|
1
|
Buma AIG, Muntinghe-Wagenaar MB, van der Noort V, de Vries R, Schuurbiers MMF, Sterk PJ, Schipper S, Meurs J, Cristescu SM, Hiltermann TJN, van den Heuvel MM. Lung cancer detection by electronic nose analysis of exhaled breath: a multi-center prospective external validation study. Ann Oncol 2025:S0923-7534(25)00125-5. [PMID: 40174676 DOI: 10.1016/j.annonc.2025.03.013] [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: 01/23/2025] [Revised: 03/12/2025] [Accepted: 03/24/2025] [Indexed: 04/04/2025] Open
Abstract
BACKGROUND Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in COPD patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population. PATIENTS AND METHODS This multi-center prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in The Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose model was assessed in various population subsets based on ROC-AUC, specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training and validation cohort were used. RESULTS Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with a ROC-AUC of 0.92 (95% CI: 0.85-0.99) in COPD patients (n=98/116; 84%) and 0.80 (95% CI: 0.75-0.85) in all participants (n=216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n=72/121; 60%) with a ROC-AUC of 0.83 (95% CI: 0.75-0.91), 94% sensitivity, 63% specificity, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centers, and clinical characteristics. CONCLUSION This multi-center prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic center, and clinical characteristics.
Collapse
Affiliation(s)
- A I G Buma
- Department of Respiratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - M Benthe Muntinghe-Wagenaar
- Department of Respiratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - V van der Noort
- Department of Biometrics, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - R de Vries
- Breathomix B.V., Leiden, The Netherlands
| | - M M F Schuurbiers
- Department of Respiratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - P J Sterk
- Emeritus, University of Amsterdam, Amsterdam, The Netherlands
| | - S Schipper
- Department of Respiratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Life Science Trace Detection Laboratory, Department of Analytical Chemistry & Chemometrics, Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - J Meurs
- Life Science Trace Detection Laboratory, Department of Analytical Chemistry & Chemometrics, Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - S M Cristescu
- Life Science Trace Detection Laboratory, Department of Analytical Chemistry & Chemometrics, Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - T J N Hiltermann
- Department of Respiratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - M M van den Heuvel
- Department of Respiratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
|
2
|
van der Aart TJ, Visser M, van Londen M, van de Wetering KMH, Ter Maaten JC, Bouma HR. The smell of sepsis: Electronic nose measurements improve early recognition of sepsis in the ED. Am J Emerg Med 2025; 88:126-133. [PMID: 39615435 DOI: 10.1016/j.ajem.2024.11.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2024] [Revised: 11/13/2024] [Accepted: 11/14/2024] [Indexed: 02/11/2025] Open
Abstract
OBJECTIVE Early recognition of sepsis is essential for timely initiation of adequate care. However, this is challenging as signs and symptoms may be absent or nonspecific. The cascade of events leading to organ failure in sepsis is characterized by immune-metabolic alterations. Volatile organic compounds (VOCs) are metabolic byproducts released in expired air. We hypothesize that measuring the VOC profile using electronic nose technology (eNose) could improve early recognition of sepsis. MATERIAL AND METHODS In this cohort study, bedside eNose measurements were collected prospectively from ED patients with suspected infections. Sepsis diagnosis was retrospectively defined based on Sepsis-3 criteria. eNose sensor data were used in a discriminant analysis to evaluate the predictive performance for early sepsis recognition. The dataset was randomly split into training (67 %) and validation (33 %) subsets. The derived discriminant function from the training subset was then applied to classify new observations in the validation subset. Model performance was evaluated using receiver operating characteristic (ROC) curves and predictive values. RESULTS We analyzed a total of 160 eNose measurements. The eNose measurements had an area under the ROC (AUROC) of 0.78 (95 % CI: 0.69-0.87) for diagnosing sepsis, with a sensitivity of 72 %, specificity of 73 %, and an overall accuracy of 73 %. The validation model showed an AUC of 0.83 (95 % CI: 0.71-0.94), sensitivity of 71 %, specificity of 83 %, and an accuracy of 80 %. CONCLUSION eNose measurements can identify sepsis among patients with a suspected infection at the ED. CLINICAL TRIAL REGISTRATION The study is embedded in the Acutelines data-biobank (www.acutelines.nl), registered in Clinicaltrials.gov (NCT04615065).
Collapse
Affiliation(s)
- T J van der Aart
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - M Visser
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - M van Londen
- Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - K M H van de Wetering
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - J C Ter Maaten
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Acute Care, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - H R Bouma
- Department of Acute Care, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
| |
Collapse
|
|
3
|
Capuano R, Ciotti M, Catini A, Bernardini S, Di Natale C. Clinical applications of volatilomic assays. Crit Rev Clin Lab Sci 2025; 62:45-64. [PMID: 39129534 DOI: 10.1080/10408363.2024.2387038] [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: 03/14/2024] [Revised: 04/23/2024] [Accepted: 07/29/2024] [Indexed: 08/13/2024]
Abstract
The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.
Collapse
Affiliation(s)
- Rosamaria Capuano
- Department of Electronic Engineering, University of Rome Tor Vergata, Roma, Italy
- Interdepartmental Center for Volatilomics, "A. D'Amico", University of Rome Tor Vergata, Rome, Italy
| | - Marco Ciotti
- Department of Laboratory Medicine, University Hospital Tor Vergata, Rome, Italy
| | - Alexandro Catini
- Department of Electronic Engineering, University of Rome Tor Vergata, Roma, Italy
- Interdepartmental Center for Volatilomics, "A. D'Amico", University of Rome Tor Vergata, Rome, Italy
| | - Sergio Bernardini
- Interdepartmental Center for Volatilomics, "A. D'Amico", University of Rome Tor Vergata, Rome, Italy
- Department of Laboratory Medicine, University Hospital Tor Vergata, Rome, Italy
- Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Corrado Di Natale
- Department of Electronic Engineering, University of Rome Tor Vergata, Roma, Italy
- Interdepartmental Center for Volatilomics, "A. D'Amico", University of Rome Tor Vergata, Rome, Italy
| |
Collapse
|
|
4
|
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.
Collapse
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
| |
Collapse
|
|
5
|
Seidl E, Licht JC, de Vries R, Ratjen F, Grasemann H. Exhaled Breath Analysis Detects the Clearance of Staphylococcus aureus from the Airways of Children with Cystic Fibrosis. Biomedicines 2024; 12:431. [PMID: 38398033 PMCID: PMC10887307 DOI: 10.3390/biomedicines12020431] [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: 01/12/2024] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Electronic nose (eNose) technology can be used to characterize volatile organic compound (VOC) mixes in breath. While previous reports have shown that eNose can detect lung infections with pathogens such as Staphylococcus aureus (SA) in people with cystic fibrosis (CF), the clinical utility of eNose for longitudinally monitoring SA infection status is unknown. METHODS In this longitudinal study, a cloud-connected eNose, the SpiroNose, was used for the breath profile analysis of children with CF at two stable visits and compared based on changes in SA infection status between visits. Data analysis involved advanced sensor signal processing, ambient correction, and statistics based on the comparison of breath profiles between baseline and follow-up visits. RESULTS Seventy-two children with CF, with a mean (IQR) age of 13.8 (9.8-16.4) years, were studied. In those with SA-positive airway cultures at baseline but SA-negative cultures at follow-up (n = 19), significant signal differences were detected between Baseline and Follow-up at three distinct eNose sensors, i.e., S4 (p = 0.047), S6 (p = 0.014), and S7 (p = 0.014). Sensor signal changes with the clearance of SA from airways were unrelated to antibiotic treatment. No changes in sensor signals were seen in patients with unchanged infection status between visits. CONCLUSIONS Our results demonstrate the potential applicability of the eNose as a non-invasive clinical tool to longitudinally monitor pulmonary SA infection status in children with CF.
Collapse
Affiliation(s)
- Elias Seidl
- Division of Respiratory Medicine, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; (E.S.); (J.-C.L.); (F.R.)
- Division of Respiratory Medicine, University Children’s Hospital Zurich, 8032 Zurich, Switzerland
| | - Johann-Christoph Licht
- Division of Respiratory Medicine, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; (E.S.); (J.-C.L.); (F.R.)
| | - Rianne de Vries
- Breathomix BV, Bargelaan 200, 2333 CW Leiden, The Netherlands;
| | - Felix Ratjen
- Division of Respiratory Medicine, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; (E.S.); (J.-C.L.); (F.R.)
- Translational Medicine Program, Research Institute, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada
| | - Hartmut Grasemann
- Division of Respiratory Medicine, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; (E.S.); (J.-C.L.); (F.R.)
- Translational Medicine Program, Research Institute, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada
| |
Collapse
|
|
6
|
Martin JDM, Claudia F, Romain AC. How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment. J Breath Res 2024; 18:026002. [PMID: 38211310 DOI: 10.1088/1752-7163/ad1d64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between 'healthy' samples (breath without additives) and 'cancer' samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.
Collapse
Affiliation(s)
- Justin D M Martin
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| | - Falzone Claudia
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| | - Anne-Claude Romain
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| |
Collapse
|
|
7
|
Brener S, Snitz K, Sobel N. An electronic nose can identify humans by the smell of their ear. Chem Senses 2024; 49:bjad053. [PMID: 38237638 PMCID: PMC10810274 DOI: 10.1093/chemse/bjad053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Indexed: 01/27/2024] Open
Abstract
Terrestrial mammals identify conspecifics by body odor. Dogs can also identify humans by body odor, and in some instances, humans can identify other humans by body odor as well. Despite the potential for a powerful biometric tool, smell has not been systematically used for this purpose. A question arising in the application of smell to biometrics is which bodily odor source should we measure. Breath is an obvious candidate, but the associated humidity can challenge many sensing devices. The armpit is also a candidate source, but it is often doused in cosmetics. Here, we test the hypothesis that the ear may provide an effective source for odor-based biometrics. The inside of the ear has relatively constant humidity, cosmetics are not typically applied inside the ear, and critically, ears contain cerumen, a potent source of volatiles. We used an electronic nose to identify 12 individuals within and across days, using samples from the armpit, lower back, and ear. In an identification setting where chance was 8.33% (1 of 12), we found that we could identify a person by the smell of their ear within a day at up to ~87% accuracy (~10 of 12, binomial P < 10-5), and across days at up to ~22% accuracy (~3 of 12, binomial P < 0.012). We conclude that humans can indeed be identified from the smell of their ear, but the results did not imply a consistent advantage over other bodily odor sources.
Collapse
Affiliation(s)
- Stephanie Brener
- The Azrieli National Center for Human Brain Imaging and Research, Weizmann Institute of Science, Rehovot 7610001, Israel
- The Department for Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Kobi Snitz
- The Azrieli National Center for Human Brain Imaging and Research, Weizmann Institute of Science, Rehovot 7610001, Israel
- The Department for Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Noam Sobel
- The Azrieli National Center for Human Brain Imaging and Research, Weizmann Institute of Science, Rehovot 7610001, Israel
- The Department for Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| |
Collapse
|
|
8
|
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: 5] [Impact Index Per Article: 2.5] [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.
Collapse
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.
| |
Collapse
|
|
9
|
Seidl E, Licht JC, Wee WB, Post M, Ratjen F, Grasemann H. Exhaled Volatile Organic Compound Profiles Differ between Children with Primary Ciliary Dyskinesia and Cystic Fibrosis. Ann Am Thorac Soc 2023; 20:1667-1672. [PMID: 37555716 DOI: 10.1513/annalsats.202302-165rl] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 08/08/2023] [Indexed: 08/10/2023] Open
Affiliation(s)
- Elias Seidl
- Division of Respiratory Medicine and Department of Pediatrics University of Toronto Toronto, Ontario, Canada
- Department of Pediatrics Ludwig Maximilian University of Munich Munich, Germany
| | - Johann-Christoph Licht
- Division of Respiratory Medicine and Department of Pediatrics University of Toronto Toronto, Ontario, Canada
- Translational Medicine Research Institute Toronto, Ontario, Canada
| | - Wallace B Wee
- Division of Respiratory Medicine and Department of Pediatrics University of Toronto Toronto, Ontario, Canada
| | - Martin Post
- Division of Respiratory Medicine and Department of Pediatrics University of Toronto Toronto, Ontario, Canada
| | - Felix Ratjen
- Division of Respiratory Medicine and Department of Pediatrics University of Toronto Toronto, Ontario, Canada
- Translational Medicine Research Institute Toronto, Ontario, Canada
| | - Hartmut Grasemann
- Division of Respiratory Medicine and Department of Pediatrics University of Toronto Toronto, Ontario, Canada
- Translational Medicine Research Institute Toronto, Ontario, Canada
| |
Collapse
|
|
10
|
Wijbenga N, de Jong NL, Hoek RA, Mathot BJ, Seghers L, Aerts JG, Bos D, Manintveld OC, Hellemons ME. Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose. Transplant Direct 2023; 9:e1533. [PMID: 37745948 PMCID: PMC10513211 DOI: 10.1097/txd.0000000000001533] [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: 04/12/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 09/26/2023] Open
Abstract
Background Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect BC in LTRs. Therefore, we aimed to assess the diagnostic accuracy of an eNose to detect BC in LTRs. Methods We performed a cross-sectional analysis within a prospective, single-center cohort study assessing the diagnostic accuracy of detecting BC using eNose technology in LTRs. In the outpatient clinic, consecutive LTR eNose measurements were collected. We assessed and classified the eNose measurements for the presence of BC. Using supervised machine learning, the diagnostic accuracy of eNose for BC was assessed in a random training and validation set. Model performance was evaluated using receiver operating characteristic analysis. Results In total, 161 LTRs were included with 80 exclusions because of various reasons. Of the remaining 81 patients, 16 (20%) were classified as BC and 65 (80%) as non-BC. eNose-based classification of patients with and without BC provided an area under the curve of 0.82 in the training set and 0.97 in the validation set. Conclusions Exhaled breath analysis using eNose technology has the potential to noninvasively detect BC.
Collapse
Affiliation(s)
- Nynke Wijbenga
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Nadine L.A. de Jong
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology and Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Rogier A.S. Hoek
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Bas J. Mathot
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Leonard Seghers
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Joachim G.J.V. Aerts
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Daniel Bos
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Olivier C. Manintveld
- Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Cardiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Merel E. Hellemons
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| |
Collapse
|
|
11
|
Wijbenga N, Muller MM, Hoek RAS, Mathot BJ, Seghers L, Aerts JGJV, de Winter BCM, Bos D, Manintveld OC, Hellemons ME. Diagnostic accuracy of eNose 'breathprints' for therapeutic drug monitoring of Tacrolimus trough levels in lung transplantation. J Breath Res 2023; 17:046010. [PMID: 37582348 DOI: 10.1088/1752-7163/acf066] [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: 08/30/2022] [Accepted: 08/15/2023] [Indexed: 08/17/2023]
Abstract
In order to prevent long-term immunity-related complications after lung transplantation, close monitoring of immunosuppressant levels using therapeutic drug monitoring (TDM) is paramount. Novel electronic nose (eNose) technology may be a non-invasive alternative to the current invasive procedures for TDM. We investigated the diagnostic and categorization capacity of eNose breathprints for Tacrolimus trough blood plasma levels (TACtrough) in lung transplant recipients (LTRs). We performed eNose measurements in stable LTR attending the outpatient clinic. We evaluated (1) the correlation between eNose measurements and TACtrough, (2) the diagnostic capacity of eNose technology for TACtrough, and (3) the accuracy of eNose technology for categorization of TACtroughinto three clinically relevant categories (low: <7µg ml-1, medium: 7-10µg ml-1, and high: >10µg ml-1). A total of 186 measurements from 86 LTR were included. There was a weak but statistically significant correlation (r= 0.21,p= 0.004) between the eNose measurements and TACtrough. The root mean squared error of prediction for the diagnostic capacity was 3.186 in the training and 3.131 in the validation set. The accuracy of categorization ranged between 45%-63% for the training set and 52%-69% in the validation set. There is a weak correlation between eNose breathprints and TACtroughin LTR. However, the diagnostic as well as categorization capacity for TACtroughusing eNose breathprints is too inaccurate to be applicable in TDM.
Collapse
Affiliation(s)
- Nynke Wijbenga
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marjolein M Muller
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Educational program Technical Medicine; Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Rotterdam, The Netherlands
- Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rogier A S Hoek
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Bas J Mathot
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Leonard Seghers
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Joachim G J V Aerts
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Brenda C M de Winter
- Department of Hospital Pharmacy, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Olivier C Manintveld
- Department of Cardiology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Merel E Hellemons
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
- Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
|
12
|
Westhoff M, Keßler M, Baumbach JI. Alveolar gradients in breath analysis. A pilot study with comparison of room air and inhaled air by simultaneous measurements using ion mobility spectrometry. J Breath Res 2023; 17:046009. [PMID: 37611565 DOI: 10.1088/1752-7163/acf338] [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: 05/26/2023] [Accepted: 08/23/2023] [Indexed: 08/25/2023]
Abstract
Analyzing exhaled breath samples, especially using a highly sensitive method such as MCC/IMS (multi-capillary column/ion mobility spectrometry), may also detect analytes that are derived from exogenous production. In this regard, there is a discussion about the optimal interpretation of exhaled breath, either by considering volatile organic compounds (VOCs) only in exhaled breath or by additionally considering the composition of room air and calculating the alveolar gradients. However, there are no data on whether the composition and concentration of VOCs in room air are identical to those in truly inhaled air directly before analyzing the exhaled breath. The current study aimed to determine whether the VOCs in room air, which are usually used for the calculation of alveolar gradients, are identical to the VOCs in truly inhaled air. For the measurement of inhaled air and room air, two IMS, each coupled with an MCC that provided a pre-separation of the VOCs, were used in parallel. One device was used for sampling room air and the other for sampling inhaled air. Each device was coupled with a newly invented system that cleaned room air and provided a clean carrier gas, whereas formerly synthetic air had to be used as a carrier gas. In this pilot study, a healthy volunteer underwent three subsequent runs of sampling of inhaled air and simultaneous sampling and analysis of room air. Three of the selected 11 peaks (P4-unknown, P5-1-Butanol, and P9-Furan, 2-methyl-) had significantly higher intensities during inspiration than in room air, and four peaks (P1-1-Propanamine, N-(phenylmethylene), P2-2-Nonanone, P3-Benzene, 1,2,4-trimethyl-, and P11-Acetyl valeryl) had higher intensities in room air. Furthermore, four peaks (P6-Benzaldehyde, P7-Pentane, 2-methyl-, P8-Acetone, and P10-2-Propanamine) showed inconsistent differences in peak intensities between inhaled air and room air. To the best of our knowledge, this is the first study to compare simultaneous sampling of room air and inhaled air using MCC/IMS. The simultaneous measurement of inhaled air and room air showed that using room air for the calculation of alveolar gradients in breath analysis resulted in different alveolar gradient values than those obtained by measuring truly inhaled air.
Collapse
Affiliation(s)
- M Westhoff
- Department of Pneumology, Sleep and Respiratory Medicine, Hemer Lung Clinic, Theo-Funccius-Str. 1, 58675 Hemer, Germany
- Witten/Herdecke University, Alfred-Herrhausen-Str. 50, 58448 Witten, Germany
| | - M Keßler
- University of Applied Sciences Münster, Hüfferstrasse 27, 48149 Münster, Germany
- B. Braun Melsungen AG, Branch Dortmund, Center of Competence Breath Analysis, Otto-Hahn-Str. 15, 44227 Dortmund, Germany
| | - J I Baumbach
- Technical University Dortmund, Faculty Bio- and Chemical Engineering, Emil-Figge-Str. 70, 44227 Dortmund, Germany
| |
Collapse
|
|
13
|
Licht JC, Seidl E, Slingers G, Waters V, de Vries R, Post M, Ratjen F, Grasemann H. Exhaled breath profiles to detect lung infection with Staphylococcus aureus in children with cystic fibrosis. J Cyst Fibros 2023; 22:888-893. [PMID: 36849333 DOI: 10.1016/j.jcf.2023.02.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/23/2022] [Accepted: 02/20/2023] [Indexed: 02/27/2023]
Abstract
BACKGROUND An electronic nose (eNose) can be used to detect volatile organic compounds (VOCs). Exhaled breath contains numerous VOCs and individuals' VOCs mixtures create distinct breath profiles. Previous reports have shown that eNose can detect lung infections. Whether eNose can detect Staphylococcus aureus airway infections in breath of children with cystic fibrosis (CF) is currently unclear. METHODS In this cross-sectional observational study, a cloud-connected eNose was used for breath profile analysis of clinically stable paediatric CF patients with airway microbiology cultures positive or negative for CF pathogens. Data-analysis involved advanced signal processing, ambient correction and statistics based on linear discriminant and receiver operating characteristics (ROC) analyses. RESULTS Breath profiles from 100 children with CF (median predicted FEV1 91%) were obtained and analysed. CF patients with positive airway cultures for any CF pathogen were distinguishable from no CF pathogens (no growth or usual respiratory flora) with accuracy of 79.0% (AUC-ROC 0.791; 95% CI: 0.669-0.913) and between patients positive for Staphylococcus aureus (SA) only and no CF pathogen with accuracy of 74.0% (AUC-ROC 0.797; 95% CI: 0.698-0.896). Similar differences were seen for Pseudomonas aeruginosa (PA) infection vs no CF pathogens (78.0% accuracy, AUC-ROC 0.876, 95% CI: 0.794-0.958). SA- and PA-specific signatures were driven by different sensors in the SpiroNose suggesting pathogen-specific breath signatures. CONCLUSIONS Breath profiles of CF patients with SA in airway cultures are distinct from those with no infection or PA infection, suggesting the utility of eNose technology in the detection of this early CF pathogen in children with CF.
Collapse
Affiliation(s)
- Johann-Christoph Licht
- Division of Respiratory Medicine, Department of Pediatrics, Hospital for Sick Children, Toronto, ON M5G 1 X 8, Canada and University of Toronto; Translational Medicine, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1 × 8, Canada
| | - Elias Seidl
- Division of Respiratory Medicine, Department of Pediatrics, Hospital for Sick Children, Toronto, ON M5G 1 X 8, Canada and University of Toronto
| | - Gitte Slingers
- Breathomix BV, Bargelaan 200, 2333 CW Leiden, the Netherlands
| | - Valerie Waters
- Translational Medicine, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1 × 8, Canada; Division of Infectious Diseases, Department of Pediatrics, Hospital for Sick Children, Toronto, ON M5G 1 X 8, Canada and University of Toronto
| | - Rianne de Vries
- Breathomix BV, Bargelaan 200, 2333 CW Leiden, the Netherlands
| | - Martin Post
- Translational Medicine, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1 × 8, Canada
| | - Felix Ratjen
- Division of Respiratory Medicine, Department of Pediatrics, Hospital for Sick Children, Toronto, ON M5G 1 X 8, Canada and University of Toronto; Translational Medicine, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1 × 8, Canada
| | - Hartmut Grasemann
- Division of Respiratory Medicine, Department of Pediatrics, Hospital for Sick Children, Toronto, ON M5G 1 X 8, Canada and University of Toronto; Translational Medicine, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1 × 8, Canada.
| |
Collapse
|
|
14
|
Vanstraelen S, Jones DR, Rocco G. Breathprinting analysis and biomimetic sensor technology to detect lung cancer. J Thorac Cardiovasc Surg 2023; 166:357-361.e1. [PMID: 36997463 DOI: 10.1016/j.jtcvs.2023.02.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/15/2023] [Accepted: 02/28/2023] [Indexed: 03/11/2023]
Affiliation(s)
- Stijn Vanstraelen
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - David R Jones
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Fiona and Stanley Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gaetano Rocco
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Fiona and Stanley Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY.
| |
Collapse
|
|
15
|
Savito L, Scarlata S, Bikov A, Carratù P, Carpagnano GE, Dragonieri S. Exhaled volatile organic compounds for diagnosis and monitoring of asthma. World J Clin Cases 2023; 11:4996-5013. [PMID: 37583852 PMCID: PMC10424019 DOI: 10.12998/wjcc.v11.i21.4996] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/08/2023] [Accepted: 07/06/2023] [Indexed: 07/26/2023] Open
Abstract
The asthmatic inflammatory process results in the generation of volatile organic compounds (VOCs), which are subsequently secreted by the airways. The study of these elements through gas chromatography-mass spectrometry (GC-MS), which can identify individual molecules with a discriminatory capacity of over 85%, and electronic-Nose (e-NOSE), which is able to perform a quick onboard pattern-recognition analysis of VOCs, has allowed new prospects for non-invasive analysis of the disease in an "omics" approach. In this review, we aim to collect and compare the progress made in VOCs analysis using the two methods and their instrumental characteristics. Studies have described the potential of GC-MS and e-NOSE in a multitude of relevant aspects of the disease in both children and adults, as well as differential diagnosis between asthma and other conditions such as wheezing, cystic fibrosis, COPD, allergic rhinitis and last but not least, the accuracy of these methods compared to other diagnostic tools such as lung function, FeNO and eosinophil count. Due to significant limitations of both methods, it is still necessary to improve and standardize techniques. Currently, e-NOSE appears to be the most promising aid in clinical practice, whereas GC-MS, as the gold standard for the structural analysis of molecules, remains an essential tool in terms of research for further studies on the pathophysiologic pathways of the asthmatic inflammatory process. In conclusion, the study of VOCs through GC-MS and e-NOSE appears to hold promise for the non-invasive diagnosis, assessment, and monitoring of asthma, as well as for further research studies on the disease.
Collapse
Affiliation(s)
- Luisa Savito
- Department of Internal Medicine, Unit of Respiratory Pathophysiology and Thoracic Endoscopy, Fondazione Policlinico Universitario Campus Bio Medico, Rome 00128, Italy
| | - Simone Scarlata
- Department of Internal Medicine, Unit of Respiratory Pathophysiology and Thoracic Endoscopy, Fondazione Policlinico Universitario Campus Bio Medico, Rome 00128, Italy
| | - Andras Bikov
- Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, United Kingdom
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, United Kingdom
| | - Pierluigi Carratù
- Department of Internal Medicine "A.Murri", University of Bari "Aldo Moro", Bari 70124, Italy
| | | | - Silvano Dragonieri
- Department of Respiratory Diseases, University of Bari, Bari 70124, Italy
| |
Collapse
|
|
16
|
Lagopati N, Valamvanos TF, Proutsou V, Karachalios K, Pippa N, Gatou MA, Vagena IA, Cela S, Pavlatou EA, Gazouli M, Efstathopoulos E. The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis. CHEMOSENSORS 2023; 11:317. [DOI: 10.3390/chemosensors11060317] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2023]
Abstract
Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.
Collapse
Affiliation(s)
- Nefeli Lagopati
- Laboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Theodoros-Filippos Valamvanos
- Laboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
- Medical Physics Unit, 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, 12462 Athens, Greece
| | - Vaia Proutsou
- Laboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
- Medical Physics Unit, 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, 12462 Athens, Greece
| | - Konstantinos Karachalios
- Laboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
- Medical Physics Unit, 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, 12462 Athens, Greece
| | - Natassa Pippa
- Section of Pharmaceutical Technology, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece
| | - Maria-Anna Gatou
- Laboratory of General Chemistry, School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 15772 Athens, Greece
| | - Ioanna-Aglaia Vagena
- Laboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Smaragda Cela
- Laboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
- Medical Physics Unit, 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, 12462 Athens, Greece
| | - Evangelia A. Pavlatou
- Laboratory of General Chemistry, School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 15772 Athens, Greece
| | - Maria Gazouli
- Laboratory of Biology, Department of Basic Medical Sciences, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
- School of Science and Technology, Hellenic Open University, 26335 Patra, Greece
| | - Efstathios Efstathopoulos
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- School of Science and Technology, Hellenic Open University, 26335 Patra, Greece
| |
Collapse
|
|
17
|
van Raaij BFM, Veltman JD, Hameete JF, Stöger JL, Geelhoed JJM. Diagnostic performance of eNose technology in COVID-19 patients after hospitalization. BMC Pulm Med 2023; 23:134. [PMID: 37081422 PMCID: PMC10117233 DOI: 10.1186/s12890-023-02407-6] [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: 01/31/2023] [Accepted: 03/31/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Volatile organic compounds (VOCs) produced by human cells reflect metabolic and pathophysiological processes which can be detected with the use of electronic nose (eNose) technology. Analysis of exhaled breath may potentially play an important role in diagnosing COVID-19 and stratification of patients based on pulmonary function or chest CT. METHODS Breath profiles of COVID-19 patients were collected with an eNose device (SpiroNose) 3 months after discharge from the Leiden University Medical Centre and matched with breath profiles from healthy individuals for analysis. Principal component analysis was performed with leave-one-out cross validation and visualised with receiver operating characteristics. COVID-19 patients were stratified in subgroups with a normal pulmonary diffusion capacity versus patients with an impaired pulmonary diffusion capacity (DLCOc < 80% of predicted) and in subgroups with a normal chest CT versus patients with COVID-19 related chest CT abnormalities. RESULTS The breath profiles of 135 COVID-19 patients were analysed and matched with 174 healthy controls. The SpiroNose differentiated between COVID-19 after hospitalization and healthy controls with an AUC of 0.893 (95-CI, 0.851-0.934). There was no difference in VOCs patterns in subgroups of COVID-19 patients based on diffusion capacity or chest CT. CONCLUSIONS COVID-19 patients have a breath profile distinguishable from healthy individuals shortly after hospitalization which can be detected using eNose technology. This may suggest ongoing inflammation or a common repair mechanism. The eNose could not differentiate between subgroups of COVID-19 patients based on pulmonary diffusion capacity or chest CT.
Collapse
Affiliation(s)
- B F M van Raaij
- Department of Internal Medicine, Section of Geriatrics and Gerontology, Leiden University Medical Centre, Albinusdreef 2, 2333ZA, Leiden, Netherlands.
| | - J D Veltman
- Department of Pulmonary Diseases, Amsterdam University Medical Centre, Amsterdam, Netherlands
| | - J F Hameete
- Department of Pulmonary Diseases, Leiden University Medical Centre, Leiden, Netherlands
| | - J L Stöger
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands
| | - J J M Geelhoed
- Department of Pulmonary Diseases, Leiden University Medical Centre, Leiden, Netherlands
| |
Collapse
|
|
18
|
Safety and tolerability of stereotactic radiotherapy combined with durvalumab with or without tremelimumab in advanced non-small cell lung cancer, the phase I SICI trial. Lung Cancer 2023; 178:96-102. [PMID: 36806899 DOI: 10.1016/j.lungcan.2023.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/23/2022] [Accepted: 02/04/2023] [Indexed: 02/10/2023]
Abstract
INTRODUCTION This phase I study primarily addresses the safety and tolerability of Stereotactic radiotherapy on the primary tumor combined with double Immune Checkpoint Inhibition (SICI) in patients with non-small cell lung cancer (NSCLC). Increasing the release of neoantigens by radiotherapy might enhance response to immunotherapy. Especially, by targeting trunk mutations in the primary tumor. MATERIALS AND METHODS In three sequential cohorts, immunotherapy regimes combined with stereotactic body radiotherapy (SBRT) on the primary tumor (1x20 Gy on 9 cc) were studied in stage IIIB/IV NSCLC patients progressing on chemotherapy. The first cohort (n = 3) received durvalumab. The second (n = 6) received a combination of tremelimumab and durvalumab followed by durvalumab monotherapy. The third cohort (n = 6) was similar except that the combination was reversed. Descriptive statistics were used to assess safety parameters and the exploratory outcomes of efficacy. Adverse events were reported using NCI CTCAE version 4.03. Exhaled breath was analyzed at baseline. RESULTS Fifteen patients were included. Median irradiated volume was 9.13 cc, on a median primary tumor volume of 79 cc. There were seven patients with grade 1-2, and two patients with grade 3 treatment related adverse events. There was 1 dose limiting toxicity (colitis) with double immunotherapy. CONCLUSION The combination of SBRT to the primary tumor and double immunotherapy in advanced NSCLC patients is safe and feasible.
Collapse
|
|
19
|
Wijbenga N, Hoek RAS, Mathot BJ, Seghers L, Moor CC, Aerts JGJV, Bos D, Manintveld OC, Hellemons ME. Diagnostic performance of electronic nose technology in chronic lung allograft dysfunction. J Heart Lung Transplant 2023; 42:236-245. [PMID: 36283951 DOI: 10.1016/j.healun.2022.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/22/2022] [Accepted: 09/12/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND There is a need for reliable biomarkers for the diagnosis of chronic lung allograft dysfunction (CLAD). In this light, we investigated the diagnostic value of exhaled breath analysis using an electronic nose (eNose) for CLAD, CLAD phenotype, and CLAD stage in lung transplant recipients (LTR). METHODS We performed eNose measurements in LTR with and without CLAD, visiting the outpatient clinic. Through supervised machine learning, the diagnostic value of eNose for CLAD was assessed in a random training and validation set. Next, we investigated the diagnostic value of the eNose measurements combined with known risk factors for CLAD. Model performance was evaluated using ROC-analysis. RESULTS We included 152 LTR (median age 60 years, 49% females), of whom 38 with CLAD. eNose-based classification of patients with and without CLAD provided an AUC of 0.86 in the training set, and 0.82 in the validation set. After adding established risk factors for CLAD (age, gender, type of transplantation, time after transplantation and prior occurrence of acute cellular rejection) to a model with the eNose data, the discriminative ability of the model improved to an AUC of 0.94 (p = 0.02) in the training set and 0.94 (p = 0.04) in the validation set. Discrimination between BOS and RAS was good (AUC 0.95). Discriminative ability for other phenotypes (AUCs ranging 0.50-0.92) or CLAD stages (AUC 0.56) was limited. CONCLUSION Exhaled breath analysis using eNose is a promising novel biomarker for enabling diagnosis and phenotyping CLAD. eNose technology could be a valuable addition to the diagnostic armamentarium for suspected graft failure in LTR.
Collapse
Affiliation(s)
- Nynke Wijbenga
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands; Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rogier A S Hoek
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands; Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Bas J Mathot
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands; Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Leonard Seghers
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands; Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Catharina C Moor
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Joachim G J V Aerts
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Olivier C Manintveld
- Department of Cardiology, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands; Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Merel E Hellemons
- Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands; Erasmus MC Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.
| |
Collapse
|
|
20
|
Sun P, Shi Y, Shi Y. Bionic sensing system and characterization of exhaled nitric oxide detection based on canine olfaction. PLoS One 2022; 17:e0279003. [PMID: 36534648 PMCID: PMC9762597 DOI: 10.1371/journal.pone.0279003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
A quantitative monitoring system for fractional exhaled nitric oxide (FENO) in homes is very important for the control of respiratory diseases such as asthma. To this end, this paper proposes a small bionic sensing system for NO detection in an electronic nose based on analysis of the structure of the canine olfactory system and the airflow pattern in the nasal cavity. The proposed system detected NO at different FENO concentration levels with different bionic sensing systems in the electronic nose, and analyzed the data comparatively. Combined with a backpropagation neural network algorithm, the bionic canine sensing system improved the recognition rate for FENO detection by up to 98.1%. Moreover, electronic noses with a canine bionic sensing system can improve the performance of trace gas detection.
Collapse
Affiliation(s)
- Pengjiao Sun
- The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin, China
- Electronics and Communication Engineering School, Jilin Technology College of Electronic Information, Jilin, China
| | - Yunbo Shi
- The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin, China
- Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin, China
- National Experimental Teaching Demonstration Center for Measurement and Control Technology and Instrumentation, Harbin University of Science and Technology, Harbin, China
- * E-mail:
| | - Yeping Shi
- The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin, China
- Electronics and Communication Engineering School, Jilin Technology College of Electronic Information, Jilin, China
| |
Collapse
|
|
21
|
Azim A, Rezwan FI, Barber C, Harvey M, Kurukulaaratchy RJ, Holloway JW, Howarth PH. Measurement of Exhaled Volatile Organic Compounds as a Biomarker for Personalised Medicine: Assessment of Short-Term Repeatability in Severe Asthma. J Pers Med 2022; 12:1635. [PMID: 36294774 PMCID: PMC9604907 DOI: 10.3390/jpm12101635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/14/2022] [Accepted: 09/26/2022] [Indexed: 11/07/2022] Open
Abstract
The measurement of exhaled volatile organic compounds (VOCs) in exhaled breath (breathomics) represents an exciting biomarker matrix for airways disease, with early research indicating a sensitivity to airway inflammation. One of the key aspects to analytical validity for any clinical biomarker is an understanding of the short-term repeatability of measures. We collected exhaled breath samples on 5 consecutive days in 14 subjects with severe asthma who had undergone extensive clinical characterisation. Principal component analysis on VOC abundance across all breath samples revealed no variance due to the day of sampling. Samples from the same patients clustered together and there was some separation according to T2 inflammatory markers. The intra-subject and between-subject variability of each VOC was calculated across the 70 samples and identified 30.35% of VOCs to be erratic: variable between subjects but also variable in the same subject. Exclusion of these erratic VOCs from machine learning approaches revealed no apparent loss of structure to the underlying data or loss of relationship with salient clinical characteristics. Moreover, cluster evaluation by the silhouette coefficient indicates more distinct clustering. We are able to describe the short-term repeatability of breath samples in a severe asthma population and corroborate its sensitivity to airway inflammation. We also describe a novel variance-based feature selection tool that, when applied to larger clinical studies, could improve machine learning model predictions.
Collapse
Affiliation(s)
- Adnan Azim
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK
| | - Faisal I. Rezwan
- Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
| | - Clair Barber
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK
| | - Matthew Harvey
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK
| | - Ramesh J. Kurukulaaratchy
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK
- David Hide Asthma and Allergy Research Centre, Isle of Wight NHS Trust, Newport PO30 5TG, UK
| | - John W. Holloway
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
| | - Peter H. Howarth
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK
| |
Collapse
|
|
22
|
Keogh RJ, Riches JC. The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime? Curr Oncol 2022; 29:7355-7378. [PMID: 36290855 PMCID: PMC9600994 DOI: 10.3390/curroncol29100578] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/22/2022] [Accepted: 09/28/2022] [Indexed: 11/07/2022] Open
Abstract
Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.
Collapse
|
|
23
|
Avian C, Mahali MI, Putro NAS, Prakosa SW, Leu JS. Fx-Net and PureNet: Convolutional Neural Network architecture for discrimination of Chronic Obstructive Pulmonary Disease from smokers and healthy subjects through electronic nose signals. Comput Biol Med 2022; 148:105913. [PMID: 35940164 DOI: 10.1016/j.compbiomed.2022.105913] [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] [Received: 04/01/2022] [Revised: 06/28/2022] [Accepted: 07/23/2022] [Indexed: 11/03/2022]
Abstract
As one of the most reliable and significant indicators, Chronic Obstructive Pulmonary Disease (COPD) becomes a robust predictor of lung cancer early detection, the world's leading cause of cancer death. One of the methods is to analyze the Volatile Organic Compounds (VOCs) in exhaled breath using electronic noses (E-noses), which have become emerging tools for analyzing breath because of their potential and promising technology for diagnosing. However, the signal processing of the E-Nose sensor becomes vital in exposing information about the subject condition, which most researchers strive to accomplish. We proposed a Convolutional Neural Network (CNN) architecture to classify COPD in smokers and non-smokers, healthy subjects, and smokers from E-Nose signals to contribute to this field. Two models were constructed following E-Nose signal processing state-of-the-arts. One was by combined feature extraction and classifier, and the second was by CNN, which directly processed the raw signal. In addition, various feature extraction and classifier (Machine Learning and CNN) used in prior research were investigated. Using 3K and 5K Fold cross-validation results demonstrated that our proposed models outperformed in Kernel Principal Component Analysis (KPCA) with Fx-ConvNet and Pure-ConvNet. They all reached maximum F1-Score with zero standard deviation values indicating a consistent result. Further experiments also showed that KPCA contributed to the increasing performance of some classifiers with average F1-Score 0.933 and 0.068 as standard deviation values.
Collapse
Affiliation(s)
- Cries Avian
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan
| | - Muhammad Izzuddin Mahali
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan; Department of Electronics and Informatics Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Indonesia
| | - Nur Achmad Sulistyo Putro
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan; Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia
| | - Setya Widyawan Prakosa
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan
| | - Jenq-Shiou Leu
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan.
| |
Collapse
|
|
24
|
Rothbart N, Stanley V, Koczulla R, Jarosch I, Holz O, Schmalz K, Hübers HW. Millimeter-wave gas spectroscopy for breath analysis of COPD patients in comparison to GC-MS. J Breath Res 2022; 16. [PMID: 35688126 DOI: 10.1088/1752-7163/ac77aa] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/10/2022] [Indexed: 01/12/2023]
Abstract
The analysis of human breath is a very active area of research, driven by the vision of a fast, easy, and non-invasive tool for medical diagnoses at the point of care. Millimeter-wave gas spectroscopy (MMWGS) is a novel, well-suited technique for this application as it provides high sensitivity, specificity and selectivity. Most of all, it offers the perspective of compact low-cost systems to be used in doctors' offices or hospitals. In this work, we demonstrate the analysis of breath samples acquired in a medical environment using MMWGS and evaluate validity, reliability, as well as limitations and perspectives of the method. To this end, we investigated 28 duplicate samples from chronic obstructive lung disease patients and compared the results to gas chromatography-mass spectrometry (GC-MS). The quantification of the data was conducted using a calibration-free fit model, which describes the data precisely and delivers absolute quantities. For ethanol, acetone, and acetonitrile, the results agree well with the GC-MS measurements and are as reliable as GC-MS. The duplicate samples deviate from the mean values by only 6% to 18%. Detection limits of MMWGS depend strongly on the molecular species. For example, acetonitrile can be traced down to 1.8 × 10-12mol by the MMWGS system, which is comparable to the GC-MS system. We observed correlations of abundances between formaldehyde and acetaldehyde as well as between acetonitrile and acetaldehyde, which demonstrates the potential of MMWGS for breath research.
Collapse
Affiliation(s)
- Nick Rothbart
- Institute of Optical Sensor Systems, German Aerospace Center (DLR), Berlin, Germany.,Department of Physics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Victoria Stanley
- Institute of Optical Sensor Systems, German Aerospace Center (DLR), Berlin, Germany.,Department of Physics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rembert Koczulla
- Schön Klinik Berchtesgadener Land, Research Institute for Pulmonary Rehabilitation, Schönau am Königssee, Germany.,Philipps-University of Marburg, Department of Pulmonary Rehabilitation, Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Inga Jarosch
- Schön Klinik Berchtesgadener Land, Research Institute for Pulmonary Rehabilitation, Schönau am Königssee, Germany.,Philipps-University of Marburg, Department of Pulmonary Rehabilitation, Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Olaf Holz
- Fraunhofer ITEM, German Center for Lung Research (BREATH, DZL), Clinical Airway Research, Hannover, Germany
| | - Klaus Schmalz
- IHP-Leibniz-Institut für Innovative Mikroelektronik, Frankfurt (Oder), Germany
| | - Heinz-Wilhelm Hübers
- Institute of Optical Sensor Systems, German Aerospace Center (DLR), Berlin, Germany.,Department of Physics, Humboldt-Universität zu Berlin, Berlin, Germany
| |
Collapse
|
|
25
|
Zhang X, Deng K, Yuan Y, Liu L, Zhang S, Wang C, Wang G, Zhang H, Wang L, Cheng G, Wood LG, Wang G. Body Composition-Specific Asthma Phenotypes: Clinical Implications. Nutrients 2022; 14:nu14122525. [PMID: 35745259 PMCID: PMC9229860 DOI: 10.3390/nu14122525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/11/2022] [Accepted: 06/13/2022] [Indexed: 02/05/2023] Open
Abstract
Background: Previous studies have indicated the limitations of body mass index for defining disease phenotypes. The description of asthma phenotypes based on body composition (BC) has not been largely reported. Objective: To identify and characterize phenotypes based on BC parameters in patients with asthma. Methods: A study with two prospective observational cohorts analyzing adult patients with stable asthma (n = 541 for training and n = 179 for validation) was conducted. A body composition analysis was performed for the included patients. A cluster analysis was conducted by applying a 2-step process with stepwise discriminant analysis. Logistic regression models were used to evaluate the association between identified phenotypes and asthma exacerbations (AEs). The same algorithm for cluster analysis in the independent validation set was used to perform an external validation. Results: Three clusters had significantly different characteristics associated with asthma outcomes. An external validation identified the similarity of the participants in training and the validation set. In the training set, cluster Training (T) 1 (29.4%) was “patients with undernutrition”, cluster T2 (18.9%) was “intermediate level of nutrition with psychological dysfunction”, and cluster T3 (51.8%) was “patients with good nutrition”. Cluster T3 had a decreased risk of moderate-to-severe and severe AEs in the following year compared with the other two clusters. The most important BC-specific factors contributing to being accurately assigned to one of these three clusters were skeletal muscle mass and visceral fat area. Conclusion: We defined three distinct clusters of asthma patients, which had distinct clinical features and asthma outcomes. Our data reinforced the importance of evaluating BC to determining nutritional status in clinical practice.
Collapse
Affiliation(s)
- Xin Zhang
- Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (X.Z.); (L.L.); (S.Z.); (G.W.); (H.Z.); (L.W.)
- Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (K.D.); (C.W.)
- Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, Sichuan University, Chengdu 610213, China
| | - Ke Deng
- Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (K.D.); (C.W.)
- Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, Sichuan University, Chengdu 610213, China
| | - Yulai Yuan
- Department of Respiratory Medicine, Traditional Chinese Medicine Hospital Affiliated to Southwest Medical University, Luzhou 646699, China;
| | - Lei Liu
- Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (X.Z.); (L.L.); (S.Z.); (G.W.); (H.Z.); (L.W.)
- Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (K.D.); (C.W.)
- Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, Sichuan University, Chengdu 610213, China
| | - Shuwen Zhang
- Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (X.Z.); (L.L.); (S.Z.); (G.W.); (H.Z.); (L.W.)
- Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, Sichuan University, Chengdu 610213, China
| | - Changyong Wang
- Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (K.D.); (C.W.)
- Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, Sichuan University, Chengdu 610213, China
| | - Gang Wang
- Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (X.Z.); (L.L.); (S.Z.); (G.W.); (H.Z.); (L.W.)
- Institute of Environmental Medicine, Karolinska Institute, 11883 Stockholm, Sweden
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institute, 11883 Stockholm, Sweden
| | - Hongping Zhang
- Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (X.Z.); (L.L.); (S.Z.); (G.W.); (H.Z.); (L.W.)
- Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, Sichuan University, Chengdu 610213, China
| | - Lei Wang
- Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (X.Z.); (L.L.); (S.Z.); (G.W.); (H.Z.); (L.W.)
- Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, Sichuan University, Chengdu 610213, China
| | - Gaiping Cheng
- Department of Clinical Nutrition, West China Hospital, Sichuan University, Chengdu 610044, China;
| | - Lisa G. Wood
- Priority Research Center for Healthy Lungs, Hunter Medical Research Institute, University of Newcastle, New Lambton, NSW 2308, Australia;
| | - Gang Wang
- Department of Respiratory and Critical Care Medicine, Clinical Research Center for Respiratory Disease, West China Hospital, Sichuan University, Chengdu 610044, China; (K.D.); (C.W.)
- Laboratory of Pulmonary Immunology and Inflammation, Frontiers Science Center for Disease-Related Molecular Network, Sichuan University, Chengdu 610213, China
- Correspondence:
| |
Collapse
|
|
26
|
Scheepers MHMC, Al-Difaie Z, Brandts L, Peeters A, van Grinsven B, Bouvy ND. Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis. JAMA Netw Open 2022; 5:e2219372. [PMID: 35767259 PMCID: PMC9244610 DOI: 10.1001/jamanetworkopen.2022.19372] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
IMPORTANCE There has been a growing interest in the use of electronic noses (e-noses) in detecting volatile organic compounds in exhaled breath for the diagnosis of cancer. However, no systematic evaluation has been performed of the overall diagnostic accuracy and methodologic challenges of using e-noses for cancer detection in exhaled breath. OBJECTIVE To provide an overview of the diagnostic accuracy and methodologic challenges of using e-noses for the detection of cancer. DATA SOURCES An electronic search was performed in the PubMed and Embase databases (January 1, 2000, to July 1, 2021). STUDY SELECTION Inclusion criteria were the following: (1) use of e-nose technology, (2) detection of cancer, and (3) analysis of exhaled breath. Exclusion criteria were (1) studies published before 2000; (2) studies not performed in humans; (3) studies not performed in adults; (4) studies that only analyzed biofluids; and (5) studies that exclusively used gas chromatography-mass spectrometry to analyze exhaled breath samples. DATA EXTRACTION AND SYNTHESIS PRISMA guidelines were used for the identification, screening, eligibility, and selection process. Quality assessment was performed using Quality Assessment of Diagnostic Accuracy Studies 2. Generalized mixed-effects bivariate meta-analysis was performed. MAIN OUTCOMES AND MEASURES Main outcomes were sensitivity, specificity, and mean area under the receiver operating characteristic curve. RESULTS This review identified 52 articles with a total of 3677 patients with cancer. All studies were feasibility studies. The sensitivity of e-noses ranged from 48.3% to 95.8% and the specificity from 10.0% to 100.0%. Pooled analysis resulted in a mean (SE) area under the receiver operating characteristic curve of 94% (95% CI, 92%-96%), a sensitivity of 90% (95% CI, 88%-92%), and a specificity of 87% (95% CI, 81%-92%). Considerable heterogeneity existed among the studies because of differences in the selection of patients, endogenous and exogenous factors, and collection of exhaled breath. CONCLUSIONS AND RELEVANCE Results of this review indicate that e-noses have a high diagnostic accuracy for the detection of cancer in exhaled breath. However, most studies were feasibility studies with small sample sizes, a lack of standardization, and a high risk of bias. The lack of standardization and reproducibility of e-nose research should be addressed in future research.
Collapse
Affiliation(s)
- Max H. M. C. Scheepers
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Zaid Al-Difaie
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Lloyd Brandts
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, the Netherlands
| | - Andrea Peeters
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, the Netherlands
| | - Bart van Grinsven
- Sensor Engineering, Faculty of Science and Engineering, Maastricht University, Maastricht, the Netherlands
| | - Nicole D. Bouvy
- Department of Surgery, Maastricht University Medical Center, Maastricht, the Netherlands
| |
Collapse
|
|
27
|
Kaloumenou M, Skotadis E, Lagopati N, Efstathopoulos E, Tsoukalas D. Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:1238. [PMID: 35161984 PMCID: PMC8840008 DOI: 10.3390/s22031238] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/30/2022] [Accepted: 02/01/2022] [Indexed: 05/07/2023]
Abstract
Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.
Collapse
Affiliation(s)
- Maria Kaloumenou
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
| | - Evangelos Skotadis
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
| | - Nefeli Lagopati
- Medical School, National and Kapodistrian University of Athens, 75, Mikras Asias Str., Goudi, 11527 Athens, Greece; (N.L.); (E.E.)
| | - Efstathios Efstathopoulos
- Medical School, National and Kapodistrian University of Athens, 75, Mikras Asias Str., Goudi, 11527 Athens, Greece; (N.L.); (E.E.)
| | - Dimitris Tsoukalas
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
| |
Collapse
|
|
28
|
Palacín J, Rubies E, Clotet E, Martínez D. Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22031120. [PMID: 35161866 PMCID: PMC8838111 DOI: 10.3390/s22031120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/19/2022] [Accepted: 01/27/2022] [Indexed: 05/26/2023]
Abstract
The artificial replication of an olfactory system is currently an open problem. The development of a portable and low-cost artificial olfactory system, also called electronic nose or eNose, is usually based on the use of an array of different gas sensors types, sensitive to different target gases. Low-cost Metal-Oxide semiconductor (MOX) gas sensors are widely used in such arrays. MOX sensors are based on a thin layer of silicon oxide with embedded heaters that can operate at different temperature set points, which usually have the disadvantages of different volatile sensitivity in each individual sensor unit and also different crossed sensitivity to different volatiles (unspecificity). This paper presents and eNose composed by an array of 16 low-cost BME680 digital miniature sensors embedding a miniature MOX gas sensor proposed to unspecifically evaluate air quality. In this paper, the inherent variability and unspecificity that must be expected from the 16 embedded MOX gas sensors, combined with signal processing, are exploited to classify two target volatiles: ethanol and acetone. The proposed eNose reads the resistance of the sensing layer of the 16 embedded MOX gas sensors, applies PCA for dimensional reduction and k-NN for classification. The validation results have shown an instantaneous classification success higher than 94% two days after the calibration and higher than 70% two weeks after, so the majority classification of a sequence of measures has been always successful in laboratory conditions. These first validation results and the low-power consumption of the eNose (0.9 W) enables its future improvement and its use in portable and battery-operated applications.
Collapse
|
|
29
|
Rutjes N, Van den Bongaardt I, Hashimoto S, Sterk P, Van Aalderen W, Terheggen-Lagro S, Brinkman P, Maitland-van der Zee AH, Van der Schee M, Haarman E. Prediction of asthma in early preschool wheezing by electronic nose analysis. Pediatr Allergy Immunol 2022; 33:e13612. [PMID: 34407242 DOI: 10.1111/pai.13612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/29/2021] [Accepted: 08/02/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Niels Rutjes
- Department of Paediatric Pulmonology, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| | - Ivo Van den Bongaardt
- Department of Paediatric Pulmonology, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| | - Simone Hashimoto
- Respiratory Disease, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| | - Peter Sterk
- Respiratory Disease, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| | - Wim Van Aalderen
- Department of Paediatric Pulmonology, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| | - Suzanne Terheggen-Lagro
- Department of Paediatric Pulmonology, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| | - Paul Brinkman
- Department of Respiratory Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | | | - Marc Van der Schee
- Department of Paediatric Pulmonology, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| | - Eric Haarman
- Department of Paediatric Pulmonology, Amsterdam UMC Location AMC, Amsterdam, The Netherlands
| |
Collapse
|
|
30
|
Exhaled Metabolite Patterns to Identify Recent Asthma Exacerbations. Metabolites 2021; 11:metabo11120872. [PMID: 34940630 PMCID: PMC8708458 DOI: 10.3390/metabo11120872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/06/2021] [Accepted: 12/10/2021] [Indexed: 11/17/2022] Open
Abstract
Asthma is a chronic respiratory disease that can lead to exacerbations, defined as acute episodes of worsening respiratory symptoms and lung function. Predicting the occurrence of these exacerbations is an important goal in asthma management. The measurement of exhaled breath by electronic nose (eNose) may allow for the monitoring of clinically unstable asthma and exacerbations. However, data on its ability to perform this is lacking. We aimed to evaluate whether eNose could identify patients that recently had asthma exacerbations. We performed a cross-sectional study, measuring exhaled breath using the SpiroNose in adults with a physician-reported diagnosis of asthma. Patients were randomly divided into a training (n = 252) and validation (n = 109) set. For the analysis of eNose signals, principal component (PC) and linear discriminant analysis (LDA) were performed. LDA, based on PC1-4, reliably discriminated between patients who had a recent exacerbation from those who had not (training receiver operating characteristic (ROC)–area under the curve (AUC) = 0.76,95% CI 0.69–0.82), (validation AUC = 0.76, 95% CI 0.64–0.87). Our study showed that, exhaled breath analysis using eNose could accurately identify asthma patients who recently had an exacerbation, and could indicate that asthma exacerbations have a specific exhaled breath pattern detectable by eNose.
Collapse
|
|
31
|
Ahmed W, White IR, Wilkinson M, Johnson CF, Rattray N, Kishore AK, Goodacre R, Smith CJ, Fowler SJ. Breath and plasma metabolomics to assess inflammation in acute stroke. Sci Rep 2021; 11:21949. [PMID: 34753981 PMCID: PMC8578671 DOI: 10.1038/s41598-021-01268-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/27/2021] [Indexed: 12/25/2022] Open
Abstract
Inflammation is strongly implicated in both injury and repair processes occurring after stroke. In this exploratory study we assessed the feasibility of repeated sampling of exhaled volatile organic compounds and performed an untargeted metabolomic analysis of plasma collected at multiple time periods after stroke. Metabolic profiles were compared with the time course of the inflammatory markers C-reactive protein (CRP) and interleukin-6 (IL-6). Serial breath sampling was well-tolerated by all patients and the measurement appears feasible in this group. We found that exhaled decanal tracks CRP and IL-6 levels post-stroke and correlates with several metabolic pathways associated with a post-stroke inflammatory response. This suggests that measurement of breath and blood metabolites could facilitate development of novel therapeutic and diagnostic strategies. Results are discussed in relation to the utility of breath analysis in stroke care, such as in monitoring recovery and complications including stroke associated infection.
Collapse
Affiliation(s)
- Waqar Ahmed
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Iain R White
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Nova Gorica, Slovenia
| | - Maxim Wilkinson
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Craig F Johnson
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Nicholas Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Amit K Kishore
- Greater Manchester Comprehensive Stroke Centre, Geoffrey Jefferson Brain Research Centre, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Salford, UK
- Division of Cardiovascular Sciences, Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Royston Goodacre
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Craig J Smith
- Greater Manchester Comprehensive Stroke Centre, Geoffrey Jefferson Brain Research Centre, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Salford, UK.
- Division of Cardiovascular Sciences, Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
| | - Stephen J Fowler
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, UK.
| |
Collapse
|
|
32
|
Kim ST, Choi IH, Li H. Identification of multi-concentration aromatic fragrances with electronic nose technology using a support vector machine. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:4710-4717. [PMID: 34617937 DOI: 10.1039/d1ay00788b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Due to the concentration effect, there is a major challenge for the electronic nose system to identify different odor samples with multiple concentrations. The development of artificial intelligence provides new ways to solve such problems. This article attempts to use support vector machine (SVM) technology to distinguish four fragrance samples with three concentrations, including roman chamomile, jasmine, lavender, and orange. The responses of these samples were collected by an 11-sensor electronic nose. After baseline correction, data smoothing, and removal of non-responsive sensors, the signals of 8 sensors were used for subsequent model analysis. Due to the concentration effect, when the primary signal intensities were used as features, the electronic nose cannot distinguish between different aroma types (accuracy less than 50%). When the normalized maximum signal intensity Xmr was used, the accuracy of the model was greatly improved. Graphic analysis and PCA showed that the normalized feature effectively eliminates the concentration effect, and appropriately reducing some sensors can enhance the ability to distinguish odors. The SVM correctly classified all 14 aromas when feeding 8 sets of data to train the radial kernel C-classification SVM. This showed that the cross-interference of the sensors was reduced, and the resolving power of the electronic nose was enhanced after the feature reduction.
Collapse
Affiliation(s)
- Sun-Tae Kim
- Daejeon University, 62 Daehak-Ro, Dong-Gu, Daejeon, 34520, Republic of Korea
| | - Il-Hwan Choi
- Daejeon University, 62 Daehak-Ro, Dong-Gu, Daejeon, 34520, Republic of Korea
| | - Hui Li
- Envors Co., Ltd, Biraeseoro 54-1, Dong Gu, Daejeon, 34528, Republic of Korea.
| |
Collapse
|
|
33
|
Zhang J, Tian Y, Luo Z, Qian C, Li W, Duan Y. Breath volatile organic compound analysis: an emerging method for gastric cancer detection. J Breath Res 2021; 15. [PMID: 34610588 DOI: 10.1088/1752-7163/ac2cde] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 10/05/2021] [Indexed: 12/14/2022]
Abstract
Gastric cancer is a common malignancy, being the fifth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths worldwide. Diagnosis of gastric cancer at the early stage is critical to effectively improve the survival rate. However, a substantial proportion of patients with gastric cancer in the early stages lack specific symptoms or are asymptomatic. Moreover, the imaging techniques currently used for gastric cancer screening, such as computed tomography and barium examination, are usually radioactive and have low sensitivity and specificity. Even though endoscopy has high accuracy for gastric cancer screening, its application is limited by the invasiveness of the technique. Breath analysis is an economic, effective, easy to perform, non-invasive detection method, and has no undesirable side effects on subjects. Extensive worldwide research has been conducted on breath volatile organic compounds (VOCs), which reveals its prospect as a potential method for gastric cancer detection. Many interesting results have been obtained and innovative methods have been introduced in this subject; hence, an extensive review would be beneficial. By providing a comprehensive list of breath VOCs identified by gastric cancer would promote further research in this field. This review summarizes the commonly used technologies for exhaled breath analysis, focusing on the application of analytical instruments in the detection of breath VOCs in gastric cancers, and the alterations in the profile of breath biomarkers in gastric cancer patients are discussed as well.
Collapse
Affiliation(s)
- Jing Zhang
- Research Center of Analytical Instrumentation, Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710069, People's Republic of China
| | - Yonghui Tian
- Research Center of Analytical Instrumentation, Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710069, People's Republic of China
| | - Zewei Luo
- Research Center of Analytical Instrumentation, Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710069, People's Republic of China
| | - Cheng Qian
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, People's Republic of China
| | - Wenwen Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, People's Republic of China
| | - Yixiang Duan
- Research Center of Analytical Instrumentation, Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710069, People's Republic of China
| |
Collapse
|
|
34
|
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: 24] [Impact Index Per Article: 6.0] [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.
Collapse
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
| |
Collapse
|
|
35
|
Buma AIG, Muller M, de Vries R, Sterk PJ, van der Noort V, Wolf-Lansdorf M, Farzan N, Baas P, van den Heuvel MM. eNose analysis for early immunotherapy response monitoring in non-small cell lung cancer. Lung Cancer 2021; 160:36-43. [PMID: 34399166 DOI: 10.1016/j.lungcan.2021.07.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/20/2021] [Accepted: 07/28/2021] [Indexed: 01/28/2023]
Abstract
OBJECTIVES Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment. MATERIALS AND METHODS This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis. RESULTS In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89-1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91-1.00). CONCLUSION Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy.
Collapse
Affiliation(s)
| | - Mirte Muller
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Rianne de Vries
- Amsterdam University Medical Center, Amsterdam, the Netherlands; Breathomix B.V. (www.breathomix.com), Leiden, the Netherlands
| | - Peter J Sterk
- Amsterdam University Medical Center, Amsterdam, the Netherlands
| | | | | | - Niloufar Farzan
- Breathomix B.V. (www.breathomix.com), Leiden, the Netherlands
| | - Paul Baas
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | | |
Collapse
|
|
36
|
Jaeschke C, Padilla M, Glöckler J, Polaka I, Leja M, Veliks V, Mitrovics J, Leja M, Mizaikoff B. Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies. Molecules 2021; 26:3776. [PMID: 34205805 PMCID: PMC8235513 DOI: 10.3390/molecules26123776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/11/2021] [Accepted: 06/14/2021] [Indexed: 11/17/2022] Open
Abstract
Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.
Collapse
Affiliation(s)
- Carsten Jaeschke
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, 89081 Ulm, Germany; (C.J.); (J.G.)
| | - Marta Padilla
- JLM Innovation GmbH, Vor dem Kreuzberg 17, 72070 Tuebingen, Germany; (M.P.); (J.M.)
| | - Johannes Glöckler
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, 89081 Ulm, Germany; (C.J.); (J.G.)
| | - Inese Polaka
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1079 Riga, Latvia; (I.P.); (M.L.); (V.V.); (M.L.)
| | - Martins Leja
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1079 Riga, Latvia; (I.P.); (M.L.); (V.V.); (M.L.)
| | - Viktors Veliks
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1079 Riga, Latvia; (I.P.); (M.L.); (V.V.); (M.L.)
| | - Jan Mitrovics
- JLM Innovation GmbH, Vor dem Kreuzberg 17, 72070 Tuebingen, Germany; (M.P.); (J.M.)
| | - Marcis Leja
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1079 Riga, Latvia; (I.P.); (M.L.); (V.V.); (M.L.)
| | - Boris Mizaikoff
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, 89081 Ulm, Germany; (C.J.); (J.G.)
| |
Collapse
|
|
37
|
Kalidoss R, Kothalam R, Manikandan A, Jaganathan SK, Khan A, Asiri AM. Socio-economic demands and challenges for non-invasive disease diagnosis through a portable breathalyzer by the incorporation of 2D nanosheets and SMO nanocomposites. RSC Adv 2021; 11:21216-21234. [PMID: 35478818 PMCID: PMC9034087 DOI: 10.1039/d1ra02554f] [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: 03/31/2021] [Accepted: 05/23/2021] [Indexed: 12/15/2022] Open
Abstract
Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials. The trace level selective detection of volatile organic compounds (VOCs) in breath facilitates the study of physiological disorder and real-time health monitoring. This review focuses on advancements in chemiresistive gas sensor technology for biomarker detection associated with different diseases. Emphasis is placed on selective biomarker detection by semiconducting metal oxide (SMO) nanostructures, 2-dimensional nanomaterials (2DMs) and nanocomposites through various optimization strategies and sensing mechanisms. Their synergistic properties for incorporation in a portable breathalyzer have been elucidated. Furthermore, the socio-economic demands of a breathalyzer in terms of recent establishment of startups globally and challenges of a breathalyzer are critically reviewed. This initiative is aimed at highlighting the challenges and scope for improvement to realize a high performance chemiresistive gas sensor for non-invasive disease diagnosis. Breath analysis for non-invasive clinical diagnostics and treatment progression has penetrated the research community owing to the technological developments in novel sensing nanomaterials.![]()
Collapse
Affiliation(s)
- Ramji Kalidoss
- Department of Biomedical Engineering, Bharath Institute of Higher Education and Research Selaiyur Tamil Nadu 600 073 India +91-9840-959832
| | - Radhakrishnan Kothalam
- Department of Chemistry, College of Engineering and Technology, SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603 203 India
| | - A Manikandan
- Department of Chemistry, Bharath Institute of Higher Education and Research Selaiyur Tamil Nadu 600 073 India.,Centre for Nanoscience and Nanotechnology, Bharath Institute of Higher Education and Research Selaiyur Tamil Nadu 600 073 India
| | - Saravana Kumar Jaganathan
- Bionanotechnology Research Group, Ton Duc Thang University Ho Chi Minh City Vietnam.,Faculty of Applied Sciences, Ton Duc Thang University Ho Chi Minh City Vietnam.,Department of Engineering, Faculty of Science and Engineering, University of Hull HU6 7RX UK
| | - Anish Khan
- Chemistry Department, Faculty of Science, King Abdulaziz University Jeddah 21589 Saudi Arabia.,Center of Excellence for Advanced Materials Research, King Abdulaziz University Jeddah 21589 Saudi Arabia
| | - Abdullah M Asiri
- Chemistry Department, Faculty of Science, King Abdulaziz University Jeddah 21589 Saudi Arabia.,Center of Excellence for Advanced Materials Research, King Abdulaziz University Jeddah 21589 Saudi Arabia
| |
Collapse
|
|
38
|
Augustus E, Zwaenepoel K, Siozopoulou V, Raskin J, Jordaens S, Baggerman G, Sorber L, Roeyen G, Peeters M, Pauwels P. Prognostic and Predictive Biomarkers in Non-Small Cell Lung Cancer Patients on Immunotherapy-The Role of Liquid Biopsy in Unraveling the Puzzle. Cancers (Basel) 2021; 13:1675. [PMID: 33918147 PMCID: PMC8036384 DOI: 10.3390/cancers13071675] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/14/2022] Open
Abstract
In the last decade, immunotherapy has been one of the most important advances in the non-small cell lung cancer (NSCLC) treatment landscape. Nevertheless, only a subset of NSCLC patients benefits from it. Currently, the only Food and Drug Administration (FDA) approved diagnostic test for first-line immunotherapy in metastatic NSCLC patients uses tissue biopsies to determine the programmed death ligand 1 (PD-L1) status. However, obtaining tumor tissue is not always feasible and puts the patient at risk. Liquid biopsy, which refers to the tumor-derived material present in body fluids, offers an alternative approach. This less invasive technique gives real-time information on the tumor characteristics. This review addresses different promising liquid biopsy based biomarkers in NSCLC patients that enable the selection of patients who benefit from immunotherapy and the monitoring of patients during this therapy. The challenges and the opportunities of blood-based biomarkers such as cell-free DNA (cfDNA), circulating tumor cells (CTCs), exosomes, epigenetic signatures, microRNAs (miRNAs) and the T cell repertoire will be addressed. This review also focuses on the less-studied feces-based and breath-based biomarkers.
Collapse
Affiliation(s)
- Elien Augustus
- Center for Oncological Research Antwerp (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp (UAntwerp), 2610 Wilrijk, Belgium; (K.Z.); (V.S.); (S.J.); (L.S.); (M.P.); (P.P.)
- Laboratory of Pathological Anatomy, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
| | - Karen Zwaenepoel
- Center for Oncological Research Antwerp (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp (UAntwerp), 2610 Wilrijk, Belgium; (K.Z.); (V.S.); (S.J.); (L.S.); (M.P.); (P.P.)
- Laboratory of Pathological Anatomy, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
| | - Vasiliki Siozopoulou
- Center for Oncological Research Antwerp (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp (UAntwerp), 2610 Wilrijk, Belgium; (K.Z.); (V.S.); (S.J.); (L.S.); (M.P.); (P.P.)
- Laboratory of Pathological Anatomy, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
| | - Jo Raskin
- Department of Pulmonology and Thoracic Oncology, Antwerp University Hospital (UZA), 2650 Edegem, Belgium;
| | - Stephanie Jordaens
- Center for Oncological Research Antwerp (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp (UAntwerp), 2610 Wilrijk, Belgium; (K.Z.); (V.S.); (S.J.); (L.S.); (M.P.); (P.P.)
- Laboratory of Pathological Anatomy, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
| | - Geert Baggerman
- Centre for Proteomics, University of Antwerp (UAntwerp), 2020 Antwerpen, Belgium;
- Health Unit, Vlaamse Instelling voor Technologisch Onderzoek (VITO), 2400 Mol, Belgium
| | - Laure Sorber
- Center for Oncological Research Antwerp (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp (UAntwerp), 2610 Wilrijk, Belgium; (K.Z.); (V.S.); (S.J.); (L.S.); (M.P.); (P.P.)
- Laboratory of Pathological Anatomy, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
| | - Geert Roeyen
- Department of Hepato-Pancreato-Biliary, Endocrine and Transplantation Surgery, Antwerp University Hospital (UZA), 2650 Edegem, Belgium;
| | - Marc Peeters
- Center for Oncological Research Antwerp (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp (UAntwerp), 2610 Wilrijk, Belgium; (K.Z.); (V.S.); (S.J.); (L.S.); (M.P.); (P.P.)
- Department of Oncology, Multidisciplinary Oncological Center Antwerp, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
| | - Patrick Pauwels
- Center for Oncological Research Antwerp (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp (UAntwerp), 2610 Wilrijk, Belgium; (K.Z.); (V.S.); (S.J.); (L.S.); (M.P.); (P.P.)
- Laboratory of Pathological Anatomy, Antwerp University Hospital (UZA), 2650 Edegem, Belgium
| |
Collapse
|
|
39
|
The Influence of Smoking Status on Exhaled Breath Profiles in Asthma and COPD Patients. Molecules 2021; 26:molecules26051357. [PMID: 33806279 PMCID: PMC7961431 DOI: 10.3390/molecules26051357] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/20/2021] [Accepted: 02/26/2021] [Indexed: 01/18/2023] Open
Abstract
Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver–operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66–0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43–0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49–0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50–0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.
Collapse
|
|
40
|
Recognizing lung cancer and stages using a self-developed electronic nose system. Comput Biol Med 2021; 131:104294. [PMID: 33647830 DOI: 10.1016/j.compbiomed.2021.104294] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/17/2021] [Accepted: 02/17/2021] [Indexed: 12/25/2022]
Abstract
Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that could be used as non-invasive biomarkers of lung cancer. Breath-based lung cancer screening has attracted wide attention on account of its convenience, low cost and easy popularization. In this paper, the research of lung cancer detection and staging is conducted by the self-developed electronic nose (e-nose) system. In order to investigate the performance of the device in distinguishing lung cancer patients from healthy controls, two feature extraction methods and two different classification models were adopted. Among all the models, kernel principal component analysis (KPCA) combined with extreme gradient boosting (XGBoost) achieved the best results among 235 breath samples. The accuracy, sensitivity and specificity of e-nose system were 93.59%, 95.60% and 91.09%, respectively. Meanwhile, the device could innovatively classify stages of 90 lung cancer patients (i.e., 44 stage III and 46 stage IV). Experimental results indicated that the recognition accuracy of lung cancer stages was more than 80%. Further experiments of this research also showed that the combination of sensor array and pattern recognition algorithms could identify and distinguish the expiratory characteristics of lung cancer, smoking and other respiratory diseases.
Collapse
|
|
41
|
Moor CC, Oppenheimer JC, Nakshbandi G, Aerts JGJV, Brinkman P, Maitland-van der Zee AH, Wijsenbeek MS. Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease. Eur Respir J 2021; 57:13993003.02042-2020. [PMID: 32732331 DOI: 10.1183/13993003.02042-2020] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 07/20/2020] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Early and accurate diagnosis of interstitial lung diseases (ILDs) remains a major challenge. Better noninvasive diagnostic tools are much needed. We aimed to assess the accuracy of exhaled breath analysis using eNose technology to discriminate between ILD patients and healthy controls, and to distinguish ILD subgroups. METHODS In this cross-sectional study, exhaled breath of consecutive ILD patients and healthy controls was analysed using eNose technology (SpiroNose). Statistical analyses were done using partial least square discriminant analysis and receiver operating characteristic analysis. Independent training and validation sets (2:1) were used in larger subgroups. RESULTS A total of 322 ILD patients and 48 healthy controls were included: sarcoidosis (n=141), idiopathic pulmonary fibrosis (IPF) (n=85), connective tissue disease-associated ILD (n=33), chronic hypersensitivity pneumonitis (n=25), idiopathic nonspecific interstitial pneumonia (n=10), interstitial pneumonia with autoimmune features (n=11) and other ILDs (n=17). eNose sensors discriminated between ILD and healthy controls, with an area under the curve (AUC) of 1.00 in the training and validation sets. Comparison of patients with IPF and patients with other ILDs yielded an AUC of 0.91 (95% CI 0.85-0.96) in the training set and an AUC of 0.87 (95% CI 0.77-0.96) in the validation set. The eNose reliably distinguished between individual diseases, with AUC values ranging from 0.85 to 0.99. CONCLUSIONS eNose technology can completely distinguish ILD patients from healthy controls and can accurately discriminate between different ILD subgroups. Hence, exhaled breath analysis using eNose technology could be a novel biomarker in ILD, enabling timely diagnosis in the future.
Collapse
Affiliation(s)
- Catharina C Moor
- Center of Excellence and European Reference Center for Interstitial Lung Disease and Sarcoidosis, Dept of Respiratory Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,These authors share first authorship
| | - Judith C Oppenheimer
- Center of Excellence and European Reference Center for Interstitial Lung Disease and Sarcoidosis, Dept of Respiratory Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,These authors share first authorship
| | - Gizal Nakshbandi
- Center of Excellence and European Reference Center for Interstitial Lung Disease and Sarcoidosis, Dept of Respiratory Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Joachim G J V Aerts
- Center of Excellence and European Reference Center for Interstitial Lung Disease and Sarcoidosis, Dept of Respiratory Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Paul Brinkman
- Dept of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Rotterdam, The Netherlands
| | - Anke-Hilse Maitland-van der Zee
- Dept of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Rotterdam, The Netherlands.,These authors share senior authorship
| | - Marlies S Wijsenbeek
- Center of Excellence and European Reference Center for Interstitial Lung Disease and Sarcoidosis, Dept of Respiratory Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,These authors share senior authorship
| |
Collapse
|
|
42
|
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: 6] [Impact Index Per Article: 1.2] [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.
Collapse
|
|
43
|
Jaeschke C, Glöckler J, Padilla M, Mitrovics J, Mizaikoff B. An eNose-based method performing drift correction for online VOC detection under dry and humid conditions. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:4724-4733. [PMID: 32930676 DOI: 10.1039/d0ay01172j] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Our recently demonstrated innovative concept of electronic nose (eNose) based on a combination of gas sensors is further tested and benchmarked in the present study. The system is a test bed for gas sensors of different principal technologies distributed within three compartments, which share a compact, very low volume, temperature-controlled sensing chamber. Here, the eNose-based analyser contains three sensing arrays of commercially available semiconducting metal oxide (MOX) gas sensors: one compartment contains 8 analog MOX sensors, while the other two compartments comprise 10 digital MOX sensors. The presented instrument is explicitly tested for the discrimination between mid-range (3-18 ppm) concentrations of different volatile organic compounds (VOCs) including acetaldehyde, acetone, ethanol, ethyl acetate, isoprene and n-pentane under dry and humid conditions, which are all considered relevant gases in future breath diagnostic applications. Since the experiments were performed in periods of time separated by around 20 days, they are affected by drift. For this reason, we explore the opportunity of drift mitigation using methods based on component removal computed by linear discriminant analysis, partial least squares discriminant analysis and direct orthogonalization, which lend themselves to future in-field applications of the developed device and sensing methodology.
Collapse
Affiliation(s)
- Carsten Jaeschke
- University of Ulm, Institute of Analytical and Bioanalytical Chemistry, Albert-Einstein-Allee 11, 89081 Ulm, Germany.
| | | | | | | | | |
Collapse
|
|
44
|
BALSAM-An Interactive Online Platform for Breath Analysis, Visualization and Classification. Metabolites 2020; 10:metabo10100393. [PMID: 33023186 PMCID: PMC7601018 DOI: 10.3390/metabo10100393] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/28/2020] [Accepted: 09/30/2020] [Indexed: 01/22/2023] Open
Abstract
The field of breath analysis lacks a fully automated analysis platform that enforces machine learning good practice and enables clinicians and clinical researchers to rapidly and reproducibly discover metabolite patterns in diseases. We present BALSAM-a comprehensive web-platform to simplify and automate this process, offering features for preprocessing, peak detection, feature extraction, visualization and pattern discovery. Our main focus is on data from multi-capillary-column ion-mobility-spectrometry. While not limited to breath data, BALSAM was developed to increase consistency and robustness in the data analysis process of breath samples, aiming to expand the array of low cost molecular diagnostics in clinics. Our platform is freely available as a web-service and in form of a publicly available docker container.
Collapse
|
|
45
|
van Bragt JJ, Brinkman P, de Vries R, Vijverberg SJ, Weersink EJ, Haarman EG, de Jongh FH, Kester S, Lucas A, in 't Veen JC, Sterk PJ, Bel EH, Maitland-van der Zee AH. Identification of recent exacerbations in COPD patients by electronic nose. ERJ Open Res 2020; 6:00307-2020. [PMID: 33447611 PMCID: PMC7792783 DOI: 10.1183/23120541.00307-2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022] Open
Abstract
Molecular profiling of exhaled breath by electronic nose (eNose) might be suitable as a noninvasive tool that can help in monitoring of clinically unstable COPD patients. However, supporting data are still lacking. Therefore, as a first step, this study aimed to determine the accuracy of exhaled breath analysis by eNose to identify COPD patients who recently exacerbated, defined as an exacerbation in the previous 3 months. Data for this exploratory, cross-sectional study were extracted from the multicentre BreathCloud cohort. Patients with a physician-reported diagnosis of COPD (n=364) on maintenance treatment were included in the analysis. Exacerbations were defined as a worsening of respiratory symptoms requiring treatment with oral corticosteroids, antibiotics or both. Data analysis involved eNose signal processing, ambient air correction and statistics based on principal component (PC) analysis followed by linear discriminant analysis (LDA). Before analysis, patients were randomly divided into a training (n=254) and validation (n=110) set. In the training set, LDA based on PCs 1-4 discriminated between patients with a recent exacerbation or no exacerbation with high accuracy (receiver operating characteristic (ROC)-area under the curve (AUC)=0.98, 95% CI 0.97-1.00). This high accuracy was confirmed in the validation set (AUC=0.98, 95% CI 0.94-1.00). Smoking, health status score, use of inhaled corticosteroids or vital capacity did not influence these results. Exhaled breath analysis by eNose can discriminate with high accuracy between COPD patients who experienced an exacerbation within 3 months prior to measurement and those who did not. This suggests that COPD patients who recently exacerbated have their own exhaled molecular fingerprint that could be valuable for monitoring purposes.
Collapse
Affiliation(s)
- Job J.M.H. van Bragt
- Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands
| | - Paul Brinkman
- Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands
| | - Rianne de Vries
- Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands
- Breathomix BV, Leiden, The Netherlands
| | - Susanne J.H. Vijverberg
- Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands
| | - Els J.M. Weersink
- Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands
| | - Eric G. Haarman
- Amsterdam UMC, Vrije Universiteit Amsterdam, Dept of Pediatric Respiratory Medicine, Amsterdam, The Netherlands
| | - Frans H.C. de Jongh
- Medisch Spectrum Twente, Dept of Pulmonary Function, Enschede, The Netherlands
| | - Sigrid Kester
- Medisch Centrum Den Bosch Oost, ’s-Hertogenbosch, The Netherlands
| | | | | | - Peter J. Sterk
- Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands
| | - Elisabeth H.D. Bel
- Amsterdam UMC, University of Amsterdam, Dept of Respiratory Medicine, Amsterdam, The Netherlands
| | | |
Collapse
|
|
46
|
Baldini C, Billeci L, Sansone F, Conte R, Domenici C, Tonacci A. Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review. BIOSENSORS-BASEL 2020; 10:bios10080084. [PMID: 32722438 PMCID: PMC7459473 DOI: 10.3390/bios10080084] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/13/2020] [Accepted: 07/21/2020] [Indexed: 12/13/2022]
Abstract
Cancer is fast becoming the most important cause of death worldwide, its mortality being mostly caused by late or wrong diagnosis. Novel strategies have been developed to identify early signs of cancer in a minimally obtrusive way, including the Electronic Nose (E-Nose) technology, user-friendly, cost- and time-saving alternative to classical approaches. This systematic review, conducted under the PRISMA guidelines, identified 60 articles directly dealing with the E-Nose application in cancer research published up to 31 January 2020. Among these works, the vast majority reported successful E-Nose use for diagnosing Lung Cancer, showing promising results especially when employing the Aeonose tool, discriminating subjects with Lung Cancer from controls in more than 80% of individuals, in most studies. In order to tailor the main limitations of the proposed approach, including the application of the protocol to advanced stage of cancer, sample heterogeneity and massive confounders, future studies should be conducted on early stage patients, and on larger cohorts, as to better characterize the specific breathprint associated with the various subtypes of cancer. This would ultimately lead to a better and faster diagnosis and to earlier treatment, possibly reducing the burden associated to such conditions.
Collapse
Affiliation(s)
- Chiara Baldini
- School of Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy;
| | - Lucia Billeci
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Francesco Sansone
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Raffaele Conte
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Claudio Domenici
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Alessandro Tonacci
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
- Correspondence:
| |
Collapse
|
|
47
|
Abdel-Aziz MI, Brinkman P, Vijverberg SJH, Neerincx AH, de Vries R, Dagelet YWF, Riley JH, Hashimoto S, Montuschi P, Chung KF, Djukanovic R, Fleming LJ, Murray CS, Frey U, Bush A, Singer F, Hedlin G, Roberts G, Dahlén SE, Adcock IM, Fowler SJ, Knipping K, Sterk PJ, Kraneveld AD, Maitland-van der Zee AH. eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy. J Allergy Clin Immunol 2020; 146:1045-1055. [PMID: 32531371 DOI: 10.1016/j.jaci.2020.05.038] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/30/2020] [Accepted: 05/05/2020] [Indexed: 02/04/2023]
Abstract
BACKGROUND Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma. OBJECTIVE We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma. METHODS Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics. RESULTS Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics. CONCLUSION eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping.
Collapse
Affiliation(s)
- Mahmoud I Abdel-Aziz
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Clinical Pharmacy, Faculty of Pharmacy, Assiut University, Assiut, Egypt
| | - Paul Brinkman
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Susanne J H Vijverberg
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Anne H Neerincx
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Rianne de Vries
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Breathomix BV, Reeuwijk, The Netherlands
| | - Yennece W F Dagelet
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - John H Riley
- Respiratory Therapeutic Unit, GlaxoSmithKline, Stockley Park, United Kingdom
| | - Simone Hashimoto
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Paediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Paolo Montuschi
- Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome
| | - Kian Fan Chung
- National Heart and Lung Institute, Imperial College London, and Royal Brompton and Harefield NHS Trust, London, United Kingdom
| | - Ratko Djukanovic
- NIHR Southampton Respiratory Biomedical Research Unit, Clinical and Experimental Sciences and Human Development and Health, University of Southampton, Southampton, United Kingdom
| | - Louise J Fleming
- National Heart and Lung Institute, Imperial College London, and Royal Brompton and Harefield NHS Trust, London, United Kingdom
| | - Clare S Murray
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, and Manchester Academic Health Science Centre and NIHR Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
| | - Urs Frey
- University Children's Hospital Basel, University of Basel, Basel, Switzerland
| | - Andrew Bush
- National Heart and Lung Institute, Imperial College London, and Royal Brompton and Harefield NHS Trust, London, United Kingdom
| | | | - Gunilla Hedlin
- Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden; Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Graham Roberts
- NIHR Southampton Respiratory Biomedical Research Unit, Clinical and Experimental Sciences and Human Development and Health, University of Southampton, Southampton, United Kingdom
| | - Sven-Erik Dahlén
- Centre for Allergy Research, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ian M Adcock
- National Heart and Lung Institute, Imperial College London, and Royal Brompton and Harefield NHS Trust, London, United Kingdom
| | - Stephen J Fowler
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, and Manchester Academic Health Science Centre and NIHR Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
| | - Karen Knipping
- Danone Nutricia Research, Utrecht, The Netherlands; Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Peter J Sterk
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Aletta D Kraneveld
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands; Institute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Anke H Maitland-van der Zee
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands; Department of Paediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
| | | | | |
Collapse
|
|
48
|
Peel AM, Wilkinson M, Sinha A, Loke YK, Fowler SJ, Wilson AM. Volatile organic compounds associated with diagnosis and disease characteristics in asthma - A systematic review. Respir Med 2020; 169:105984. [PMID: 32510334 DOI: 10.1016/j.rmed.2020.105984] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 03/30/2020] [Accepted: 04/19/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Metabolomics refers to study of the metabolome, the entire set of metabolites produced by a biological system. The application of metabolomics to exhaled breath samples - breathomics - is a rapidly growing field with potential application to asthma diagnosis and management. OBJECTIVES We aimed to review the adult asthma breathomic literature and present a comprehensive list of volatile organic compounds identified by asthma breathomic models. METHODS We undertook a systematic search for literature on exhaled volatile organic compounds in adult asthma. We assessed the quality of studies and performed a qualitative synthesis. RESULTS We identified twenty studies; these were methodologically heterogenous with a variable risk of bias. Studies almost universally reported breathomics to be capable of differentiating - with moderate or greater accuracy - between samples from healthy controls and those with asthma; and to be capable of phenotyping disease. However, there was little concordance in the compounds upon which discriminatory models were based. CONCLUSION Results to-date are promising but validation in independent prospective cohorts is needed. This may be challenging given the high levels of inter-individual variation. However, large-scale, multi-centre studies are underway and validation efforts have been aided by the publication of technical standards likely to increase inter-study comparability. Successful validation of breathomic models for diagnosis and phenotyping would constitute an important step towards personalised medicine in asthma.
Collapse
Affiliation(s)
- Adam M Peel
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK.
| | - Maxim Wilkinson
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester; Manchester Academic Health Science Centre and NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Ashnish Sinha
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Yoon K Loke
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Stephen J Fowler
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester; Manchester Academic Health Science Centre and NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Andrew M Wilson
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| |
Collapse
|
|
49
|
Duanmu F, Shen Z, Liu Q, Zhong S, Ji H. A WO3-CuWO4 nanostructured heterojunction for enhanced n-butanol sensing performance. CHINESE CHEM LETT 2020. [DOI: 10.1016/j.cclet.2019.07.032] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
|
|
50
|
Rodríguez-Aguilar M, Díaz de León-Martínez L, Gorocica-Rosete P, Padilla RP, Thirión-Romero I, Ornelas-Rebolledo O, Flores-Ramírez R. Identification of breath-prints for the COPD detection associated with smoking and household air pollution by electronic nose. Respir Med 2020; 163:105901. [PMID: 32125969 DOI: 10.1016/j.rmed.2020.105901] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 02/06/2020] [Accepted: 02/11/2020] [Indexed: 01/10/2023]
Abstract
PURPOSE The analysis of breath-print, has been proposed as an attractive alternative to investigate possible biomarkers of Chronic Obstructive Pulmonary Disease (COPD). The aim of the present study was to discriminate between healthy subjects, patients with COPD associated with smoking (COPD-S) and patients with COPD associated with household air pollution (COPD-HAP). METHODS A cross-sectional study of 294 participants was conducted, 88 with smoking associated COPD, 28 associated with HAP and 178 healthy subjects. Breath-print analysis was performed by using the Cyranose 320 electronic nose. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA) and Support Vector Machine (SVM) and the test's diagnostic power by means of ROC (Receiver Operating Characteristic) curves. RESULTS The results indicated that the breath-print of patients with COPD is different from the one of healthy subjects explaining a variability of 93.8% with a correct prediction of 97.8% and correct classification of 100%,also positive and negative predictive value of 96.5 and 100% respectively. Furthermore, the breath-print of exhaled breath from patients with COPD-S and COPD-HAP does not present any difference. CONCLUSIONS The breath-print of exhaled breath from patients with COPD-S and COPD-HAP does not present any difference, which demonstrates that the breath-print is related to the disease and not to causality. With these results, the analysis of the breath-print of COPD is proposed as an alternative for a screening method in future clinical applications.
Collapse
Affiliation(s)
- Maribel Rodríguez-Aguilar
- Centro de Investigación Aplicada en Ambiente y Salud, CIACYT, Facultad de Medicina, Universidad Autónoma de San Luis Potosí, Av. Venustiano Carranza 2405, CP 78210, San Luis Potosí, S.L.P, Mexico
| | - Lorena Díaz de León-Martínez
- Centro de Investigación Aplicada en Ambiente y Salud, CIACYT, Facultad de Medicina, Universidad Autónoma de San Luis Potosí, Av. Venustiano Carranza 2405, CP 78210, San Luis Potosí, S.L.P, Mexico
| | - Patricia Gorocica-Rosete
- Biochemestry Research Department of National Institute Respiratory Diseases, Ismael Cosío Villegas, Mexico City, Mexico
| | - Rogelio Pérez Padilla
- Smoking and COPD Research Department of National Institute Respiratory Diseases, Ismael Cosío Villegas, Mexico City, Mexico
| | - Ireri Thirión-Romero
- Smoking and COPD Research Department of National Institute Respiratory Diseases, Ismael Cosío Villegas, Mexico City, Mexico
| | - Omar Ornelas-Rebolledo
- Labinnova Center of Investigation in Breath for Early Detection Diseases, Guadalajara, Mexico
| | - Rogelio Flores-Ramírez
- Coordinación para la Innovación y Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Avenida Sierra Leona No. 550, CP 78210, Colonia Lomas Segunda Sección, San Luis Potosí, SLP, Mexico.
| |
Collapse
|