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Mujawar MA, Gohel H, Bhardwaj SK, Srinivasan S, Hickman N, Kaushik A. Nano-enabled biosensing systems for intelligent healthcare: towards COVID-19 management. Mater Today Chem 2020; 17:100306. [PMID: 32835155 PMCID: PMC7274574 DOI: 10.1016/j.mtchem.2020.100306] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 05/28/2020] [Accepted: 05/30/2020] [Indexed: 05/18/2023]
Abstract
Biosensors are emerging as efficient (sensitive and selective) and affordable analytical diagnostic tools for early-stage disease detection, as required for personalized health wellness management. Low-level detection of a targeted disease biomarker (pM level) has emerged extremely useful to evaluate the progression of disease under therapy. Such collected bioinformatics and its multi-aspects-oriented analytics is in demand to explore the effectiveness of a prescribed treatment, optimize therapy, and correlate biomarker level with disease pathogenesis. Owing to nanotechnology-enabled advancements in sensing unit fabrication, device integration, interfacing, packaging, and sensing performance at point-of-care (POC) has rendered diagnostics according to the requirements of disease management and patient disease profile i.e. in a personalized manner. Efforts are continuously being made to promote the state of art biosensing technology as a next-generation non-invasive disease diagnostics methodology. Keeping this in view, this progressive opinion article describes personalized health care management related analytical tools which can provide access to better health for everyone, with overreaching aim to manage healthy tomorrow timely. Considering accomplishments and predictions, such affordable intelligent diagnostics tools are urgently required to manage COVID-19 pandemic, a life-threatening respiratory infectious disease, where a rapid, selective and sensitive detection of human beta severe acute respiratory system coronavirus (SARS-COoV-2) protein is the key factor.
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Affiliation(s)
- M A Mujawar
- Department of Electrical and Computer Engineering, College of Engineering and Computing, Florida International University, Miami, FL, 33174, USA
| | - H Gohel
- Department of Computer Science, School of Art and Sciences, University of Houston, Victoria, TX, USA
| | - S K Bhardwaj
- Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands
| | - S Srinivasan
- NnaoBioTech Laboratory, Department of Natural Sciences, Division of Sciences, Art, & Mathematics, Florida Polytechnic University, Lakeland, FL, 33805, USA
| | - N Hickman
- NnaoBioTech Laboratory, Department of Natural Sciences, Division of Sciences, Art, & Mathematics, Florida Polytechnic University, Lakeland, FL, 33805, USA
| | - A Kaushik
- NnaoBioTech Laboratory, Department of Natural Sciences, Division of Sciences, Art, & Mathematics, Florida Polytechnic University, Lakeland, FL, 33805, USA
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Li W, Jia Z, Xie D, Chen K, Cui J, Liu H. Recognizing lung cancer using a homemade e-nose: A comprehensive study. Comput Biol Med 2020; 120:103706. [PMID: 32250850 DOI: 10.1016/j.compbiomed.2020.103706] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 03/17/2020] [Accepted: 03/17/2020] [Indexed: 01/15/2023]
Abstract
In recent years, breath analysis has been used as a tool for lung cancer detection and many gas sensors were developed for this purpose. Although they are fabricated with advanced materials, for now, gas sensors are still limited in their medical application due to their unfavorable performance. Here, we hypothesized that a combination of diverse types of sensors could aid in improving the detection performance. We fabricated an e-nose based on 10 gas sensors of 4 types and directly tested it using samples from 153 healthy participants and 115 lung cancer patients, without gas pre-concentration. Additionally, we studied and compared five feature extraction algorithms. The extracted features were then used in 2 optimized clustering algorithms and 3 supervised classification strategies, and their performance was investigated. As a result, "breath-prints" for all subjects were successfully obtained. The combined features extracted by LDA and Fast ICA formed the best feature space. Within this feature space, both clustering algorithms grouped all "breath-prints" into exactly 2 clusters with an Adjusted Rand Index greater than 0.95. Among the 3 supervised classification strategies, random forest with 3-fold cross validation showed the best performance with 86.42% of mean classification accuracy and 0.87 of AUC, which was somewhat better than many recently reported sensor arrays. It can be concluded that, the diversity of sensors may play a role in improving the performance of the e-nose though to what extent still requires evaluation.
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Affiliation(s)
- Wang Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, PR China
| | - Ziru Jia
- Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, PR China
| | - Dandan Xie
- Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, PR China
| | - Ke Chen
- Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, PR China
| | - Jianguo Cui
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, PR China
| | - Hongying Liu
- Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, PR China.
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