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Zhao Y, Wang X, Sun T, Shan P, Zhan Z, Zhao Z, Jiang Y, Qu M, Lv Q, Wang Y, Liu P, Chen S. Artificial intelligence-driven electrochemical immunosensing biochips in multi-component detection. BIOMICROFLUIDICS 2023; 17:041301. [PMID: 37614678 PMCID: PMC10444200 DOI: 10.1063/5.0160808] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/01/2023] [Indexed: 08/25/2023]
Abstract
Electrochemical Immunosensing (EI) combines electrochemical analysis and immunology principles and is characterized by its simplicity, rapid detection, high sensitivity, and specificity. EI has become an important approach in various fields, such as clinical diagnosis, disease prevention and treatment, environmental monitoring, and food safety. However, EI multi-component detection still faces two major bottlenecks: first, the lack of cost-effective and portable detection platforms; second, the difficulty in eliminating batch differences and accurately decoupling signals from multiple analytes. With the gradual maturation of biochip technology, high-throughput analysis and portable detection utilizing the advantages of miniaturized chips, high sensitivity, and low cost have become possible. Meanwhile, Artificial Intelligence (AI) enables accurate decoupling of signals and enhances the sensitivity and specificity of multi-component detection. We believe that by evaluating and analyzing the characteristics, benefits, and linkages of EI, biochip, and AI technologies, we may considerably accelerate the development of EI multi-component detection. Therefore, we propose three specific prospects: first, AI can enhance and optimize the performance of the EI biochips, addressing the issue of multi-component detection for portable platforms. Second, the AI-enhanced EI biochips can be widely applied in home care, medical healthcare, and other areas. Third, the cross-fusion and innovation of EI, biochip, and AI technologies will effectively solve key bottlenecks in biochip detection, promoting interdisciplinary development. However, challenges may arise from AI algorithms that are difficult to explain and limited data access. Nevertheless, we believe that with technological advances and further research, there will be more methods and technologies to overcome these challenges.
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Affiliation(s)
- Yuliang Zhao
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066000, Hebei, China
| | - Xiaoai Wang
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066000, Hebei, China
| | - Tingting Sun
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066000, Hebei, China
| | - Peng Shan
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066000, Hebei, China
| | - Zhikun Zhan
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066000, Hebei, China
| | - Zhongpeng Zhao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing 100071, China
| | - Yongqiang Jiang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing 100071, China
| | - Mingyue Qu
- The PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Qingyu Lv
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing 100071, China
| | - Ying Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Peng Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing 100071, China
| | - Shaolong Chen
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences (AMMS), Beijing 100071, China
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Zhao Y, Zhang H, Li Y, Yu X, Cai Y, Sha X, Wang S, Zhan Z, Xu J, Liu L. AI powered electrochemical multi-component detection of insulin and glucose in serum. Biosens Bioelectron 2021; 186:113291. [PMID: 33971527 DOI: 10.1016/j.bios.2021.113291] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/09/2021] [Accepted: 04/27/2021] [Indexed: 12/27/2022]
Abstract
Multi-component detection of insulin and glucose in serum is of great importance and urgently needed in clinical diagnosis and treatment due to its economy and practicability. However, insulin and glucose can hardly be determined by traditional electrochemical detection methods. Their mixed oxidation currents and rare involvement in the reaction process make it difficult to decouple them. In this study, AI algorithms are introduced to power the electrochemical method to conquer this problem. First, the current curves of insulin, glucose, and their mixed solution are obtained using cyclic voltammetry. Then, seven features of the cyclic voltammetry curve are extracted as characteristic values for detecting the concentrations of insulin and glucose. Finally, after training using machine learning algorithms, insulin and glucose concentrations are decoupled and regressed accurately. The entire detection process only takes three minutes. It can detect insulin at the pmol level and glucose at the mmol level, which meets the basic clinical requirements. The average relative error in predicting insulin concentrations is around 6.515%, and that in predicting glucose concentrations is around 4.36%. To verify the performance and effectiveness of the proposed method, it is used to determine the concentrations of insulin and glucose in fetal bovine serum and real clinical serum samples. The results are satisfactory, demonstrating that the method can meet basic clinical needs. This multi-component testing system delivers acceptable detect limit and accuracy and has the merits of low cost and high efficiency, holding great potential for use in clinical diagnosis.
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Affiliation(s)
- Yuliang Zhao
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China
| | - Hongyu Zhang
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China.
| | - Yang Li
- School of Electrical Engineering, Yanshan University at Qinhuangdao, Qinhuangdao, 066004, China
| | - Xiaodong Yu
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China
| | - Yi Cai
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China
| | - Xiaopeng Sha
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China
| | - Shuyu Wang
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China
| | - Zhikun Zhan
- School of Electrical Engineering, Yanshan University at Qinhuangdao, Qinhuangdao, 066004, China.
| | - Jianghong Xu
- Qinhuangdao Hospital of Traditional Chinese Medicine, Qinhuangdao, 066004, China
| | - Lianqing Liu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110000, China.
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Kang A, Ryu J, Lee J, Kim S, Lee CY, Yun WS. Calibration curve-free electrochemical quantitation by micro-nano multi-scale gap devices. Mikrochim Acta 2021; 188:200. [PMID: 34041606 DOI: 10.1007/s00604-021-04861-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/13/2021] [Indexed: 12/25/2022]
Abstract
Quantitation without relying on the calibration curve has long been an issue of overcoming analytical problems accompanied with the inherent limitations of the calibration curve fitting errors. Here, we report on a calibration curve-free method for electrochemical quantitation based on a multi-scale gap device (MGD). The MGD is an integrated device having a series of interdigitated electrodes (IDE) with micro-to-nano gap distances. The device shows a gap-dependent redox current of the analyte when subjected to the electrochemical cycling between the two facing electrodes of its componential IDEs. Based on the fact that the current increases as the gap distance decreases, the analyte concentration could be directly estimated: the rate of increase in the current was directly proportional to the analyte concentration. The calibration curve was not necessary for the quantitation. The accuracy of this MGD approach was better than that of an IDE collection of the same gap distance, which was deteriorated at the larger gap distances particularly. The MGD-based quantitation of dopamine, potassium ferricyanide, and aminophenol was demonstrated in a relatively broad range of concentrations (100 nM-5 mM).
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Affiliation(s)
- Aeyeon Kang
- Department of Chemistry, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Jiho Ryu
- Department of Chemistry, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Jisu Lee
- Department of Chemistry, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Seunghun Kim
- Department of Chemistry, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Cho Yeon Lee
- Nano/Bio Fusion Technology Research Center, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Wan Soo Yun
- Department of Chemistry, Sungkyunkwan University, Suwon, 16419, South Korea.
- Nano/Bio Fusion Technology Research Center, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
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Kim RY, Rivera H, Evarts SE, Rodríguez-Martínez JA, Willis RR, Galloway DB, Falih F, McCall MJ, Smith SJ, Perz K, Smotkin ES. A Laser-Activated Membrane Introduction Mass Spectrometry Study of Proton Spillover Promoted Alkane Dehydrogenation. Anal Chem 2020; 92:13462-13469. [PMID: 32907325 DOI: 10.1021/acs.analchem.0c02886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Operando high-throughput evaluation of heterogeneous catalysts by laser-activated membrane introduction mass spectrometry (LAMIMS) elucidates the Pt loading dependence of methylcyclohexane dehydrogenation on platinized γ-alumina beads. A CO2 marking laser rapidly and sequentially heats catalyst beads positioned on a heat-dissipating carbon paper support that overlays a silicone membrane, separating the bead library reaction zone from a quadrupole mass analyzer. The toluene m/z peak varies logarithmically with Pt loading, suggesting that reactivity includes factors that are negatively correlated to Pt loading. These factors may include the Pt/γ-Al2O3 surface interfacial region as one component of a heterogeneous catalytically active surface area/mass. This work demonstrates LAMIMS as a broadly applicable high-throughput operando screening method for heterogeneous catalysts.
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Affiliation(s)
- Ryan Yongtae Kim
- Department of Chemical and Environmental Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Harry Rivera
- Department of Chemistry, University of Puerto Rico at Rio Piedras, San Juan, Puerto Rico 00931, United States
| | - Sara E Evarts
- Department of Chemistry & Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - José A Rodríguez-Martínez
- Department of Chemistry, University of Puerto Rico at Rio Piedras, San Juan, Puerto Rico 00931, United States
| | - Richard R Willis
- UOP LLC, a Honeywell Company, Des Plaines, Illinois 60016 United States
| | | | - Falaah Falih
- UOP LLC, a Honeywell Company, Des Plaines, Illinois 60016 United States
| | - Michael J McCall
- UOP LLC, a Honeywell Company, Des Plaines, Illinois 60016 United States
| | - S Jackson Smith
- Department of Chemistry & Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Kyra Perz
- Department of Chemistry & Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Eugene S Smotkin
- Department of Chemical and Environmental Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States.,Department of Chemistry, University of Puerto Rico at Rio Piedras, San Juan, Puerto Rico 00931, United States.,Department of Chemistry & Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
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Skuratovsky A, Soto RJ, Porter MD. Adaptable Detection Strategies in Membrane-Based Immunoassays: Calibration-Free Quantitation with Surface-Enhanced Raman Scattering Readout. Anal Chem 2018; 90:7769-7776. [DOI: 10.1021/acs.analchem.8b01958] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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