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S K, Saquib M, Poojary H, Illanad G, Valavan D, M S, Nayak R, Mazumder N, Ghosh C. Skin emitted volatiles analysis for noninvasive diagnosis: the current advances in sample preparation techniques for biomedical application. RSC Adv 2024; 14:12009-12020. [PMID: 38623290 PMCID: PMC11017966 DOI: 10.1039/d4ra01579g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/10/2024] [Indexed: 04/17/2024] Open
Abstract
Human skin emits a series of volatile compounds from the skin due to various metabolic processes, microbial activity, and several external factors. Changes in the concentration of skin volatile metabolites indicate many diseases, including diabetes, cancer, and infectious diseases. Researchers focused on skin-emitted compounds to gain insight into the pathophysiology of various diseases. In the case of skin volatolomics research, it is noteworthy that sample preparation, sampling protocol, analytical techniques, and comprehensive validation are important for the successful integration of skin metabolic profiles into regular clinical settings. Solid-phase microextraction techniques and polymer-based active sorbent traps were developed to capture the skin-emitted volatile compounds. The primary advantage of these sample preparation techniques is the ability to efficiently and targetedly capture skin metabolites, thus improving the detection of the biomarkers associated with various diseases. In further research, polydimethyl-based patches were utilized for skin research due to their biocompatibility and thermal stability properties. The microextraction sampling tools coupled with high sensitive Gas Chromatography-Mass Spectrometer provided a potential platform for skin volatolomes, thus emerging as a state-of-the-art analytical technique. Later, technological advancements, including the design of wearable sensors, have enriched skin-based research as it can integrate the information from skin-emitted volatile profiles into a portable platform. However, individual-specific hydration, temperature, and skin conditions can influence variations in skin volatile concentration. Considering the subject-specific skin depth, sampling time standardization, and suitable techniques may improve the skin sampling techniques for the potential discovery of various skin-based marker compounds associated with diseases. Here, we have summarised the current research progress, limitations, and technological advances in skin-based sample preparation techniques for disease diagnosis, monitoring, and personalized healthcare applications.
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Affiliation(s)
- Keerthana S
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal Karnataka 576104 India
| | - Mohammad Saquib
- Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal Karnataka 576104 India
| | - Harshika Poojary
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal Karnataka 576104 India
| | - Gouri Illanad
- Department of Biotechnology, KLE Technological University Hubballi Karnataka 580021 India
| | - Divyadarshini Valavan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal Karnataka 576104 India
| | - Selvakumar M
- Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal Karnataka 576104 India
| | - Ramakrishna Nayak
- Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal Karnataka 576104 India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education Manipal Karnataka 576104 India
| | - Chiranjit Ghosh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education Manipal Karnataka 576104 India
- Harvard Medical School 25 Shattuck Street Boston 02115 MA USA
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2
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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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3
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Zhang F, Yang L, Wei J, Tian X. Non-Invasive Blood Pressure Tracking of Spontaneous Hypertension Rats Using an Electronic Nose. SENSORS (BASEL, SWITZERLAND) 2023; 24:238. [PMID: 38203100 PMCID: PMC10781391 DOI: 10.3390/s24010238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024]
Abstract
Traditional noninvasive blood pressure measurement methods in experimental animals are time consuming and difficult to operate, particularly for large numbers of animals. In this study, the possibility of sensing fecal odor to estimate the blood pressure status of spontaneous hypertension rats (SHRs) was explored with the aim of establishing a new method for non-invasive monitoring of blood pressure. The body weight and blood pressure of SHRs kept increasing with growth, and the odor information monitored using an E-nose varied with the blood pressure status, particularly for sensors S6 and S7. The fecal information was analyzed using principal component analysis, canonical discriminant analysis and multilayer perception neural networks (MLP) to discriminate SHRs from normal ones, with a 100% correct classification rate. For better prediction of blood pressure, the model built using multiple linear regression analysis, partial least squares regression analysis and multilayer perceptron neural network analysis were used, with coefficients of determination (R2) ranging from 0.8036 to 0.9926. Moreover, the best prediction model for blood pressure was established using MLP analysis with an R2¬ higher than 0.91. Thus, changes in blood pressure levels can be tracked non-invasively, and normotension can be distinguished from hypertension or even at different hypertension levels based on the odor information of rat feces, providing a foundation for non-invasive health monitoring. This work might provide potential instructions for functional food research aimed at lowering blood pressure.
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Affiliation(s)
- Fumei Zhang
- China-Malaysia National Joint Laboratory, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China; (F.Z.); (L.Y.); (J.W.)
- Department of Medicine, Northwest Minzu University, Lanzhou 730124, China
| | - Lijing Yang
- China-Malaysia National Joint Laboratory, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China; (F.Z.); (L.Y.); (J.W.)
- School of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China
| | - Jia Wei
- China-Malaysia National Joint Laboratory, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China; (F.Z.); (L.Y.); (J.W.)
- School of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China
| | - Xiaojing Tian
- China-Malaysia National Joint Laboratory, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China; (F.Z.); (L.Y.); (J.W.)
- School of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China
- Gannan Yak Milk Research Institute, Gannan 747000, China
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4
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Makhubela PCK, Rohwer ER, Naudé Y. Detection of tuberculosis-associated compounds from human skin by GCxGC-TOFMS. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1231:123937. [PMID: 37995549 DOI: 10.1016/j.jchromb.2023.123937] [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: 07/07/2023] [Revised: 09/28/2023] [Accepted: 10/10/2023] [Indexed: 11/25/2023]
Abstract
Tuberculosis (TB) remains a global health concern. This study aimed to investigate the potential of human skin volatile organic compounds (VOCs) as prospective biomarkers for TB diagnosis. It employed a non-invasive approach using a wearable silicone rubber band for VOC sampling, comprehensive gas chromatography - time of flight mass spectrometry (GCxGC-TOFMS), and chemometric techniques. Both targeted and untargeted biochemical screening was utilized to explore biochemical differences between healthy individuals and those with TB infection. Results confirmed a correlation between compounds found in this study, and those reported for TB from other biofluids. In a comparison to known TB-associated compounds from other biofluids our analysis established the presence of 27 of these compounds emanating from human skin. Additionally, 16 previously unreported compounds were found as potential biomarkers. The diagnostic ability of the VOCs selected by statistical methods was investigated using predictive modelling techniques. Artificial neural network multi-layered perceptron (ANN) yielded two compounds, 1H-indene, 2,3 dihydro-1,1,3-trimethyl-3-phenyl; and heptane-3-ethyl-2-methyl, as the most discriminatory, and could differentiate between TB-positive (n = 15) and TB-negative (n = 23) individuals with an area under the receiver operating characteristic curve (AUROC) of 92 %, a sensitivity of 100 % and a specificity of 94 % for six targeted features. For untargeted analysis, ANN assigned 3-methylhexane as the most discriminatory between TB-positive and TB- negative individuals. An AUROC of 98.5 %, a sensitivity of 83 %, and a specificity of 88 % were obtained for 16 untargeted features as chosen by high performance variable selection. The obtained values compare highly favourable to alternative diagnostic methods such as breath analysis and GeneXpert. Consequently, human skin VOCs hold considerable potential as a TB diagnostic screening test.
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Affiliation(s)
- Portia C K Makhubela
- Department of Chemistry, University of Pretoria, Private Bag X20, Hatfield, 0028 Pretoria, South Africa
| | - Egmont R Rohwer
- Department of Chemistry, University of Pretoria, Private Bag X20, Hatfield, 0028 Pretoria, South Africa
| | - Yvette Naudé
- Department of Chemistry, University of Pretoria, Private Bag X20, Hatfield, 0028 Pretoria, South Africa.
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5
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Brebu M, Simion VE, Andronie V, Jaimes-Mogollón AL, Beleño-Sáenz KDJ, Ionescu F, Welearegay TG, Suschinel R, de Lema JB, Ionescu R. Putative volatile biomarkers of bovine tuberculosis infection in breath, skin and feces of cattle. Mol Cell Biochem 2023; 478:2473-2480. [PMID: 36840799 DOI: 10.1007/s11010-023-04676-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 02/09/2023] [Indexed: 02/26/2023]
Abstract
Bovine tuberculosis (bTB) is an infectious disease with significant impact on animal health, public health and international trade. Standard bTB screening in live cattle consists in injecting tuberculin and measuring the swelling at the place of injection few days later. This procedure is expensive, time-consuming, logistically challenging, and is not conclusive before performing confirmatory tests and additional analysis. The analysis of the volatile organic compounds (VOCs) emitted by non-invasive biological samples can provide an alternative diagnostic approach suitable for bTB screening. In the present study, we analyzed VOC samples emitted through the breath, feces and skin of 18 cows diagnosed with bTB from three farms from Romania, as well as of 27 negative cows for bTB from the same farms. Analytical studies employing gas chromatography coupled to mass spectrometry revealed 80 VOCs emitted through the breath, 200 VOCs released by feces, and 80 VOCs emitted through the skin. Statistical analysis of these compounds allowed the identification of 3 tentative breath VOC biomarkers (acetone; 4-methyldecane; D-limonene), 9 tentative feces VOC biomarkers (toluene; [(1,1-dimethylethyl)thio]acetic acid; alpha-thujene; camphene; phenol; o-cymene; 3-(1,1-dimethylethyl)-2,2,4,4-tetramethyl-3-pentanol; 2,5-dimethylhexane-2,5-dihydroperoxide; 2,4-di-tert-butylphenol), and 3 tentative skin VOC biomarkers (ammonia; 1-methoxy-2-propanol; toluene). The possible pathway of these volatile biomarkers is discussed.
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Affiliation(s)
- Mihai Brebu
- "Petru Poni" Institute of Macromolecular Chemistry, Aleea Grigore Ghica Voda 41A, 700487, Iasi, Romania
| | - Violeta Elena Simion
- Faculty of Veterinary Medicine, Spiru Haret University, Bd. Basarabia 256, 030171, Bucharest, Romania
| | - Viorel Andronie
- Faculty of Veterinary Medicine, Spiru Haret University, Bd. Basarabia 256, 030171, Bucharest, Romania
| | - Aylen Lisset Jaimes-Mogollón
- GISM Group, Faculty of Engineering and Architecture, University of Pamplona, Ciudad Universitaria, Via Bucaramanga Km 1, 543050, Pamplona, Colombia
| | - Kelvin de Jesús Beleño-Sáenz
- GISM Group, Faculty of Engineering and Architecture, University of Pamplona, Ciudad Universitaria, Via Bucaramanga Km 1, 543050, Pamplona, Colombia
- Department of Mechatronics Engineering, Universidad Autónoma del Caribe, Calle 90 #46-112, 080020, Barranquilla, Colombia
| | - Florina Ionescu
- The Ångström Laboratory, Department of Materials Science and Engineering, Uppsala University, P.O. Box 35, 75103, Uppsala, Sweden
| | - Tesfalem Geremariam Welearegay
- The Ångström Laboratory, Department of Materials Science and Engineering, Uppsala University, P.O. Box 35, 75103, Uppsala, Sweden
| | - Raluca Suschinel
- Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Kreutzwaldi 62, 51006, Tartu, Estonia
| | - Jose Bruno de Lema
- Institute of Veterinary Medicine and Animal Sciences, Estonian University of Life Sciences, Kreutzwaldi 62, 51006, Tartu, Estonia
- D Asociation, Paseo de la Montaña 14, 08402, Granollers, Barcelona, Spain
| | - Radu Ionescu
- D Asociation, Paseo de la Montaña 14, 08402, Granollers, Barcelona, Spain.
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6
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Zeid AM, Mostafa IM, Lou B, Xu G. Advances in miniaturized nanosensing platforms for analysis of pathogenic bacteria and viruses. LAB ON A CHIP 2023; 23:4160-4172. [PMID: 37668185 DOI: 10.1039/d3lc00674c] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Pathogenic bacteria and viruses are the main causes of infectious diseases all over the world. Early diagnosis of such infectious diseases is a critical step in management of their spread and treatment of the infection in its early stages. Therefore, the innovation of smart sensing platforms for point-of-care diagnosis of life-threatening infectious diseases such as COVID-19 is a prerequisite to isolate the patients and provide them with suitable treatment strategies. The developed diagnostic sensors should be highly sensitive, specific, ultrafast, portable, cheap, label-free, and selective. In recent years, different nanosensors have been developed for the detection of bacterial and viral pathogens. We focus here on label-free miniaturized nanosensing platforms that were efficiently applied for pathogenic detection in biological matrices. Such devices include nanopore sensors and nanostructure-integrated lab-on-a-chip sensors that are characterized by portability, simplicity, cost-effectiveness, and ultrafast analysis because they avoid the time-consuming sample preparation steps. Furthermore, nanopore-based sensors could afford single-molecule counting of viruses in biological specimens, yielding high-sensitivity and high-accuracy detection. Moreover, non-invasive nanosensors that are capable of detecting volatile organic compounds emitted from the diseased organ to the skin, urine, or exhaled breath were also reviewed. The merits and applications of all these nanosensors for analysis of pathogenic bacteria and viruses in biological matrices will be discussed in detail, emphasizing the importance of artificial intelligence in advancing specific nanosensors.
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Affiliation(s)
- Abdallah M Zeid
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China.
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Islam M Mostafa
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China.
- University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Baohua Lou
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China.
| | - Guobao Xu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China.
- University of Science and Technology of China, Hefei, Anhui 230026, China
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7
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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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8
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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 PMCID: PMC11223676 DOI: 10.1021/acsnano.2c12606] [Citation(s) in RCA: 194] [Impact Index Per Article: 194.0] [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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9
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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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10
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Fu L, Feng Y, Ren T, Yang M, Yang Q, Lin Y, Zeng H, Zhang J, Liu L, Li Q, He M, Zhang P, Chen H, Deng G. Detecting latent tuberculosis infection with a breath test using mass spectrometer: A pilot cross-sectional study. Biosci Trends 2023; 17:73-77. [PMID: 36596559 DOI: 10.5582/bst.2022.01476] [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: 01/02/2023]
Abstract
Mycobacterium tuberculosis (M.tb) infects a quarter of the world's population and may progress to active tuberculosis (ATB). There is no gold standard for diagnosing latent tuberculosis infection (LTBI). Some immunodiagnostic tests are recommended to detect LTBI but can not distinguish ATB from LTBI. The breath test is useful for diagnosing ATB compared to healthy subjects but was never studied for LTBI. This proof-of-concept study (Chinese Clinical Trials Registry number: ChiCTR2200058346) was the first to explore a novel, rapid, and simple LTBI detection method via breath test on high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). The case group of LTBI subjects (n = 185) and the control group (n = 250), which included ATB subgroup (n = 121) and healthy control (HC) subgroup (n = 129), were enrolled. The LTBI detection model indicated that a breath test via HPPI-TOFMS could distinguish LTBI from the control with a sensitivity of 80.0% (95% CI: 67.6%, 92.4%) and a specificity of 80.8% (95% CI: 71.8%, 89.9%). Nevertheless, further intensive studies with a larger sample size are required for clinical application.
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Affiliation(s)
- Liang Fu
- Division Two of Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yong Feng
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Tantan Ren
- Division Two of Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Min Yang
- Division Two of Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Qianting Yang
- Guangdong Key Lab for Diagnosis & Treatment of Emerging Infectious Disease, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yi Lin
- Division Two of Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Hui Zeng
- Medical Examination Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Jiaohong Zhang
- Pulmonary Diseases Out-patient Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Lei Liu
- Division Two of Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Qingyun Li
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Mengqi He
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Peize Zhang
- Division Two of Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Haibin Chen
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, China
| | - Guofang Deng
- Division Two of Pulmonary Diseases Department, The Third People's Hospital of Shenzhen, National clinical research center for infectious disease, Southern University of Science and Technology, Shenzhen, Guangdong, China
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11
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Kia P, Ruman U, Pratiwi AR, Hussein MZ. Innovative Therapeutic Approaches Based on Nanotechnology for the Treatment and Management of Tuberculosis. Int J Nanomedicine 2023; 18:1159-1191. [PMID: 36919095 PMCID: PMC10008450 DOI: 10.2147/ijn.s364634] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 02/06/2023] [Indexed: 03/11/2023] Open
Abstract
Tuberculosis (TB), derived from bacterium named Mycobacterium tuberculosis, has become one of the worst infectious and contagious illnesses in the world after HIV/AIDS. Long-term therapy, a high pill burden, lack of compliance, and strict management regimens are disadvantages which resulted in the extensively drug-resistant (XDR) along with multidrug-resistant (MDR) in the treatment of TB. One of the main thrust areas for the current scenario is the development of innovative intervention tools for early diagnosis and therapeutics towards Mycobacterium tuberculosis (MTB). This review discusses various nanotherapeutic agents that have been developed for MTB diagnostics, anti-TB drugs and vaccine. Undoubtedly, the concept of employing nanoparticles (NPs) has strong potential in this therapy and offers impressive outcomes to conquer the disease. Nanocarriers with different types were designed for drug delivery applications via various administration methods. Controlling and maintaining the drug release might be an example of the benefits of utilizing a drug-loaded NP in TB therapy over conventional drug therapy. Furthermore, the drug-encapsulated NP is able to lessen dosage regimen and can resolve the problems of insufficient compliance. Over the past decade, NPs were developed in both diagnostic and therapeutic methods, while on the other hand, the therapeutic system has increased. These "theranostic" NPs were designed for nuclear imaging, optical imaging, ultrasound, imaging with magnetic resonance and the computed tomography, which includes both single-photon computed tomography and positron emission tomography. More specifically, the current manuscript focuses on the status of therapeutic and diagnostic approaches in the treatment of TB.
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Affiliation(s)
- Pooneh Kia
- Institute of Bioscience, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Umme Ruman
- Nanomaterials Synthesis and Characterization Laboratory (NSCL), Institute of Nanoscience and Nanotechnology (ION2), Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
| | - Ariyati Retno Pratiwi
- Department of Oral Biology, Faculty of Dentistry, Universitas Brawijaya, Malang, Indonesia
| | - Mohd Zobir Hussein
- Nanomaterials Synthesis and Characterization Laboratory (NSCL), Institute of Nanoscience and Nanotechnology (ION2), Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia
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12
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Deng Z, Guo L, Chen X, Wu W. Smart Wearable Systems for Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052479. [PMID: 36904682 PMCID: PMC10007426 DOI: 10.3390/s23052479] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 06/12/2023]
Abstract
Smart wearable systems for health monitoring are highly desired in personal wisdom medicine and telemedicine. These systems make the detecting, monitoring, and recording of biosignals portable, long-term, and comfortable. The development and optimization of wearable health-monitoring systems have focused on advanced materials and system integration, and the number of high-performance wearable systems has been gradually increasing in recent years. However, there are still many challenges in these fields, such as balancing the trade-off between flexibility/stretchability, sensing performance, and the robustness of systems. For this reason, more evolution is required to promote the development of wearable health-monitoring systems. In this regard, this review summarizes some representative achievements and recent progress of wearable systems for health monitoring. Meanwhile, a strategy overview is presented about selecting materials, integrating systems, and monitoring biosignals. The next generation of wearable systems for accurate, portable, continuous, and long-term health monitoring will offer more opportunities for disease diagnosis and treatment.
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Affiliation(s)
- Zhiyong Deng
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, China
- Nuclear Power Institute of China, Huayang, Shuangliu District, Chengdu 610213, China
| | - Lihao Guo
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Ximeng Chen
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
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13
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Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities. Metabolites 2023; 13:metabo13020203. [PMID: 36837822 PMCID: PMC9960124 DOI: 10.3390/metabo13020203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/21/2023] [Accepted: 01/28/2023] [Indexed: 01/31/2023] Open
Abstract
Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82-88% sensitivity and 80-86% specificity on the test data.
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14
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Ghosh A, Nag S, Gomes A, Gosavi A, Ghule G, Kundu A, Purohit B, Srivastava R. Applications of Smart Material Sensors and Soft Electronics in Healthcare Wearables for Better User Compliance. MICROMACHINES 2022; 14:121. [PMID: 36677182 PMCID: PMC9862021 DOI: 10.3390/mi14010121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
The need for innovation in the healthcare sector is essential to meet the demand of a rapidly growing population and the advent of progressive chronic ailments. Over the last decade, real-time monitoring of health conditions has been prioritized for accurate clinical diagnosis and access to accelerated treatment options. Therefore, the demand for wearable biosensing modules for preventive and monitoring purposes has been increasing over the last decade. Application of machine learning, big data analysis, neural networks, and artificial intelligence for precision and various power-saving approaches are used to increase the reliability and acceptance of smart wearables. However, user compliance and ergonomics are key areas that need focus to make the wearables mainstream. Much can be achieved through the incorporation of smart materials and soft electronics. Though skin-friendly wearable devices have been highlighted recently for their multifunctional abilities, a detailed discussion on the integration of smart materials for higher user compliance is still missing. In this review, we have discussed the principles and applications of sustainable smart material sensors and soft electronics for better ergonomics and increased user compliance in various healthcare devices. Moreover, the importance of nanomaterials and nanotechnology is discussed in the development of smart wearables.
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Affiliation(s)
- Arnab Ghosh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Sagnik Nag
- Department of Biotechnology, School of Biosciences & Technology, Vellore Institute of Technology (VIT), Tiruvalam Road, Vellore 632014, Tamil Nadu, India
| | - Alyssa Gomes
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Apurva Gosavi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Gauri Ghule
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Aniket Kundu
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Buddhadev Purohit
- DTU Bioengineering, Technical University of Denmark, Søltofts Plads 221, 2800 Kongens Lyngby, Denmark
| | - Rohit Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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15
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Gashimova E, Temerdashev A, Porkhanov V, Polyakov I, Perunov D, Dmitrieva E. Non-invasive Exhaled Breath and Skin Analysis to Diagnose Lung Cancer: Study of Age Effect on Diagnostic Accuracy. ACS OMEGA 2022; 7:42613-42628. [PMID: 36440120 PMCID: PMC9685768 DOI: 10.1021/acsomega.2c06132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Development of simple, fast, and non-invasive tests for lung cancer diagnostics is essential for clinical practice. In this paper, exhaled breath and skin were studied as potential objects to diagnose lung cancer. The influence of age on the performance of diagnostic models was studied. Gas chromatography in combination with mass spectrometry (MS) was used to analyze the exhaled breath of 110 lung cancer patients and 212 healthy individuals of various ages. Peak area ratios of volatile organic compounds (VOCs) were used for data analysis instead of VOC peak areas. Various machine learning algorithms were applied to create diagnostic models, and their performance was compared. The best results on the test data set were achieved using artificial neural networks (ANNs): classification of patients with lung cancer and young healthy volunteers: 88 ± 4% sensitivity and 83 ± 3% specificity; classification of patients with lung cancer and old healthy individuals: 81 ± 3% sensitivity and 85 ± 1% specificity. The difference between performance of models based on young and old healthy groups was minor. The results obtained have shown that metabolic dysregulation driven by the disease biology is too high, which significantly overlaps the age effect. The influence of tumor localization and histological type on exhaled breath samples of lung cancer patients was studied. Statistically significant differences between some parameters in these samples were observed. A possibility of assessing the disease status by skin analysis in the Zakharyin-Ged zones using an electronic nose based on the quartz crystal microbalance sensor system was evaluated. Diagnostic models created using ANNs allow us to classify the skin composition of patients with lung cancer and healthy subjects of different ages with a sensitivity of 69 ± 2% and a specificity of 68 ± 8% for the young healthy group and a sensitivity of 74 ± 7% and a specificity of 66 ± 6% for the old healthy group. Primary results of skin analysis in the Zakharyin-Ged zones for the lung cancer diagnosis have shown its utility, but further investigation is required to confirm the results obtained.
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Affiliation(s)
- Elina Gashimova
- Department
of Analytical Chemistry, Kuban State University, Krasnodar350040, Russia
| | - Azamat Temerdashev
- Department
of Analytical Chemistry, Kuban State University, Krasnodar350040, Russia
| | - Vladimir Porkhanov
- Research
Institute—Regional Clinical Hospital No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar350086, Russia
| | - Igor Polyakov
- Research
Institute—Regional Clinical Hospital No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar350086, Russia
| | - Dmitry Perunov
- Research
Institute—Regional Clinical Hospital No 1 n.a. Prof. S.V. Ochapovsky, Krasnodar350086, Russia
| | - Ekaterina Dmitrieva
- Department
of Analytical Chemistry, Kuban State University, Krasnodar350040, Russia
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16
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Zhang P, Ren T, Chen H, Li Q, He M, Feng Y, Wang L, Huang T, Yuan J, Deng G, Lu H. A feasibility study of COVID-19 detection using breath analysis by high-pressure photon ionization time-of-flight mass spectrometry. J Breath Res 2022; 16. [PMID: 36052728 DOI: 10.1088/1752-7163/ac8ea1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 08/11/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND SARS-CoV-2 has caused a tremendous threat to global health. PCR and antigen testing have played a prominent role in the detection of SARS-CoV-2-infected individuals and disease control. An efficient, reliable detection tool is still urgently needed to halt the global COVID-19 pandemic. Recently, FDA emergency approved VOC as an alternative test for COVID-19 detection. METHODS AND MATERIALS In this case-control study, we prospectively and consecutively recruited 95 confirmed COVID-19 patients and 106 healthy controls in the designated hospital for treatment of COVID-19 patients in Shenzhen, China. Exhaled breath samples were collected and stored in customized bags and then detected by HPPI-TOFMS for volatile organic components (VOCs). Machine learning (ML) algorithms were employed for COVID-19 detection model construction. Participants were randomly assigned in a 5:2:3 ratio to the training, validation, and blinded test sets. The sensitivity (SEN), specificity (SPE), and other general metrics were employed for the VOCs based COVID-19 detection model performance evaluation. RESULTS The VOCs based COVID-19 detection model achieved good performance, with a SEN of 92.2% (95% CI: 83.8%, 95.6%), a SPE of 86.1% (95% CI: 74.8%, 97.4%) on blinded test set. Five potential VOC ions related to COVID-19 infection were discovered, which are significantly different between COVID-19 infected patients and controls. CONCLUSIONS This study evaluated a simple, fast, non-invasive VOCs-based COVID-19 detection method and demonstrated that it has good sensitivity and specificity in distinguishing COVID-19 infected patients from controls. It has great potential for fast and accurate COVID-19 detection.
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Affiliation(s)
- Peize Zhang
- Department of Pulmonary medicine and Tuberculosis, Shenzhen Third People's Hospital, No. 29, Bulan Road, Longgang District, Shenzhen, 518112, CHINA
| | - Tantan Ren
- Department of Pulmonary medicine and Tuberculosis, Shenzhen Third People's Hospital, No. 29, Bulan Road, Longgang District, Shenzhen, 518112, CHINA
| | - Haibin Chen
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, 3rd Gangnanli, Fengtai Distinct, Beijing, 100071, CHINA
| | - Qingyun Li
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, 3rd Gangnanli, Fengtai Distinct, Beijing, 100071, CHINA
| | - Mengqi He
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, 3rd Gangnanli, Fengtai Distinct, Beijing, 100071, CHINA
| | - Yong Feng
- Breax Laboratory, PCAB Research Center of Breath and Metabolism,, 3rd Gangnanli, Fengtai Distinct, Beijing, 100071, CHINA
| | - Lei Wang
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, 3rd Gangnanli, Fengtai Distinct, Beijing, 100071, CHINA
| | - Ting Huang
- Department of Disease Control, Shenzhen Third People's Hospital, No. 29, Bulan Road, Longgang District, Beijing, 100071, CHINA
| | - Jing Yuan
- Department of Infectious Disease, Shenzhen Third People's Hospital, No. 29, Bulan Road, Longgang District, Shenzhen, 518112, CHINA
| | - Guofang Deng
- Department of Pulmonary medicine and Tuberculosis,, Shenzhen Third People's Hospital, No. 29, Bulan Road, Longgang District, Shenzhen, Shenzhen, 518112, CHINA
| | - Hongzhou Lu
- Department of Infectious Disease, Shenzhen Third People's Hospital, No. 29, Bulan Road, Longgang District, Shenzhen, 518112, CHINA
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17
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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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18
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The Human Skin Volatolome: A Systematic Review of Untargeted Mass Spectrometry Analysis. Metabolites 2022; 12:metabo12090824. [PMID: 36144228 PMCID: PMC9504915 DOI: 10.3390/metabo12090824] [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: 07/26/2022] [Revised: 08/27/2022] [Accepted: 08/28/2022] [Indexed: 11/16/2022] Open
Abstract
The analysis of volatile organic compounds (VOCs) can provide important clinical information (entirely non-invasively); however, the exact extent to which VOCs from human skin can be signatures of health and disease is unknown. This systematic review summarises the published literature concerning the methodology, application, and volatile profiles of skin VOC studies. An online literature search was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis, to identify human skin VOC studies using untargeted mass spectrometry (MS) methods. The principal outcome was chemically verified VOCs detected from the skin. Each VOC was cross-referenced using the CAS number against the Human Metabolome and KEGG databases to evaluate biological origins. A total of 29 studies identified 822 skin VOCs from 935 participants. Skin VOCs were commonly sampled from the hand (n = 9) or forearm (n = 7) using an absorbent patch (n = 15) with analysis by gas chromatography MS (n = 23). Twenty-two studies profiled the skin VOCs of healthy subjects, demonstrating a volatolome consisting of aldehydes (18%), carboxylic acids (12%), alkanes (12%), fatty alcohols (9%), ketones (7%), benzenes and derivatives (6%), alkenes (2%), and menthane monoterpenoids (2%). Of the VOCs identified, 13% had putative endogenous origins, 46% had tentative exogenous origins, and 40% were metabolites from mixed metabolic pathways. This review has comprehensively profiled the human skin volatolome, demonstrating the presence of a distinct VOC signature of healthy skin, which can be used as a reference for future researchers seeking to unlock the clinical potential of skin volatolomics. As significant proportions of identified VOCs have putative exogenous origins, strategies to minimise their presence through methodological refinements and identifying confounding compounds are discussed.
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19
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Mansour E, Sherbo S, Saliba W, Kloper V, Haick H. Effect of the Dispersion Process and Nanoparticle Quality on Chemical Sensing Performance. ACS OMEGA 2022; 7:22484-22491. [PMID: 35811934 PMCID: PMC9260890 DOI: 10.1021/acsomega.2c01668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
On the surface of chemiresistive films, the scarce heterogeneity of a molecularly capped gold nanoparticle (MCGNP) colloidal dispersion and uneven evaporation of the MCGNP-contained drying drop applied to this surface are among the main factors that affect reproducibility, and repeatable fabrication of thin films of MCGNPs. This article shows that an increase in reproducibility and repeatability is possible using a dispersant and a surfactant during the deposition and annealing processes of the MCGNP. The results show higher sensitivity and accuracy of the sensors for the detection of volatile organic compounds in air and an increased limit of detection. These simple and practical additions might serve as a launching pad for fabrication of other types of thin-film-based sensors.
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Affiliation(s)
- Elias Mansour
- The
Department of Chemical Engineering, Technion
− Israel Institute of Technology, Haifa 3200003, Israel
| | - Shay Sherbo
- 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
| | - Viki Kloper
- The
Department of Chemical Engineering, Technion
− Israel Institute of Technology, Haifa 3200003, 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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20
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Abstract
This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.
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Affiliation(s)
- Muhammad Khatib
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
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21
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Wang Y, Haick H, Guo S, Wang C, Lee S, Yokota T, Someya T. Skin bioelectronics towards long-term, continuous health monitoring. Chem Soc Rev 2022; 51:3759-3793. [PMID: 35420617 DOI: 10.1039/d2cs00207h] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Skin bioelectronics are considered as an ideal platform for personalised healthcare because of their unique characteristics, such as thinness, light weight, good biocompatibility, excellent mechanical robustness, and great skin conformability. Recent advances in skin-interfaced bioelectronics have promoted various applications in healthcare and precision medicine. Particularly, skin bioelectronics for long-term, continuous health monitoring offer powerful analysis of a broad spectrum of health statuses, providing a route to early disease diagnosis and treatment. In this review, we discuss (1) representative healthcare sensing devices, (2) material and structure selection, device properties, and wireless technologies of skin bioelectronics towards long-term, continuous health monitoring, (3) healthcare applications: acquisition and analysis of electrophysiological, biophysical, and biochemical signals, and comprehensive monitoring, and (4) rational guidelines for the design of future skin bioelectronics for long-term, continuous health monitoring. Long-term, continuous health monitoring of advanced skin bioelectronics will open unprecedented opportunities for timely disease prevention, screening, diagnosis, and treatment, demonstrating great promise to revolutionise traditional medical practices.
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Affiliation(s)
- Yan Wang
- Department of Chemical Engineering, Guangdong Technion-Israel Institute of Technology (GTIIT), Shantou, Guangdong 515063, China.,Technion-Israel Institute of Technology (IIT), Haifa 32000, Israel.,Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan. .,Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion, Guangdong Technion - Israel Institute of Technology, Shantou, Guangdong 515063, China
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Shuyang Guo
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan.
| | - Chunya Wang
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan.
| | - Sunghoon Lee
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan.
| | - Tomoyuki Yokota
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan.
| | - Takao Someya
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan.
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22
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Arora M, Zambrzycki SC, Levy JM, Esper A, Frediani JK, Quave CL, Fernández FM, Kamaleswaran R. Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS. Metabolites 2022; 12:232. [PMID: 35323675 PMCID: PMC8953436 DOI: 10.3390/metabo12030232] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/03/2022] [Accepted: 03/04/2022] [Indexed: 12/24/2022] Open
Abstract
Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications.
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Affiliation(s)
- Mehak Arora
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30332, USA;
| | - Stephen C. Zambrzycki
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA; (S.C.Z.); (F.M.F.)
| | - Joshua M. Levy
- Department of Otolaryngology—Head and Neck Surgery, Emory University School of Medicine, Atlanta, GA 30332, USA;
| | - Annette Esper
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University School of Medicine, Atlanta, GA 30332, USA;
- Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA 30332, USA
| | - Jennifer K. Frediani
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA 30332, USA;
| | - Cassandra L. Quave
- Department of Dermatology, Emory University School of Medicine, Atlanta, GA 30332, USA;
- Center for the Study of Human Health, Emory College of Arts and Sciences, Atlanta, GA 30332, USA
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA; (S.C.Z.); (F.M.F.)
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30332, USA;
- Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA 30332, USA
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA 30332, USA
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Determination of tuberculosis-related volatile organic biomarker methyl nicotinate in vapor using fluorescent assay based on quantum dots and cobalt-containing porphyrin nanosheets. Mikrochim Acta 2022; 189:108. [PMID: 35171382 DOI: 10.1007/s00604-022-05212-w] [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: 11/29/2021] [Accepted: 01/31/2022] [Indexed: 10/19/2022]
Abstract
Methyl nicotinate (MN) is a representative and typical volatile organic marker of Mycobacterium tuberculosis, and the specific detection of MN in human breath facilitates non-invasive, rapid, and accurate epidemic screening of tuberculosis infection. Herein, we constructed a fluorescent assay consisted of CdTe quantum dots (QD) and cobalt-metalized tetrakis(4-carboxyphenyl) porphyrin (CoTCPP) nanosheets to determine methyl nicotinate (MN) in vapor samples. Red-emission QD (λex=370 nm, λem=658 nm) acts as signal switches whose fluorescence signals can be effectively quenched by CoTCPP nanosheets but restored in the presence of MN. The strategy relied on the distinct binding affinity of cobalt ion and MN. MN restored the fluorescence of QD quenched by CoTCPP in a concentration-dependent manner, which exhibited a well-linear relationship in the range 1-100 μM, and a limit of detection of 0.59 μM. The proposed platform showed sensitivity and selectivity to detect MN in vapor samples with satisfactory RSD below 3.33%. The method is cheap, simple, and relatively rapid (detected within 4 min), which suggests a potential in tuberculosis diagnosis in resource- and professional-lacked areas.
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24
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Hu W, Wu W, Jian Y, Haick H, Zhang G, Qian Y, Yuan M, Yao M. Volatolomics in healthcare and its advanced detection technology. NANO RESEARCH 2022; 15:8185-8213. [PMID: 35789633 PMCID: PMC9243817 DOI: 10.1007/s12274-022-4459-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 05/21/2023]
Abstract
Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.
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Affiliation(s)
- Wenwen Hu
- School of Aerospace Science and Technology, Xidian University, Xi’an, 730107 China
| | - Weiwei Wu
- Interdisciplinary Research Center of Smart Sensors, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an, 730107 China
| | - Yingying Jian
- Interdisciplinary Research Center of Smart Sensors, School of Advanced Materials and Nanotechnology, Xidian University, Xi’an, 730107 China
| | - Hossam Haick
- Faculty of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200002 Israel
| | - Guangjian Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 China
| | - Yun Qian
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006 China
| | - Miaomiao Yuan
- The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033 China
| | - Mingshui Yao
- State Key Laboratory of Multi-phase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 310006 China
- Institute for Integrated Cell-Material Sciences, Kyoto University Institute for Advanced Study, Kyoto University, Kyoto, 606-8501 Japan
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Zheng Y, Omar R, Hu Z, Duong T, Wang J, Haick H. Bioinspired Triboelectric Nanosensors for Self-Powered Wearable Applications. ACS Biomater Sci Eng 2021; 9:2087-2102. [PMID: 34961316 DOI: 10.1021/acsbiomaterials.1c01106] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The sustainable operation of wearable sensors plays an important role in continuous and longtime health monitoring. Conventional batteries, which are bulky and rigid, do not satisfy these requirements and, rather, cause additional economic burdens and environmental problems by regular replacement of power sources. This article provides a review on an alternative solution in the form of self-powered devices that can harvest energy from the surrounding environment to support the operation of the wearable sensor. The Review starts with an introduction of the self-powered triboelectric nanosensors (TENSs) and its two independent modules: the energy harvester and the sensing module. The Review continues with the TENS-related bioinspired designs for wearable applications, while providing a bird's-eye view of their characteristics and applications. The ongoing challenges and prospects for providing personal healthcare with self-powered TENS are presented and discussed.
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Affiliation(s)
- Youbin Zheng
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Rawan Omar
- 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
| | - Tuan Duong
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Jing Wang
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, 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.,School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an 710126, P. R. China
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26
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Vishinkin R, Busool R, Mansour E, Fish F, Esmail A, Kumar P, Gharaa A, Cancilla JC, Torrecilla JS, Skenders G, Leja M, Dheda K, Singh S, Haick H. Profiles of Volatile Biomarkers Detect Tuberculosis from Skin. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100235. [PMID: 34075714 PMCID: PMC8336503 DOI: 10.1002/advs.202100235] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/19/2021] [Indexed: 02/05/2023]
Abstract
Tuberculosis (TB) is an infectious disease that threatens >10 million people annually. Despite advances in TB diagnostics, patients continue to receive an insufficient diagnosis as TB symptoms are not specific. Many existing biodiagnostic tests are slow, have low clinical performance, and can be unsuitable for resource-limited settings. According to the World Health Organization (WHO), a rapid, sputum-free, and cost-effective triage test for real-time detection of TB is urgently needed. This article reports on a new diagnostic pathway enabling a noninvasive, fast, and highly accurate way of detecting TB. The approach relies on TB-specific volatile organic compounds (VOCs) that are detected and quantified from the skin headspace. A specifically designed nanomaterial-based sensors array translates these findings into a point-of-care diagnosis by discriminating between active pulmonary TB patients and controls with sensitivity above 90%. This fulfills the WHO's triage test requirements and poses the potential to become a TB triage test.
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Affiliation(s)
- Rotem Vishinkin
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of TechnologyHaifa3200003Israel
| | - Rami Busool
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of TechnologyHaifa3200003Israel
| | - Elias Mansour
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of TechnologyHaifa3200003Israel
| | - Falk Fish
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of TechnologyHaifa3200003Israel
| | - Ali Esmail
- Centre for Lung Infection and ImmunityDivision of PulmonologyDepartment of Medicine and UCT Lung Institute & South African MRC/UCT Centre for the Study of Antimicrobial ResistanceUniversity of Cape TownCape Town 7925South Africa
| | - Parveen Kumar
- All India Institute of Medical SciencesNew Delhi110029India
| | - Alaa Gharaa
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of TechnologyHaifa3200003Israel
| | | | - Jose S. Torrecilla
- Department of Chemical and Materials EngineeringComplutense University of MadridMadrid28040Spain
| | - Girts Skenders
- Institute of Clinical and Preventive MedicineUniversity of Latvia and Riga east University HospitalRigaLV1079Latvia
| | - Marcis Leja
- Institute of Clinical and Preventive MedicineUniversity of Latvia and Riga east University HospitalRigaLV1079Latvia
| | - Keertan Dheda
- Centre for Lung Infection and ImmunityDivision of PulmonologyDepartment of Medicine and UCT Lung Institute & South African MRC/UCT Centre for the Study of Antimicrobial ResistanceUniversity of Cape TownCape Town 7925South Africa
- Faculty of Infectious and Tropical DiseasesDepartment of Infection BiologyLondon School of Hygiene and Tropical MedicineLondonWC1E 7HTUK
| | - Sarman Singh
- All India Institute of Medical SciencesNew Delhi110029India
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of TechnologyHaifa3200003Israel
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