JCCM, Moor CC. Differentiating interstitial lung diseases from other respiratory diseases using electronic nose technology. Respir Res 2023; 24:271. [PMID: 37932795 PMCID: PMC10626662 DOI: 10.1186/s12931-023-02575-3] [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: 08/25/2023] [Accepted: 10/22/2023] [Indexed: 11/08/2023] Open
Abstract
INTRODUCTION Interstitial lung disease (ILD) may be difficult to distinguish from other respiratory diseases due to overlapping clinical presentation. Recognition of ILD is often late, causing delay which has been associated with worse clinical outcome. Electronic nose (eNose) sensor technology profiles volatile organic compounds in exhaled breath and has potential to detect ILD non-invasively. We assessed the accuracy of differentiating breath profiles of patients with ILD from patients with asthma, chronic obstructive pulmonary disease (COPD), and lung cancer using eNose technology. METHODS Patients with ILD, asthma, COPD, and lung cancer, regardless of stage or treatment, were included in a cross-sectional study in two hospitals. Exhaled breath was analysed using an eNose (SpiroNose) and clinical data were collected. Datasets were split in training and test sets for independent validation of the model. Data were analyzed with partial least squares discriminant and receiver operating characteristic analyses. RESULTS 161 patients with ILD and 161 patients with asthma (n = 65), COPD (n = 50) or lung cancer (n = 46) were included. Breath profiles of patients with ILD differed from all other diseases with an area under the curve (AUC) of 0.99 (95% CI 0.97-1.00) in the test set. Moreover, breath profiles of patients with ILD could be accurately distinguished from the individual diseases with an AUC of 1.00 (95% CI 1.00-1.00) for asthma, AUC of 0.96 (95% CI 0.90-1.00) for COPD, and AUC of 0.98 (95% CI 0.94-1.00) for lung cancer in test sets. Results were similar after excluding patients who never smoked. CONCLUSIONS Exhaled breath of patients with ILD can be distinguished accurately from patients with other respiratory diseases using eNose technology. eNose has high potential as an easily accessible point-of-care medical test for identification of ILD amongst patients with respiratory symptoms, and could possibly facilitate earlier referral and diagnosis of patients suspected of ILD.
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Affiliation(s)
- Iris G van der Sar
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marlies S Wijsenbeek
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Gert-Jan Braunstahl
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Respiratory Medicine, Franciscus Gasthuis & Vlietland, Center of Excellence for Asthma, COPD, and Respiratory Allergy, Rotterdam, The Netherlands
| | - Jason O Loekabino
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Anne-Marie C Dingemans
- Department of Respiratory Medicine, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Johannes C C M In 't Veen
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Respiratory Medicine, Franciscus Gasthuis & Vlietland, Center of Excellence for Asthma, COPD, and Respiratory Allergy, Rotterdam, The Netherlands
| | - Catharina C Moor
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Hagens LA, Heijnen NFL, Smit MR, Verschueren ARM, Nijsen TME, Geven I, Presură CN, Rietman R, Fenn DW, Brinkman P, Schultz MJ, Bergmans DCJJ, Schnabel RM, Bos LDJ. Octane in exhaled breath to diagnose acute respiratory distress syndrome in invasively ventilated intensive care unit patients. ERJ Open Res 2023; 9:00214-2023. [PMID: 37850212 PMCID: PMC10577595 DOI: 10.1183/23120541.00214-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/03/2023] [Indexed: 10/19/2023] Open
Abstract
Background The concentration of exhaled octane has been postulated as a reliable biomarker for acute respiratory distress syndrome (ARDS) using metabolomics analysis with gas chromatography and mass spectrometry (GC-MS). A point-of-care (POC) breath test was developed in recent years to accurately measure octane at the bedside. The aim of the present study was to validate the diagnostic accuracy of exhaled octane for ARDS using a POC breath test in invasively ventilated intensive care unit (ICU) patients. Methods This was an observational cohort study of consecutive patients receiving invasive ventilation for at least 24 h, recruited in two university ICUs. GC-MS and POC breath tests were used to quantify the exhaled octane concentration. ARDS was assessed by three experts following the Berlin definition and used as the reference standard. The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy. Results 519 patients were included and 190 (37%) fulfilled the criteria for ARDS. The median (interquartile range) concentration of octane using the POC breath test was not significantly different between patients with ARDS (0.14 (0.05-0.37) ppb) and without ARDS (0.11 (0.06-0.26) ppb; p=0.64). The AUC for ARDS based on the octane concentration in exhaled breath using the POC breath test was 0.52 (95% CI 0.46-0.57). Analysis of exhaled octane with GC-MS showed similar results. Conclusions Octane in exhaled breath has insufficient diagnostic accuracy for ARDS. This disqualifies the use of octane as a biomarker in the diagnosis of ARDS and challenges most of the research performed up to now in the field of exhaled breath metabolomics.
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Affiliation(s)
- Laura A Hagens
- Department of Intensive Care, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
| | - Nanon F L Heijnen
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Marry R Smit
- Department of Intensive Care, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
| | | | - Tamara M E Nijsen
- Sleep and Respiratory Solutions, Philips Research, Eindhoven, The Netherlands
| | - Inge Geven
- Sleep and Respiratory Solutions, Philips Research, Eindhoven, The Netherlands
| | - Cristian N Presură
- Sleep and Respiratory Solutions, Philips Research, Eindhoven, The Netherlands
| | - Ronald Rietman
- Sleep and Respiratory Solutions, Philips Research, Eindhoven, The Netherlands
| | - Dominic W Fenn
- Department of Respiratory Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Laboratory of Experimental Intensive Care and Anesthesiology (LEICA), Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
| | - Paul Brinkman
- Department of Respiratory Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
| | - Marcus J Schultz
- Department of Intensive Care, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Mahidol-Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok, Thailand
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Dennis C J J Bergmans
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht, The Netherlands
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, Maastricht, The Netherlands
| | - Ronny M Schnabel
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lieuwe D J Bos
- Department of Intensive Care, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Department of Respiratory Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Laboratory of Experimental Intensive Care and Anesthesiology (LEICA), Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
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van der Sar IG, van Jaarsveld N, Spiekerman IA, Toxopeus FJ, Langens QL, Wijsenbeek MS, Dauwels J, Moor CC. Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis. J Breath Res 2023; 17:047104. [PMID: 37595574 DOI: 10.1088/1752-7163/acf1bf] [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: 12/02/2022] [Accepted: 08/18/2023] [Indexed: 08/20/2023]
Abstract
Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patterns can be used to diagnose medical conditions. Sarcoidosis is an often difficult to diagnose disease, as no standard procedure or conclusive test exists. An accurate diagnostic model based on eNose data could therefore be helpful in clinical decision-making. The aim of this paper is to evaluate the performance of various dimensionality reduction methods and classifiers in order to design an accurate diagnostic model for sarcoidosis. Various methods of dimensionality reduction and multiple hyperparameter optimised classifiers were tested and cross-validated on a dataset of patients with pulmonary sarcoidosis (n= 224) and other interstitial lung disease (n= 317). Best performing methods were selected to create a model to diagnose patients with sarcoidosis. Nested cross-validation was applied to calculate the overall diagnostic performance. A classification model with feature selection and random forest (RF) classifier showed the highest accuracy. The overall diagnostic performance resulted in an accuracy of 87.1% and area-under-the-curve of 91.2%. After comparing different dimensionality reduction methods and classifiers, a highly accurate model to diagnose a patient with sarcoidosis using eNose data was created. The RF classifier and feature selection showed the best performance. The presented systematic approach could also be applied to other eNose datasets to compare methods and select the optimal diagnostic model.
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Affiliation(s)
- Iris G van der Sar
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Nynke van Jaarsveld
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Imme A Spiekerman
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Floor J Toxopeus
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Quint L Langens
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Marlies S Wijsenbeek
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Justin Dauwels
- Department of Microelectronics, Delft University of Technology, Delft, The Netherlands
| | - Catharina C Moor
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
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Shahbazi Khamas S, Alizadeh Bahmani AH, Vijverberg SJ, Brinkman P, Maitland-van der Zee AH. Exhaled volatile organic compounds associated with risk factors for obstructive pulmonary diseases: a systematic review. ERJ Open Res 2023; 9:00143-2023. [PMID: 37650089 PMCID: PMC10463028 DOI: 10.1183/23120541.00143-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/21/2023] [Indexed: 09/01/2023] Open
Abstract
Background Asthma and COPD are among the most common respiratory diseases. To improve the early detection of exacerbations and the clinical course of asthma and COPD new biomarkers are needed. The development of noninvasive metabolomics of exhaled air into a point-of-care tool is an appealing option. However, risk factors for obstructive pulmonary diseases can potentially introduce confounding markers due to altered volatile organic compound (VOC) patterns being linked to these risk factors instead of the disease. We conducted a systematic review and presented a comprehensive list of VOCs associated with these risk factors. Methods A PRISMA-oriented systematic search was conducted across PubMed, Embase and Cochrane Libraries between 2000 and 2022. Full-length studies evaluating VOCs in exhaled breath were included. A narrative synthesis of the data was conducted, and the Newcastle-Ottawa Scale was used to assess the quality of included studies. Results The search yielded 2209 records and, based on the inclusion/exclusion criteria, 24 articles were included in the qualitative synthesis. In total, 232 individual VOCs associated with risk factors for obstructive pulmonary diseases were found; 58 compounds were reported more than once and 12 were reported as potential markers of asthma and/or COPD in other studies. Critical appraisal found that the identified studies were methodologically heterogeneous and had a variable risk of bias. Conclusion We identified a series of exhaled VOCs associated with risk factors for asthma and/or COPD. Identification of these VOCs is necessary for the further development of exhaled metabolites-based point-of-care tests in these obstructive pulmonary diseases.
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Affiliation(s)
- Shahriyar Shahbazi Khamas
- Department of Pulmonary Medicine, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam, the Netherlands
| | - Amir Hossein Alizadeh Bahmani
- Department of Pulmonary Medicine, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam, the Netherlands
| | - Susanne J.H. Vijverberg
- Department of Pulmonary Medicine, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam, the Netherlands
| | - Paul Brinkman
- Department of Pulmonary Medicine, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam, the Netherlands
- These authors contributed equally
| | - Anke H. Maitland-van der Zee
- Department of Pulmonary Medicine, Amsterdam UMC location, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam, the Netherlands
- These authors contributed equally
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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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Sas V, Cherecheș-Panța P, Borcau D, Schnell CN, Ichim EG, Iacob D, Coblișan AP, Drugan T, Man SC. Breath Prints for Diagnosing Asthma in Children. J Clin Med 2023; 12:jcm12082831. [PMID: 37109167 PMCID: PMC10146639 DOI: 10.3390/jcm12082831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/04/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
Electronic nose (e-nose) is a new technology applied for the identification of volatile organic compounds (VOC) in breath air. Measuring VOC in exhaled breath can adequately identify airway inflammation, especially in asthma. Its noninvasive character makes e-nose an attractive technology applicable in pediatrics. We hypothesized that an electronic nose could discriminate the breath prints of patients with asthma from controls. A cross-sectional study was conducted and included 35 pediatric patients. Eleven cases and seven controls formed the two training models (models A and B). Another nine cases and eight controls formed the external validation group. Exhaled breath samples were analyzed using Cyranose 320, Smith Detections, Pasadena, CA, USA. The discriminative ability of breath prints was investigated by principal component analysis (PCA) and canonical discriminative analysis (CDA). Cross-validation accuracy (CVA) was calculated. For the external validation step, accuracy, sensitivity and specificity were calculated. Duplicate sampling of exhaled breath was obtained for ten patients. E-nose was able to discriminate between the controls and asthmatic patient group with a CVA of 63.63% and an M-distance of 3.13 for model A and a CVA of 90% and an M-distance of 5.55 for model B in the internal validation step. In the second step of external validation, accuracy, sensitivity and specificity were 64%, 77% and 50%, respectively, for model A, and 58%, 66% and 50%, respectively, for model B. Between paired breath sample fingerprints, there were no significant differences. An electronic nose can discriminate pediatric patients with asthma from controls, but the accuracy obtained in the external validation was lower than the CVA obtained in the internal validation step.
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Affiliation(s)
- Valentina Sas
- Department of Pediatrics, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400124 Cluj-Napoca, Romania
- Clinical Hospital for Pediatric Emergencies, 400124 Cluj-Napoca, Romania
| | - Paraschiva Cherecheș-Panța
- Department of Pediatrics, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400124 Cluj-Napoca, Romania
- Clinical Hospital for Pediatric Emergencies, 400124 Cluj-Napoca, Romania
| | - Diana Borcau
- Clinical Hospital for Pediatric Emergencies, 400124 Cluj-Napoca, Romania
| | - Cristina-Nicoleta Schnell
- Department of Pediatrics, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400124 Cluj-Napoca, Romania
- Clinical Hospital for Pediatric Emergencies, 400124 Cluj-Napoca, Romania
| | - Edita-Gabriela Ichim
- Department of Pediatrics, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400124 Cluj-Napoca, Romania
- Clinical Hospital for Pediatric Emergencies, 400124 Cluj-Napoca, Romania
| | - Daniela Iacob
- Department of Pediatrics, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400124 Cluj-Napoca, Romania
- Clinical Hospital for Pediatric Emergencies, 400124 Cluj-Napoca, Romania
| | - Alina-Petronela Coblișan
- Clinical Hospital for Pediatric Emergencies, 400124 Cluj-Napoca, Romania
- Department of Nursing, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400124 Cluj-Napoca, Romania
| | - Tudor Drugan
- Department of Medical Informatics and Biostatistics, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400124 Cluj-Napoca, Romania
| | - Sorin-Claudiu Man
- Department of Pediatrics, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400124 Cluj-Napoca, Romania
- Clinical Hospital for Pediatric Emergencies, 400124 Cluj-Napoca, Romania
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Issitt T, Wiggins L, Veysey M, Sweeney S, Brackenbury W, Redeker K. Volatile compounds in human breath: critical review and meta-analysis. J Breath Res 2022; 16. [PMID: 35120340 DOI: 10.1088/1752-7163/ac5230] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 02/04/2022] [Indexed: 11/12/2022]
Abstract
Volatile compounds contained in human breath reflect the inner workings of the body. A large number of studies have been published that link individual components of breath to disease, but diagnostic applications remain limited, in part due to inconsistent and conflicting identification of breath biomarkers. New approaches are therefore required to identify effective biomarker targets. Here, volatile organic compounds have been identified in the literature from four metabolically and physiologically distinct diseases and grouped into chemical functional groups (e.g. - methylated hydrocarbons or aldehydes; based on known metabolic and enzymatic pathways) to support biomarker discovery and provide new insight on existing data. Using this functional grouping approach, principal component analysis doubled explanatory capacity from 19.1% to 38% relative to single individual compound approaches. Random forest and linear discriminant analysis reveal 93% classification accuracy for cancer. This review and meta-analysis provides insight for future research design by identifying volatile functional groups associated with disease. By incorporating our understanding of the complexities of the human body, along with accounting for variability in methodological and analytical approaches, this work demonstrates that a suite of targeted, functional volatile biomarkers, rather than individual biomarker compounds, will improve accuracy and success in diagnostic research and application.
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Affiliation(s)
- Theo Issitt
- Biology, University of York, University of York, York, York, YO10 5DD, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Laura Wiggins
- Biology, University of York, University of York, York, York, YO10 5DD, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Martin Veysey
- The University of Newcastle, School of Medicine & Public Health, Callaghan, New South Wales, 2308, AUSTRALIA
| | - Sean Sweeney
- Biology, University of York, University of York, York, York, YO10 5DD, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - William Brackenbury
- Biology, University of York, University of York, York, York, YO10 5DD, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Kelly Redeker
- Biology, University of York, Biology Dept. University of York, York, York, North Yorkshire, YO10 5DD, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Wojnowski W, Kalinowska K. Machine Learning and Electronic Noses for Medical Diagnostics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
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Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose. Expert Rev Mol Diagn 2021; 21:1223-1233. [PMID: 34415806 DOI: 10.1080/14737159.2021.1971079] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung cancer are detected with MOS sensor array-based e-noses. The e-nose device with the sensor array, data acquisition system, and pattern recognition can detect the variations of volatile organic compounds (VOC) present in the expelled breath of patients and healthy controls. MATERIALS AND METHODS This work presents the e-nose equipment design, study subjects selection, breath sampling procedures, and various data analysis tools. The developed e-nose system is tested in 40 patients with lung cancer, 48 patients with COPD, and 90 healthy controls. RESULTS In differentiating lung cancer and COPD from controls, support vector machine (SVM) with 3-fold cross-validation outperformed all other classifiers with an accuracy of 92.3% in cross-validation. In external validation, the same discrimination was achieved by k-nearest neighbors (k-NN) with 75.0% accuracy. CONCLUSION The reported results show that the VOC analysis with an e-nose system holds exceptional possibilities in noninvasive disease diagnosis applications.
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Binson VA, Subramoniam M, Mathew L. Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose. J Breath Res 2021; 15. [PMID: 34243176 DOI: 10.1088/1752-7163/ac1326] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/09/2021] [Indexed: 01/22/2023]
Abstract
This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.
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Affiliation(s)
- V A Binson
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.,Department of Electronics Engineering, Saintgits College of Engineering, Kottayam, Kerala, India
| | - M Subramoniam
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - Luke Mathew
- Department of Pulmonology, Believers Church Medical College Hospital, Thiruvalla, Kerala, India
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Gashimova E, Osipova A, Temerdashev A, Porkhanov V, Polyakov I, Perunov D, Dmitrieva E. Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath. Biomark Med 2021; 15:821-829. [PMID: 34223778 DOI: 10.2217/bmm-2020-0828] [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: 01/11/2023] Open
Abstract
Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.
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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 named after Prof. SV Ochapovsky, Krasnodar, Russia
| | - Igor Polyakov
- Research Institute - Regional Clinical Hospital No. 1 named after Prof. SV Ochapovsky, Krasnodar, Russia
| | - Dmitry Perunov
- Research Institute - Regional Clinical Hospital No. 1 named after Prof. SV Ochapovsky, Krasnodar, Russia
| | - Ekaterina Dmitrieva
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia
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Khoubnasabjafari M, Mogaddam MRA, Rahimpour E, Soleymani J, Saei AA, Jouyban A. Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications. Crit Rev Anal Chem 2021; 52:1461-1487. [PMID: 33691552 DOI: 10.1080/10408347.2021.1889961] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.
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Affiliation(s)
- Maryam Khoubnasabjafari
- Tuberculosis and Lung Diseases Research Center and Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.,Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohamad Reza Afshar Mogaddam
- Food and Drug Safety Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Elaheh Rahimpour
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Jafar Soleymani
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Liver and Gastrointestinal Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Amir Ata Saei
- Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry I, Karolinska Institutet, Stockholm, Sweden
| | - Abolghasem Jouyban
- Food and Drug Safety Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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14
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Machine Learning and Electronic Noses for Medical Diagnostics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_329-1] [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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15
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The Role of Electronic Noses in Phenotyping Patients with Chronic Obstructive Pulmonary Disease. BIOSENSORS-BASEL 2020; 10:bios10110171. [PMID: 33187142 PMCID: PMC7697924 DOI: 10.3390/bios10110171] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/05/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a common progressive disorder of the respiratory system which is currently the third leading cause of death worldwide. Exhaled breath analysis is a non-invasive method to study lung diseases, and electronic noses have been extensively used in breath research. Studies with electronic noses have proved that the pattern of exhaled volatile organic compounds is different in COPD. More recent investigations have reported that electronic noses could potentially distinguish different endotypes (i.e., neutrophilic vs. eosinophilic) and are able to detect microorganisms in the airways responsible for exacerbations. This article will review the published literature on electronic noses and COPD and help in identifying methodological, physiological, and disease-related factors which could affect the results.
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16
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Kalidoss R, Umapathy S, Kothalam R, Sakthivelu U. Adsorption kinetics feature extraction from breathprint obtained by graphene based sensors for diabetes diagnosis. J Breath Res 2020; 15:016005. [PMID: 33049727 DOI: 10.1088/1752-7163/abc09b] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The correlation between blood glucose and breath acetone suggested by several studies has spurred the research community to develop an electronic (e-nose) for diabetes diagnosis. Herein, we have validated the in-house graphene based sensors with known acetone concentration. The sensor performances such as sensitivity, selectivity and stability (SSS) suggested their potential use in acquiring breath print. The 10% higher mean saturation voltage for 30 diabetic subjects ensured a discrimination accuracy of 65% with a positive correlation (r = 0.88) between biochemically measured and non-invasively estimated (glycated haemoglobin) HbA1c. For the improvement of classification rate, thirteen features associated with the adsorption kinetics were extracted from the breathprint from each of the three sensors. The features given as an input to the Naïve Bayes classification model fetched an accuracy of 68.33%. Elimination of redundant features by distinction index and one-R feature ranking algorithm results in Naïve Bayes algorithm with improved performances. The success rate has improved to 70% using the subset of features ranked by one-R algorithm. These results indicated the use of feature ranking algorithms and prediction models for the improvement in accuracy of our in-house fabricated graphene based sensors.
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Affiliation(s)
- Ramji Kalidoss
- Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science & Technology, Tamil Nadu, Kattankulathur 603203, India. Department of Biomedical Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu 600073, India
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17
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Kort S, Brusse-Keizer M, Gerritsen JW, Schouwink H, Citgez E, de Jongh F, van der Maten J, Samii S, van den Bogart M, van der Palen J. Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters. ERJ Open Res 2020; 6:00221-2019. [PMID: 32201682 PMCID: PMC7073409 DOI: 10.1183/23120541.00221-2019] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 01/14/2020] [Indexed: 12/25/2022] Open
Abstract
Introduction Exhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis. Methods Based on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis. Results NSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89). Conclusions Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer. Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose significantly improves the diagnostic accuracy to detect the presence or absence of lung cancerhttp://bit.ly/38ps6fH
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Affiliation(s)
- Sharina Kort
- Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | | | | | - Hugo Schouwink
- Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Emanuel Citgez
- Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Frans de Jongh
- Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jan van der Maten
- Dept of Pulmonary Medicine, Medisch Centrum Leeuwarden, Leeuwarden, the Netherlands
| | - Suzy Samii
- Dept of Pulmonary Medicine, Deventer Ziekenhuis, Deventer, the Netherlands
| | | | - Job van der Palen
- Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands.,Dept of Research Methodology, Measurement, and Data Analysis, University of Twente, Enschede, the Netherlands
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18
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Chen CY, Lin WC, Yang HY. Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research. Respir Res 2020; 21:45. [PMID: 32033607 PMCID: PMC7006122 DOI: 10.1186/s12931-020-1285-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 01/07/2020] [Indexed: 01/07/2023] Open
Abstract
Background Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique. Methods We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy. Results A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement. Conclusions There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.
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Affiliation(s)
- Chung-Yu Chen
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Douliu, Taiwan
| | - Wei-Chi Lin
- Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, Taipei, Taiwan
| | - Hsiao-Yu Yang
- Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, Taipei, Taiwan. .,Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, Taipei, Taiwan. .,Department of Public Health, National Taiwan University College of Public Health, Taipei, Taiwan. .,Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei, Taiwan. .,Innovation and Policy Center for Population Health and Sustainable Environment, College of Public Health, National Taiwan University, Taipei, Taiwan.
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19
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Abstract
PURPOSE OF REVIEW The long-term management goals of the inflammatory airway diseases asthma and chronic obstructive pulmonary disease (COPD) are similar and focus on symptom control and reduction of exacerbation frequency and severity. Treatable traits have recently been postulated as a management concept which complements the traditional diagnostic labels 'asthma' and 'COPD', thereby focusing on therapy targeted to a patients' individual disease-associated characteristics. Exhaled volatile organic compounds (VOCs) may be utilized as noninvasive biomarker for disease activity or manifestation in asthma and COPD. In this review, we provide an overview of the current achievements concerning exhaled breath analysis in the field of uncontrolled chronic airways diseases. RECENT FINDINGS Monitoring of (airway) inflammation and identification of (molecular) phenotypic characteristics in asthma and COPD through exhaled VOC analysis by either mass spectrometry (MS) based or sensor-driven electronic nose technology (eNose) seems to be feasible, however pending confirmation could hamper the valorization of breathomics into clinical tests. SUMMARY Exhaled VOC analysis and the management of asthma and COPD through the concept of pulmonary treatable traits are an interesting match. To develop exhaled breath analysis into an added value for pulmonary treatable traits, multicentre studies are required following international standards for study populations, sampling methods and analytical strategies enabling external validation.
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20
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Xu B, Moradi M, Kuplicki R, Stewart JL, McKinney B, Sen S, Paulus MP. Machine Learning Analysis of Electronic Nose in a Transdiagnostic Community Sample With a Streamlined Data Collection Approach: No Links Between Volatile Organic Compounds and Psychiatric Symptoms. Front Psychiatry 2020; 11:503248. [PMID: 33192639 PMCID: PMC7524957 DOI: 10.3389/fpsyt.2020.503248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 08/24/2020] [Indexed: 11/25/2022] Open
Abstract
Non-intrusive, easy-to-use and pragmatic collection of biological processes is warranted to evaluate potential biomarkers of psychiatric symptoms. Prior work with relatively modest sample sizes suggests that under highly-controlled sampling conditions, volatile organic compounds extracted from the human breath (exhalome), often measured by an electronic nose ("e-nose"), may be related to physical and mental health. The present study utilized a streamlined data collection approach and attempted to replicate and extend prior e-nose links to mental health in a standard research setting within large transdiagnostic community dataset (N = 1207; 746 females; 18-61 years) who completed a screening visit at the Laureate Institute for Brain Research between 07/2016 and 05/2018. Factor analysis was used to obtain latent exhalome variables, and machine learning approaches were employed using these latent variables to predict three types of symptoms independent of each other (depression, anxiety, and substance use disorder) within separate training and a test sets. After adjusting for age, gender, body mass index, and smoking status, the best fitting algorithm produced by the training set accounted for nearly 0% of the test set's variance. In each case the standard error included the zero line, indicating that models were not predictive of clinical symptoms. Although some sample variance was predicted, findings did not generalize to out-of-sample data. Based on these findings, we conclude that the exhalome, as measured by the e-nose within a less-controlled environment than previously reported, is not able to provide clinically useful assessments of current depression, anxiety or substance use severity.
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Affiliation(s)
- Bohan Xu
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | - Mahdi Moradi
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
| | - Brett McKinney
- Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States.,Department of Mathematics, College of Engineering & Natural Sciences, University of Tulsa, Tulsa, OK, United States
| | - Sandip Sen
- Department of Computer Science, Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States.,Department of Psychiatry, School of Medicine, University of California San Diego, San Diego, CA, United States
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21
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Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning. SENSORS 2019; 19:s19235207. [PMID: 31783711 PMCID: PMC6928873 DOI: 10.3390/s19235207] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 11/18/2019] [Accepted: 11/25/2019] [Indexed: 11/16/2022]
Abstract
We used a 16-channel e-nose demonstrator based on micro-capacitive sensors with functionalized surfaces to measure the response of 30 different sensors to the vapours from 11 different substances, including the explosives 1,3,5-trinitro-1,3,5-triazinane (RDX), 1-methyl-2,4-dinitrobenzene (DNT) and 2-methyl-1,3,5-trinitrobenzene (TNT). A classification model was developed using the Random Forest machine-learning algorithm and trained the models on a set of signals, where the concentration and flow of a selected single vapour were varied independently. It is demonstrated that our classification models are successful in recognizing the signal pattern of different sets of substances. An excellent accuracy of 96% was achieved for identifying the explosives from among the other substances. These experiments clearly demonstrate that the silane monolayers used in our sensors as receptor layers are particularly well suited to selecting and recognizing TNT and similar types of explosives from among other substances.
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22
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Le Maout P, Laquintinie PS, Lahuec C, Seguin F, Wojkiewicz JL, Redon N, Dupont L. A low cost, handheld E-nose for renal diseases early diagnosis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:2817-2820. [PMID: 30440987 DOI: 10.1109/embc.2018.8512847] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
In the last decade, the use of electronic olfaction systems for the early diagnosis of several pathologies by breath analysis has been investigated. In this study, an electronic nose including seven polyaniline sensors has been developed. An impedance measurement circuit and a micro-computer to process the sensor responses were studied to give a pre-diagnosis conclusion. The measurement accuracy is 97% when the E-nose is exposed to a simulated human breath and different concentration of ammonia, from 500 ppb to 2.8 ppm. The described prototype weights about 300 g and can be used for 14 hours with a smartphone battery.
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Kononov A, Korotetsky B, Jahatspanian I, Gubal A, Vasiliev A, Arsenjev A, Nefedov A, Barchuk A, Gorbunov I, Kozyrev K, Rassadina A, Iakovleva E, Sillanpää M, Safaei Z, Ivanenko N, Stolyarova N, Chuchina V, Ganeev A. Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer. J Breath Res 2019; 14:016004. [PMID: 31505480 DOI: 10.1088/1752-7163/ab433d] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated March 15, 2017.
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Affiliation(s)
- Aleksandr Kononov
- St Petersburg State University, Universitetskaya nab.7/9, 199034, St Petersburg, Russia
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Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank. J Clin Med 2019; 8:jcm8101698. [PMID: 31623141 PMCID: PMC6832325 DOI: 10.3390/jcm8101698] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 10/06/2019] [Accepted: 10/14/2019] [Indexed: 12/27/2022] Open
Abstract
Background: There is an increasing interest in employing electronic nose technology in the diagnosis and monitoring of lung diseases. Interstitial lung diseases (ILD) are challenging in regard to setting an accurate diagnosis in a timely manner. Thus, there is a high unmet need in non-invasive diagnostic tests. This single-center explorative study aimed to evaluate the usefulness of electronic nose (Aeonose®) in the diagnosis of ILDs. Methods: Exhaled volatile organic compound (VOC) signatures were obtained by Aeonose® in 174 ILD patients, 23 patients with chronic obstructive pulmonary disease (COPD), and 33 healthy controls (HC). Results: By dichotomous comparison of VOC’s between ILD, COPD, and HC, a discriminating algorithm was established. In addition, direct analyses between the ILD subgroups, e.g., cryptogenic organizing pneumonia (COP, n = 28), idiopathic pulmonary fibrosis (IPF, n = 51), and connective tissue disease-associated ILD (CTD-ILD, n = 25) were performed. Area under the Curve (AUC) and Matthews’s correlation coefficient (MCC) were used to interpret the data. In direct comparison of the different ILD subgroups to HC, the algorithms developed on the basis of the Aeonose® signatures allowed safe separation between IPF vs. HC (AUC of 0.95, MCC of 0.73), COP vs. HC (AUC 0.89, MCC 0.67), and CTD-ILD vs. HC (AUC 0.90, MCC 0.69). Additionally, to a case-control study design, the breath patterns of ILD subgroups were compared to each other. Following this approach, the sensitivity and specificity showed a relevant drop, which results in a poorer performance of the algorithm to separate the different ILD subgroups (IPF vs. COP with MCC 0.49, IPF vs. CTD-ILD with MCC 0.55, and COP vs. CT-ILD with MCC 0.40). Conclusions: The Aeonose® showed some potential in separating ILD subgroups from HC. Unfortunately, when applying the algorithm to distinguish ILD subgroups from each other, the device showed low specificity. We suggest that artificial intelligence or principle compound analysis-based studies of a much broader data set of patients with ILDs may be much better suited to train these devices.
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Azim A, Barber C, Dennison P, Riley J, Howarth P. Exhaled volatile organic compounds in adult asthma: a systematic review. Eur Respir J 2019; 54:13993003.00056-2019. [PMID: 31273044 DOI: 10.1183/13993003.00056-2019] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 06/13/2019] [Indexed: 12/11/2022]
Abstract
The search for biomarkers that can guide precision medicine in asthma, particularly those that can be translated to the clinic, has seen recent interest in exhaled volatile organic compounds (VOCs). Given the number of studies reporting "breathomics" findings and its growing integration in clinical trials, we performed a systematic review of the literature to summarise current evidence and understanding of breathomics technology in asthma.A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed (CRD42017084145) of MEDLINE, Embase and the Cochrane databases to search for any reports that assessed exhaled VOCs in adult asthma patients, using the following terms (asthma AND (volatile organic compounds AND exhaled) OR breathomics).Two authors independently determined the eligibility of 2957 unique records, of which 66 underwent full-text review. Data extraction and risk of bias assessment was performed on the 22 studies deemed to fulfil the search criteria. The studies are described in terms of methodology and the evidence narratively summarised under the following clinical headings: diagnostics, phenotyping, treatment stratification, treatment monitoring and exacerbation prediction/assessment.Our review found that most studies were designed to assess diagnostic potential rather than focus on underlying biology or treatable traits. Results are generally limited by a lack of methodological standardisation and external validation and by insufficiently powered studies, but there is consistency across the literature that exhaled VOCs are sensitive to underlying inflammation. Modern studies are applying robust breath analysis workflows to large multi-centre study designs, which should unlock the full potential of measurement of exhaled volatile organic compounds in airways diseases such as asthma.
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Affiliation(s)
- Adnan Azim
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK .,National Institute for Health Research (NIHR) Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Clair Barber
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.,National Institute for Health Research (NIHR) Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Paddy Dennison
- National Institute for Health Research (NIHR) Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - John Riley
- Galaxy Asthma, GSK, Medicines Research Centre, Stevenage, UK
| | - Peter Howarth
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.,National Institute for Health Research (NIHR) Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
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The electronic nose technology in clinical diagnosis: A systematic review. Porto Biomed J 2019; 4:e42. [PMID: 31930178 PMCID: PMC6924976 DOI: 10.1097/j.pbj.0000000000000042] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 06/06/2019] [Indexed: 12/13/2022] Open
Abstract
Supplemental Digital Content is available in the text Background: Volatile organic compounds (VOC) are end products of human metabolism (normal and disease-associated) that can be mainly excreted in breath, urine, and feces. Therefore, VOC can be very useful as markers of diseases and helpful for clinicians since its sampling is noninvasive, inexpensive, and painless. Electronic noses, or eNoses, provide an easy and inexpensive way to analyze gas samples. Thus, this device may be used for diagnosis, monitoring or phenotyping diseases according to specific breathprints (breath profile). Objective: In this review, we summarize data showing the ability of eNose to be used as a noninvasive tool to improve diagnosis in clinical settings. Methods: A PRISMA-oriented search was performed in PubMed and Cochrane Library. Only studies performed in humans and published since 2000 were included. Results: A total of 48 original articles, 21 reviews, and 7 other documents were eligible and fully analyzed. The quality assessment of the selected studies was conducted according to the Standards for Reporting of Diagnostic Accuracy. Airway obstructive diseases were the most studied and Cyranose 320 was the most used eNose. Conclusions: Several case–control studies were performed to test this technology in diverse fields. More than a half of the selected studies showed good accuracy. However, there are some limitations regarding sampling methodology, analysis, reproducibility, and external validation that need to be standardized. Additionally, it is urgent to test this technology in intend-to-treat populations. Thus, it is possible to think in the contribution of VOC analysis by eNoses in a clinical setting.
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Bannier MAGE, van de Kant KDG, Jöbsis Q, Dompeling E. Feasibility and diagnostic accuracy of an electronic nose in children with asthma and cystic fibrosis. J Breath Res 2019; 13:036009. [PMID: 30213921 DOI: 10.1088/1752-7163/aae158] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The measurement of volatile organic compounds (VOCs) in exhaled breath is a promising tool for diagnosing and monitoring various lung diseases in children. Gas chromatography mass spectrometry (GC-MS) analysis is a frequently used standard technique for VOCs analysis. However, as GC-MS is an expensive and time-consuming technique, hand-held devices or electronic noses have been developed. Recently, the Aeonose was introduced as an easy-to-use hand-held eNose capable of point-of-care testing. Although first results using this eNose in adults are promising, studies in children are lacking. We therefore performed a cross-sectional study in 55 children and adolescents ≥6 years of age (20 children with moderate to severe asthma, 13 children with CF, and 22 healthy controls). The feasibility of the Aeonose was high (>98% successful measurements). The diagnostic accuracy was high for discriminating asthma from CF (Area Under the Receiver Operating Characteristic Curve [AUC] 0.90 [95% Confidence Interval 0.78-1.00] sensitivity 89% [65%-98%], specificity 77% [46%-94%]), and for the distinction between CF and healthy controls (AUC 0.87 [0.74-1.00], sensitivity 85% [54%-97%], specificity 77% [54%-91%]). However, the diagnostic accuracy for the discrimination between asthma and healthy controls was modest (AUC 0.79 [0.63-0.94], sensitivity 74% [49%-90%], specificity 91% [69%-98%]). This is the first study to report test results of the Aeonose in children and adolescents ≥6 years. This eNose showed a high feasibility with modest to good diagnostic accuracies in asthma and CF. This study was registered at clinicaltrial.gov (NCT03377686).
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Affiliation(s)
- Michiel A G E Bannier
- Department of Paediatric Respiratory Medicine, School for Public Health and Primary Care (CAPHRI), Maastricht University Medical Centre+, Maastricht, The Netherlands
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Fasola S, Ferrante G, Sabatini A, Santonico M, Zompanti A, Grasso S, Antonelli Incalzi R, La Grutta S. Repeatability of exhaled breath fingerprint collected by a modern sampling system in asthmatic and healthy children. J Breath Res 2019; 13:036007. [PMID: 30965288 DOI: 10.1088/1752-7163/ab1765] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
E-noses provide potential non-invasive metabolic biomarkers for diagnosing and monitoring pulmonary diseases. The primary aim of the present study was to assess the within-day and between-day repeatability of a modern breath sampling system (Pneumopipe® plus an array of e-nose sensors) in asthmatic and healthy children. The secondary aim was to compare the repeatability of the breath sampling system, spirometry and exhaled nitric oxide (eNO). Fifteen children (age 6-11 years) with asthma and thirty healthy children matched by age and gender (1:2 allocation) were recruited; of them, three healthy children did not complete the study. All measurements were collected twice during the baseline visit, 30 min apart, and once during the final visit, after 7 d. Repeatability was assessed through the intra-cluster correlation coefficient (ICC), and a significance test was performed to detect an at least 'fair' repeatability (ICC > 0.2). In asthmatic children, the within-day (0-30 min) ICCs for e-nose sensors (8 sensors × 4 desorption temperatures) ranged from 0.24 to 0.84 (median 0.57, IQR 0.47-0.71), while the between-day (0-7 d) ICCs ranged from 0.25 to 0.83 (median 0.66, IQR 0.55-0.72). In healthy children, the within-day ICCs for e-nose sensors ranged from 0.29 to 0.85 (median 0.58, IQR 0.49-0.63), while the between-day ICCs ranged from 0.33 to 0.82 (median 0.55, IQR 0.49-0.63). In both groups, most of the within-day and between-day ICCs for e-nose sensors were statistically significant. Moreover, the within-day and between-day ICCs for all spirometry parameters and eNO were significant and similar to those of the most reliable sensors. The modern breath sampling system showed more than acceptable within-day and between-day repeatability, in both asthmatic and healthy children. The present study was registered on the central registration system ClinicalTrials.gov (ID: NCT03025061).
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Affiliation(s)
- Salvatore Fasola
- National Research Council of Italy, Institute of Biomedicine and Molecular Immunology, Palermo, Italy
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Wojnowski W, Dymerski T, Gębicki J, Namieśnik J. Electronic Noses in Medical Diagnostics. Curr Med Chem 2019; 26:197-215. [PMID: 28982314 DOI: 10.2174/0929867324666171004164636] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Revised: 05/24/2016] [Accepted: 09/05/2016] [Indexed: 01/13/2023]
Abstract
BACKGROUND Electronic nose technology is being developed in order to analyse complex mixtures of volatiles in a way parallel to biologic olfaction. When applied in the field of medicine, the use of such devices should enable the identification and discrimination between different diseases. In this review, a comprehensive summary of research in medical diagnostics using electronic noses is presented. A special attention has been paid to the application of these devices and sensor technologies, in response to current trends in medicine. METHODS Peer-reviewed research literature pertaining to the subject matter was identified based on a search of bibliographic databases. The quality and relevance of retrieved papers was assessed using standard tools. Their content was critically reviewed and certain information contained therein was compiled in tabularized form. RESULTS The majority of reviewed studies show promising results, often surpassing the accuracy and sensitivity of established diagnostic methods. However, only a relatively small number of devices have been field tested. The methods used for sample collection and data processing in various studies were listed in a table, together with electronic nose models used in these investigations. CONCLUSION Despite the fact that devices equipped with arrays of chemical sensors are not routinely used in everyday medical practice, their prospective use would solve some established issues in medical diagnostics, as well as lead to developments in prophylactics by facilitating a widespread use of non-invasive screening tests.
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Affiliation(s)
- Wojciech Wojnowski
- Department of Analytical Chemistry, Chemical Faculty, Gdansk University of Technology, Gdansk, Poland
| | - Tomasz Dymerski
- Department of Analytical Chemistry, Chemical Faculty, Gdansk University of Technology, Gdansk, Poland
| | - Jacek Gębicki
- Department of Chemical and Process Engineering, Chemical Faculty, Gdansk University of Technology, Gdansk, Poland
| | - Jacek Namieśnik
- Department of Analytical Chemistry, Chemical Faculty, Gdansk University of Technology, Gdansk, Poland
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Kielmann M, Senge MO. Molecular Engineering of Free-Base Porphyrins as Ligands-The N-H⋅⋅⋅X Binding Motif in Tetrapyrroles. Angew Chem Int Ed Engl 2019; 58:418-441. [PMID: 30067890 PMCID: PMC6391963 DOI: 10.1002/anie.201806281] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Indexed: 12/15/2022]
Abstract
The core N-H units of planar porphyrins are often inaccessible to forming hydrogen-bonding complexes with acceptor molecules. This is due to the fact that the amine moieties are "shielded" by the macrocyclic system, impeding the formation of intermolecular H-bonds. However, methods exist to modulate the tetrapyrrole conformations and to reshape the vector of N-H orientation outwards, thus increasing their availability and reactivity. Strategies include the use of porpho(di)methenes and phlorins (calixphyrins), as well as saddle-distorted porphyrins. The former form cavities due to interruption of the aromatic system. The latter are highly basic systems and capable of binding anions and neutral molecules via N-H⋅⋅⋅X-type H-bonds. This Review discusses the role of porphyrin(oid) ligands in various coordination-type complexes, means to access the core for hydrogen bonding, the concept of conformational control, and emerging applications, such as organocatalysis and sensors.
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Affiliation(s)
- Marc Kielmann
- School of ChemistrySFI Tetrapyrrole LaboratoryTrinity Biomedical Sciences InstituteTrinity College DublinThe University of Dublin152–160 Pearse StreetDublin 2Ireland
| | - Mathias O. Senge
- School of ChemistrySFI Tetrapyrrole LaboratoryTrinity Biomedical Sciences InstituteTrinity College DublinThe University of Dublin152–160 Pearse StreetDublin 2Ireland
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31
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Hu W, Wan L, Jian Y, Ren C, Jin K, Su X, Bai X, Haick H, Yao M, Wu W. Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing. ADVANCED MATERIALS TECHNOLOGIES 2018:1800488. [DOI: 10.1002/admt.201800488] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Affiliation(s)
- Wenwen Hu
- School of Aerospace Science and TechnologyXidian University Shaanxi 710126 P. R. China
| | - Liangtian Wan
- The Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceSchool of SoftwareDalian University of Technology Dalian 116620 China
| | - Yingying Jian
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Cong Ren
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Ke Jin
- School of Aerospace Science and TechnologyXidian University Shaanxi 710126 P. R. China
| | - Xinghua Su
- School of Materials Science and EngineeringChang'an University Xi'an 710061 China
| | - Xiaoxia Bai
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Hossam Haick
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of Technology Haifa 3200003 Israel
| | - Mingshui Yao
- Fujian Institute of Research on the Structure of MatterChinese Academy of Sciences Fuzhou 350002 P. R. China
| | - Weiwei Wu
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
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32
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Tirzïte M, Bukovskis M, Strazda G, Jurka N, Taivans I. Detection of lung cancer with electronic nose and logistic regression analysis. J Breath Res 2018; 13:016006. [PMID: 30221629 DOI: 10.1088/1752-7163/aae1b8] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Lung cancer is a very common malignancy with a low five-year survival rate. Artificial olfactory sensor (electronic nose) is a tool that recently has been studied as a probable optimal screening tool for early detection of lung cancer, but still no statistical method has been put forward as the preferable one. The aim of the study was to explore the use of logistic regression analysis (LRA) to analyse patients' exhaled breath samples with electronic nose in order to differentiate lung cancer patients (regardless of the stage of the cancer) from patients with other lung diseases and healthy individuals. Patients with histologically or cytologically verified, untreated lung cancer, patients with other lung diseases such as benign lung tumors, chronic obstructive pulmonary disease, asthma, pneumonia, etc, and healthy volunteers were enrolled in the study, in total 252 cancer patients and 223 patients without cancer. Breath sample collection and analysis were performed with Cyranose 320 sensor device and data further analysed using LRA. The LRA correctly differentiated lung cancer patients from no-cancer patients. The overall sensitivity in detecting patients having cancer was 95.8% for smokers and 96.2% for non-smokers and the overall specificity was 90.6% for non-smokers and 92.3% for smokers. Exhaled breath analysis by electronic nose using LRA is able to discriminate lung cancer patients from patients with other lung diseases and from healthy individuals.
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Affiliation(s)
- Madara Tirzïte
- Faculty of Medicine, University of Latvia, Raina Blv. 19, Riga, Latvia
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33
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Kielmann M, Senge MO. Molekulares Engineering freier Porphyrinbasen als Liganden - das N-H⋅⋅⋅X-Bindungsmotiv in Tetrapyrrolen. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201806281] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Marc Kielmann
- School of Chemistry; SFI Tetrapyrrole Laboratory; Trinity Biomedical Sciences Institute; Trinity College Dublin; The University of Dublin; 152-160 Pearse Street Dublin 2 Irland
| | - Mathias O. Senge
- School of Chemistry; SFI Tetrapyrrole Laboratory; Trinity Biomedical Sciences Institute; Trinity College Dublin; The University of Dublin; 152-160 Pearse Street Dublin 2 Irland
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34
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Ahmed WM, Brinkman P, Weda H, Knobel HH, Xu Y, Nijsen TM, Goodacre R, Rattray N, Vink TJ, Santonico M, Pennazza G, Montuschi P, Sterk PJ, Fowler SJ. Methodological considerations for large-scale breath analysis studies: lessons from the U-BIOPRED severe asthma project. J Breath Res 2018; 13:016001. [PMID: 30272570 DOI: 10.1088/1752-7163/aae557] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Methods for breath sampling and analysis require robust quality assessment to minimise the risk of false discoveries. Planning large-scale multi-site breath metabolite profiling studies also requires careful consideration of systematic and random variation as a result of sampling and analysis techniques. In this study we use breath sample data from the recent U-BIOPRED cohort to evaluate and discuss some important methodological considerations such as batch variation and correction, variation between sites, storage and transportation, as well as inter-instrument analytical differences. Based on this we provide a summary of recommended best practices for new large scale multi-site studies.
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Affiliation(s)
- Waqar M Ahmed
- School of Biological Sciences, University of Manchester, United Kingdom
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35
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Ganeev AA, Gubal AR, Lukyanov GN, Arseniev AI, Barchuk AA, Jahatspanian IE, Gorbunov IS, Rassadina AA, Nemets VM, Nefedov AO, Korotetsky BA, Solovyev ND, Iakovleva E, Ivanenko NB, Kononov AS, Sillanpaa M, Seeger T. Analysis of exhaled air for early-stage diagnosis of lung cancer: opportunities and challenges. RUSSIAN CHEMICAL REVIEWS 2018. [DOI: 10.1070/rcr4831] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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36
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Virtanen J, Hokkinen L, Karjalainen M, Kontunen A, Vuento R, Numminen J, Rautiainen M, Oksala N, Roine A, Kivekäs I. In vitro detection of common rhinosinusitis bacteria by the eNose utilising differential mobility spectrometry. Eur Arch Otorhinolaryngol 2018; 275:2273-2279. [PMID: 30043078 DOI: 10.1007/s00405-018-5055-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 07/02/2018] [Indexed: 12/12/2022]
Abstract
Acute rhinosinusitis (ARS) is a sudden, symptomatic inflammation of the nasal and paranasal mucosa. It is usually caused by respiratory virus infection, but bacteria complicate for a small number of ARS patients. The differential diagnostics between viral and bacterial pathogens is difficult and currently no rapid methodology exists, so antibiotics are overprescribed. The electronic nose (eNose) has shown the ability to detect diseases from gas mixtures. Differential mobility spectrometry (DMS) is a next-generation device that can separate ions based on their different mobility in high and low electric fields. Five common rhinosinusitis bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, and Pseudomonas aeruginosa) were analysed in vitro with DMS. Classification was done using linear discriminant analysis (LDA) and k-nearest neighbour (KNN). The results were validated using leave-one-out cross-validation and separate train and test sets. With the latter, 77% of the bacteria were classified correctly with LDA. The comparative figure with KNN was 79%. In one train-test set, P. aeruginosa was excluded and the four most common ARS bacteria were analysed with LDA and KNN; the correct classification rate was 83 and 85%, respectively. DMS has shown its potential in detecting rhinosinusitis bacteria in vitro. The applicability of DMS needs to be studied with rhinosinusitis patients.
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Affiliation(s)
- Jussi Virtanen
- Department of Otorhinolaryngology, Faculty of Medicine and Life Sciences, University of Tampere and Tampere University Hospital, PL 2000, 33521, Tampere, Finland.
| | - Lauri Hokkinen
- Department of Otorhinolaryngology, Faculty of Medicine and Life Sciences, University of Tampere and Tampere University Hospital, PL 2000, 33521, Tampere, Finland.,Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Markus Karjalainen
- BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
| | - Anton Kontunen
- BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
| | - Risto Vuento
- Department of Microbiology, Fimlab Laboratories Ltd, Tampere, Finland
| | - Jura Numminen
- Department of Otorhinolaryngology, Faculty of Medicine and Life Sciences, University of Tampere and Tampere University Hospital, PL 2000, 33521, Tampere, Finland
| | - Markus Rautiainen
- Department of Otorhinolaryngology, Faculty of Medicine and Life Sciences, University of Tampere and Tampere University Hospital, PL 2000, 33521, Tampere, Finland
| | - Niku Oksala
- Department of Surgery, Faculty of Medicine and Life Sciences, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - Antti Roine
- Department of Surgery, Hatanpää Hospital and University of Tampere, Tampere, Finland
| | - Ilkka Kivekäs
- Department of Otorhinolaryngology, Faculty of Medicine and Life Sciences, University of Tampere and Tampere University Hospital, PL 2000, 33521, Tampere, Finland
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Dutta D, Chong NS, Lim SH. Endogenous volatile organic compounds in acute myeloid leukemia: origins and potential clinical applications. J Breath Res 2018; 12:034002. [PMID: 29463782 DOI: 10.1088/1752-7163/aab108] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Not unlike many cancer types, acute myeloid leukemia (AML) exhibits many metabolic changes and reprogramming, causing changes in lipid metabolism. Some of the distinct molecular abnormalities associated with AML also modify the metabolic changes. Both processes result in changes in the production of endogenous volatile organic compounds (VOCs). The increasing availability of highly sensitive methods for detecting trace chemicals provides the opportunity to investigate the role of patient-specific VOC finger-prints as biomarkers for detecting early relapse or minimal residual disease in AML. Since VOC production is reliant on metabolic activities, when combined with currently available methods, VOC analysis may identify within a group of patients with flow cytometric or molecular evidence of residual disease those most at risk for disease relapse.
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Affiliation(s)
- Dibyendu Dutta
- Department of Professional Sciences, Middle Tennessee State University, Murfreesboro, Tennessee, United States of America
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van Oort PM, Povoa P, Schnabel R, Dark P, Artigas A, Bergmans DCJJ, Felton T, Coelho L, Schultz MJ, Fowler SJ, Bos LD. The potential role of exhaled breath analysis in the diagnostic process of pneumonia-a systematic review. J Breath Res 2018; 12:024001. [PMID: 29292698 DOI: 10.1088/1752-7163/aaa499] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Diagnostic strategies currently used for pneumonia are time-consuming, lack accuracy and suffer from large inter-observer variability. Exhaled breath contains thousands of volatile organic compounds (VOCs), which include products of host and pathogen metabolism. In this systematic review we investigated the use of so-called 'breathomics' for diagnosing pneumonia. A Medline search yielded 18 manuscripts reporting on animal and human studies using organic and inorganic molecules in exhaled breath, that all could be used to answer whether analysis of VOC profiles could potentially improve the diagnostic process of pneumonia. Papers were categorised based on their specific aims; the exclusion of pneumonia; the detection of specific respiratory pathogens; and whether targeted or untargeted VOC analysis was used. Ten studies reported on the association between VOCs and presence of pneumonia. Eight studies demonstrated a difference in exhaled VOCs between pneumonia and controls; in the individual studies this discrimination was based on unique sets of VOCs. Eight studies reported on the accuracy of a breath test for a specific respiratory pathogen: five of these concerned pre-clinical studies in animals. All studies were valued as having a high risk of bias, except for one study that used an external validation cohort. The findings in the identified studies are promising. However, as yet no breath test has been shown to have sufficient diagnostic accuracy for pneumonia. We are in need of studies that further translate the knowledge from discovery studies to clinical practice.
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Affiliation(s)
- Pouline M van Oort
- Department of Intensive Care, Academic Medical Centre, Amsterdam, The Netherlands
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Abstract
INTRODUCTION Human breath can contain thousands of volatile organic compounds (VOCs) and semi-volatile compounds that are related to metabolism and other biochemical processes. The presence of cancer cells can affect the identity and abundances of chemicals in breath when compared to those in healthy control subjects, which can be used to indicate the likelihood of a patient having cancer. Recently, the chemical analysis of exhaled breath from patients has been shown to be promising for diagnosing many different types of cancers, including lung, breast, colon, head, neck, and prostate, along with pre-cancerous conditions (dysplasia). AREAS COVERED Here, we reviewed the sampling, analytical and data analysis methods reported in the recent patent literature related to cancer breath testing (2014-2017). In addition, the different types of cancer biomarkers that were disclosed are discussed. EXPERT OPINION The major advantages of breath testing compared to conventional X-ray and imaging based methods includes simplicity of use, non-invasiveness, and the potential to detect cancer at a relatively early stage. Such methods are also suitable to perform population screening because of their non-invasiveness. However, the establishment of standard sampling, detection and quantification methods for breath testing is required before the methods can be employed for clinical diagnosis.
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Affiliation(s)
- K M Mohibul Kabir
- a School of Chemistry , University of New South Wales, NSW , Sydney , Australia
| | - William A Donald
- a School of Chemistry , University of New South Wales, NSW , Sydney , Australia
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Henderson B, Khodabakhsh A, Metsälä M, Ventrillard I, Schmidt FM, Romanini D, Ritchie GAD, te Lintel Hekkert S, Briot R, Risby T, Marczin N, Harren FJM, Cristescu SM. Laser spectroscopy for breath analysis: towards clinical implementation. APPLIED PHYSICS. B, LASERS AND OPTICS 2018; 124:161. [PMID: 30956412 PMCID: PMC6428385 DOI: 10.1007/s00340-018-7030-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 07/19/2018] [Indexed: 05/08/2023]
Abstract
Detection and analysis of volatile compounds in exhaled breath represents an attractive tool for monitoring the metabolic status of a patient and disease diagnosis, since it is non-invasive and fast. Numerous studies have already demonstrated the benefit of breath analysis in clinical settings/applications and encouraged multidisciplinary research to reveal new insights regarding the origins, pathways, and pathophysiological roles of breath components. Many breath analysis methods are currently available to help explore these directions, ranging from mass spectrometry to laser-based spectroscopy and sensor arrays. This review presents an update of the current status of optical methods, using near and mid-infrared sources, for clinical breath gas analysis over the last decade and describes recent technological developments and their applications. The review includes: tunable diode laser absorption spectroscopy, cavity ring-down spectroscopy, integrated cavity output spectroscopy, cavity-enhanced absorption spectroscopy, photoacoustic spectroscopy, quartz-enhanced photoacoustic spectroscopy, and optical frequency comb spectroscopy. A SWOT analysis (strengths, weaknesses, opportunities, and threats) is presented that describes the laser-based techniques within the clinical framework of breath research and their appealing features for clinical use.
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Affiliation(s)
- Ben Henderson
- Trace Gas Research Group, Molecular and Laser Physics, IMM, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - Amir Khodabakhsh
- Trace Gas Research Group, Molecular and Laser Physics, IMM, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - Markus Metsälä
- Department of Chemistry, University of Helsinki, PO Box 55, 00014 Helsinki, Finland
| | | | - Florian M. Schmidt
- Department of Applied Physics and Electronics, Umeå University, 90187 Umeå, Sweden
| | - Daniele Romanini
- University of Grenoble Alpes, CNRS, LIPhy, 38000 Grenoble, France
| | - Grant A. D. Ritchie
- Department of Chemistry, Physical and Theoretical Chemistry Laboratory, University of Oxford, South Parks Road, Oxford, OX1 3QZ UK
| | | | - Raphaël Briot
- University of Grenoble Alpes, CNRS, TIMC-IMAG, 38000 Grenoble, France
- Emergency Department and Mobile Intensive Care Unit, Grenoble University Hospital, Grenoble, France
| | - Terence Risby
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, USA
| | - Nandor Marczin
- Section of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
- Centre of Anaesthesia and Intensive Care, Semmelweis University, Budapest, Hungary
| | - Frans J. M. Harren
- Trace Gas Research Group, Molecular and Laser Physics, IMM, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - Simona M. Cristescu
- Trace Gas Research Group, Molecular and Laser Physics, IMM, Radboud University, 6525 AJ Nijmegen, The Netherlands
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Kielmann M, Prior C, Senge MO. Porphyrins in troubled times: a spotlight on porphyrins and their metal complexes for explosives testing and CBRN defense. NEW J CHEM 2018. [DOI: 10.1039/c7nj04679k] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A critical perspective on (metallo)porphyrins in security-related applications: the past, present and future of explosives detection, CBRN defense, and beyond.
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Affiliation(s)
- Marc Kielmann
- School of Chemistry
- SFI Tetrapyrrole Laboratory
- Trinity Biomedical Sciences Institute
- Trinity College Dublin
- The University of Dublin
| | - Caroline Prior
- School of Chemistry
- SFI Tetrapyrrole Laboratory
- Trinity Biomedical Sciences Institute
- Trinity College Dublin
- The University of Dublin
| | - Mathias O. Senge
- Medicinal Chemistry
- Trinity Translational Medicine Institute
- Trinity Centre for Health Sciences
- Trinity College Dublin
- The University of Dublin
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Neerincx AH, Vijverberg SJH, Bos LDJ, Brinkman P, van der Schee MP, de Vries R, Sterk PJ, Maitland-van der Zee AH. Breathomics from exhaled volatile organic compounds in pediatric asthma. Pediatr Pulmonol 2017; 52:1616-1627. [PMID: 29082668 DOI: 10.1002/ppul.23785] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 07/21/2017] [Indexed: 12/19/2022]
Abstract
Asthma is the most common chronic disease in children, and is characterized by airway inflammation, bronchial hyperresponsiveness, and airflow obstruction. Asthma diagnosis, phenotyping, and monitoring are still challenging with currently available methods, such as spirometry, FE NO or sputum analysis. The analysis of volatile organic compounds (VOCs) in exhaled breath could be an interesting non-invasive approach, but has not yet reached clinical practice. This review describes the current status of breath analysis in the diagnosis and monitoring of pediatric asthma. Furthermore, features of an ideal breath test, different breath analysis techniques, and important methodological issues are discussed. Although only a (small) number of studies have been performed in pediatric asthma, of which the majority is focusing on asthma diagnosis, these studies show moderate to good prediction accuracy (80-100%, with models including 6-28 VOCs), thereby qualifying breathomics for future application. However, standardization of procedures, longitudinal studies, as well as external validation are needed in order to further develop breathomics into clinical tools. Such a non-invasive tool may be the next step toward stratified and personalized medicine in pediatric respiratory disease.
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Affiliation(s)
- Anne H Neerincx
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, The Netherlands
| | - Susanne J H Vijverberg
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, The Netherlands
| | - Lieuwe D J Bos
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, The Netherlands.,Department of Intensive Care Medicine, Academic Medical Centre, University of Amsterdam, The Netherlands
| | - Paul Brinkman
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, The Netherlands
| | - Marc P van der Schee
- Department of Paediatric Respiratory Medicine, Academic Medical Centre, University of Amsterdam, The Netherlands
| | - Rianne de Vries
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, The Netherlands
| | - Peter J Sterk
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, The Netherlands
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43
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Yang HY, Peng HY, Chang CJ, Chen PC. Diagnostic accuracy of breath tests for pneumoconiosis using an electronic nose. J Breath Res 2017; 12:016001. [PMID: 28795953 DOI: 10.1088/1752-7163/aa857d] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Breath analyses have attracted substantial attention as screens for occupational environmental lung disease. The objective of this study was to develop breath tests for pneumoconiosis by analysing volatile organic compounds using an electronic nose. A case-control study was designed. We screened 102 subjects from a cohort of stone workers. After excluding three subjects with poorly controlled diabetes mellitus and one subject with asthma, 98 subjects were enrolled, including 34 subjects with pneumoconiosis and 64 healthy controls. We analysed the subjects' breath using an electronic nose with 32 nanocomposite sensors. Data were randomly split into 80% for model building and 20% for validation. Using a linear discriminate analysis, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were 67.9%, 88.0%, 80.8%, and 0.91, respectively, in the training set and 66.7%, 71.4%, 70.0%, and 0.86, respectively, in the test set. In subgroup analysis divided by smoking status, the AUROCs for current smokers, former smokers, and subjects who never smoked were 0.94, 0.93, and 0.99, respectively. In subgroup analysis divided by gender, the AUROCs for males and females were 0.95 and 0.99, respectively. Breath tests may have potential as a screen for pneumoconiosis. A multi-centre study is warranted, and the procedures must be standardized before clinical application.
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Affiliation(s)
- Hsiao-Yu Yang
- Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, Taipei, Taiwan. Department of Public Health, National Taiwan University College of Public Health, Taipei, Taiwan. Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei, Taiwan
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44
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Fijten RRR, Smolinska A, Drent M, Dallinga JW, Mostard R, Pachen DM, van Schooten FJ, Boots AW. The necessity of external validation in exhaled breath research: a case study of sarcoidosis. J Breath Res 2017; 12:016004. [DOI: 10.1088/1752-7163/aa8409] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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45
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Lamote K, Brinkman P, Vandermeersch L, Vynck M, Sterk PJ, Van Langenhove H, Thas O, Van Cleemput J, Nackaerts K, van Meerbeeck JP. Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study. Oncotarget 2017; 8:91593-91602. [PMID: 29207669 PMCID: PMC5710949 DOI: 10.18632/oncotarget.21335] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 07/16/2017] [Indexed: 01/02/2023] Open
Abstract
RATIONALE Malignant pleural mesothelioma (MPM) is mainly caused by previous exposure to asbestos fibers and has a poor prognosis. Due to a long latency period between exposure and diagnosis, MPM incidence is expected to peak between 2020-2025. Screening of asbestos-exposed individuals is believed to improve early detection and hence, MPM management. Recent developments focus on breath analysis for screening since breath contains volatile organic compounds (VOCs) which reflect the cell's metabolism. OBJECTIVES The goal of this cross-sectional, case-control study is to identify VOCs in exhaled breath of MPM patients with gas chromatography-mass spectrometry (GC-MS) and to assess breath analysis to screen for MPM using an electronic nose (eNose). METHODS Breath and background samples were taken from 64 subjects: 16 healthy controls (HC), 19 asymptomatic former asbestos-exposed (AEx) individuals, 15 patients with benign asbestos-related diseases (ARD) and 14 MPM patients. Samples were analyzed with both GC-MS and eNose. RESULTS Using GC-MS, AEx individuals were discriminated from MPM patients with 97% accuracy, with diethyl ether, limonene, nonanal, methylcyclopentane and cyclohexane as important VOCs. This was validated by eNose analysis. MPM patients were discriminated from AEx+ARD participants by GC-MS and eNose with 94% and 74% accuracy, respectively. The sensitivity, specificity, positive and negative predictive values were 100%, 91%, 82%, 100% for GC-MS and 82%, 55%, 82%, 55% for eNose, respectively. CONCLUSION This study shows accurate discrimination of patients with MPM from asymptomatic asbestos-exposed persons at risk by GC-MS and eNose analysis of exhaled VOCs and provides proof-of-principle of breath analysis for MPM screening.
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Affiliation(s)
- Kevin Lamote
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
- Department of Internal Medicine, Ghent University, Ghent, Belgium
| | - Paul Brinkman
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Lore Vandermeersch
- Department of Sustainable Organic Chemistry and Technology, EnVOC Research Group, Ghent University, Ghent, Belgium
| | - Matthijs Vynck
- Department of Mathematical Modelling, Statistics and Bio-Informatics, Ghent University, Ghent, Belgium
| | - Peter J. Sterk
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Herman Van Langenhove
- Department of Sustainable Organic Chemistry and Technology, EnVOC Research Group, Ghent University, Ghent, Belgium
| | - Olivier Thas
- Department of Mathematical Modelling, Statistics and Bio-Informatics, Ghent University, Ghent, Belgium
| | | | - Kristiaan Nackaerts
- Department of Respiratory Diseases, KU Leuven, University Hospitals Leuven, Leuven, Belgium
| | - Jan P. van Meerbeeck
- Department of Internal Medicine, Ghent University, Ghent, Belgium
- Thoracic Oncology/MOCA, Antwerp University Hospital, Edegem, Belgium
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46
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Scarlata S, Pennazza G, Santonico M, Santangelo S, Rossi Bartoli I, Rivera C, Vernile C, De Vincentis A, Antonelli Incalzi R. Screening of Obstructive Sleep Apnea Syndrome by Electronic-Nose Analysis of Volatile Organic Compounds. Sci Rep 2017; 7:11938. [PMID: 28931931 PMCID: PMC5607284 DOI: 10.1038/s41598-017-12108-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 08/30/2017] [Indexed: 01/14/2023] Open
Abstract
Obstructive Sleep Apnea Syndrome (OSAS) carries important social and economic implications. Once the suspicion of OSAS has arisen, Polysomnography (PSG) represents the diagnostic gold standard. However, about 45% of people who have undergone PSG are free from OSAS. Thus, efforts should be made to improve the selection of subjects. We verified whether the pattern of Volatile Organic Compounds (VOCs) helps to select patients amenable to PSG. We studied 136 subjects (20 obese non-OSAS, 20 hypoxic OSAS, 20 non-hypoxic OSAS, and 20 non-hypoxic Chronic Obstructive Pulmonary Disease (COPD) vs 56 healthy controls) without any criteria of exclusion for comorbidity to deal with a real-life population. VOCs patterns were analyzed using electronic-nose (e-nose) technology. A Discriminant Analysis (Partial Least Square-Discriminant Analysis) was performed to predict respiratory functions and PSG parameters. E-nose distinguished controls (100% correct classification) from others and identified 60% of hypoxic, and 35% of non-hypoxic OSAS patients. Similarly, it identified 60% of COPD patients. One-by-one group comparison yielded optimal discrimination of OSAS vs controls and of COPD vs controls (100% correct classification). In conclusion, e-nose technology applied to breath-analysis can discriminate non-respiratory from respiratory diseased populations in real-life multimorbid populations and exclude OSAS. If confirmed, this evidence may become pivotal for screening purposes.
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Affiliation(s)
- Simone Scarlata
- Geriatrics, Department of Respiratory Pathophysiology, Campus Bio-Medico University and Teaching Hospital, Rome, Italy.
| | - Giorgio Pennazza
- Centre for Integrated Research - CIR, Department of Electronics for Sensor Systems, Campus Bio-Medico University, Rome, Italy
| | - Marco Santonico
- Centre for Integrated Research - CIR, Department of Electronics for Sensor Systems, Campus Bio-Medico University, Rome, Italy
| | - Simona Santangelo
- Geriatrics, Department of Respiratory Pathophysiology, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Isaura Rossi Bartoli
- Geriatrics, Department of Respiratory Pathophysiology, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Chiara Rivera
- Geriatrics, Department of Respiratory Pathophysiology, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Chiara Vernile
- Centre for Integrated Research - CIR, Department of Electronics for Sensor Systems, Campus Bio-Medico University, Rome, Italy
| | - Antonio De Vincentis
- Department of Hepatology, Chair of Internal Medicine, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
| | - Raffaele Antonelli Incalzi
- Geriatrics, Department of Respiratory Pathophysiology, Campus Bio-Medico University and Teaching Hospital, Rome, Italy.,Department of Hepatology, Chair of Internal Medicine, Campus Bio-Medico University and Teaching Hospital, Rome, Italy
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47
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Siegel AP, Daneshkhah A, Hardin DS, Shrestha S, Varahramyan K, Agarwal M. Analyzing breath samples of hypoglycemic events in type 1 diabetes patients: towards developing an alternative to diabetes alert dogs. J Breath Res 2017; 11:026007. [PMID: 28569238 DOI: 10.1088/1752-7163/aa6ac6] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Diabetes is a disease that involves dysregulation of metabolic processes. Patients with type 1 diabetes (T1D) require insulin injections and measured food intake to maintain clinical stability, manually tracking their results by measuring blood glucose levels. Low blood glucose levels, hypoglycemia, can be extremely dangerous and can result in seizures, coma, or even death. Canines trained as diabetes alert dogs (DADs) have demonstrated the ability to detect hypoglycemia from breath, which led us to hypothesize that hypoglycemia, a metabolic dysregulation leading to low blood glucose levels, could be identified through analyzing volatile organic compounds (VOCs) contained within breath. We hoped to replicate the canines' detection ability and success by analytically using gas chromatography/mass spectrometry of VOCs in 128 breath samples collected from 52 youths with T1D at two different diabetes camps. We used different tests for significance including Ranksum, Student's T-test, and difference between means, and found a subset of 56 traces of potential metabolites. Principle component and linear discriminant analysis (LDA) confirmed a hypoglycemic signature likely resides within this group. Supervised machine learning combined with LDA narrowed the list of likely components to seven. The technique of leave one out cross validation demonstrated the model thus developed has a sensitivity of 91% (95% confidence interval (CI) [57.1, 94.7]) and a specificity of 84% (95% CI [73.0, 92.7]) at identifying hypoglycemia. Confidence intervals were obtained by bootstrapping. These results demonstrate that it is possible to differentiate breath samples obtained during hypoglycemic events from all other breath samples by analytical means and could lead to developing a simple analytical monitoring device as an alternative to using DADs.
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Affiliation(s)
- Amanda P Siegel
- Integrated Nanosystems Development Institute, Indiana University-Purdue University Indianapolis, IN, United States of America. Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, IN, United States of America
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48
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Kort S, Brusse-Keizer M, Gerritsen JW, van der Palen J. Data analysis of electronic nose technology in lung cancer: generating prediction models by means of Aethena. J Breath Res 2017; 11:026006. [DOI: 10.1088/1752-7163/aa6b08] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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49
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Horváth I, Barnes PJ, Loukides S, Sterk PJ, Högman M, Olin AC, Amann A, Antus B, Baraldi E, Bikov A, Boots AW, Bos LD, Brinkman P, Bucca C, Carpagnano GE, Corradi M, Cristescu S, de Jongste JC, Dinh-Xuan AT, Dompeling E, Fens N, Fowler S, Hohlfeld JM, Holz O, Jöbsis Q, Van De Kant K, Knobel HH, Kostikas K, Lehtimäki L, Lundberg J, Montuschi P, Van Muylem A, Pennazza G, Reinhold P, Ricciardolo FLM, Rosias P, Santonico M, van der Schee MP, van Schooten FJ, Spanevello A, Tonia T, Vink TJ. A European Respiratory Society technical standard: exhaled biomarkers in lung disease. Eur Respir J 2017; 49:49/4/1600965. [PMID: 28446552 DOI: 10.1183/13993003.00965-2016] [Citation(s) in RCA: 369] [Impact Index Per Article: 52.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 01/09/2017] [Indexed: 12/19/2022]
Abstract
Breath tests cover the fraction of nitric oxide in expired gas (FeNO), volatile organic compounds (VOCs), variables in exhaled breath condensate (EBC) and other measurements. For EBC and for FeNO, official recommendations for standardised procedures are more than 10 years old and there is none for exhaled VOCs and particles. The aim of this document is to provide technical standards and recommendations for sample collection and analytic approaches and to highlight future research priorities in the field. For EBC and FeNO, new developments and advances in technology have been evaluated in the current document. This report is not intended to provide clinical guidance on disease diagnosis and management.Clinicians and researchers with expertise in exhaled biomarkers were invited to participate. Published studies regarding methodology of breath tests were selected, discussed and evaluated in a consensus-based manner by the Task Force members.Recommendations for standardisation of sampling, analysing and reporting of data and suggestions for research to cover gaps in the evidence have been created and summarised.Application of breath biomarker measurement in a standardised manner will provide comparable results, thereby facilitating the potential use of these biomarkers in clinical practice.
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Affiliation(s)
- Ildiko Horváth
- Dept of Pulmonology, National Korányi Institute of Pulmonology, Budapest, Hungary
| | - Peter J Barnes
- National Heart and Lung Institute, Imperial College London, Royal Brompton Hospital, London, UK
| | | | - Peter J Sterk
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Marieann Högman
- Centre for Research & Development, Uppsala University/Gävleborg County Council, Gävle, Sweden
| | - Anna-Carin Olin
- Occupational and Environmental Medicine, Sahlgrenska Academy and University Hospital, Goteborg, Sweden
| | - Anton Amann
- Innsbruck Medical University, Innsbruck, Austria
| | - Balazs Antus
- Dept of Pathophysiology, National Korányi Institute of Pulmonology, Budapest, Hungary
| | | | - Andras Bikov
- Dept of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Agnes W Boots
- Dept of Pharmacology and Toxicology, University of Maastricht, Maastricht, The Netherlands
| | - Lieuwe D Bos
- Intensive Care, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Paul Brinkman
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Caterina Bucca
- Biomedical Sciences and Human Oncology, Universita' di Torino, Turin, Italy
| | | | | | - Simona Cristescu
- Dept of Molecular and Laser Physics, Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - Johan C de Jongste
- Dept of Pediatrics/Respiratory Medicine, Erasmus MC-Sophia Childrens' Hospital, Rotterdam, The Netherlands
| | | | - Edward Dompeling
- Dept of Paediatrics/Family Medicine Research School CAPHRI, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Niki Fens
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Stephen Fowler
- Respiratory Research Group, University of Manchester Wythenshawe Hospital, Manchester, UK
| | - Jens M Hohlfeld
- Clinical Airway Research, Fraunhofer Institute of Toxicology and Experimental Medicine (ITEM), Hannover, Germany.,Medizinische Hochschule Hannover, Hannover, Germany
| | - Olaf Holz
- Clinical Airway Research, Fraunhofer Institute of Toxicology and Experimental Medicine (ITEM), Hannover, Germany
| | - Quirijn Jöbsis
- Department of Paediatric Respiratory Medicine, Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands
| | - Kim Van De Kant
- Dept of Paediatrics/Family Medicine Research School CAPHRI, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Hugo H Knobel
- Philips Research, High Tech Campus 11, Eindhoven, The Netherlands
| | | | | | - Jon Lundberg
- Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Paolo Montuschi
- Pharmacology, Catholic University of the Sacred Heart, Rome, Italy
| | - Alain Van Muylem
- Hopital Erasme Cliniques Universitaires de Bruxelles, Bruxelles, Belgium
| | - Giorgio Pennazza
- Faculty of Engineering, University Campus Bio-Medico, Rome, Italy
| | - Petra Reinhold
- Institute of Molecular Pathogenesis, Friedrich Loeffler Institut, Jena, Germany
| | - Fabio L M Ricciardolo
- Clinic of Respiratory Disease, Dept of Clinical and Biological Sciences, University of Torino, Torino, Italy
| | - Philippe Rosias
- Dept of Paediatrics/Family Medicine Research School CAPHRI, Maastricht University Medical Centre, Maastricht, The Netherlands.,Dept of Pediatrics, Maasland Hospital, Sittard, The Netherlands
| | - Marco Santonico
- Faculty of Engineering, University Campus Bio-Medico, Rome, Italy
| | - Marc P van der Schee
- Dept of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | | | | | - Thomy Tonia
- European Respiratory Society, Lausanne, Switzerland
| | - Teunis J Vink
- Philips Research, High Tech Campus 11, Eindhoven, The Netherlands
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50
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Leopold JH, Bos LDJ, Colombo C, Sterk PJ, Schultz MJ, Abu-Hanna A. Non-invasive breath monitoring with eNose does not improve glucose diagnostics in critically ill patients in comparison to continuous glucose monitoring in blood. J Breath Res 2017; 11:026002. [PMID: 28260695 DOI: 10.1088/1752-7163/aa6488] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Continuous glucose monitoring (CGM) can be beneficial in critically ill patients. Current CGM devices rely on subcutaneous or blood plasma glucose measurements and consequently there is an increased risk of infections and the possibility of loss of blood with each measurement. A potential method to continuously and non-invasively measure blood glucose levels is using exhaled breath. A correlation between blood glucose levels and volatile organic compounds (VOCs) in the exhaled breath was already reported. VOCs can be analyzed continuously using a so-called electronic nose (eNose). We hypothesize that continuous exhaled breath analysis using an eNose can be used to accurately predict blood glucose levels in intubated, mechanically ventilated ICU-patients. Mechanically ventilated patients whose blood glucose concentration was monitored with a CGM device were eligible. An eNose with four metal oxide sensors was used to continuously measure changes in exhaled breath. After pre-processing the data, several regression models were trained, consisting of: (1) only eNose sensor values; (2) only the 1st and 2nd principal components (PC) of eNose values; (3) eNose sensor values and last known blood glucose value as random effect; (4) 1st and 2nd PC of eNose sensor values and CGM value of one minute ago as fixed effect; (5) CGM value of one minute ago as fixed effect. Model performance was measured using the R 2 value, the akaike information criterion and the Clarke error grid. Twenty-three patients were included in the study and 1165 hours of measurements were collected. Performance was low in models 1, 2 and 3 with a mean R 2 of 0.07 [95%-CI: 0.00-0.28], 0.10 [95%-CI: 0.00-0.40] and 0.30 [0.02-0.79], respectively. Performance in models 4 and 5 was better with a mean R 2 of 0.77 [0.02-1.00]. Subsequently, eNose data in model 4 had no added value over using CGM only in model 5. Continuous exhaled breath analysis using this eNose cannot be used to accurately predict blood glucose levels in intubated, mechanically ventilated ICU-patients.
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Affiliation(s)
- Jan Hendrik Leopold
- Department of Intensive Care, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
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