1
|
Machine learning–based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold nanocluster as a single probe. Anal Bioanal Chem 2022; 414:8365-8378. [DOI: 10.1007/s00216-022-04372-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/25/2022] [Accepted: 10/06/2022] [Indexed: 11/01/2022]
|
2
|
Monreal-Trigo J, Alcañiz M, Martínez-Bisbal MC, Loras A, Pascual L, Ruiz-Cerdá JL, Ferrer A, Martínez-Máñez R. New bladder cancer non-invasive surveillance method based on voltammetric electronic tongue measurement of urine. iScience 2022; 25:104829. [PMID: 36034216 PMCID: PMC9399275 DOI: 10.1016/j.isci.2022.104829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 06/14/2022] [Accepted: 07/20/2022] [Indexed: 11/08/2022] Open
Abstract
Bladder cancer (BC) is the sixth leading cause of death by cancer. Depending on the invasiveness of tumors, patients with BC will undergo surgery and surveillance lifelong, owing the high rate of recurrence and progression. In this context, the development of strategies to support non-invasive BC diagnosis is focusing attention. Voltammetric electronic tongue (VET) has been demonstrated to be of use in the analysis of biofluids. Here, we present the implementation of a VET to study 207 urines to discriminate BC and non-BC for diagnosis and surveillance to detect recurrences. Special attention has been paid to the experimental setup to improve reproducibility in the measurements. PLSDA analysis together with variable selection provided a model with high sensitivity, specificity, and area under the ROC curve AUC (0.844, 0.882, and 0.917, respectively). These results pave the way for the development of non-invasive low-cost and easy-to-use strategies to support BC diagnosis and follow-up. Bladder cancer (BC) and control urines were studied by voltammetric electronic tongue A PLSDA model was obtained with high sensitivity, specificity, and accuracy (84/88/86) 103/122 BC urines and 7⅝5 control urines were predicted correctly The electronic tongue has the potential for non-invasive BC diagnostics and follow-up
Collapse
|
3
|
Fortunati S, Giliberti C, Giannetto M, Bolchi A, Ferrari D, Donofrio G, Bianchi V, Boni A, De Munari I, Careri M. Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor. BIOSENSORS 2022; 12:bios12060426. [PMID: 35735573 PMCID: PMC9220900 DOI: 10.3390/bios12060426] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/03/2022] [Accepted: 06/15/2022] [Indexed: 05/04/2023]
Abstract
An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.
Collapse
Affiliation(s)
- Simone Fortunati
- Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy; (S.F.); (C.G.); (A.B.); (D.F.)
| | - Chiara Giliberti
- Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy; (S.F.); (C.G.); (A.B.); (D.F.)
| | - Marco Giannetto
- Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy; (S.F.); (C.G.); (A.B.); (D.F.)
- Correspondence: (M.G.); (M.C.)
| | - Angelo Bolchi
- Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy; (S.F.); (C.G.); (A.B.); (D.F.)
| | - Davide Ferrari
- Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy; (S.F.); (C.G.); (A.B.); (D.F.)
| | - Gaetano Donofrio
- Dipartimento di Scienze Medico-Veterinarie, Università di Parma, Strada del Taglio 10, 43126 Parma, Italy;
| | - Valentina Bianchi
- Dipartimento di Ingegneria e Architettura, Università di Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy; (V.B.); (A.B.); (I.D.M.)
| | - Andrea Boni
- Dipartimento di Ingegneria e Architettura, Università di Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy; (V.B.); (A.B.); (I.D.M.)
| | - Ilaria De Munari
- Dipartimento di Ingegneria e Architettura, Università di Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy; (V.B.); (A.B.); (I.D.M.)
| | - Maria Careri
- Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università di Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy; (S.F.); (C.G.); (A.B.); (D.F.)
- Correspondence: (M.G.); (M.C.)
| |
Collapse
|
4
|
Faura G, Boix-Lemonche G, Holmeide AK, Verkauskiene R, Volke V, Sokolovska J, Petrovski G. Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning. SENSORS 2022; 22:s22030718. [PMID: 35161465 PMCID: PMC8839630 DOI: 10.3390/s22030718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/20/2021] [Accepted: 12/26/2021] [Indexed: 12/13/2022]
Abstract
In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.
Collapse
Affiliation(s)
- Georgina Faura
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
- Department of Medical Biochemistry, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
| | - Gerard Boix-Lemonche
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
| | | | - Rasa Verkauskiene
- Institute of Endocrinology, Medical Academy, Lithuanian University of Health Sciences, LT-50009 Kaunas, Lithuania;
| | - Vallo Volke
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia;
- Institute of Biomedical and Transplant Medicine, Department of Medical Sciences, Tartu University Hospital, L. Puusepa Street, 51014 Tartu, Estonia
| | | | - Goran Petrovski
- Center for Eye Research, Department of Ophthalmology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway; (G.F.); (G.B.-L.)
- Department of Ophthalmology, Oslo University Hospital, 0450 Oslo, Norway
- Correspondence: ; Tel.: +47-9222-6158
| |
Collapse
|
5
|
Abstract
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.
Collapse
Affiliation(s)
- Feiyun Cui
- Department of Chemical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, Massachusetts 01609, United States
| | - Yun Yue
- Department of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - Yi Zhang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Ziming Zhang
- Department of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - H. Susan Zhou
- Department of Chemical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, Massachusetts 01609, United States
| |
Collapse
|
6
|
Duan Q, Lee J, Zheng S, Chen J, Luo R, Feng Y, Xu Z. A color-spectral machine learning path for analysis of five mixed amino acids. Chem Commun (Camb) 2020; 56:1058-1061. [DOI: 10.1039/c9cc07186e] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
A data path between mixed amino acid analysis and machine learning.
Collapse
Affiliation(s)
- Qiannan Duan
- State Key Laboratory of Pollution Control and Resource Reuse
- Jiangsu Key Laboratory of Vehicle Emissions Control
- School of the Environment
- Nanjing University
- Nanjing 210023
| | - Jianchao Lee
- Department of Environmental Science
- Shaanxi Normal University
- Xi’an 710062
- China
| | - Shourong Zheng
- State Key Laboratory of Pollution Control and Resource Reuse
- Jiangsu Key Laboratory of Vehicle Emissions Control
- School of the Environment
- Nanjing University
- Nanjing 210023
| | - Jiayuan Chen
- Department of Environmental Science
- Shaanxi Normal University
- Xi’an 710062
- China
| | - Run Luo
- Department of Environmental Science
- Shaanxi Normal University
- Xi’an 710062
- China
| | - Yunjin Feng
- Department of Environmental Science
- Shaanxi Normal University
- Xi’an 710062
- China
| | - Zhaoyi Xu
- State Key Laboratory of Pollution Control and Resource Reuse
- Jiangsu Key Laboratory of Vehicle Emissions Control
- School of the Environment
- Nanjing University
- Nanjing 210023
| |
Collapse
|
7
|
Duan Q, Lee J. Fast-developing machine learning support complex system research in environmental chemistry. NEW J CHEM 2020. [DOI: 10.1039/c9nj05717j] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Machine learning will radically accelerate analysis of complex material networks in environmental chemistry.
Collapse
Affiliation(s)
- Qiannan Duan
- Department of Environment Science
- Shaanxi Normal University
- Xi’an 710062
- China
- State Key Laboratory of Pollution Control and Resource Reuse
| | - Jianchao Lee
- Department of Environment Science
- Shaanxi Normal University
- Xi’an 710062
- China
| |
Collapse
|
8
|
Tonello S, Stradolini F, Abate G, Uberti D, Serpelloni M, Carrara S, Sardini E. Electrochemical detection of different p53 conformations by using nanostructured surfaces. Sci Rep 2019; 9:17347. [PMID: 31758050 PMCID: PMC6874615 DOI: 10.1038/s41598-019-53994-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Accepted: 11/07/2019] [Indexed: 11/09/2022] Open
Abstract
Protein electrochemistry represents a powerful technique for investigating the function and structure of proteins. Currently available biochemical assays provide limited information related to the conformational state of proteins and high costs. This work provides novel insights into the electrochemical investigation of the metalloprotein p53 and its redox products using label-free direct electrochemistry and label-based antibody-specific approaches. First, the redox activities of different p53 redox products were qualitatively investigated on carbon-based electrodes. Then, focusing on the open p53 isoform (denatured p53), a quantitative analysis was performed, comparing the performances of different bulk and nanostructured materials (carbon and platinum). Overall, four different p53 products could be successfully discriminated, from wild type to denatured. Label-free analysis suggested a single electron exchange with electron transfer rate constants on the order of 1 s-1. Label-based analysis showed decreasing affinity of pAb240 towards denatured, oxidized and nitrated p53. Furthermore, platinum nanostructured electrodes showed the highest enhancement of the limit of detection in the quantitative analysis (100 ng/ml). Overall, the obtained results represent a first step towards the implementation of highly requested complex integrated devices for clinical practices, with the aim to go beyond simple protein quantification.
Collapse
Affiliation(s)
- Sarah Tonello
- Department of Information Engineering, University of Brescia, Brescia, Italy.
| | | | - Giulia Abate
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Daniela Uberti
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Mauro Serpelloni
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Sandro Carrara
- Integrated Systems Laboratory (LSI), EPFL, Lausanne, Switzerland
| | - Emilio Sardini
- Department of Information Engineering, University of Brescia, Brescia, Italy
| |
Collapse
|
9
|
Herrera-Chacón A, Torabi F, Faridbod F, Ghasemi JB, González-Calabuig A, del Valle M. Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines. SENSORS 2019; 19:s19225002. [PMID: 31744128 PMCID: PMC6891414 DOI: 10.3390/s19225002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/05/2019] [Accepted: 11/13/2019] [Indexed: 02/06/2023]
Abstract
The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO3, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.
Collapse
Affiliation(s)
- Anna Herrera-Chacón
- Sensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Spain; (A.H.-C.); (F.T.); (A.G.-C.)
| | - Farzad Torabi
- Sensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Spain; (A.H.-C.); (F.T.); (A.G.-C.)
- Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran 1417466191, Iran;
| | - Farnoush Faridbod
- Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran 1417466191, Iran;
| | - Jahan B. Ghasemi
- School of Chemistry, College of Science, University of Tehran, Tehran 1417466191, Iran;
| | - Andreu González-Calabuig
- Sensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Spain; (A.H.-C.); (F.T.); (A.G.-C.)
| | - Manel del Valle
- Sensors and Biosensors Group, Department of Chemistry Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Spain; (A.H.-C.); (F.T.); (A.G.-C.)
- Correspondence: ; Tel.: +34-93-581-3235
| |
Collapse
|
10
|
Kotani A, Kitamura K, Kusu F, Yamamoto K, Hakamata H. Voltammetric Determination of Amino Acids Based on the Measurement of Reduction Prepeak of Quinone Caused by Surplus Acid after Neutralization. ELECTROANAL 2018. [DOI: 10.1002/elan.201800480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Akira Kotani
- Department of Analytical Chemistry, School of Pharmacy; Tokyo University of Pharmacy and Life Sciences
| | - Kanae Kitamura
- Department of Analytical Chemistry, School of Pharmacy; Tokyo University of Pharmacy and Life Sciences
| | - Fumiyo Kusu
- Department of Analytical Chemistry, School of Pharmacy; Tokyo University of Pharmacy and Life Sciences
| | - Kazuhiro Yamamoto
- Department of Analytical Chemistry, School of Pharmacy; Tokyo University of Pharmacy and Life Sciences
| | - Hideki Hakamata
- Department of Analytical Chemistry, School of Pharmacy; Tokyo University of Pharmacy and Life Sciences
| |
Collapse
|
11
|
Wei Z, Yang Y, Wang J, Zhang W, Ren Q. The measurement principles, working parameters and configurations of voltammetric electronic tongues and its applications for foodstuff analysis. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2017.08.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
12
|
Artificial neural network for the voltamperometric quantification of diclofenac in presence of other nonsteroidal anti-inflammatory drugs and some commercial excipients. J Electroanal Chem (Lausanne) 2017. [DOI: 10.1016/j.jelechem.2017.08.029] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|