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Takallu S, Aiyelabegan HT, Zomorodi AR, Alexandrovna KV, Aflakian F, Asvar Z, Moradi F, Behbahani MR, Mirzaei E, Sarhadi F, Vakili-Ghartavol R. Nanotechnology improves the detection of bacteria: Recent advances and future perspectives. Heliyon 2024; 10:e32020. [PMID: 38868076 PMCID: PMC11167352 DOI: 10.1016/j.heliyon.2024.e32020] [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: 02/28/2024] [Revised: 04/23/2024] [Accepted: 05/27/2024] [Indexed: 06/14/2024] Open
Abstract
Nanotechnology has advanced significantly, particularly in biomedicine, showing promise for nanomaterial applications. Bacterial infections pose persistent public health challenges due to the lack of rapid pathogen detection methods, resulting in antibiotic overuse and bacterial resistance, threatening the human microbiome. Nanotechnology offers a solution through nanoparticle-based materials facilitating early bacterial detection and combating resistance. This study explores recent research on nanoparticle development for controlling microbial infections using various nanotechnology-driven detection methods. These approaches include Surface Plasmon Resonance (SPR) Sensors, Surface-Enhanced Raman Scattering (SERS) Sensors, Optoelectronic-based sensors, Bacteriophage-Based Sensors, and nanotechnology-based aptasensors. These technologies provide precise bacteria detection, enabling targeted treatment and infection prevention. Integrating nanoparticles into detection approaches holds promise for enhancing patient outcomes and mitigating harmful bacteria spread in healthcare settings.
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Affiliation(s)
- Sara Takallu
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Abolfazl Rafati Zomorodi
- Department of Bacteriology & Virology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Fatemeh Aflakian
- Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Zahra Asvar
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Farhad Moradi
- Department of Bacteriology & Virology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mahrokh Rajaee Behbahani
- Department of Bacteriology & Virology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Esmaeil Mirzaei
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Firoozeh Sarhadi
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Roghayyeh Vakili-Ghartavol
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
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Xie X, Yu W, Wang L, Yang J, Tu X, Liu X, Liu S, Zhou H, Chi R, Huang Y. SERS-based AI diagnosis of lung and gastric cancer via exhaled breath. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 314:124181. [PMID: 38527410 DOI: 10.1016/j.saa.2024.124181] [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: 12/26/2023] [Revised: 03/13/2024] [Accepted: 03/20/2024] [Indexed: 03/27/2024]
Abstract
Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.
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Affiliation(s)
- Xin Xie
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Wenrou Yu
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Li Wang
- School of Optoelectronics Engineering, Chongqing University, Chongqing 401331, China
| | - Junjun Yang
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Xiaobin Tu
- Department of Oncology and Department of Hematology, Chongqing Wulong People's Hospital, Chongqing 408500, China
| | - Xiaochun Liu
- Department of Oncology and Department of Hematology, Chongqing Wulong People's Hospital, Chongqing 408500, China
| | - Shihong Liu
- Department of Geriatric Oncology and Department of Palliative Care, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Han Zhou
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Runwei Chi
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Yingzhou Huang
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China.
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Liu BN, Gao XL, Piao Y. Mapping the intellectual structure and emerging trends for the application of nanomaterials in gastric cancer: A bibliometric study. World J Gastrointest Oncol 2024; 16:2181-2199. [PMID: 38764848 PMCID: PMC11099444 DOI: 10.4251/wjgo.v16.i5.2181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/11/2024] [Accepted: 03/21/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Recent reviews have outlined the main nanomaterials used in relation to gastrointestinal tumors and described the basic properties of these materials. However, the research hotspots and trends in the application of nanomaterials in gastric cancer (GC) remain obscure. AIM To demonstrate the knowledge structure and evolutionary trends of research into the application of nanomaterials in GC. METHODS Publications related to the application of nanomaterials in GC were retrieved from the Web of Science Core Collection for this systematic review and bibliometric study. VOSviewer and CiteSpace were used for bibliometric and visualization analyses. RESULTS From 2000 to 2022, the application of nanomaterials in GC developed rapidly. The keyword co-occurrence analysis showed that the related research topics were divided into three clusters: (1) The application of nanomaterials in GC treatment; (2) The application and toxicity of nanomaterials in GC diagnosis; and (3) The effects of nanomaterials on the biological behavior of GC cells. Complexes, silver nanoparticles, and green synthesis are the latest high-frequency keywords that represent promising future research directions. CONCLUSION The application of nanomaterials in GC diagnosis and treatment and the mechanisms of their effects on GC cells have been major themes in this field over the past 23 years.
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Affiliation(s)
- Bo-Na Liu
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang 110015, Liaoning Province, China
| | - Xiao-Li Gao
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang 110015, Liaoning Province, China
| | - Ying Piao
- Department of Oncology, General Hospital of Northern Theater Command, Shenyang 110015, Liaoning Province, China
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Kwon H, Kamboj O, Song A, Alarcón-Correa M, Remke J, Moafian F, Miksch B, Goyal R, Kim DY, Hamprecht FA, Fischer P. Scalable Optical Nose Realized with a Chemiresistively Modulated Light-Emitter Array. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2402287. [PMID: 38696529 DOI: 10.1002/adma.202402287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/10/2024] [Indexed: 05/04/2024]
Abstract
Biological olfaction relies on a large number of receptors that function as sensors to detect gaseous molecules. It is challenging to realize artificial olfactory systems that contain similarly large numbers of sensory materials. It is shown that combinatorial materials processing with vapor deposition can be used to fabricate large arrays of distinct chemiresistive sensing materials. By combining these with light-emitting diodes, an array of chemiresistively-modulated light-emitting diodes, or ChemLEDs, that permit a simultaneous optical read-out in response to an analyte is obtained. The optical nose uses a common voltage source and ground for all sensing elements and thus eliminates the need for complex wiring of individual sensors. This optical nose contains one hundred ChemLEDs and generates unique light patterns in response to gases and their mixtures. Optical pattern recognition methods enable the quantitative prediction of the corresponding concentrations and compositions, thereby paving the way for massively parallel artificial olfactory systems. ChemLEDs open the possibility to explore demanding gas sensing applications, including in environmental, food quality monitoring, and potentially diagnostic settings.
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Affiliation(s)
- Hyunah Kwon
- Institute for Molecular Systems Engineering and Advanced Materials, Heidelberg University, INF 225, 69120, Heidelberg, Germany
- Max Planck Institute for Medical Research, Jahnstrasse 29, 69120, Heidelberg, Germany
| | - Ocima Kamboj
- IWR, Heidelberg University, INF 205, 69120, Heidelberg, Germany
| | - Alexander Song
- Institute for Molecular Systems Engineering and Advanced Materials, Heidelberg University, INF 225, 69120, Heidelberg, Germany
- Max Planck Institute for Medical Research, Jahnstrasse 29, 69120, Heidelberg, Germany
| | - Mariana Alarcón-Correa
- Institute for Molecular Systems Engineering and Advanced Materials, Heidelberg University, INF 225, 69120, Heidelberg, Germany
- Max Planck Institute for Medical Research, Jahnstrasse 29, 69120, Heidelberg, Germany
| | - Julia Remke
- IWR, Heidelberg University, INF 205, 69120, Heidelberg, Germany
| | - Fahimeh Moafian
- IWR, Heidelberg University, INF 205, 69120, Heidelberg, Germany
| | - Björn Miksch
- Max Planck Institute for Intelligent Systems, Heisenbergstrasse 3, 70569, Stuttgart, Germany
| | - Rahul Goyal
- Institute for Molecular Systems Engineering and Advanced Materials, Heidelberg University, INF 225, 69120, Heidelberg, Germany
- Max Planck Institute for Medical Research, Jahnstrasse 29, 69120, Heidelberg, Germany
| | - Dong Yeong Kim
- Max Planck Institute for Solid State Research, Heisenbergstrasse 1, 70569, Stuttgart, Germany
- Major of Semiconductor Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 48513, Republic of Korea
| | | | - Peer Fischer
- Institute for Molecular Systems Engineering and Advanced Materials, Heidelberg University, INF 225, 69120, Heidelberg, Germany
- Max Planck Institute for Medical Research, Jahnstrasse 29, 69120, Heidelberg, Germany
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, 03722, Republic of Korea
- Department of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul, 03722, Republic of Korea
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5
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Saeki Y, Maki N, Nemoto T, Inada K, Minami K, Tamura R, Imamura G, Cho-Isoda Y, Kitazawa S, Kojima H, Yoshikawa G, Sato Y. Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array. Lung Cancer 2024; 190:107514. [PMID: 38447302 DOI: 10.1016/j.lungcan.2024.107514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/12/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
INTRODUCTION Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. METHODS Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. RESULTS Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. CONCLUSION Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.
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Affiliation(s)
- Yusuke Saeki
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Naoki Maki
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Takahiro Nemoto
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Katsushige Inada
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan
| | - Kosuke Minami
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; International Center for Young Scientists (ICYS), National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Ryo Tamura
- World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Ibaraki, Japan; Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan; Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Ibaraki, Japan; Center for Basic Research on Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan
| | - Gaku Imamura
- Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; World Premier International (WPI) Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Ibaraki, Japan; Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Yukiko Cho-Isoda
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan
| | - Shinsuke Kitazawa
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan
| | - Hiroshi Kojima
- Department of Medical Oncology, Ibaraki Prefectural Central Hospital, Ibaraki, Japan; Ibaraki Clinical Education and Training Center, University of Tsukuba Hospital, Ibaraki, Japan
| | - Genki Yoshikawa
- Center for Functional Sensor & Actuator (CFSN), Research Center for Functional Materials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki, Japan; Materials Science and Engineering, Graduate School of Pure and Applied Science, University of Tsukuba, Ibaraki, Japan
| | - Yukio Sato
- Department of Thoracic Surgery, University of Tsukuba, Ibaraki, Japan.
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Mezmale L, Ślefarska-Wolak D, Bhandari MP, Ager C, Veliks V, Patsko V, Lukashenko A, Dias-Neto E, Nunes DN, Bartelli TF, Pelosof AG, Sztokfisz CZ, Murillo R, Królicka A, Mayhew CA, Leja M, Haick H, Mochalski P. Volatilomic profiles of gastric juice in gastric cancer patients. J Breath Res 2024; 18:026010. [PMID: 38467063 DOI: 10.1088/1752-7163/ad324f] [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: 06/19/2023] [Accepted: 03/11/2024] [Indexed: 03/13/2024]
Abstract
Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer.
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Affiliation(s)
- Linda Mezmale
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga, Latvia
- Riga East University Hospital, Riga, Latvia
- Riga Stradins University, LV-1007, Riga, Latvia
| | - Daria Ślefarska-Wolak
- Institute for Breath Research, Universität Innsbruck, Innsbruck and Dornbirn, Austria
- Institute of Chemistry, Jan Kochanowski University of Kielce, Kielce, Poland
| | - Manohar Prasad Bhandari
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga, Latvia
| | - Clemens Ager
- Institute for Breath Research, Universität Innsbruck, Innsbruck and Dornbirn, Austria
| | - Viktors Veliks
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga, Latvia
| | | | | | - Emmanuel Dias-Neto
- Medical Genomics group and Endoscopy Center, A.C.Camargo Cancer Center, São Paulo, Brazil
| | - Diana Noronha Nunes
- Medical Genomics group and Endoscopy Center, A.C.Camargo Cancer Center, São Paulo, Brazil
| | | | | | | | - Raúl Murillo
- University Hospital San Ignacio, Bogotá, Colombia
| | - Agnieszka Królicka
- Department of Building Materials Technology, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30, Krakow, Poland
| | - Chris A Mayhew
- Institute for Breath Research, Universität Innsbruck, Innsbruck and Dornbirn, Austria
| | - Marcis Leja
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga, Latvia
- Riga East University Hospital, Riga, Latvia
- Digestive Diseases Centre GASTRO, Riga, Latvia
| | - Hossam Haick
- Department of Chemical Engineering and Russel Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
| | - Pawel Mochalski
- Institute for Breath Research, Universität Innsbruck, Innsbruck and Dornbirn, Austria
- Institute of Chemistry, Jan Kochanowski University of Kielce, Kielce, Poland
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Noriega Landa E, Quaye GE, Su X, Badmos S, Holbrook KL, Polascik TJ, Adams ES, Deivasigamani S, Gao Q, Annabi MH, Habib A, Lee WY. Urinary fatty acid biomarkers for prostate cancer detection. PLoS One 2024; 19:e0297615. [PMID: 38335180 PMCID: PMC10857612 DOI: 10.1371/journal.pone.0297615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/09/2024] [Indexed: 02/12/2024] Open
Abstract
The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.
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Affiliation(s)
- Elizabeth Noriega Landa
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, Texas, United States of America
| | - George E. Quaye
- Department of Mathematical Sciences, University of Texas at El Paso, El Paso, Texas, United States of America
| | - Xiaogang Su
- Department of Mathematical Sciences, University of Texas at El Paso, El Paso, Texas, United States of America
| | - Sabur Badmos
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, Texas, United States of America
| | - Kiana L. Holbrook
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, Texas, United States of America
| | - Thomas J. Polascik
- Department of Urological Surgery, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Eric S. Adams
- Department of Urological Surgery, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Sriram Deivasigamani
- Department of Urological Surgery, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Qin Gao
- Biologics Analytical Operations, Gilead Sciences Incorporated, Oceanside, California, United States of America
| | | | - Ahsan Habib
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, Texas, United States of America
| | - Wen-Yee Lee
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, Texas, United States of America
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8
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Omar R, Saliba W, Khatib M, Zheng Y, Pieters C, Oved H, Silberman E, Zohar O, Hu Z, Kloper V, Broza YY, Dvir T, Grinberg Dana A, Wang Y, Haick H. Biodegradable, Biocompatible, and Implantable Multifunctional Sensing Platform for Cardiac Monitoring. ACS Sens 2024; 9:126-138. [PMID: 38170944 PMCID: PMC10825867 DOI: 10.1021/acssensors.3c01755] [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: 08/24/2023] [Revised: 11/17/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024]
Abstract
Cardiac monitoring after heart surgeries is crucial for health maintenance and detecting postoperative complications early. However, current methods like rigid implants have limitations, as they require performing second complex surgeries for removal, increasing infection and inflammation risks, thus prompting research for improved sensing monitoring technologies. Herein, we introduce a nanosensor platform that is biodegradable, biocompatible, and integrated with multifunctions, suitable for use as implants for cardiac monitoring. The device has two electrochemical biosensors for sensing lactic acid and pH as well as a pressure sensor and a chemiresistor array for detecting volatile organic compounds. Its biocompatibility with myocytes has been tested in vitro, and its biodegradability and sensing function have been proven with ex vivo experiments using a three-dimensional (3D)-printed heart model and 3D-printed cardiac tissue patches. Moreover, an artificial intelligence-based predictive model was designed to fuse sensor data for more precise health assessment, making it a suitable candidate for clinical use. This sensing platform promises impactful applications in the realm of cardiac patient care, laying the foundation for advanced life-saving developments.
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Affiliation(s)
- Rawan Omar
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Walaa Saliba
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Muhammad Khatib
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Youbin Zheng
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Calvin Pieters
- Department
of Chemical Engineering, Technion-Israel
Institute of Technology, Haifa 320003, Israel
| | - Hadas Oved
- Shmunis
School of Biomedicine and Cancer Research, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eric Silberman
- Shmunis
School of Biomedicine and Cancer Research, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Orr Zohar
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Zhipeng Hu
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Viki Kloper
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Yoav Y. Broza
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Tal Dvir
- Shmunis
School of Biomedicine and Cancer Research, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Department
Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- The
Chaoul Center for Nanoscale Systems, Tel
Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv 6997801, Israel
- Sagol Center
for Regenerative Biotechnology, Tel Aviv
University, Tel Aviv 6997801, Israel
| | - Alon Grinberg Dana
- Department
of Chemical Engineering, Technion-Israel
Institute of Technology, Haifa 320003, Israel
| | - Yan Wang
- Department
of Chemical Engineering, Guangdong Technion-Israel
Institute of Technology (GTIIT), Shantou 515063, Guangdong, China
| | - Hossam Haick
- Department
of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
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Martin JDM, Claudia F, Romain AC. How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment. J Breath Res 2024; 18:026002. [PMID: 38211310 DOI: 10.1088/1752-7163/ad1d64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between 'healthy' samples (breath without additives) and 'cancer' samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.
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Affiliation(s)
- Justin D M Martin
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| | - Falzone Claudia
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| | - Anne-Claude Romain
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
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10
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Badmos S, Noriega-Landa E, Holbrook KL, Quaye GE, Su X, Gao Q, Chacon AA, Adams E, Polascik TJ, Feldman AS, Annabi MM, Lee WY. Urinary volatile organic compounds in prostate cancer biopsy pathologic risk stratification using logistic regression and multivariate analysis models. Am J Cancer Res 2024; 14:192-209. [PMID: 38323272 PMCID: PMC10839326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/15/2023] [Indexed: 02/08/2024] Open
Abstract
Prostate cancer (PCa) is the second leading cause of cancer-related death in American men after lung cancer. The current PCa diagnostic method, the serum prostate-specific antigen (PSA) test, is not specific, thus, alternatives are needed to avoid unnecessary biopsies and over-diagnosis of clinically insignificant PCa. To explore the application of metabolomics in such effort, urine samples were collected from 386 male adults aged 44-93 years, including 247 patients with biopsy-proven PCa and 139 with biopsy-proven negative results. The PCa-positive group was further subdivided into two groups: low-grade (ISUP Grade Group = 1; n = 139) and intermediate/high-grade (ISUP Grade Group ≥ 2; n = 108). Volatile organic compounds (VOCs) in urine were extracted by stir bar sorptive extraction (SBSE) and analyzed using thermal desorption with gas chromatography and mass spectrometry (GC-MS). We used machine learning tools to develop and evaluate models for PCa diagnosis and prognosis. In total, 22,538 VOCs were identified in the urine samples. With regularized logistic regression, our model for PCa diagnosis yielded an area under the curve (AUC) of 0.99 and 0.88 for the training and testing sets respectively. Furthermore, the model for differentiating between low-grade and intermediate/high-grade PCa yielded an average AUC of 0.78 based on a repeated test-sample approach for cross-validation. These novel methods using urinary VOCs and logistic regression were developed to fill gaps in PCa screening and assessment of PCa grades prior to biopsy. Our study findings provide a promising alternative or adjunct to current PCa screening and diagnostic methods to better target patients for biopsy and mitigate the challenges associated with over-diagnosis and over-treatment of PCa.
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Affiliation(s)
- Sabur Badmos
- Department of Chemistry and Biochemistry, University of Texas at El PasoEl Paso, Texas, USA
| | | | - Kiana L Holbrook
- Department of Chemistry and Biochemistry, University of Texas at El PasoEl Paso, Texas, USA
| | - George E Quaye
- Department of Mathematical Sciences, University of Texas at El PasoEl Paso, Texas, USA
| | - Xiaogang Su
- Department of Mathematical Sciences, University of Texas at El PasoEl Paso, Texas, USA
| | - Qin Gao
- Department of Chemistry and Biochemistry, University of Texas at El PasoEl Paso, Texas, USA
- PDM Biologics Analytical Operations, Gilead Sciences Inc.Oceanside, California, USA
| | - Angelica A Chacon
- Department of Chemistry and Biochemistry, University of Texas at El PasoEl Paso, Texas, USA
| | - Eric Adams
- Department of Urological Surgery, Duke University Medical CenterDurham, North Carolina, USA
| | - Thomas J Polascik
- Department of Urological Surgery, Duke University Medical CenterDurham, North Carolina, USA
| | - Adam S Feldman
- Department of Urology, Massachusetts General HospitalBoston, Massachusetts, USA
| | | | - Wen-Yee Lee
- Department of Chemistry and Biochemistry, University of Texas at El PasoEl Paso, Texas, USA
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11
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Gallos IK, Tryfonopoulos D, Shani G, Amditis A, Haick H, Dionysiou DD. Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions. Diagnostics (Basel) 2023; 13:3673. [PMID: 38132257 PMCID: PMC10743128 DOI: 10.3390/diagnostics13243673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.
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Affiliation(s)
- Ioannis K. Gallos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Dimitrios Tryfonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Gidi Shani
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Angelos Amditis
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Hossam Haick
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Dimitra D. Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
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12
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Gaye O, Fall CB, Jalloh M, Faye B, Jobin M, Cussenot O. Detection of urological cancers by the signature of organic volatile compounds in urine, from dogs to electronic noses. Curr Opin Urol 2023; 33:437-444. [PMID: 37678152 DOI: 10.1097/mou.0000000000001128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
PURPOSE OF REVIEW Urine volatile organic compound (VOC) testing for early detection of urological cancers is a minimally invasive and promising method. The objective of this review was to present the results of recently published work on this subject. RECENT FINDINGS Organic volatile compounds are produced through oxidative stress and peroxidation of cell membranes, and they are eliminated through feces, urine, and sweat. Studies looking for VOCs in urine for the diagnosis of urological cancers have mostly focused on bladder and prostate cancers. However, the number of patients included in the studies was small. The electronic nose was the most widely used means of detecting VOCs in urine for the detection of urological cancers. MOS sensors and pattern recognition machine learning were more used for the composition of electronic noses. Early detection of urological cancers by detection of VOCs in urine is a method with encouraging results with sensitivities ranging from 27 to 100% and specificities ranging from 72 to 94%. SUMMARY The olfactory signature of urine from patients with urological cancers is a promising biomarker for the early diagnosis of urological cancers. The electronic nose with its ability to recognize complex odors is an excellent alterative to canine diagnosis and analytical techniques. Nevertheless, additional research improving the technology of Enoses and the methodology of the studies is necessary for its implementation in daily clinical practice.
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Affiliation(s)
- Oumar Gaye
- Urology Department, Dalal Jamm Hospital
- University Cheikh Anta Diop
| | | | - Mohamed Jalloh
- Urology Department, Idrissa Pouye General Hospital, Dakar, Senegal
| | | | - Marc Jobin
- HEPIA, University of Applied Sciences of Western Switzerland (HES-SO), Genève, Switzerland
| | - Olivier Cussenot
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- CeRePP, Paris, France
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13
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Jiao B, Zhang S, Bei Y, Bu G, Yuan L, Zhu Y, Yang Q, Xu T, Zhou L, Liu Q, Ouyang Z, Yang X, Feng Y, Tang B, Chen H, Shen L. A detection model for cognitive dysfunction based on volatile organic compounds from a large Chinese community cohort. Alzheimers Dement 2023; 19:4852-4862. [PMID: 37032600 DOI: 10.1002/alz.13053] [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: 10/18/2022] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 04/11/2023]
Abstract
INTRODUCTION We explored whether volatile organic compound (VOC) detection can serve as a screening tool to distinguish cognitive dysfunction (CD) from cognitively normal (CN) individuals. METHODS The cognitive function of 1467 participants was assessed and their VOCs were detected. Six machine learning algorithms were conducted and the performance was determined. The plasma neurofilament light chain (NfL) was measured. RESULTS Distinguished VOC patterns existed between CD and CN groups. The CD detection model showed good accuracy with an area under the receiver-operating characteristic curve (AUC) of 0.876. In addition, we found that 10 VOC ions showed significant differences between CD and CN individuals (p < 0.05); three VOCs were significantly related to plasma NfL (p < 0.005). Moreover, a combination of VOCs with NfL showed the best discriminating power (AUC = 0.877). DISCUSSION Detection of VOCs from exhaled breath samples has the potential to provide a novel solution for the dilemma of CD screening.
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Affiliation(s)
- Bin Jiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Sizhe Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yuzhang Bei
- Department of Neurology, Liuyang Jili Hospital, Changsha, China
| | - Guiwen Bu
- Department of Neurology, Liuyang Jili Hospital, Changsha, China
| | - Li Yuan
- Department of Neurology, Liuyang Jili Hospital, Changsha, China
| | - Yuan Zhu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qijie Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Tianyan Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Lu Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qianqian Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ziyu Ouyang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xuan Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Feng
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Haibin Chen
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
- Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China
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14
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Cao H, Shi H, Tang J, Xu Y, Ling Y, Lu X, Yang Y, Zhang X, Wang H. Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip. iScience 2023; 26:107821. [PMID: 37731613 PMCID: PMC10507157 DOI: 10.1016/j.isci.2023.107821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/06/2023] [Accepted: 08/31/2023] [Indexed: 09/22/2023] Open
Abstract
Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace aldehydes at the ppt level. For rapid preconcentration, we demonstrate that the nozzle-like microfluidic concentrator allows the enrichment of rare gaseous analytes by five-fold in only 0.01 ms. The enriched gas is subsequently captured and detected by an integrated silicon-based SERS chip, which is made of zeolitic imidazolate framework-8 coated silver nanoparticles grown in situ on a silicon wafer. After SERS measurement, a fully connected deep neural network is built to extract faint features in the spectral dataset and discriminate volatile organic compound classes. We demonstrate that six kinds of gaseous aldehydes at 100 ppt could be detected and classified with an identification accuracy of ∼80.9% by using this platform.
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Affiliation(s)
- Haiting Cao
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China
| | - Huayi Shi
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China
| | - Jie Tang
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China
| | - Yanan Xu
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China
| | - Yufan Ling
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, 199 Renai Road, Suzhou 215123, China
| | - Xing Lu
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China
| | - Yang Yang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Xiaojie Zhang
- Department of Experimental Center, Medical College of Soochow University, Suzhou, Jiangsu 215123, China
| | - Houyu Wang
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China
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15
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Poeta E, Liboà A, Mistrali S, Núñez-Carmona E, Sberveglieri V. Nanotechnology and E-Sensing for Food Chain Quality and Safety. SENSORS (BASEL, SWITZERLAND) 2023; 23:8429. [PMID: 37896524 PMCID: PMC10610592 DOI: 10.3390/s23208429] [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: 08/03/2023] [Revised: 10/02/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023]
Abstract
Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.
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Affiliation(s)
- Elisabetta Poeta
- Department of Life Sciences, University of Modena and Reggio Emilia, Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy
| | - Aris Liboà
- Department of Chemistry, Life Science and Environmental Sustainability, University of Parma, Parco Area delle Scienze, 11/a, 43124 Parma, PR, Italy;
| | - Simone Mistrali
- Nano Sensor System srl (NASYS), Via Alfonso Catalani, 9, 42124 Reggio Emilia, RE, Italy;
| | - Estefanía Núñez-Carmona
- National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy;
| | - Veronica Sberveglieri
- Nano Sensor System srl (NASYS), Via Alfonso Catalani, 9, 42124 Reggio Emilia, RE, Italy;
- National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy;
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16
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Mansour E, Saliba W, Broza YY, Frankfurt O, Zuri L, Ginat K, Palzur E, Shamir A, Haick H. Continuous Monitoring of Psychosocial Stress by Non-Invasive Volatilomics. ACS Sens 2023; 8:3215-3224. [PMID: 37494456 DOI: 10.1021/acssensors.3c00945] [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: 07/28/2023]
Abstract
Stress is becoming increasingly commonplace in modern times, making it important to have accurate and effective detection methods. Currently, detection methods such as self-evaluation and clinical questionnaires are subjective and unsuitable for long-term monitoring. There have been significant studies into biomarkers such as HRV, cortisol, electrocardiography, and blood biomarkers, but the use of multiple electrodes for electrocardiography or blood tests is impractical for real-time stress monitoring. To this end, there is a need for non-invasive sensors to monitor stress in real time. This study looks at the possibility of using breath and skin VOC fingerprinting as stress biomarkers. The Trier social stress test (TSST) was used to induce acute stress and HRV, cortisol, and anxiety levels were measured before, during, and after the test. GC-MS and sensor array were used to collect and measure VOCs. A prediction model found eight different stress-related VOCs with an accuracy of up to 78%, and a molecularly capped gold nanoparticle-based sensor revealed a significant difference in breath VOC fingerprints between the two groups. These stress-related VOCs either changed or returned to baseline after the stress induction, suggesting different metabolic pathways at different times. A correlation analysis revealed an association between VOCs and cortisol levels and a weak correlation with either HRV or anxiety levels, suggesting that VOCs may include complementary information in stress detection. This study shows the potential of VOCs as stress biomarkers, paving the way into developing a real-time, objective, non-invasive stress detection tool for well-being and early detection of stress-related diseases.
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Affiliation(s)
- Elias Mansour
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Walaa Saliba
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Yoav Y Broza
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Ora Frankfurt
- Maale Hacarmel Mental Health Center, Tirat Carmel 3911917, Israel
| | - Liat Zuri
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Karen Ginat
- Mazor Mental Health Center, Akko 2423314, Israel
| | - Eilam Palzur
- Eliachar Research Laboratory, Galilee Medical Center, P.O. Box 21, Nahariya 2210001, Israel
| | - Alon Shamir
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 3200003, Israel
- Mazor Mental Health Center, Akko 2423314, Israel
| | - Hossam Haick
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
- The Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
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17
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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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18
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Walsh CM, Fadel MG, Jamel SH, Hanna GB. Breath Testing in the Surgical Setting: Applications, Challenges, and Future Perspectives. Eur Surg Res 2023; 64:315-322. [PMID: 37311421 PMCID: PMC10614239 DOI: 10.1159/000531504] [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: 02/17/2023] [Accepted: 06/05/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND The potential for exhaled breath to be a valuable diagnostic tool is often overlooked as it can be difficult to imagine how a barely visible sample of breath could hold such a rich source of information about the state of our health. However, technological advances over the last 50 years have enabled us to detect volatile organic compounds (VOCs) present in exhaled breath, and this provides the key to understanding the wealth of information contained within these readily available samples. SUMMARY VOCs are produced as a by-product of metabolism; hence, changes in the underlying physiological processes will be reflected in the exact composition of VOCs in exhaled breath. It has been shown that characteristic changes occur in the breath VOC profile associated with certain diseases including cancer, which may enable the non-invasive detection of cancer at primary care level for patients with vague symptoms. The use of breath testing as a diagnostic tool has many advantages. It is non-invasive and quick, and the test is widely accepted by patients and clinicians. However, breath samples provide a snapshot of the VOCs present in a particular patient at a given point in time, so this can be heavily influenced by external factors such as diet, smoking, and the environment. These must all be accounted for when attempting to draw conclusions about disease status. This review focuses on the current applications for breath testing in the field of surgery, as well as discussing the challenges encountered with developing a breath test in a clinical environment. The future of breath testing in the surgical setting is also discussed, including the translation of breath research into clinical practice. KEY MESSAGES Analysis of VOCs in exhaled breath can identify the presence of underlying disease including cancer as well as other infectious or inflammatory conditions. Despite the patient factors, environmental factors, storage, and transport considerations that must be accounted for, breath testing demonstrates ideal characteristics for a triage test, being non-invasive, simple, and universally acceptable to patients and clinicians. Many novel biomarkers and diagnostic tests fail to translate into clinical practice because their potential clinical application does not align with the requirements and unmet needs of the healthcare sector. Non-invasive breath testing, however, has the great potential to revolutionise the early detection of diseases, such as cancer, in the surgical setting for patients with vague symptoms.
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Affiliation(s)
- Caoimhe M Walsh
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Michael G Fadel
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Sara H Jamel
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - George B Hanna
- Department of Surgery and Cancer, Imperial College London, London, UK
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Luo Y, Abidian MR, Ahn JH, Akinwande D, Andrews AM, Antonietti M, Bao Z, Berggren M, Berkey CA, Bettinger CJ, Chen J, Chen P, Cheng W, Cheng X, Choi SJ, Chortos A, Dagdeviren C, Dauskardt RH, Di CA, Dickey MD, Duan X, Facchetti A, Fan Z, Fang Y, Feng J, Feng X, Gao H, Gao W, Gong X, Guo CF, Guo X, Hartel MC, He Z, Ho JS, Hu Y, Huang Q, Huang Y, Huo F, Hussain MM, Javey A, Jeong U, Jiang C, Jiang X, Kang J, Karnaushenko D, Khademhosseini A, Kim DH, Kim ID, Kireev D, Kong L, Lee C, Lee NE, Lee PS, Lee TW, Li F, Li J, Liang C, Lim CT, Lin Y, Lipomi DJ, Liu J, Liu K, Liu N, Liu R, Liu Y, Liu Y, Liu Z, Liu Z, Loh XJ, Lu N, Lv Z, Magdassi S, Malliaras GG, Matsuhisa N, Nathan A, Niu S, Pan J, Pang C, Pei Q, Peng H, Qi D, Ren H, Rogers JA, Rowe A, Schmidt OG, Sekitani T, Seo DG, Shen G, Sheng X, Shi Q, Someya T, Song Y, Stavrinidou E, Su M, Sun X, Takei K, Tao XM, Tee BCK, Thean AVY, Trung TQ, Wan C, Wang H, Wang J, Wang M, Wang S, Wang T, Wang ZL, Weiss PS, Wen H, Xu S, Xu T, Yan H, Yan X, Yang H, Yang L, Yang S, Yin L, Yu C, Yu G, Yu J, Yu SH, Yu X, Zamburg E, Zhang H, Zhang X, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhao S, Zhao X, Zheng Y, Zheng YQ, Zheng Z, Zhou T, Zhu B, Zhu M, Zhu R, Zhu Y, Zhu Y, Zou G, Chen X. Technology Roadmap for Flexible Sensors. ACS NANO 2023; 17:5211-5295. [PMID: 36892156 DOI: 10.1021/acsnano.2c12606] [Citation(s) in RCA: 160] [Impact Index Per Article: 160.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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Affiliation(s)
- Yifei Luo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Innovative Centre for Flexible Devices (iFLEX), School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Mohammad Reza Abidian
- Department of Biomedical Engineering, University of Houston, Houston, Texas 77024, United States
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Anne M Andrews
- Department of Chemistry and Biochemistry, California NanoSystems Institute, and Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, and Hatos Center for Neuropharmacology, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Markus Antonietti
- Colloid Chemistry Department, Max Planck Institute of Colloids and Interfaces, 14476 Potsdam, Germany
| | - Zhenan Bao
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Magnus Berggren
- Laboratory of Organic Electronics, Department of Science and Technology, Campus Norrköping, Linköping University, 83 Linköping, Sweden
- Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC), SE-100 44 Stockholm, Sweden
| | - Christopher A Berkey
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94301, United States
| | - Christopher John Bettinger
- Department of Biomedical Engineering and Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Jun Chen
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Peng Chen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Wenlong Cheng
- Nanobionics Group, Department of Chemical and Biological Engineering, Monash University, Clayton, Australia, 3800
- Monash Institute of Medical Engineering, Monash University, Clayton, Australia3800
| | - Xu Cheng
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, PR China
| | - Seon-Jin Choi
- Division of Materials of Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Alex Chortos
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Canan Dagdeviren
- Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Reinhold H Dauskardt
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94301, United States
| | - Chong-An Di
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Michael D Dickey
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Xiangfeng Duan
- Department of Chemistry and Biochemistry, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Antonio Facchetti
- Department of Chemistry and the Materials Research Center, Northwestern University, Evanston, Illinois 60208, United States
| | - Zhiyong Fan
- Department of Electronic and Computer Engineering and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
| | - Yin Fang
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Jianyou Feng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Xue Feng
- Laboratory of Flexible Electronics Technology, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
| | - Huajian Gao
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California, 91125, United States
| | - Xiwen Gong
- Department of Chemical Engineering, Department of Materials Science and Engineering, Department of Electrical Engineering and Computer Science, Applied Physics Program, and Macromolecular Science and Engineering Program, University of Michigan, Ann Arbor, Michigan, 48109 United States
| | - Chuan Fei Guo
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiaojun Guo
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Martin C Hartel
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Zihan He
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - John S Ho
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
| | - Youfan Hu
- School of Electronics and Center for Carbon-Based Electronics, Peking University, Beijing 100871, China
| | - Qiyao Huang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Yu Huang
- Department of Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Fengwei Huo
- Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, PR China
| | - Muhammad M Hussain
- mmh Labs, Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Ali Javey
- Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Unyong Jeong
- Department of Materials Science and Engineering, Pohang University of Science and Engineering (POSTECH), Pohang, Gyeong-buk 37673, Korea
| | - Chen Jiang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Xingyu Jiang
- Department of Biomedical Engineering, Southern University of Science and Technology, No 1088, Xueyuan Road, Xili, Nanshan District, Shenzhen, Guangdong 518055, PR China
| | - Jiheong Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Daniil Karnaushenko
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz 09126, Germany
| | | | - Dae-Hyeong Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Il-Doo Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Dmitry Kireev
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Lingxuan Kong
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- National University of Singapore Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
- NUS Graduate School-Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Nae-Eung Lee
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Kyunggi-do 16419, Republic of Korea
| | - Pooi See Lee
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Singapore-HUJ Alliance for Research and Enterprise (SHARE), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Engineering Research, Research Institute of Advanced Materials, Seoul National University, Soft Foundry, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Fengyu Li
- College of Chemistry and Materials Science, Jinan University, Guangzhou, Guangdong 510632, China
| | - Jinxing Li
- Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Neuroscience Program, BioMolecular Science Program, and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan 48823, United States
| | - Cuiyuan Liang
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Chwee Teck Lim
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
- Mechanobiology Institute, National University of Singapore, Singapore 117411, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 119276, Singapore
| | - Yuanjing Lin
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Darren J Lipomi
- Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093-0448, United States
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Kai Liu
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Nan Liu
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing 100875, PR China
| | - Ren Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Yuxin Liu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Department of Biomedical Engineering, N.1 Institute for Health, Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119077, Singapore
| | - Yuxuan Liu
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Zhiyuan Liu
- Neural Engineering Centre, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 518055
| | - Zhuangjian Liu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
| | - Nanshu Lu
- Department of Aerospace Engineering and Engineering Mechanics, Department of Electrical and Computer Engineering, Department of Mechanical Engineering, Department of Biomedical Engineering, Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Zhisheng Lv
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
| | - Shlomo Magdassi
- Institute of Chemistry and the Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - George G Malliaras
- Electrical Engineering Division, Department of Engineering, University of Cambridge CB3 0FA, Cambridge United Kingdom
| | - Naoji Matsuhisa
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Arokia Nathan
- Darwin College, University of Cambridge, Cambridge CB3 9EU, United Kingdom
| | - Simiao Niu
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Jieming Pan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Changhyun Pang
- School of Chemical Engineering and Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Qibing Pei
- Department of Materials Science and Engineering, Department of Mechanical and Aerospace Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Huisheng Peng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Dianpeng Qi
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Huaying Ren
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, 90095, United States
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, Illinois 60208, United States
- Department of Materials Science and Engineering, Department of Mechanical Engineering, Department of Biomedical Engineering, Departments of Electrical and Computer Engineering and Chemistry, and Department of Neurological Surgery, Northwestern University, Evanston, Illinois 60208, United States
| | - Aaron Rowe
- Becton, Dickinson and Company, 1268 N. Lakeview Avenue, Anaheim, California 92807, United States
- Ready, Set, Food! 15821 Ventura Blvd #450, Encino, California 91436, United States
| | - Oliver G Schmidt
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz 09126, Germany
- Material Systems for Nanoelectronics, Chemnitz University of Technology, Chemnitz 09107, Germany
- Nanophysics, Faculty of Physics, TU Dresden, Dresden 01062, Germany
| | - Tsuyoshi Sekitani
- The Institute of Scientific and Industrial Research (SANKEN), Osaka University, Osaka, Japan 5670047
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Guozhen Shen
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Xing Sheng
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Center for Flexible Electronics Technology, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - Qiongfeng Shi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- National University of Singapore Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
| | - Takao Someya
- Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Yanlin Song
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing, Beijing 100190, China
| | - Eleni Stavrinidou
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, SE-601 74 Norrkoping, Sweden
| | - Meng Su
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing, Beijing 100190, China
| | - Xuemei Sun
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Kuniharu Takei
- Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
| | - Xiao-Ming Tao
- Research Institute for Intelligent Wearable Systems, School of Fashion and Textiles, Hong Kong Polytechnic University, Hong Kong, China
| | - Benjamin C K Tee
- Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- iHealthtech, National University of Singapore, Singapore 119276, Singapore
| | - Aaron Voon-Yew Thean
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Tran Quang Trung
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Kyunggi-do 16419, Republic of Korea
| | - Changjin Wan
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
| | - Huiliang Wang
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Joseph Wang
- Department of Nanoengineering, University of California, San Diego, California 92093, United States
| | - Ming Wang
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chip and Systems, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China
- the Shanghai Qi Zhi Institute, 41th Floor, AI Tower, No.701 Yunjin Road, Xuhui District, Shanghai 200232, China
| | - Sihong Wang
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois, 60637, United States
| | - Ting Wang
- State Key Laboratory of Organic Electronics and Information Displays and Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- Georgia Institute of Technology, Atlanta, Georgia 30332-0245, United States
| | - Paul S Weiss
- California NanoSystems Institute, Department of Chemistry and Biochemistry, Department of Bioengineering, and Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Hanqi Wen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
- Institute of Flexible Electronics Technology of THU, Jiaxing, Zhejiang, China 314000
| | - Sheng Xu
- Department of Nanoengineering, Department of Electrical and Computer Engineering, Materials Science and Engineering Program, and Department of Bioengineering, University of California San Diego, La Jolla, California, 92093, United States
| | - Tailin Xu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518060, PR China
| | - Hongping Yan
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Xuzhou Yan
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Hui Yang
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, China, 300072
| | - Le Yang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Department of Materials Science and Engineering, National University of Singapore (NUS), 9 Engineering Drive 1, #03-09 EA, Singapore 117575, Singapore
| | - Shuaijian Yang
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Lan Yin
- School of Materials Science and Engineering, The Key Laboratory of Advanced Materials of Ministry of Education, State Key Laboratory of New Ceramics and Fine Processing, and Center for Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Cunjiang Yu
- Department of Engineering Science and Mechanics, Department of Biomedical Engineering, Department of Material Science and Engineering, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania, 16802, United States
| | - Guihua Yu
- Materials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States
| | - Jing Yu
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Shu-Hong Yu
- Department of Chemistry, Institute of Biomimetic Materials and Chemistry, Hefei National Research Center for Physical Science at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Evgeny Zamburg
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Haixia Zhang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Xiangyu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Xiaosheng Zhang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xueji Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, PR China
| | - Yihui Zhang
- Applied Mechanics Laboratory, Department of Engineering Mechanics; Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, PR China
| | - Yu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Siyuan Zhao
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Xuanhe Zhao
- Department of Mechanical Engineering, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - Yuanjin Zheng
- Center for Integrated Circuits and Systems, School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yu-Qing Zheng
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Zijian Zheng
- Department of Applied Biology and Chemical Technology, Faculty of Science, Research Institute for Intelligent Wearable Systems, Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Tao Zhou
- Center for Neural Engineering, Department of Engineering Science and Mechanics, The Huck Institutes of the Life Sciences, Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Ming Zhu
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
| | - Rong Zhu
- Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, Los Angeles, California, 90064, United States
| | - Yong Zhu
- Department of Mechanical and Aerospace Engineering, Department of Materials Science and Engineering, and Department of Biomedical Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Guijin Zou
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Xiaodong Chen
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Laboratory for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Costantini M, Filianoti A, Anceschi U, Bove AM, Brassetti A, Ferriero M, Mastroianni R, Misuraca L, Tuderti G, Ciliberto G, Simone G, Torregiani G. Human Urinary Volatilome Analysis in Renal Cancer by Electronic Nose. BIOSENSORS 2023; 13:bios13040427. [PMID: 37185502 PMCID: PMC10136259 DOI: 10.3390/bios13040427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/14/2023] [Accepted: 03/21/2023] [Indexed: 05/17/2023]
Abstract
Currently, in clinical practice there are still no useful markers available that are able to diagnose renal cancer in the early stages in the context of population screening. This translates into very high costs for healthcare systems around the world. Analysing urine using an electronic nose (EN) provides volatile organic compounds that can be easily used in the diagnosis of urological diseases. Although no convincing results have been published, some previous studies suggest that dogs trained to sniff urine can recognize different types of tumours (bladder, lung, breast cancer) with different success rates. We therefore hypothesized that urinary volatilome profiling may be able to distinguish patients with renal cancer from healthy controls. A total of 252 individuals, 110 renal patients and 142 healthy controls, were enrolled in this pilot monocentric study. For each participant, we collected, stabilized (at 37 °C) and analysed urine samples using a commercially available electronic nose (Cyranose 320®). Principal component (PCA) analyses, discriminant analysis (CDA) and ROC curves were performed to provide a complete statistical analysis of the sensor responses. The best discriminating principal component groups were identified with univariable ANOVA analysis. The study correctly identified 79/110 patients and 127/142 healthy controls, respectively (specificity 89.4%, sensitivity 71.8%, positive predictive value 84.04%, negative predictive value 80.37%). In order to test the study efficacy, the Cross Validated Accuracy was calculated (CVA 81.7%, p < 0.001). At ROC analysis, the area under the curve was 0.85. The results suggest that urine volatilome profiling by e-Nose seems a promising, accurate and non-invasive diagnostic tool in discriminating patients from controls. The low costs and ease of execution make this test useful in clinical practice.
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Affiliation(s)
- Manuela Costantini
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Alessio Filianoti
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
- Department of Urology, San Filippo Neri Hospital, 00135 Rome, Italy
| | - Umberto Anceschi
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Alfredo Maria Bove
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Aldo Brassetti
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | | | - Riccardo Mastroianni
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Leonardo Misuraca
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Gabriele Tuderti
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Gennaro Ciliberto
- Scientific Direction, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Giuseppe Simone
- Department of Urology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Giulia Torregiani
- Department of Anesthesiology and Intensive Care Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
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21
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Li T, Zhu X, Hai X, Bi S, Zhang X. Recent Progress in Sensor Arrays: From Construction Principles of Sensing Elements to Applications. ACS Sens 2023; 8:994-1016. [PMID: 36848439 DOI: 10.1021/acssensors.2c02596] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
The traditional sensors are designed based on the "lock-and-key" strategy with high selectivity and specificity for detecting specific analytes, which however are not suitable for detecting multiple analytes simultaneously. With the help of pattern recognition technologies, the sensor arrays excel in distinguishing subtle changes caused by multitarget analytes with similar structures in a complex system. To construct a sensor array, the multiple sensing elements are undoubtedly indispensable units that will selectively interact with targets to generate the unique "fingerprints" based on the distinct responses, enabling the identification among various analytes through pattern recognition methods. This comprehensive review mainly focuses on the construction strategies and principles of sensing elements, as well as the applications of sensor array for identification and detection of target analytes in a wide range of fields. Furthermore, the present challenges and further perspectives of sensor arrays are discussed in detail.
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Affiliation(s)
- Tian Li
- College of Chemistry and Chemical Engineering, Research Center for Intelligent and Wearable Technology, Qingdao University, Qingdao 266071, P. R. China
| | - Xueying Zhu
- College of Chemistry and Chemical Engineering, Research Center for Intelligent and Wearable Technology, Qingdao University, Qingdao 266071, P. R. China
| | - Xin Hai
- College of Chemistry and Chemical Engineering, Research Center for Intelligent and Wearable Technology, Qingdao University, Qingdao 266071, P. R. China
| | - Sai Bi
- College of Chemistry and Chemical Engineering, Research Center for Intelligent and Wearable Technology, Qingdao University, Qingdao 266071, P. R. China
| | - Xueji Zhang
- School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, Guangdong 518060, P. R. China
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22
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Mansour E, Palzur E, Broza YY, Saliba W, Kaisari S, Goldstein P, Shamir A, Haick H. Noninvasive Detection of Stress by Biochemical Profiles from the Skin. ACS Sens 2023; 8:1339-1347. [PMID: 36848629 DOI: 10.1021/acssensors.3c00011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Stress is a leading cause of several disease types, yet it is underdiagnosed as current diagnostic methods are mainly based on self-reporting and interviews that are highly subjective, inaccurate, and unsuitable for monitoring. Although some physiological measurements exist (e.g., heart rate variability and cortisol), there are no reliable biological tests that quantify the amount of stress and monitor it in real time. In this article, we report a novel way to measure stress quickly, noninvasively, and accurately. The overall detection approach is based on measuring volatile organic compounds (VOCs) emitted from the skin in response to stress. Sprague Dawley male rats (n = 16) were exposed to underwater trauma. Sixteen naive rats served as a control group (n = 16). VOCs were measured before, during, and after induction of the traumatic event, by gas chromatography linked with mass spectrometry determination and quantification, and an artificially intelligent nanoarray for easy, inexpensive, and portable sensing of the VOCs. An elevated plus maze during and after the induction of stress was used to evaluate the stress response of the rats, and machine learning was used for the development and validation of a computational stress model at each time point. A logistic model classifier with stepwise selection yielded a 66-88% accuracy in detecting stress with a single VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector machine) model showed a 66-72% accuracy in detecting stress with the artificially intelligent nanoarray. The current study highlights the potential of VOCs as a noninvasive, automatic, and real-time stress predictor for mental health.
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Affiliation(s)
- Elias Mansour
- Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Eilam Palzur
- Eliachar Research Laboratory, Galilee Medical Center, P.O. Box 21, Nahariya 2210001, Israel
| | - Yoav Y Broza
- Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Walaa Saliba
- Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Sharon Kaisari
- Integrative Pain Laboratory (iPainLab), School of Public Health, University of Haifa, Haifa 2611001, Israel
| | - Pavel Goldstein
- Integrative Pain Laboratory (iPainLab), School of Public Health, University of Haifa, Haifa 2611001, Israel
| | - Alon Shamir
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 3200003, Israel
- Mazor Mental Health Center, Akko 2423314, Israel
| | - Hossam Haick
- Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
- Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
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23
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Einoch Amor R, Levy J, Broza YY, Vangravs R, Rapoport S, Zhang M, Wu W, Leja M, Behar JA, Haick H. Liquid Biopsy-Based Volatile Organic Compounds from Blood and Urine and Their Combined Data Sets for Highly Accurate Detection of Cancer. ACS Sens 2023; 8:1450-1461. [PMID: 36926819 DOI: 10.1021/acssensors.2c02422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.
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Affiliation(s)
- Reef Einoch Amor
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Jeremy Levy
- The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering and Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Yoav Y Broza
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Reinis Vangravs
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga LV-1004, Latvia.,Department of Research, Riga East University Hospital, Digestive Diseases Centre GASTRO, Riga 1586, Latvia
| | - Shelley Rapoport
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Min Zhang
- School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Shaanxi 710126, P.R. China
| | - Marcis Leja
- Institute of Clinical and Preventive Medicine & Faculty of Medicine, University of Latvia, Riga LV-1004, Latvia.,Department of Research, Riga East University Hospital, Digestive Diseases Centre GASTRO, Riga 1586, Latvia
| | - Joachim A Behar
- The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering and Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
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24
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Maiti KS. Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review. Molecules 2023; 28:2320. [PMID: 36903576 PMCID: PMC10005715 DOI: 10.3390/molecules28052320] [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: 01/12/2023] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.
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Affiliation(s)
- Kiran Sankar Maiti
- Max–Planck–Institut für Quantenoptik, Hans-Kopfermann-Straße 1, 85748 Garching, Germany; ; Tel.: +49-289-14054
- Lehrstuhl für Experimental Physik, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany
- Laser-Forschungslabor, Klinikum der Universität München, Fraunhoferstrasse 20, 82152 Planegg, Germany
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25
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Kort S, Brusse-Keizer M, Schouwink H, Citgez E, de Jongh FH, van Putten JWG, van den Borne B, Kastelijn EA, Stolz D, Schuurbiers M, van den Heuvel MM, van Geffen WH, van der Palen J. Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study. Chest 2023; 163:697-706. [PMID: 36243060 DOI: 10.1016/j.chest.2022.09.042] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 09/02/2022] [Accepted: 09/23/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. RESEARCH QUESTION This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? STUDY DESIGN AND METHODS In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. RESULTS A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. INTERPRETATION Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer. CLINICAL TRIAL REGISTRATION The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.
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Affiliation(s)
- Sharina Kort
- Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands.
| | - Marjolein Brusse-Keizer
- Medical School Twente, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands
| | - Hugo Schouwink
- Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands
| | - Emanuel Citgez
- Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands
| | - Frans H de Jongh
- Department of Respiratory Medicine, Medisch Spectrum Twente Enschede, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands
| | - Jan W G van Putten
- Department of Respiratory Medicine, Martini Ziekenhuis, Groningen, The Netherlands
| | - Ben van den Borne
- Department of Respiratory Medicine, Catharina Ziekenhuis, Eindhoven, The Netherlands
| | - Elisabeth A Kastelijn
- Department of Respiratory Medicine, Sint Antonius Ziekenhuis, Utrecht, The Netherlands
| | - Daiana Stolz
- Clinic for Pulmonary Medicine and Respiratory Cell Research, Universitätspital Basel, Basel, Switzerland; Clinic for Respiratory Medicine, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Milou Schuurbiers
- Department of Respiratory Medicine, Radboud UMC, Nijmegen, The Netherlands
| | | | - Wouter H van Geffen
- Department of Respiratory Medicine, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Job van der Palen
- Medical School Twente, Enschede, The Netherlands; Universiteit of Twente, Faculty of Behavioural Management and Social Sciences, Enschede, The Netherlands
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26
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Tzouvadaki I, Prodromakis T. Large-scale nano-biosensing technologies. FRONTIERS IN NANOTECHNOLOGY 2023. [DOI: 10.3389/fnano.2023.1127363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
Abstract
Nanoscale technologies have brought significant advancements to modern diagnostics, enabling unprecedented bio-chemical sensitivities that are key to disease monitoring. At the same time, miniaturized biosensors and their integration across large areas enabled tessellating these into high-density biosensing panels, a key capability for the development of high throughput monitoring: multiple patients as well as multiple analytes per patient. This review provides a critical overview of various nanoscale biosensing technologies and their ability to unlock high testing throughput without compromising detection resilience. We report on the challenges and opportunities each technology presents along this direction and present a detailed analysis on the prospects of both commercially available and emerging biosensing technologies.
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27
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Kiss H, Örlős Z, Gellért Á, Megyesfalvi Z, Mikáczó A, Sárközi A, Vaskó A, Miklós Z, Horváth I. Exhaled Biomarkers for Point-of-Care Diagnosis: Recent Advances and New Challenges in Breathomics. MICROMACHINES 2023; 14:391. [PMID: 36838091 PMCID: PMC9964519 DOI: 10.3390/mi14020391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
Cancers, chronic diseases and respiratory infections are major causes of mortality and present diagnostic and therapeutic challenges for health care. There is an unmet medical need for non-invasive, easy-to-use biomarkers for the early diagnosis, phenotyping, predicting and monitoring of the therapeutic responses of these disorders. Exhaled breath sampling is an attractive choice that has gained attention in recent years. Exhaled nitric oxide measurement used as a predictive biomarker of the response to anti-eosinophil therapy in severe asthma has paved the way for other exhaled breath biomarkers. Advances in laser and nanosensor technologies and spectrometry together with widespread use of algorithms and artificial intelligence have facilitated research on volatile organic compounds and artificial olfaction systems to develop new exhaled biomarkers. We aim to provide an overview of the recent advances in and challenges of exhaled biomarker measurements with an emphasis on the applicability of their measurement as a non-invasive, point-of-care diagnostic and monitoring tool.
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Affiliation(s)
- Helga Kiss
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Zoltán Örlős
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Áron Gellért
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Zsolt Megyesfalvi
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Angéla Mikáczó
- Department of Pulmonology, University of Debrecen, Nagyerdei krt 98, 4032 Debrecen, Hungary
| | - Anna Sárközi
- Department of Pulmonology, University of Debrecen, Nagyerdei krt 98, 4032 Debrecen, Hungary
| | - Attila Vaskó
- Department of Pulmonology, University of Debrecen, Nagyerdei krt 98, 4032 Debrecen, Hungary
| | - Zsuzsanna Miklós
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
| | - Ildikó Horváth
- National Koranyi Institute for Pulmonology, Koranyi F Street 1, 1121 Budapest, Hungary
- Department of Pulmonology, University of Debrecen, Nagyerdei krt 98, 4032 Debrecen, Hungary
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28
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Haick H. The diagnostic breathprint of cancer; the past and the future. Br J Cancer 2023; 128:448-450. [PMID: 36261582 PMCID: PMC9938276 DOI: 10.1038/s41416-022-01987-0] [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: 07/27/2022] [Revised: 08/26/2022] [Accepted: 09/07/2022] [Indexed: 11/08/2022] Open
Abstract
The main milestones in the exploration and validation of cancer breathprint for the advancement of personalised diagnosis and medicine are summarised here, with a special attention to the appraisal and translation of the accumulating knowledge from the laboratory to the Point-of-Care phase. An outlook into the opportunities of the use of breathprints and their wider availability for healthcare is offered.
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Affiliation(s)
- Hossam Haick
- Department of Chemical Engineering and Department of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, 3200003, Israel.
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29
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Mochalski P, Leja M, Ślefarska-Wolak D, Mezmale L, Patsko V, Ager C, Królicka A, Mayhew CA, Shani G, Haick H. Identification of Key Volatile Organic Compounds Released by Gastric Tissues as Potential Non-Invasive Biomarkers for Gastric Cancer. Diagnostics (Basel) 2023; 13:diagnostics13030335. [PMID: 36766440 PMCID: PMC9914709 DOI: 10.3390/diagnostics13030335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/08/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Volatilomics is a powerful tool capable of providing novel biomarkers for medical diagnosis and therapy monitoring. The objective of this study is to identify potential volatile biomarkers of gastric cancer. METHODS The volatilomic signatures of gastric tissues obtained from two distinct populations were investigated using gas chromatography with mass spectrometric detection. RESULTS Amongst the volatiles emitted, nineteen showed differences in their headspace concentrations above the normal and cancer tissues in at least one population of patients. Headspace levels of seven compounds (hexanal, nonanal, cyclohexanone, 2-nonanone, pyrrole, pyridine, and phenol) were significantly higher above the cancer tissue, whereas eleven volatiles (ethyl acetate, acetoin, 2,3-butanedione, 3-methyl-1-butanol, 2-pentanone, γ-butyrolactone, DL-limonene, benzaldehyde, 2-methyl-1-propanol, benzonitrile, and 3-methyl-butanal) were higher above the non-cancerous tissue. One compound, isoprene, exhibited contradictory alterations in both cohorts. Five compounds, pyridine, ethyl acetate, acetoin, 2,3-butanedione, and 3-methyl-1-butanol, showed consistent cancer-related changes in both populations. CONCLUSIONS Pyridine is found to be the most promising biomarker candidate for detecting gastric cancer. The difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes, or pathways. The results of this study confirm that the chemical fingerprint formed by volatiles in gastric tissue is altered by gastric cancer.
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Affiliation(s)
- Paweł Mochalski
- Institute of Chemistry, Jan Kochanowski University of Kielce, PL-25406 Kielce, Poland
- Institute for Breath Research, University of Innsbruck, A-6850 Dornbirn, Austria
- Correspondence:
| | - Marcis Leja
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Digestive Diseases Centre GASTRO, LV-1586 Riga, Latvia
- Riga East University Hospital, LV-1586 Riga, Latvia
| | - Daria Ślefarska-Wolak
- Institute of Chemistry, Jan Kochanowski University of Kielce, PL-25406 Kielce, Poland
- Institute for Breath Research, University of Innsbruck, A-6850 Dornbirn, Austria
| | - Linda Mezmale
- Institute of Clinical and Preventive Medicine, Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Riga East University Hospital, LV-1586 Riga, Latvia
| | | | - Clemens Ager
- Institute for Breath Research, University of Innsbruck, A-6850 Dornbirn, Austria
| | - Agnieszka Królicka
- Department of Building Materials Technology, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30, PL-30059 Krakow, Poland
| | - Chris A. Mayhew
- Institute for Breath Research, University of Innsbruck, A-6850 Dornbirn, Austria
| | - Gidi Shani
- Department of Chemical Engineering, Russel Berrie Nanotechnology Institute, Technicon—Israel Institute of Technology, Haifa 3200003, Israel
| | - Hossam Haick
- Department of Chemical Engineering, Russel Berrie Nanotechnology Institute, Technicon—Israel Institute of Technology, Haifa 3200003, Israel
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30
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Bauër P, Leemans M, Audureau E, Gilbert C, Armal C, Fromantin I. Remote Medical Scent Detection of Cancer and Infectious Diseases With Dogs and Rats: A Systematic Review. Integr Cancer Ther 2022; 21:15347354221140516. [PMID: 36541180 PMCID: PMC9791295 DOI: 10.1177/15347354221140516] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Remote medical scent detection of cancer and infectious diseases with dogs and rats has been an increasing field of research these last 20 years. If validated, the possibility of implementing such a technique in the clinic raises many hopes. This systematic review was performed to determine the evidence and performance of such methods and assess their potential relevance in the clinic. METHODS Pubmed and Web of Science databases were independently searched based on PRISMA standards between 01/01/2000 and 01/05/2021. We included studies aiming at detecting cancers and infectious diseases affecting humans with dogs or rats. We excluded studies using other animals, studies aiming to detect agricultural diseases, diseases affecting animals, and others such as diabetes and neurodegenerative diseases. Only original articles were included. Data about patients' selection, samples, animal characteristics, animal training, testing configurations, and performances were recorded. RESULTS A total of 62 studies were included. Sensitivity and specificity varied a lot among studies: While some publications report low sensitivities of 0.17 and specificities around 0.29, others achieve rates of 1 sensitivity and specificity. Only 6 studies were evaluated in a double-blind screening-like situation. In general, the risk of performance bias was high in most evaluated studies, and the quality of the evidence found was low. CONCLUSIONS Medical detection using animals' sense of smell lacks evidence and performances so far to be applied in the clinic. What odors the animals detect is not well understood. Further research should be conducted, focusing on patient selection, samples (choice of materials, standardization), and testing conditions. Interpolations of such results to free running detection (direct contact with humans) should be taken with extreme caution. Considering this synthesis, we discuss the challenges and highlight the excellent odor detection threshold exhibited by animals which represents a potential opportunity to develop an accessible and non-invasive method for disease detection.
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Affiliation(s)
- Pierre Bauër
- Institut Curie, Paris, France,Univ Paris Est Creteil, INSERM, IMRB, Team CEpiA
| | - Michelle Leemans
- Univ Paris Est Creteil, INSERM, IMRB, Team CEpiA,Michelle Leemans, Univ Paris Est Creteil, INSERM, IMRB, Team CEpiA, 61 Av. du Général de Gaulle, 94000 Créteil, F-94010 Créteil, France.
| | | | - Caroline Gilbert
- Muséum National d’Histoire Naturelle, Brunoy, France,Ecole nationale vétérinaire d’Alfort, Maisons-Alfort cedex, France
| | | | - Isabelle Fromantin
- Institut Curie, Paris, France,Univ Paris Est Creteil, INSERM, IMRB, Team CEpiA
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31
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Karas D, Bužga M, Stejskal D, Kocna P, Holéczy P, Novotná A, Švagera Z. Breath Tests Used in the Context of Bariatric Surgery. Diagnostics (Basel) 2022; 12:diagnostics12123170. [PMID: 36553178 PMCID: PMC9777764 DOI: 10.3390/diagnostics12123170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/01/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022] Open
Abstract
This review article focuses on the use of breath tests in the field of bariatrics and obesitology. The first part of the review is an introduction to breath test problematics with a focus on their use in bariatrics. The second part provides a brief history of breath testing. Part three describes how breath tests are used for monitoring certain processes in various organs and various substances in exhaled air and how the results are analyzed and evaluated. The last part covers studies that described the use of breath tests for monitoring patients that underwent bariatric treatments. Although the number of relevant studies is small, this review could promote the future use of breath testing in the context of bariatric treatments.
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Affiliation(s)
- Daniel Karas
- Institute of Laboratory Medicine, Faculty of Medicine, University of Ostrava, Syllabova 19, 703 00 Ostrava, Czech Republic
| | - Marek Bužga
- Department of Human Movement Studies, Faculty of Education, University of Ostrava, Fráni Šrámka 3, 709 00 Ostrava, Czech Republic
- Department of Physiology and Pathophysiology, Faculty of Medicine, University of Ostrava, Syllabova 19, 703 00 Ostrava, Czech Republic
- Institute of Laboratory Medicine, University Hospital Ostrava, 17. Listopadu 1790/5, 708 52 Ostrava, Czech Republic
- Correspondence:
| | - David Stejskal
- Institute of Laboratory Medicine, Faculty of Medicine, University of Ostrava, Syllabova 19, 703 00 Ostrava, Czech Republic
- Institute of Laboratory Medicine, University Hospital Ostrava, 17. Listopadu 1790/5, 708 52 Ostrava, Czech Republic
| | - Petr Kocna
- Institute of Medical Biochemistry and Laboratory Diagnostics, 1st Faculty of Medicine, Charles University in Prague, Kateřinská 1660/32, 121 08 Prague, Czech Republic
| | - Pavol Holéczy
- Department of Surgery, Vítkovice Hospital, Zalužanského 1192/15, 703 00 Ostrava, Czech Republic
- Department of Surgical Disciplines, Faculty of Medicine, University of Ostrava, Syllabova 19, 703 00 Ostrava, Czech Republic
| | - Adéla Novotná
- Department of Epidemiology and Public Health, Faculty of Medicine, University of Ostrava, Syllabova 19, 703 00 Ostrava, Czech Republic
| | - Zdeněk Švagera
- Institute of Laboratory Medicine, Faculty of Medicine, University of Ostrava, Syllabova 19, 703 00 Ostrava, Czech Republic
- Institute of Laboratory Medicine, University Hospital Ostrava, 17. Listopadu 1790/5, 708 52 Ostrava, Czech Republic
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32
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Zhao Y, Dong B, Benkstein KD, Chen L, Steffens KL, Semancik S. Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring. ACS APPLIED MATERIALS & INTERFACES 2022; 14:54411-54422. [PMID: 36418023 DOI: 10.1021/acsami.2c11153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. We demonstrate a deep learning-assisted plasmonic sensing platform that can detect and quantify gas-phase biomarkers in breath-related backgrounds of varying complexity. The sensing interface consisted of Au/SiO2 nanopillars covered with a 15 nm metal-organic framework. A small camera was utilized to capture the plasmonic sensing responses as images, which were subjected to deep learning signal processing. The approach has been demonstrated at a classification accuracy of 95 to 98% for the diabetic ketosis marker acetone within a concentration range of 0.5-80 μmol/mol. The reported work provides a thorough exploration of single-sensor capabilities and sets the basis for more advanced utilization of artificial intelligence in sensing applications.
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Affiliation(s)
- Yangyang Zhao
- Biomolecular Measurement Division, Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland20899, United States
- Sensing Labs, Inc., Rockville, Maryland20850, United States
| | - Boqun Dong
- Sensing Labs, Inc., Rockville, Maryland20850, United States
| | - Kurt D Benkstein
- Biomolecular Measurement Division, Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland20899, United States
| | - Lei Chen
- Center for Nanoscale Science and Technology, Physical Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland20899, United States
| | - Kristen L Steffens
- Biomolecular Measurement Division, Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland20899, United States
| | - Steve Semancik
- Biomolecular Measurement Division, Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland20899, United States
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33
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Yang K, Zhang C, Zhu K, Qian Z, Yang Z, Wu L, Zong S, Cui Y, Wang Z. A Programmable Plasmonic Gas Microsystem for Detecting Arbitrarily Combinated Volatile Organic Compounds (VOCs) with Ultrahigh Resolution. ACS NANO 2022; 16:19335-19345. [PMID: 36278500 DOI: 10.1021/acsnano.2c08906] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
For gas sensors, the ultrasensitive and highly selective detection of multiple components is of great significance in a wide range of applications extending from environment to healthcare, which is still a long-term challenge due to the single sensing mechanism of most sensors. Here, we combine the advantages of microfluidic chips and surface-enhanced Raman spectroscopy (SERS) spectra to fabricate a smart single-chip for simultaneously detecting an arbitrary combination of VOCs that incorporates different detection units, working on either a physisorption or chemisorption mechanism. Full integration of microfluidic and multiplex nanostructure components on one chip permits programmable design for sensing multifarious volatile compounds, and enables on-chip signal amplifications with increased reproducibility. As a proof-of-principle experiment, we demonstrate the simultaneous identification of 9 different gases that belong to aromatic compounds, aldehydes, ketones, or sulfides in one mixture, with high sensitivity (ppb level), high selectivity, and high robustness (error ∼8%). We further evaluated the application of our universal gas sensor in two scenarios including indoor air pollution monitoring and exhaled breath-based disease diagnosis. We expect that our design will improve the various practical applications of gas sensors.
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Affiliation(s)
- Kuo Yang
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
| | - Congyu Zhang
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
| | - Kai Zhu
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
| | - Ziting Qian
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
| | - Zhaoyan Yang
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
| | - Lei Wu
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
| | - Shenfei Zong
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
| | - Yiping Cui
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
| | - Zhuyuan Wang
- Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
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34
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Capman NSS, Zhen XV, Nelson JT, Chaganti VRSK, Finc RC, Lyden MJ, Williams TL, Freking M, Sherwood GJ, Bühlmann P, Hogan CJ, Koester SJ. Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose. ACS NANO 2022; 16:19567-19583. [PMID: 36367841 DOI: 10.1021/acsnano.2c10240] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.
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Affiliation(s)
- Nyssa S S Capman
- Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, Minnesota 55455, United States
- Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, Minnesota 55455, United States
| | - Xue V Zhen
- Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States
| | - Justin T Nelson
- Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States
| | - V R Saran Kumar Chaganti
- Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, Minnesota 55455, United States
| | - Raia C Finc
- Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States
| | - Michael J Lyden
- Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States
| | - Thomas L Williams
- Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States
| | - Mike Freking
- Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States
| | - Gregory J Sherwood
- Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States
| | - Philippe Bühlmann
- Department of Chemistry, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States
| | - Christopher J Hogan
- Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, Minnesota 55455, United States
| | - Steven J Koester
- Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, Minnesota 55455, United States
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Emam S, Nasrollahpour M, Allen JP, He Y, Hussein H, Shah HS, Tavangarian F, Sun NX. A handheld electronic device with the potential to detect lung cancer biomarkers from exhaled breath. Biomed Microdevices 2022; 24:41. [PMID: 36399220 DOI: 10.1007/s10544-022-00638-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2022] [Indexed: 11/19/2022]
Abstract
Lung cancer is the leading cause of cancer death in the United States. It has the lowest 5-year survival rate among the most common cancers and therefore, early diagnosis is critical to improve the survival rate. In this paper, a new handheld electronic device is proposed to detect nine lung cancer biomarkers in the exhaled breath. An electrochemical gas sensor was produced through deposition of a thin layer of graphene and Prussian blue on a chromium-modified silicon substrate. Selective binding of the analyte was formed by molecular imprinting polymer (MIP). Subsequent polymerization and removal of the analyte yielded a layer of a conductive polymer on top of the sensor containing molecularly imprinted cavities selective for the target molecule. The sensors were tested over 1-20 parts per trillion (ppt) level of concentration while the sensor resistance has been monitored as the sensors react to the analyte by resistance change. Pentane sensor was also tested for selectivity. A printed circuit board was designed to measure the resistance of each sensor and send the data to a developed application in smartphone through Bluetooth. This handheld device has the potential to be used as a diagnostic method in the near future.
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Affiliation(s)
- Shadi Emam
- Mechanical Engineering Program, School of Science, Engineering and Technology, Pennsylvania State University, Harrisburg, Middletown, PA, 17057, USA.
| | - Mehdi Nasrollahpour
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - John Patrick Allen
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Yifan He
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Hussein Hussein
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Harsh Shailesh Shah
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Fariborz Tavangarian
- Mechanical Engineering Program, School of Science, Engineering and Technology, Pennsylvania State University, Harrisburg, Middletown, PA, 17057, USA
| | - Nian-Xiang Sun
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
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36
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Neta S, Ariel G, Yossi Y, Amir A, Ben MM. The Locust antenna as an odor discriminator. Biosens Bioelectron 2022; 221:114919. [DOI: 10.1016/j.bios.2022.114919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022]
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Sukul P, Trefz P, Schubert JK, Miekisch W. Advanced setup for safe breath sampling and patient monitoring under highly infectious conditions in the clinical environment. Sci Rep 2022; 12:17926. [PMID: 36289276 PMCID: PMC9606119 DOI: 10.1038/s41598-022-22581-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/17/2022] [Indexed: 01/20/2023] Open
Abstract
Being the proximal matrix, breath offers immediate metabolic outlook of respiratory infections. However, high viral load in exhalations imposes higher transmission risk that needs improved methods for safe and repeatable analysis. Here, we have advanced the state-of-the-art methods for real-time and offline mass-spectrometry based analysis of exhaled volatile organic compounds (VOCs) under SARS-CoV-2 and/or similar respiratory conditions. To reduce infection risk, the general experimental setups for direct and offline breath sampling are modified. Certain mainstream and side-stream viral filters are examined for direct and lab-based applications. Confounders/contributions from filters and optimum operational conditions are assessed. We observed immediate effects of infection safety mandates on breath biomarker profiles. Main-stream filters induced physiological and analytical effects. Side-stream filters caused only systematic analytical effects. Observed substance specific effects partly depended on compound's origin and properties, sampling flow and respiratory rate. For offline samples, storage time, -conditions and -temperature were crucial. Our methods provided repeatable conditions for point-of-care and lab-based breath analysis with low risk of disease transmission. Besides breath VOCs profiling in spontaneously breathing subjects at the screening scenario of COVID-19/similar test centres, our methods and protocols are applicable for moderately/severely ill (even mechanically-ventilated) and highly contagious patients at the intensive care.
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Affiliation(s)
- Pritam Sukul
- grid.10493.3f0000000121858338Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Department of Anaesthesiology and Intensive Care, University Medicine Rostock, Schillingallee 35, 18057 Rostock, Germany
| | - Phillip Trefz
- grid.10493.3f0000000121858338Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Department of Anaesthesiology and Intensive Care, University Medicine Rostock, Schillingallee 35, 18057 Rostock, Germany
| | - Jochen K. Schubert
- grid.10493.3f0000000121858338Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Department of Anaesthesiology and Intensive Care, University Medicine Rostock, Schillingallee 35, 18057 Rostock, Germany
| | - Wolfram Miekisch
- grid.10493.3f0000000121858338Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Department of Anaesthesiology and Intensive Care, University Medicine Rostock, Schillingallee 35, 18057 Rostock, Germany
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38
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Cao Z, Zhou C, Wang J, Wei G, Li T, Zhuang K. Theoretical Study of Adsorption Behavior of Dimethylamine and Ammonia on Al- and Ga-Doped BN Monolayer Surfaces Based on DFT. ACS OMEGA 2022; 7:37857-37866. [PMID: 36312343 PMCID: PMC9607678 DOI: 10.1021/acsomega.2c04963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Endogenous volatile organic compounds (VOCs) can reflect human health status and be used for clinical diagnosis and health monitoring. Dimethylamine and ammonia are the signature VOC gases of nephropathy. In order to find a potential gas sensitivity material for the detection of both signature VOC gases of nephropathy, this paper investigated the adsorption properties of dimethylamine and ammonia on Al- and Ga-doped BN monolayers based on density functional theory. Through analyzing the adsorption energy, adsorption distance, charge transfer, density of states, and HOMO/LUMO, the results indicated that the adsorption effect of Al- and Ga-doped BN monolayers to dimethylamine and ammonia is probably good, and these nanomaterials have the potential to be applied for nephropathy monitoring and clinical diagnosis.
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Affiliation(s)
- Zhengqin Cao
- College
of Electrical Engineering, Chongqing University
of Science and Technology, Chongqing401331, China
| | - Changli Zhou
- College
of Electrical Engineering, Chongqing University
of Science and Technology, Chongqing401331, China
| | - Jia Wang
- College
of medical informatics, Chongqing Medical
University, Chongqing400016, China
| | - Gang Wei
- College
of Electrical Engineering, Chongqing University
of Science and Technology, Chongqing401331, China
| | - Ting Li
- Traditional
Chinese medicine hospital of Jiulongpo district in Chongqing, Chongqing400050, China
| | - Kai Zhuang
- College
of Electrical Engineering, Chongqing University
of Science and Technology, Chongqing401331, China
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39
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Manipulation by Plasmodium Parasites of Anopheles Mosquito Behavior and Human Odors. Acta Parasitol 2022; 67:1463-1470. [PMID: 36260195 DOI: 10.1007/s11686-022-00621-6] [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: 08/08/2022] [Accepted: 09/20/2022] [Indexed: 11/01/2022]
Abstract
PURPOSE The phenomenon of parasites manipulating host phenotypes is well documented; the best-known examples are manipulations of host behavior. More recently, there has been interest in whether parasites can manipulate host odor phenotypes to enhance their attractiveness to vectors. We review here evidence that Plasmodium-infected mosquitoes have enhanced attraction to human hosts, especially when the parasite is sufficiently developed to be transmissible. We also review evidence suggesting that malaria-infected host odors elicit greater mosquito attraction compared to uninfected controls. METHODS We reviewed and summarized the relevant literature. RESULTS Though evidence is mounting that supports both premises we reviewed, there are several confounds that complicate interpretation. These include differences in Plasmodium and mosquito species studied, stage of infection tested, age of human participants in trials, and methods used to quantify volatiles. In addition, a key requirement to support the hypothesis of manipulation by parasites is that costs of manipulation be identified, and ideally, quantified. CONCLUSIONS Substantial progress has been made to unlock the importance of odor for enhancing transmission of Plasmodium. However, there needs to be more replication using similar methods to better define the odor parameters involved in this enhancement.
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Peña A, Aguilera JD, Matatagui D, de la Presa P, Horrillo C, Hernando A, Marín P. Real-Time Monitoring of Breath Biomarkers with A Magnetoelastic Contactless Gas Sensor: A Proof of Concept. BIOSENSORS 2022; 12:871. [PMID: 36291006 PMCID: PMC9599754 DOI: 10.3390/bios12100871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/26/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
In the quest for effective gas sensors for breath analysis, magnetoelastic resonance-based gas sensors (MEGSs) are remarkable candidates. Thanks to their intrinsic contactless operation, they can be used as non-invasive and portable devices. However, traditional monitoring techniques are bound to slow detection, which hinders their application to fast bio-related reactions. Here we present a method for real-time monitoring of the resonance frequency, with a proof of concept for real-time monitoring of gaseous biomarkers based on resonance frequency. This method was validated with a MEGS based on a Metglass 2826 MB microribbon with a polyvinylpyrrolidone (PVP) nanofiber electrospun functionalization. The device provided a low-noise (RMS = 1.7 Hz), fast (<2 min), and highly reproducible response to humidity (Δf = 46−182 Hz for 17−95% RH), ammonia (Δf = 112 Hz for 40 ppm), and acetone (Δf = 44 Hz for 40 ppm). These analytes are highly important in biomedical applications, particularly ammonia and acetone, which are biomarkers related to diseases such as diabetes. Furthermore, the capability of distinguishing between breath and regular air was demonstrated with real breath measurements. The sensor also exhibited strong resistance to benzene, a common gaseous interferent in breath analysis.
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Affiliation(s)
- Alvaro Peña
- Instituto de Magnetismo Aplicado (IMA), Universidad Complutense de Madrid-Administrador de Infraestructuras Ferroviarias (UCM-ADIF), 28230 Las Rozas, Spain
| | - Juan Diego Aguilera
- Instituto de Magnetismo Aplicado (IMA), Universidad Complutense de Madrid-Administrador de Infraestructuras Ferroviarias (UCM-ADIF), 28230 Las Rozas, Spain
| | - Daniel Matatagui
- Instituto de Magnetismo Aplicado (IMA), Universidad Complutense de Madrid-Administrador de Infraestructuras Ferroviarias (UCM-ADIF), 28230 Las Rozas, Spain
- Departamento de Física de Materiales, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
- Grupo de Tecnología de Sensores Avanzados (SENSAVAN), Instituto de Tecnologías Físicas y de la Información (ITEFI), Consejo Superior de Investigaciones Científicas (CSIC), 28006 Madrid, Spain
| | - Patricia de la Presa
- Instituto de Magnetismo Aplicado (IMA), Universidad Complutense de Madrid-Administrador de Infraestructuras Ferroviarias (UCM-ADIF), 28230 Las Rozas, Spain
- Departamento de Física de Materiales, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
| | - Carmen Horrillo
- Grupo de Tecnología de Sensores Avanzados (SENSAVAN), Instituto de Tecnologías Físicas y de la Información (ITEFI), Consejo Superior de Investigaciones Científicas (CSIC), 28006 Madrid, Spain
| | - Antonio Hernando
- Instituto de Magnetismo Aplicado (IMA), Universidad Complutense de Madrid-Administrador de Infraestructuras Ferroviarias (UCM-ADIF), 28230 Las Rozas, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
- Instituto Madrileño de Estudios Avanzados (IMDEA) Nanociencia, 28049 Madrid, Spain
- Departamento de Ingeniería, Universidad de Nebrija, 28015 Madrid, Spain
| | - Pilar Marín
- Instituto de Magnetismo Aplicado (IMA), Universidad Complutense de Madrid-Administrador de Infraestructuras Ferroviarias (UCM-ADIF), 28230 Las Rozas, Spain
- Departamento de Física de Materiales, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
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41
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Woollam M, Grocki P, Schulz E, Siegel AP, Deiss F, Agarwal M. Evaluating Polyvinylidene Fluoride - Carbon Black Composites as Solid Phase Microextraction Coatings for the Detection of Urinary Volatile Organic Compounds by Gas Chromatography-Mass Spectrometry. J Chromatogr A 2022; 1685:463606. [DOI: 10.1016/j.chroma.2022.463606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/23/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022]
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Abumeeiz M, Elliott L, Olla P. Use of Breath Analysis for Diagnosing COVID-19: Opportunities, Challenges, and Considerations for Future Pandemic Responses. Disaster Med Public Health Prep 2022; 16:2137-2140. [PMID: 34649631 PMCID: PMC8576132 DOI: 10.1017/dmp.2021.317] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/31/2021] [Accepted: 10/03/2021] [Indexed: 01/01/2023]
Abstract
Due to the coronavirus disease 2019 (COVID-19) pandemic, there is currently a need for accurate, rapid, and easy-to-administer diagnostic tools to help communities manage local outbreaks and assess the spread of disease. The use of artificial intelligence within the domain of breath analysis techniques has shown to have potential in diagnosing a variety of diseases, such as cancer and lung disease, by analyzing volatile organic compounds (VOCs) in exhaled breath. This combined with their rapid, easy-to-use, and noninvasive nature makes them a good candidate for use in diagnosing COVID-19 in large scale public health operations. However, there remains issues with their implementation when it comes to the infrastructure currently available to support their use on a broad scale. This includes issues of standardization, and whether or not a characteristic VOC pattern can be identified for COVID-19. Despite these difficulties, breathalyzers offer potential to assist in pandemic responses and their use should be investigated.
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43
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Liu H, Duan L, Xia K, Chen Y, Li Y, Deng S, Xu J, Hou Z. Microwave Synthesized 2D WO 3 Nanosheets for VOCs Gas Sensors. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:nano12183211. [PMID: 36144999 PMCID: PMC9506399 DOI: 10.3390/nano12183211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/10/2022] [Accepted: 09/13/2022] [Indexed: 05/20/2023]
Abstract
As an n-type semiconductor material, tungsten oxide (WO3) has good application prospects in the field of gas sensing. Herein, using oxalic acid (OA), citric acid (CA) and tartaric acid (TA) as auxiliary agents, three homogeneous tungsten oxide nanosheets were prepared by the rapid microwave-assisted hydrothermal method. The potential exhaled gases of various diseases were screened for the gas sensitivity test. Compared with WO3-OA and WO3-TA, WO3-CA exhibits significant sensitivity to formaldehyde, acetone and various alkanes. Photoluminescence (PL) chromatography and photoelectric properties show that its excellent gas sensitivity is due to its abundant oxygen vacancies and high surface charge migration rate, which can provide more preferential reaction sites with gas molecules. The experiment is of great significance for the sensor selection of the large disease exhaled gas sensor array.
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Affiliation(s)
- He Liu
- School of Chemistry and Chemical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Lingyao Duan
- School of Chemistry and Chemical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Kedong Xia
- School of Chemistry and Chemical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Yang Chen
- Shanghai Yaolu Instrument & Equipment Co., Ltd., Shanghai 200444, China
- NEST Lab, Department of Physics, Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
| | - Yunling Li
- School of Chemistry and Chemical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Shaoxin Deng
- School of Chemistry and Chemical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Jiaqiang Xu
- NEST Lab, Department of Physics, Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
| | - Zhenyu Hou
- School of Chemistry and Chemical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
- Correspondence:
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44
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Einoch Amor R, Zinger A, Broza YY, Schroeder A, Haick H. Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers. Adv Healthc Mater 2022; 11:e2200356. [PMID: 35765713 DOI: 10.1002/adhm.202200356] [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: 02/14/2022] [Revised: 05/24/2022] [Indexed: 01/27/2023]
Abstract
Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.
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Affiliation(s)
- Reef Einoch Amor
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Assaf Zinger
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Yoav Y Broza
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Avi Schroeder
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
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Shuai M, Fu Y, Zhong HL, Gou W, Jiang Z, Liang Y, Miao Z, Xu JJ, Huynh T, Wahlqvist ML, Chen YM, Zheng JS. Mapping the human gut mycobiome in middle-aged and elderly adults: multiomics insights and implications for host metabolic health. Gut 2022; 71:1812-1820. [PMID: 35017200 PMCID: PMC9380515 DOI: 10.1136/gutjnl-2021-326298] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/26/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVE The human gut fungal community, known as the mycobiome, plays a fundamental role in the gut ecosystem and health. Here we aimed to investigate the determinants and long-term stability of gut mycobiome among middle-aged and elderly adults. We further explored the interplay between gut fungi and bacteria on metabolic health. DESIGN The present study included 1244 participants from the Guangzhou Nutrition and Health Study. We characterised the long-term stability and determinants of the human gut mycobiome, especially long-term habitual dietary consumption. The comprehensive multiomics analyses were performed to investigate the ecological links between gut bacteria, fungi and faecal metabolome. Finally, we examined whether the interaction between gut bacteria and fungi could modulate the metabolic risk. RESULTS The gut fungal composition was temporally stable and mainly determined by age, long-term habitual diet and host physiological states. Specifically, compared with middle-aged individuals, Blastobotrys and Agaricomycetes spp were depleted, while Malassezia was enriched in the elderly. Dairy consumption was positively associated with Saccharomyces but inversely associated with Candida. Notably, Saccharomycetales spp interacted with gut bacterial diversity to influence insulin resistance. Bidirectional mediation analyses indicated that bacterial function or faecal histidine might causally mediate an impact of Pichia on blood cholesterol. CONCLUSION We depict the sociodemographic and dietary determinants of human gut mycobiome in middle-aged and elderly individuals, and further reveal that the gut mycobiome may be closely associated with the host metabolic health through regulating gut bacterial functions and metabolites.
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Affiliation(s)
- Menglei Shuai
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Yuanqing Fu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Hai-li Zhong
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health; Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wanglong Gou
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China,Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Zengliang Jiang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China,Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Yuhui Liang
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Zelei Miao
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Jin-Jian Xu
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health; Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Tien Huynh
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Mark L Wahlqvist
- Monash Asia Institute, Monash University, Clayton, Victoria, Australia .,Institute of Nutrition and Health, Qingdao University, Qingdao, Shandong, China.,Institute of Population Health, National Health Research Institutes, Zhunan, Taiwan, China
| | - Yu-ming Chen
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health; Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ju-Sheng Zheng
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China .,Westlake Intelligent Biomarker Discovery Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
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Freddi S, Sangaletti L. Trends in the Development of Electronic Noses Based on Carbon Nanotubes Chemiresistors for Breathomics. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:nano12172992. [PMID: 36080029 PMCID: PMC9458156 DOI: 10.3390/nano12172992] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/21/2022] [Accepted: 08/25/2022] [Indexed: 06/12/2023]
Abstract
The remarkable potential of breath analysis in medical care and diagnosis, and the consequent development of electronic noses, is currently attracting the interest of the research community. This is mainly due to the possibility of applying the technique for early diagnosis, screening campaigns, or tracking the effectiveness of treatment. Carbon nanotubes (CNTs) are known to be good candidates for gas sensing, and they have been recently considered for the development of electronic noses. The present work has the aim of reviewing the available literature on the development of CNTs-based electronic noses for breath analysis applications, detailing the functionalization procedure used to prepare the sensors, the breath sampling techniques, the statistical analysis methods, the diseases under investigation, and the population studied. The review is divided in two main sections: one focusing on the e-noses completely based on CNTs and one reporting on the e-noses that feature sensors based on CNTs, along with sensors based on other materials. Finally, a classification is presented among studies that report on the e-nose capability to discriminate biomarkers, simulated breath, and animal or human breath.
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Zhang T, Chen J, Lu Y, Yang X, Ouyang Z. Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network. PLoS One 2022; 17:e0273355. [PMID: 35994484 PMCID: PMC9394838 DOI: 10.1371/journal.pone.0273355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 08/05/2022] [Indexed: 12/04/2022] Open
Abstract
Objectives This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network. Methods Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field. Results 1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992–2000), the development period (2001–2015), and the rapid growth period (2016–2021). There were two technology frontiers in the budding period (1992–2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001–2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016–2021), namely digital pathology (DP), deep learning (DL) algorithms—convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system. Conclusions Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking.
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Affiliation(s)
- Ting Zhang
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Juan Chen
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Yan Lu
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Xiaoyi Yang
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Zhaolian Ouyang
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
- * E-mail:
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Pugliese G, Trefz P, Weippert M, Pollex J, Bruhn S, Schubert JK, Miekisch W, Sukul P. Real-time metabolic monitoring under exhaustive exercise and evaluation of ventilatory threshold by breathomics: Independent validation of evidence and advances. Front Physiol 2022; 13:946401. [PMID: 36035465 PMCID: PMC9412033 DOI: 10.3389/fphys.2022.946401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/08/2022] [Indexed: 12/29/2022] Open
Abstract
Breath analysis was coupled with ergo-spirometry for non-invasive profiling of physio-metabolic status under exhaustive exercise. Real-time mass-spectrometry based continuous analysis of exhaled metabolites along with breath-resolved spirometry and heart rate monitoring were executed while 14 healthy adults performed ergometric ramp exercise protocol until exhaustion. Arterial blood lactate level was analyzed at defined time points. Respiratory-cardiac parameters and exhalation of several blood-borne volatiles changed continuously with the course of exercise and increasing workloads. Exhaled volatiles mirrored ventilatory and/or hemodynamic effects and depended on the origin and/or physicochemical properties of the substances. At the maximum workload, endogenous isoprene, methanethiol, dimethylsulfide, acetaldehyde, butanal, butyric acid and acetone concentrations decreased significantly by 74, 25, 35, 46, 21, 2 and 2%, respectively. Observed trends in exogenous cyclohexadiene and acetonitrile mimicked isoprene profile due to their similar solubility and volatility. Assignment of anaerobic threshold was possible via breath acetone. Breathomics enabled instant profiling of physio-metabolic effects and anaerobic thresholds during exercise. Profiles of exhaled volatiles indicated effects from muscular vasoconstriction, compartmental distribution of perfusion, extra-alveolar gas-exchange and energy homeostasis. Sulfur containing compounds and butyric acid turned out to be interesting for investigations of combined diet and exercise programs. Reproducible metabolic breath patterns have enhanced scopes of breathomics in sports science/medicine.
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Affiliation(s)
- Giovanni Pugliese
- Department of Anesthesiology and Intensive Care Medicine, Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Rostock University Medical Centre, Rostock, Germany
- Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany
| | - Phillip Trefz
- Department of Anesthesiology and Intensive Care Medicine, Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Rostock University Medical Centre, Rostock, Germany
| | | | - Johannes Pollex
- Institute of Sport Science, University of Rostock, Rostock, Germany
| | - Sven Bruhn
- Institute of Sport Science, University of Rostock, Rostock, Germany
| | - Jochen K. Schubert
- Department of Anesthesiology and Intensive Care Medicine, Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Rostock University Medical Centre, Rostock, Germany
| | - Wolfram Miekisch
- Department of Anesthesiology and Intensive Care Medicine, Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Rostock University Medical Centre, Rostock, Germany
| | - Pritam Sukul
- Department of Anesthesiology and Intensive Care Medicine, Rostock Medical Breath Research Analytics and Technologies (ROMBAT), Rostock University Medical Centre, Rostock, Germany
- *Correspondence: Pritam Sukul,
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A multifunctional upconversion nanoparticles probe for Cu2+ sensing and pattern recognition of biothiols. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2021.11.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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50
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Zheng Y, Zhao Y, Bai M, Gu H, Li X. Metal-organic frameworks as a therapeutic strategy for lung diseases. J Mater Chem B 2022; 10:5666-5695. [PMID: 35848605 DOI: 10.1039/d2tb00690a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Lung diseases remain a global burden today. Lower respiratory tract infections alone cause more than 3 million deaths worldwide each year and are on the rise every year. In particular, with coronavirus disease raging worldwide since 2019, we urgently require a treatment for lung disease. Metal organic frameworks (MOFs) have a broad application prospect in the biomedical field due to their remarkable properties. The unique properties of MOFs allow them to be applied as delivery materials for different drugs; diversified structural design endows MOFs with diverse functions; and they can be designed as various MOF-drug synergistic systems. This review concentrates on the synthesis design and applications of MOF based drugs against lung diseases, and discusses the possibility of preparing MOF-based inhalable formulations. Finally, we discuss the chances and challenges of using MOFs for targeting lung diseases in clinical practice.
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Affiliation(s)
- Yu Zheng
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
| | - Yuxin Zhao
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
| | - Mengting Bai
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
| | - Huang Gu
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
| | - Xiaofang Li
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
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