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Liu X, Sun Y, Song H, Zhang W, Liu T, Chu Z, Gu X, Ma Z, Jin W. Nanomaterials-based electrochemical biosensors for diagnosis of COVID-19. Talanta 2024; 274:125994. [PMID: 38547841 DOI: 10.1016/j.talanta.2024.125994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 03/15/2024] [Accepted: 03/24/2024] [Indexed: 05/04/2024]
Abstract
Since the outbreak of corona virus disease 2019 (COVID-19), this pandemic has caused severe death and infection worldwide. Owing to its strong infectivity, long incubation period, and nonspecific symptoms, the early diagnosis is essential to reduce risk of the severe illness. The electrochemical biosensor, as a fast and sensitive technique for quantitative analysis of body fluids, has been widely studied to diagnose different biomarkers caused at different infective stages of COVID-19 virus (SARS-CoV-2). Recently, many reports have proved that nanomaterials with special architectures and size effects can effectively promote the biosensing performance on the COVID-19 diagnosis, there are few comprehensive summary reports yet. Therefore, in this review, we will pay efforts on recent progress of advanced nanomaterials-facilitated electrochemical biosensors for the COVID-19 detections. The process of SARS-CoV-2 infection in humans will be briefly described, as well as summarizing the types of sensors that should be designed for different infection processes. Emphasis will be supplied to various functional nanomaterials which dominate the biosensing performance for comparison, expecting to provide a rational guidance on the material selection of biosensor construction for people. Finally, we will conclude the perspective on the design of superior nanomaterials-based biosensors facing the unknown virus in future.
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Affiliation(s)
- Xinxin Liu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Yifan Sun
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Huaiyu Song
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Wei Zhang
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China
| | - Tao Liu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China.
| | - Zhenyu Chu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Xiaoping Gu
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China.
| | - Zhengliang Ma
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China
| | - Wanqin Jin
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China.
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Szymborski TR, Berus SM, Nowicka AB, Słowiński G, Kamińska A. Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy. Biomedicines 2024; 12:167. [PMID: 38255271 PMCID: PMC10813688 DOI: 10.3390/biomedicines12010167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/23/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.
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Affiliation(s)
- Tomasz R. Szymborski
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland;
| | - Sylwia M. Berus
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland;
| | - Ariadna B. Nowicka
- Institute for Materials Research and Quantum Engineering, Poznan University of Technology, Piotrowo 3, 60-965 Poznan, Poland;
| | - Grzegorz Słowiński
- Department of Software Engineering, Warsaw School of Computer Science, Lewartowskiego 17, 00-169 Warsaw, Poland;
| | - Agnieszka Kamińska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland;
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