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Long GA, Xu Q, Sunkara J, Woodbury R, Brown K, Huang JJ, Xie Z, Chen X, Fu XA, Huang J. A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds. Diagn Microbiol Infect Dis 2024; 109:116309. [PMID: 38692202 DOI: 10.1016/j.diagmicrobio.2024.116309] [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: 11/23/2023] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/03/2024]
Abstract
BACKGROUND The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection. METHODS This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted. RESULTS The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS. CONCLUSION Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.
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Affiliation(s)
- Grace A Long
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | - Qian Xu
- Biometrics and Data Science, Fosun Pharma, Beijing, PR China
| | - Jahnavi Sunkara
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | - Reagan Woodbury
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | - Katherine Brown
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | | | - Zhenzhen Xie
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA
| | - Xiaoyu Chen
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA.
| | - Xiao-An Fu
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA.
| | - Jiapeng Huang
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA..
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2
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Xie Z, Morris JD, Pan J, Cooke EA, Sutaria SR, Balcom D, Marimuthu S, Parrish LW, Aliesky H, Huang JJ, Rai SN, Arnold FW, Huang J, Nantz MH, Fu XA. Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath. Sci Rep 2024; 14:14568. [PMID: 38914586 PMCID: PMC11196736 DOI: 10.1038/s41598-024-61735-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 05/09/2024] [Indexed: 06/26/2024] Open
Abstract
COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics.
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Affiliation(s)
- Zhenzhen Xie
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA
| | - James D Morris
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA
| | - Jianmin Pan
- Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- The Cancer Data Science Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Biostatistics and Informatics Shared Resource, University of Cincinnati Cancer Center, Cincinnati, OH, USA
| | - Elizabeth A Cooke
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | - Saurin R Sutaria
- Department of Chemistry, University of Louisville, Louisville, KY, USA
| | - Dawn Balcom
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Subathra Marimuthu
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Leslie W Parrish
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Holly Aliesky
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | | | - Shesh N Rai
- Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- The Cancer Data Science Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Biostatistics and Informatics Shared Resource, University of Cincinnati Cancer Center, Cincinnati, OH, USA
| | - Forest W Arnold
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jiapeng Huang
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, USA.
| | - Michael H Nantz
- Department of Chemistry, University of Louisville, Louisville, KY, USA.
| | - Xiao-An Fu
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA.
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Padawer D, Qadan A, Karameh M, Darawshy F, Laor A, Banker S, Fridlender ZG. Breath of Health: spectroscopy-based breath test for the detection of SARS-CoV-2. Infect Dis (Lond) 2024; 56:376-383. [PMID: 38424673 DOI: 10.1080/23744235.2024.2313020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 01/27/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Nucleic acid amplification tests (NAAT) are considered the gold standard for COVID-19 diagnosis. These tests require professional manpower and equipment, long processing and swab sampling which is unpleasant to the patients. Several volatile organic compounds (VOCs) have been identified in the breath of COVID-19 patients. Detection of these VOCs using a breath test could help rapidly identify COVID-19 patients. OBJECTIVE Assess the accuracy of 'Breath of Health' (BOH) COVID-19 Fourier-transform infra-red (FTIR) Spectroscopy-based breath test. METHODS Breath samples from patients with or without symptoms suggestive for COVID-19 who had NAAT results were collected using Tedlar bags and were blindly analysed using BOH FTIR spectroscopy. BOH Measures several VOCs simultaneously and differentiating positive and negative results. BOH results were compared to NAAT results as gold standard. RESULTS Breath samples from 531 patients were analysed. The sensitivity of BOH breath test was found to be 79.5% and specificity was 87.2%. Positive predictive value (PPV) was 74.7% and negative predictive value (NPV) 90.0%. Calculated accuracy rate was 84.8% and area under the curve 0.834. Subgroup analysis revealed that the NPV of patients without respiratory symptoms was superior over the NPV of symptomatic patients (94.7% vs 80.7%, P-value < 0.0001) and PPV of patients with respiratory symptoms outranks the PPV of individuals without symptoms (85.3% vs 69.2%, P-value 0.0196). CONCLUSION We found BOH COVID-19 breath test to be a patient-friendly, rapid, non-invasive diagnostic test with high accuracy rate and NPV that could efficiently rule out COVID-19 especially among individuals with low pre-test probability.
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Affiliation(s)
- Dan Padawer
- Institute of Pulmonary Medicine, Hadassah Medical Center, Jerusalem, Israel
- Department of Internal Medicine D, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Abed Qadan
- Department of Internal Medicine D, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Mutaz Karameh
- Department of Internal Medicine D, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
- Heart Institute, Hadassah Medical Center, Jerusalem, Israel
| | - Fares Darawshy
- Institute of Pulmonary Medicine, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Arie Laor
- Breath of Health Ltd, Rehovot, Israel
| | | | - Zvi G Fridlender
- Institute of Pulmonary Medicine, Hadassah Medical Center, Jerusalem, Israel
- Department of Internal Medicine D, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
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Picciariello A, Dezi A, Vincenti L, Spampinato MG, Zang W, Riahi P, Scott J, Sharma R, Fan X, Altomare DF. Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer-Preliminary Data. SENSORS (BASEL, SWITZERLAND) 2024; 24:2343. [PMID: 38610554 PMCID: PMC11014225 DOI: 10.3390/s24072343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients' exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.
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Affiliation(s)
| | - Agnese Dezi
- Department of Precision and Regenerative Medicine and Ionian Area and Interdepartmental Research Center for Pelvic Floor Diseases (CIRPAP), University Aldo Moro of Bari, 70124 Bari, Italy
| | - Leonardo Vincenti
- Surgical Unit, IRCCS de Bellis, Castellana Grotte, 70013 Bari, Italy;
| | | | - Wenzhe Zang
- Biomedical Engineering Department, University of Michigan, 1101 Beal Ave., Ann Arbor, MI 48109, USA; (W.Z.); (J.S.); (R.S.); (X.F.)
| | - Pamela Riahi
- Biomedical Engineering Department, University of Michigan, 1101 Beal Ave., Ann Arbor, MI 48109, USA; (W.Z.); (J.S.); (R.S.); (X.F.)
| | - Jared Scott
- Biomedical Engineering Department, University of Michigan, 1101 Beal Ave., Ann Arbor, MI 48109, USA; (W.Z.); (J.S.); (R.S.); (X.F.)
| | - Ruchi Sharma
- Biomedical Engineering Department, University of Michigan, 1101 Beal Ave., Ann Arbor, MI 48109, USA; (W.Z.); (J.S.); (R.S.); (X.F.)
| | - Xudong Fan
- Biomedical Engineering Department, University of Michigan, 1101 Beal Ave., Ann Arbor, MI 48109, USA; (W.Z.); (J.S.); (R.S.); (X.F.)
| | - Donato F. Altomare
- Department of Precision and Regenerative Medicine and Ionian Area and Interdepartmental Research Center for Pelvic Floor Diseases (CIRPAP), University Aldo Moro of Bari, 70124 Bari, Italy
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Zang W, Huang X, Sharma R, Fan X. 1D-Guided Differential Rescaling of a Contour Plot in Comprehensive 2D Gas Chromatography. Anal Chem 2024; 96:3960-3969. [PMID: 38386846 PMCID: PMC10919281 DOI: 10.1021/acs.analchem.4c00202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024]
Abstract
A 1D-guided differential rescaling algorithm for a contour plot is developed based on our recently proposed comprehensive two-dimensional gas chromatography (GC × GC) system with a first-dimensional (1D) detector added. Chromatograms obtained from 1D and second-dimensional (2D) detectors are both incorporated during the data processing. As compared to the conventional contour plot methods using only 2D data, our algorithm can significantly improve precision and consistency of GC × GC results in terms of retention times, peak widths, and peak areas or volumes, regardless of modulation time selection, modulation phase shift fluctuations, and modulation duty cycle. The peak identification, quantification, and capacity can therefore be enhanced. Furthermore, the 1D-guided differential rescaling method is shown to better handle the coelution and missing peak issues often encountered in the conventional methods. Finally, the new method exhibits high versatility in 1D and 2D detector selection, which greatly broadens GC × GC utility. Our method can easily be adapted to other two-dimensional chromatography systems that have direct access to 1D chromatograms.
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Affiliation(s)
- Wenzhe Zang
- Department
of Biomedical Engineering, University of
Michigan, Ann Arbor, Michigan 48109, United States
- Center
for Wireless Integrated MicroSensing and Systems (WIMS), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max
Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Xiaheng Huang
- Department
of Biomedical Engineering, University of
Michigan, Ann Arbor, Michigan 48109, United States
- Center
for Wireless Integrated MicroSensing and Systems (WIMS), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max
Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department
of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ruchi Sharma
- Department
of Biomedical Engineering, University of
Michigan, Ann Arbor, Michigan 48109, United States
- Center
for Wireless Integrated MicroSensing and Systems (WIMS), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max
Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Xudong Fan
- Department
of Biomedical Engineering, University of
Michigan, Ann Arbor, Michigan 48109, United States
- Center
for Wireless Integrated MicroSensing and Systems (WIMS), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max
Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
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6
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Sutaria SR, Morris JD, Xie Z, Cooke EA, Silvers SM, Long GA, Balcom D, Marimuthu S, Parrish LW, Aliesky H, Arnold FW, Huang J, Fu XA, Nantz MH. A feasibility study on exhaled breath analysis using UV spectroscopy to detect COVID-19. J Breath Res 2023; 18:016004. [PMID: 37875100 PMCID: PMC10620812 DOI: 10.1088/1752-7163/ad0646] [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: 04/28/2023] [Revised: 09/14/2023] [Accepted: 10/24/2023] [Indexed: 10/26/2023]
Abstract
A 23-subject feasibility study is reported to assess how UV absorbance measurements on exhaled breath samples collected from silicon microreactors can be used to detect COVID-19. The silicon microreactor technology chemoselectively preconcentrates exhaled carbonyl volatile organic compounds and subsequent methanol elution provides samples for analysis. The underlying scientific rationale that viral infection will induce an increase in exhaled carbonyls appears to be supported by the results of the feasibility study. The data indicate statistically significant differences in measured UV absorbance values between healthy and symptomatic COVID-19 positive subjects in the wavelength range from 235 nm to 305 nm. Factors such as subject age were noted as potential confounding variables.
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Affiliation(s)
- Saurin R Sutaria
- Departments of Chemistry, University of Louisville, Louisville, KY 40292, United States of America
| | - James D Morris
- Chemical Engineering, University of Louisville, Louisville, KY 40292, United States of America
| | - Zhenzhen Xie
- Chemical Engineering, University of Louisville, Louisville, KY 40292, United States of America
| | - Elizabeth A Cooke
- Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Shavonne M Silvers
- Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Grace A Long
- Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Dawn Balcom
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Subathra Marimuthu
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Leslie W Parrish
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Holly Aliesky
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Forest W Arnold
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Jiapeng Huang
- Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Xiao-An Fu
- Chemical Engineering, University of Louisville, Louisville, KY 40292, United States of America
| | - Michael H Nantz
- Departments of Chemistry, University of Louisville, Louisville, KY 40292, United States of America
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7
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Cao L, Zang W, Sharma R, Tabartehfarahani A, Thota C, Devi Sivakumar A, Lam A, Fan X, Ward KR, Ansari S. Automated Gas Chromatography Peak Alignment: A Deep Learning Approach using Greedy Optimization and Simulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082708 DOI: 10.1109/embc40787.2023.10340662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The clinical significance of volatile organic compounds (VOC) in detecting diseases has been established over the past decades. Gas chromatography (GC) devices enable the measurement of these VOCs. Chromatographic peak alignment is one of the important yet challenging steps in analyzing chromatogram signals. Traditional semi-automated alignment algorithms require manual intervention by an operator which is slow, expensive and inconsistent. A pipeline is proposed to train a deep-learning model from artificial chromatograms simulated from a small, annotated dataset, and a postprocessing step based on greedy optimization to align the signals.Clinical Relevance- Breath VOCs have been shown to have a significant diagnostic power for various diseases including asthma, acute respiratory distress syndrome and COVID-19. Automatic analysis of chromatograms can lead to improvements in the diagnosis and management of such diseases.
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Li J, Hannon A, Yu G, Idziak LA, Sahasrabhojanee A, Govindarajan P, Maldonado YA, Ngo K, Abdou JP, Mai N, Ricco AJ. Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection. ACS Sens 2023; 8:2309-2318. [PMID: 37224474 DOI: 10.1021/acssensors.3c00367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We adapted an existing, spaceflight-proven, robust "electronic nose" (E-Nose) that uses an array of electrical resistivity-based nanosensors mimicking aspects of mammalian olfaction to conduct on-site, rapid screening for COVID-19 infection by measuring the pattern of sensor responses to volatile organic compounds (VOCs) in exhaled human breath. We built and tested multiple copies of a hand-held prototype E-Nose sensor system, composed of 64 chemically sensitive nanomaterial sensing elements tailored to COVID-19 VOC detection; data acquisition electronics; a smart tablet with software (App) for sensor control, data acquisition and display; and a sampling fixture to capture exhaled breath samples and deliver them to the sensor array inside the E-Nose. The sensing elements detect the combination of VOCs typical in breath at parts-per-billion (ppb) levels, with repeatability of 0.02% and reproducibility of 1.2%; the measurement electronics in the E-Nose provide measurement accuracy and signal-to-noise ratios comparable to benchtop instrumentation. Preliminary clinical testing at Stanford Medicine with 63 participants, their COVID-19-positive or COVID-19-negative status determined by concomitant RT-PCR, discriminated between these two categories of human breath with a 79% correct identification rate using "leave-one-out" training-and-analysis methods. Analyzing the E-Nose response in conjunction with body temperature and other non-invasive symptom screening using advanced machine learning methods, with a much larger database of responses from a wider swath of the population, is expected to provide more accurate on-the-spot answers. Additional clinical testing, design refinement, and a mass manufacturing approach are the main steps toward deploying this technology to rapidly screen for active infection in clinics and hospitals, public and commercial venues, or at home.
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Affiliation(s)
- Jing Li
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Ami Hannon
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - George Yu
- Variable, Inc., Chattanooga, Tennessee 37406, United States
| | - Luke A Idziak
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | | | | | - Yvonne A Maldonado
- School of Medicine, Stanford University, Stanford, California 94305, United States
| | - Khoa Ngo
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - John P Abdou
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Nghia Mai
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Antonio J Ricco
- NASA Ames Research Center, Moffett Field, California 94035, United States
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9
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Huang X, Sharma R, Sivakumar AD, Yang S, Fan X. Ultrathin Silica Integration for Enhancing Reliability of Microfluidic Photoionization Detectors. Anal Chem 2023; 95:8496-8504. [PMID: 37278057 DOI: 10.1021/acs.analchem.3c00354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Microfluidic photoionization detectors (μPIDs) based on silicon chips can rapidly and sensitively detect volatile compounds. However, the applications of μPID are limited by the manual assembly process using glue, which may outgas and clog the fluidic channel, and by the short lifetime of the vacuum ultraviolet (VUV) lamps (especially, argon lamps). Here, we developed a gold-gold cold welding-based microfabrication process to integrate ultrathin (10 nm) silica into μPID. The silica coating enables direct bonding of the VUV window to silicon under amicable conditions and works as a moisture and plasma exposure barrier for VUV windows that are susceptible to hygroscopicity and solarization. Detailed characterization of the silica coating was conducted, showing that the 10 nm silica coating allows 40-80% VUV transmission from 8.5 to 11.5 eV. It is further shown that the silica-protected μPID maintained 90% of its original sensitivity after 2200 h of exposure to ambient (dew point = 8.0 ± 1.8 °C), compared to 39% without silica. Furthermore, argon plasma inside an argon VUV lamp was identified as the dominant degradation source for the LiF window with color centers formation in UV-vis and VUV transmission spectra. Ultrathin silica was then also demonstrated effective in protecting the LiF from argon plasma exposure. Lastly, thermal annealing was found to bleach the color centers and restore VUV transmission of degraded LiF windows effectively, which will lead to future development of a new type of VUV lamp and the corresponding μPID (and PID in general) that can be mass produced with a high yield, a longer lifetime, and better regenerability.
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Affiliation(s)
- Xiaheng Huang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ruchi Sharma
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Anjali Devi Sivakumar
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Shuo Yang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Xudong Fan
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Center for Wireless Integrated MicroSensing and Systems (WIMS2), University of Michigan, Ann Arbor, Michigan 48109, United States
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan 48109, United States
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