1
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Lopez Baltazar JM, Gu W, Bocková M, Yu Q. Immunoassays for Extracellular Vesicle Detection via Transmembrane Proteins Using Surface Plasmon Resonance Biosensors. ACS Sens 2024; 9:3594-3603. [PMID: 38912608 DOI: 10.1021/acssensors.4c00558] [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: 06/25/2024]
Abstract
Extracellular vesicles (EVs) are preeminent carriers of biomarkers and have become the subject of intense biomedical research for medical diagnostics using biosensors. To create effective EV-based immunoassays, it is imperative to develop surface chemistry approaches with optimal EV detection targeting transmembrane protein biomarkers that are not affected by cell-to-cell variability. Here, we developed a series of immunoassays for the detection of EVs derived from mouse monocyte cells using surface plasmon resonance (SPR) biosensors. We chemically immobilized antibodies onto mixed self-assembled monolayers of oligo ethylene glycol (OEG) alkanethiolates with carboxylic and hydroxylic terminal groups. The effects of antibody clonality (monoclonal vs polyclonal) and antibody surface coverage in targeting EVs via CD81 tetraspanins were investigated. We determined binding kinetic parameters, establishing trends from steric hindrance effects and epitope recognition properties of antibodies. Our results indicate that a 40% surface coverage of polyclonal antibodies covalently linked onto a mixed SAM with 10% of terminated -COOH groups yields a promising approach for EV detection with a linear range of 1.9 × 108-1.9 × 109 EVs/mL and a limit of detection of 5.9 × 106 EVs/mL. This optimal immunoassay exhibits a 1.92 nM equilibrium dissociation constant for bound EVs, suggesting a high binding affinity when CD81 is targeted. Our study provides important insights into surface chemistry development for EV detection targeted via transmembrane protein biomarkers using antibodies, which has promising applications for disease diagnostics.
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Affiliation(s)
- Jesus M Lopez Baltazar
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Wenchao Gu
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Markéta Bocková
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
- Institute of Photonics and Electronics, AS CR Chaberská57, Prague 182 51, Czech Republic
| | - Qiuming Yu
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
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2
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Flynn CD, Chang D. Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities. Diagnostics (Basel) 2024; 14:1100. [PMID: 38893627 PMCID: PMC11172335 DOI: 10.3390/diagnostics14111100] [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: 05/05/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.
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Affiliation(s)
- Connor D. Flynn
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Dingran Chang
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA
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3
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Lourenço AA, Amaral PHR, Paim AAO, Marques GF, Gomes-de-Pontes L, da Mata CPSM, da Fonseca FG, Pérez JCG, Coelho-dos-Reis JGA. Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data. Front Public Health 2024; 12:1347334. [PMID: 38807995 PMCID: PMC11130428 DOI: 10.3389/fpubh.2024.1347334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/30/2024] [Indexed: 05/30/2024] Open
Abstract
The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient's outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.
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Affiliation(s)
- Alice Aparecida Lourenço
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Adriana Alves Oliveira Paim
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Geovane Ferreira Marques
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Leticia Gomes-de-Pontes
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Flávio Guimarães da Fonseca
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- CT Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Juan Carlos González Pérez
- Departamento de Física, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Jordana Grazziela Alves Coelho-dos-Reis
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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4
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Costa JNY, Pimentel GJC, Poker JA, Merces L, Paschoalino WJ, Vieira LCS, Castro ACH, Alves WA, Ayres LB, Kubota LT, Santhiago M, Garcia CD, Piazzetta MHO, Gobbi AL, Shimizu FM, Lima RS. Single-Response Duplexing of Electrochemical Label-Free Biosensor from the Same Tag. Adv Healthc Mater 2024; 13:e2303509. [PMID: 38245830 DOI: 10.1002/adhm.202303509] [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: 10/12/2023] [Revised: 01/16/2024] [Indexed: 01/22/2024]
Abstract
Multiplexing is a valuable strategy to boost throughput and improve clinical accuracy. Exploiting the vertical, meshed design of reproducible and low-cost ultra-dense electrochemical chips, the unprecedented single-response multiplexing of typical label-free biosensors is reported. Using a cheap, handheld one-channel workstation and a single redox probe, that is, ferro/ferricyanide, the recognition events taking place on two spatially resolved locations of the same working electrode can be tracked along a single voltammetry scan by collecting the electrochemical signatures of the probe in relation to different quasi-reference electrodes, Au (0 V) and Ag/AgCl ink (+0.2 V). This spatial isolation prevents crosstalk between the redox tags and interferences over functionalization and binding steps, representing an advantage over the existing non-spatially resolved single-response multiplex strategies. As proof of concept, peptide-tethered immunosensors are demonstrated to provide the duplex detection of COVID-19 antibodies, thereby doubling the throughput while achieving 100% accuracy in serum samples. The approach is envisioned to enable broad applications in high-throughput and multi-analyte platforms, as it can be tailored to other biosensing devices and formats.
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Affiliation(s)
- Juliana N Y Costa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
- Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo, 09210-580, Brazil
| | - Gabriel J C Pimentel
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, 13083-970, Brazil
| | - Júlia A Poker
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, 13083-970, Brazil
| | - Leandro Merces
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, 09126, Chemnitz, Germany
| | - Waldemir J Paschoalino
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
| | - Luis C S Vieira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
| | - Ana C H Castro
- Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo, 09210-580, Brazil
| | - Wendel A Alves
- Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo, 09210-580, Brazil
| | - Lucas B Ayres
- Department of Chemistry, Clemson University, Clemson, SC, 29634, USA
| | - Lauro T Kubota
- Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo, 09210-580, Brazil
| | - Murilo Santhiago
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
| | - Carlos D Garcia
- Department of Chemistry, Clemson University, Clemson, SC, 29634, USA
| | - Maria H O Piazzetta
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
| | - Angelo L Gobbi
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
| | - Flávio M Shimizu
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
| | - Renato S Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, 13083-970, Brazil
- Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo, 09210-580, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, 13083-970, Brazil
- Department of Chemistry, Clemson University, Clemson, SC, 29634, USA
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo, 13565-590, Brazil
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5
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Zhou J, Dong J, Hou H, Huang L, Li J. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications. LAB ON A CHIP 2024; 24:1307-1326. [PMID: 38247405 DOI: 10.1039/d3lc01012k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.
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Affiliation(s)
- Jianhua Zhou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jianpei Dong
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Hongwei Hou
- Beijing Life Science Academy, Beijing 102209, China
| | - Lu Huang
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jinghong Li
- Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Tsinghua University, Beijing 100084, China.
- New Cornerstone Science Laboratory, Shenzhen 518054, China
- Beijing Life Science Academy, Beijing 102209, China
- Center for BioAnalytical Chemistry, Hefei National Laboratory of Physical Science at Microscale, University of Science and Technology of China, Hefei 230026, China
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6
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Jeong MH, Im H, Dahl JB. Non-contact microfluidic analysis of the stiffness of single large extracellular vesicles from IDH1-mutated glioblastoma cells. ADVANCED MATERIALS TECHNOLOGIES 2023; 8:2201412. [PMID: 37649709 PMCID: PMC10465107 DOI: 10.1002/admt.202201412] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Indexed: 09/01/2023]
Abstract
In preparation for leveraging extracellular vesicles (EVs) for disease diagnostics and therapeutics, fundamental research is being done to understand EV biological, chemical, and physical properties. Most published studies have investigated nanoscale EVs and focused on EV biochemical content. There is much less understanding of large microscale EV characteristics and EV mechanical properties. We recently introduced a non-contact microfluidic technique that measures the stiffness of large EVs (>1 μm diameter). This pilot study probes the robustness of the microfluidic technique to distinguish between EV populations by comparing stiffness distributions of large EVs derived from glioblastoma cell lines. EVs derived from cells expressing the IDH1 mutation, a common glioblastoma mutation known to disrupt lipid metabolism, were stiffer than those expressed from wild-type cells in a statistical comparison of sample medians. A supporting lipidomics analysis showed that the IDH1 mutation increased the amount of saturated lipids in EVs. Taken together, these data encourage further investigation into the potential of high-throughput microfluidics to distinguish between large EV populations that differ in biomolecular composition. These findings contribute to the understanding of EV biomechanics, in particular for the less studied microscale EVs.
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Affiliation(s)
- Mi Ho Jeong
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Joanna B Dahl
- Engineering Department, University of Massachusetts Boston, Boston, MA 02025, USA
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7
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Nicoliche CYN, da Silva GS, Gomes-de-Pontes L, Schleder GR, Lima RS. Single-Response Electronic Tongue and Machine Learning Enable the Multidetermination of Extracellular Vesicle Biomarkers for Cancer Diagnostics Without Recognition Elements. Methods Mol Biol 2023; 2679:83-94. [PMID: 37300610 DOI: 10.1007/978-1-0716-3271-0_6] [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: 06/12/2023]
Abstract
Platforms based on impedimetric electronic tongue (nonselective sensor) and machine learning are promising to bring disease screening biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point-of-care, thus contributing to rationalize and decentralize laboratory tests with social and economic impacts being achieved. By combining a low-cost and scalable electronic tongue with machine learning, in this chapter, we describe the simultaneous determination of two extracellular vesicle (EV) biomarkers, i.e., the concentrations of EV and carried proteins, in mice blood with Ehrlich tumor from a single impedance spectrum without using biorecognizing elements. This tumor shows primary features of mammary tumor cells. Pencil HB core electrodes are integrated into polydimethylsiloxane (PDMS) microfluidic chip. The platform shows the highest throughput in comparison with the methods addressed in the literature to determine EV biomarkers.
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Affiliation(s)
- Caroline Y N Nicoliche
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil
- Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
| | | | - Leticia Gomes-de-Pontes
- Department of Immunology, Institute of Biomedical Sciences, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Gabriel R Schleder
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Renato S Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil.
- Institute of Chemistry, University of Campinas, Campinas, SP, Brazil.
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, SP, Brazil.
- Federal University of ABC, Santo André, SP, Brazil.
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8
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Xing G, Ai J, Wang N, Pu Q. Recent progress of smartphone-assisted microfluidic sensors for point of care testing. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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9
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Castro ACH, Bezerra ÍRS, Pascon AM, da Silva GH, Philot EA, de Oliveira VL, Mancini RSN, Schleder GR, Castro CE, de Carvalho LRS, Fernandes BHV, Cilli EM, Sanches PRS, Santhiago M, Charlie-Silva I, Martinez DST, Scott AL, Alves WA, Lima RS. Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests. ACS NANO 2022; 16:14239-14253. [PMID: 35969505 PMCID: PMC9397565 DOI: 10.1021/acsnano.2c04364] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/11/2022] [Indexed: 05/16/2023]
Abstract
Limitations of the recognition elements in terms of synthesis, cost, availability, and stability have impaired the translation of biosensors into practical use. Inspired by nature to mimic the molecular recognition of the anti-SARS-CoV-2 S protein antibody (AbS) by the S protein binding site, we synthesized the peptide sequence of Asn-Asn-Ala-Thr-Asn-COOH (abbreviated as PEP2003) to create COVID-19 screening label-free (LF) biosensors based on a carbon electrode, gold nanoparticles (AuNPs), and electrochemical impedance spectroscopy. The PEP2003 is easily obtained by chemical synthesis, and it can be adsorbed on electrodes while maintaining its ability for AbS recognition, further leading to a sensitivity 3.4-fold higher than the full-length S protein, which is in agreement with the increase in the target-to-receptor size ratio. Peptide-loaded LF devices based on noncovalent immobilization were developed by affording fast and simple analyses, along with a modular functionalization. From studies by molecular docking, the peptide-AbS binding was found to be driven by hydrogen bonds and hydrophobic interactions. Moreover, the peptide is not amenable to denaturation, thus addressing the trade-off between scalability, cost, and robustness. The biosensor preserves 95.1% of the initial signal for 20 days when stored dry at 4 °C. With the aid of two simple equations fitted by machine learning (ML), the method was able to make the COVID-19 screening of 39 biological samples into healthy and infected groups with 100.0% accuracy. By taking advantage of peptide-related merits combined with advances in surface chemistry and ML-aided accuracy, this platform is promising to bring COVID-19 biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point of care, with social and economic impacts being achieved.
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Affiliation(s)
- Ana C. H. Castro
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Ítalo R. S. Bezerra
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Aline M. Pascon
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Gabriela H. da Silva
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
| | - Eric A. Philot
- Center for Mathematics, Computing and Cognition,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Vivian L. de Oliveira
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
- Laboratory of Immunology, Heart Institute,
University of São Paulo, São Paulo, São
Paulo 05508-000, Brazil
| | - Rodrigo S. N. Mancini
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Gabriel R. Schleder
- John A. Paulson School of Engineering and Applied
Sciences, Harvard University, Cambridge, Massachusetts 02138,
United States
| | - Carlos E. Castro
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | | | | | - Eduardo M. Cilli
- Institute of Chemistry, São Paulo
State University, Araraquara, São Paulo 14800-900,
Brazil
| | - Paulo R. S. Sanches
- Institute of Chemistry, São Paulo
State University, Araraquara, São Paulo 14800-900,
Brazil
| | - Murilo Santhiago
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Ives Charlie-Silva
- Institute of Biomedical Sciences,
University of São Paulo, São Paulo, São
Paulo 05508-000, Brazil
| | - Diego S. T. Martinez
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
| | - Ana L. Scott
- Center for Mathematics, Computing and Cognition,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Wendel A. Alves
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Renato S. Lima
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
- Institute of Chemistry, University of
Campinas, Campinas, São Paulo 13083-970,
Brazil
- São Carlos Institute of Chemistry,
University of São Paulo, São Carlos, São
Paulo 09210-580, Brazil
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10
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Theel EK, Schwaminger SP. Microfluidic Approaches for Affinity-Based Exosome Separation. Int J Mol Sci 2022; 23:ijms23169004. [PMID: 36012270 PMCID: PMC9409173 DOI: 10.3390/ijms23169004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 12/13/2022] Open
Abstract
As a subspecies of extracellular vesicles (EVs), exosomes have provided promising results in diagnostic and theranostic applications in recent years. The nanometer-sized exosomes can be extracted by liquid biopsy from almost all body fluids, making them especially suitable for mainly non-invasive point-of-care (POC) applications. To achieve this, exosomes must first be separated from the respective biofluid. Impurities with similar properties, heterogeneity of exosome characteristics, and time-related biofouling complicate the separation. This practical review presents the state-of-the-art methods available for the separation of exosomes. Furthermore, it is shown how new separation methods can be developed. A particular focus lies on the fabrication and design of microfluidic devices using highly selective affinity separation. Due to their compactness, quick analysis time and portable form factor, these microfluidic devices are particularly suitable to deliver fast and reliable results for POC applications. For these devices, new manufacturing methods (e.g., laminating, replica molding and 3D printing) that use low-cost materials and do not require clean rooms are presented. Additionally, special flow routes and patterns that increase contact surfaces, as well as residence time, and thus improve affinity purification are displayed. Finally, various analyses are shown that can be used to evaluate the separation results of a newly developed device. Overall, this review paper provides a toolbox for developing new microfluidic affinity devices for exosome separation.
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Affiliation(s)
- Eike K. Theel
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching bei München, Germany
| | - Sebastian P. Schwaminger
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching bei München, Germany
- Division of Medicinal Chemistry, Otto Loewi Research Center, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria
- Correspondence:
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11
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Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals. Talanta 2022; 243:123327. [DOI: 10.1016/j.talanta.2022.123327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/14/2022] [Accepted: 02/16/2022] [Indexed: 11/20/2022]
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12
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Ferreira LF, Giordano GF, Gobbi AL, Piazzetta MHO, Schleder GR, Lima RS. Real-Time and In Situ Monitoring of the Synthesis of Silica Nanoparticles. ACS Sens 2022; 7:1045-1057. [PMID: 35417147 DOI: 10.1021/acssensors.1c02697] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The real-time and in situ monitoring of the synthesis of nanomaterials (NMs) remains a challenging task, which is of pivotal importance by assisting fundamental studies (e.g., synthesis kinetics and colloidal phenomena) and providing optimized quality control. In fact, the lack of reproducibility in the synthesis of NMs is a bottleneck against the translation of nanotechnologies into the market toward daily practice. Here, we address an impedimetric millifluidic sensor with data processing by machine learning (ML) as a sensing platform to monitor silica nanoparticles (SiO2NPs) over a 24 h synthesis from a single measurement. The SiO2NPs were selected as a model NM because of their extensive applications. Impressively, simple ML-fitted descriptors were capable of overcoming interferences derived from SiO2NP adsorption over the signals of polarizable Au interdigitate electrodes to assure the determination of the size and concentration of nanoparticles over synthesis while meeting the trade-off between accuracy and speed/simplicity of computation. The root-mean-square errors were calculated as ∼2.0 nm (size) and 2.6 × 1010 nanoparticles mL-1 (concentration). Further, the robustness of the ML size descriptor was successfully challenged in data obtained along independent syntheses using different devices, with the global average accuracy being 103.7 ± 1.9%. Our work advances the developments required to transform a closed flow system basically encompassing the reactional flask and an impedimetric sensor into a scalable and user-friendly platform to assess the in situ synthesis of SiO2NPs. Since the sensor presents a universal response principle, the method is expected to enable the monitoring of other NMs. Such a platform may help to pave the way for translating "sense-act" systems into practice use in nanotechnology.
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Affiliation(s)
- Larissa F. Ferreira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Gabriela F. Giordano
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Angelo L. Gobbi
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Maria H. O. Piazzetta
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Gabriel R. Schleder
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Renato S. Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences, Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 13566-590, Brazil
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13
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Barbosa JA, Freitas VMS, Vidotto LHB, Schleder GR, de Oliveira RAG, da Rocha JF, Kubota LT, Vieira LCS, Tolentino HCN, Neckel IT, Gobbi AL, Santhiago M, Lima RS. Biocompatible Wearable Electrodes on Leaves toward the On-Site Monitoring of Water Loss from Plants. ACS APPLIED MATERIALS & INTERFACES 2022; 14:22989-23001. [PMID: 35311272 DOI: 10.1021/acsami.2c02943] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Impedimetric wearable sensors are a promising strategy for determining the loss of water content (LWC) from leaves because they can afford on-site and nondestructive quantification of cellular water from a single measurement. Because the water content is a key marker of leaf health, monitoring of the LWC can lend key insights into daily practice in precision agriculture, toxicity studies, and the development of agricultural inputs. Ongoing challenges with this monitoring are the on-leaf adhesion, compatibility, scalability, and reproducibility of the electrodes, especially when subjected to long-term measurements. This paper introduces a set of sensing material, technological, and data processing solutions that overwhelm such obstacles. Mass-production-suitable electrodes consisting of stand-alone Ni films obtained by well-established microfabrication methods or ecofriendly pyrolyzed paper enabled reproducible determination of the LWC from soy leaves with optimized sensibilities of 27.0 (Ni) and 17.5 kΩ %-1 (paper). The freestanding design of the Ni electrodes was further key to delivering high on-leaf adhesion and long-term compatibility. Their impedances remained unchanged under the action of wind at velocities of up to 2.00 m s-1, whereas X-ray nanoprobe fluorescence assays allowed us to confirm the Ni sensor compatibility by the monitoring of the soy leaf health in an electrode-exposed area. Both electrodes operated through direct transfer of the conductive materials on hairy soy leaves using an ordinary adhesive tape. We used a hand-held and low-power potentiostat with wireless connection to a smartphone to determine the LWC over 24 h. Impressively, a machine-learning model was able to convert the sensing responses into a simple mathematical equation that gauged the impairments on the water content at two temperatures (30 and 20 °C) with reduced root-mean-square errors (0.1% up to 0.3%). These data suggest broad applicability of the platform by enabling direct determination of the LWC from leaves even at variable temperatures. Overall, our findings may help to pave the way for translating "sense-act" technologies into practice toward the on-site and remote investigation of plant drought stress. These platforms can provide key information for aiding efficient data-driven management and guiding decision-making steps.
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Affiliation(s)
- Júlia A Barbosa
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 09210-580, Brazil
| | - Vitoria M S Freitas
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Faculty of Chemical Engineering, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Lourenço H B Vidotto
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Gabriel R Schleder
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Ricardo A G de Oliveira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Jaqueline F da Rocha
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
| | - Lauro T Kubota
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Luis C S Vieira
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Hélio C N Tolentino
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Itamar T Neckel
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Angelo L Gobbi
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
| | - Murilo Santhiago
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
| | - Renato S Lima
- Brazilian Nanotechnology National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo 13083-970, Brazil
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 09210-580, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
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14
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Wu N, Zhang XY, Xia J, Li X, Yang T, Wang JH. Ratiometric 3D DNA Machine Combined with Machine Learning Algorithm for Ultrasensitive and High-Precision Screening of Early Urinary Diseases. ACS NANO 2021; 15:19522-19534. [PMID: 34813275 DOI: 10.1021/acsnano.1c06429] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Urinary extracellular vesicles (uEVs) have received considerable attention as a potential biomarker source for the diagnosis of urinary diseases. Present studies mainly focus on the discovery of biomarkers based on high-throughput proteomics, while limited efforts have been paid to applying the uEVs' biomarkers for the diagnosis of early urinary disease. Herein, we demonstrate a diagnosis protocol to realize ultrasensitive detection of uEVs and accurate classification of early urinary diseases, by combing a ratiometric three-dimensional (3D) DNA machine with machine learning (ML). The ratiometric 3D DNA machine platform is constructed by conjugating a padlock probe (PLP) containing cytosine-rich (C-rich) sequences, anchor strands, and nucleic-acid-stabilized silver nanoclusters (DNAAgNCs) onto the magnetic nanoparticles (MNPs). The competitive binding of uEVs with the aptamer releases the walker strand, thus the ratiometric 3D DNA machine was activated to undergo an accurate amplification reaction and produce a ratiometric fluorescence signal. The present biosensor offers a detection limit of 9.9 × 103 particles mL-1 with a linear range of 104-108 particles mL-1 for uEVs. Two ML algorithms, K-nearest neighbor (KNN) and support vector machine (SVM), were subsequently applied for analyzing the correlation between the sensing signals of uEV multibiomarkers and the clinical state. The disease diagnostic accuracy of optimal biomarker combination reaches 95% and 100% by analyzing with KNN and SVM, and the disease type classification exhibits an accuracy of 94.7% and 89.5%, respectively. Moreover, the protocol results in 100% accurate visual identification of clinical uEV samples from individuals with disease or as healthy control by a t-distributed stochastic neighbor embedding (tSNE) algorithm.
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Affiliation(s)
- Na Wu
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China
| | - Xin-Yu Zhang
- General Hospital of Northern Theater Command, Shenyang 110015, China
- Dalian Medical University, Dalian 116044, China
| | - Jie Xia
- Product Research Institute, Research and Development Center, Huayou Nonferrous Industrial Group, Zhejiang Huayou Cobalt Co., Ltd., Quzhou 324000, China
| | - Xin Li
- General Hospital of Northern Theater Command, Shenyang 110015, China
| | - Ting Yang
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China
| | - Jian-Hua Wang
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China
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15
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Diaz-Armas GG, Cervantes-Gonzalez AP, Martinez-Duarte R, Perez-Gonzalez VH. Electrically driven microfluidic platforms for exosome manipulation and characterization. Electrophoresis 2021; 43:327-339. [PMID: 34717000 DOI: 10.1002/elps.202100202] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/06/2021] [Accepted: 10/25/2021] [Indexed: 01/15/2023]
Abstract
Exosomes are small extracellular vesicles that can be obtained from several body fluids such as blood and urine. Since these vesicles can carry biomarkers and other cargo, they have application in healthcare diagnostics and therapeutics, such as liquid biopsies and drug delivery. Yet, their identification and separation from a sample remain challenging due to their high degree of heterogeneity and their co-existence with other bioparticles. In this contribution, we review the state-of-the-art on electrical techniques and methods to displace, selectively trap/isolate, and detect/characterize exosomes in microfluidic devices. Although there are many reviews focused on exosome separation using benchtop equipment, such as ultracentrifugation, there are limited reviews focusing on the use of electrical phenomena in microfluidic devices for exosome manipulation and detection. Here, we highlight contributions published during the past decade and present perspectives for this research field for the near future, outlining challenges to address in years to come.
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Affiliation(s)
- Gladys G Diaz-Armas
- Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, Mexico
| | | | - Rodrigo Martinez-Duarte
- Multiscale Manufacturing Laboratory, Department of Mechanical Engineering, Clemson University, Clemson, South Carolina, USA
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16
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Min L, Wang B, Bao H, Li X, Zhao L, Meng J, Wang S. Advanced Nanotechnologies for Extracellular Vesicle-Based Liquid Biopsy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2102789. [PMID: 34463056 PMCID: PMC8529441 DOI: 10.1002/advs.202102789] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Indexed: 05/09/2023]
Abstract
Extracellular vesicles (EVs) are emerging as a new source of biomarkers in liquid biopsy because of their wide presence in most body fluids and their ability to load cargoes from disease-related cells. Owing to the crucial role of EVs in disease diagnosis and treatment, significant efforts have been made to isolate, detect, and analyze EVs with high efficiency. A recent overview of advanced EV detection nanotechnologies is discussed here. First, several key challenges in EV-based liquid biopsies are introduced. Then, the related pivotal advances in nanotechnologies for EV isolation based on physical features, chemical affinity, and the combination of nanostructures and chemical affinity are summarized. Next, a summary of high-sensitivity sensors for EV detection and advanced approaches for single EV detection are provided. Later, EV analysis is introduced in practical clinical scenarios, and the application of machine learning in this field is highlighted. Finally, future opportunities for the development of next-generation nanotechnologies for EV detection are presented.
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Affiliation(s)
- Li Min
- Department of GastroenterologyBeijing Friendship HospitalCapital Medical UniversityNational Clinical Research Center for Digestive DiseasesBeijing Digestive Disease CenterBeijing Key Laboratory for Precancerous Lesion of Digestive DiseaseBeijing100050P. R. China
| | - Binshuai Wang
- Department of UrologyPeking University Third HospitalBeijing100191P. R. China
| | - Han Bao
- Key Laboratory of Bio‐inspired Materials and Interfacial ScienceCAS Center for Excellence in NanoscienceTechnical Institute of Physics and ChemistryChinese Academy of SciencesBeijing100190P. R. China
- University of Chinese Academy of SciencesBeijing100049P. R. China
| | - Xinran Li
- Department of UrologyPeking University Third HospitalBeijing100191P. R. China
| | - Libo Zhao
- Echo Biotech Co., Ltd.Beijing102206P. R. China
| | - Jingxin Meng
- Key Laboratory of Bio‐inspired Materials and Interfacial ScienceCAS Center for Excellence in NanoscienceTechnical Institute of Physics and ChemistryChinese Academy of SciencesBeijing100190P. R. China
- University of Chinese Academy of SciencesBeijing100049P. R. China
| | - Shutao Wang
- Key Laboratory of Bio‐inspired Materials and Interfacial ScienceCAS Center for Excellence in NanoscienceTechnical Institute of Physics and ChemistryChinese Academy of SciencesBeijing100190P. R. China
- University of Chinese Academy of SciencesBeijing100049P. R. China
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17
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Giordano GF, Freitas VMS, Schleder GR, Santhiago M, Gobbi AL, Lima RS. Bifunctional Metal Meshes Acting as a Semipermeable Membrane and Electrode for Sensitive Electrochemical Determination of Volatile Compounds. ACS APPLIED MATERIALS & INTERFACES 2021; 13:35914-35923. [PMID: 34309352 DOI: 10.1021/acsami.1c07874] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The monitoring of toxic inorganic gases and volatile organic compounds has brought the development of field-deployable, sensitive, and scalable sensors into focus. Here, we attempted to meet these requirements by using concurrently microhole-structured meshes as (i) a membrane for the gas diffusion extraction of an analyte from a donor sample and (ii) an electrode for the sensitive electrochemical determination of this target with the receptor electrolyte at rest. We used two types of meshes with complementary benefits, i.e., Ni mesh fabricated by robust, scalable, and well-established methods for manufacturing specific designs and stainless steel wire mesh (SSWM), which is commercially available at a low cost. The diffusion of gas (from a donor) was conducted in headspace mode, thus minimizing issues related to mesh fouling. When compared with the conventional polytetrafluoroethylene (PTFE) membrane, both the meshes (40 μm hole diameter) led to a higher amount of vapor collected into the electrolyte for subsequent detection. This inedited fashion produced a kind of reverse diffusion of the analyte dissolved into the electrolyte (receptor), i.e., from the electrode to bulk, which further enabled highly sensitive analyses. Using Ni mesh coated with Ni(OH)2 nanoparticles, the limit of detection reached for ethanol was 24-fold lower than the data attained by a platform with a PTFE membrane and placement of the electrode into electrolyte bulk. This system was applied in the determination of ethanol in complex samples related to the production of ethanol biofuel. It is noteworthy that a simple equation fitted by machine learning was able to provide accurate assays (accuracies from 97 to 102%) by overcoming matrix effect-related interferences on detection performance. Furthermore, preliminary measurements demonstrated the successful coating of the meshes with gold films as an alternative raw electrode material and the monitoring of HCl utilizing Au-coated SSWMs. These strategies extend the applicability of the platform that may help to develop valuable volatile sensing solutions.
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Affiliation(s)
- Gabriela F Giordano
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
| | - Vitoria M S Freitas
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
- Faculty of Chemical Engineering, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Gabriel R Schleder
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
| | - Murilo Santhiago
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
| | - Angelo L Gobbi
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
| | - Renato S Lima
- Brazilian Center for Research in Energy and Materials, Brazilian Nanotechnology National Laboratory, Campinas, São Paulo 13083-970, Brazil
- Institute of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
- São Carlos Institute of Chemistry, University of São Paulo, São Carlos, São Paulo 09210-580, Brazil
- Federal University of ABC, Santo André, São Paulo 09210-580, Brazil
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18
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Sun BR, Zhou AG, Li X, Yu HZ. Development and Application of Mobile Apps for Molecular Sensing: A Review. ACS Sens 2021; 6:1731-1744. [PMID: 33955727 DOI: 10.1021/acssensors.1c00512] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Modern smartphone-based sensing devices are generally standalone detection platforms that can transduce signals (via the built-in USB port, audio jack, or camera), perform analysis through mobile applications (apps), and display results on the screen/user interface. The advancement toward this ultimate form of on-site chemical analysis and point-of-care diagnosis is tied closely with the evolution of mobile technology. Previous reviews in the field mainly focused on the physical platforms while overlooking the role of mobile apps in such devices. There exist three general stages throughout the development: (1) early generation telemedicine, (2) mobile phone-assisted clinical diagnosis (without apps), and (3) mobile app-based sensing devices for various analytes. This review presents the key breakthroughs during each stage, recent development, remaining challenges, and future perspectives of the field. Representative examples, spanning from the pioneering point-of-care testing to the latest devices with integrated mobile apps, are classified by their sensing mechanisms. The review also discusses the scarcity of open-source apps dedicated to molecular sensing. With the introduction of more open-source and commercial apps, the mobile app-based detection system is anticipated to dominate point-of-care diagnosis and on-site molecular sensing in our opinion.
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Affiliation(s)
- Brigitta R. Sun
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Alvin G. Zhou
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Xiaochun Li
- College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P.R. China
| | - Hua-Zhong Yu
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
- College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P.R. China
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Rodrigues JF, Florea L, de Oliveira MCF, Diamond D, Oliveira ON. Big data and machine learning for materials science. DISCOVER MATERIALS 2021; 1:12. [PMID: 33899049 PMCID: PMC8054236 DOI: 10.1007/s43939-021-00012-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/01/2021] [Indexed: 05/11/2023]
Abstract
Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.
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Affiliation(s)
- Jose F. Rodrigues
- Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil
| | - Larisa Florea
- SFI Research Centre for Advanced Materials and BioEngineering Research Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Maria C. F. de Oliveira
- Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil
| | - Dermot Diamond
- Insight Centre for Data Analytics, National Centre for Sensor Research, Dublin City University, Dublin 9, Dublin, Ireland
| | - Osvaldo N. Oliveira
- São Carlos Institute of Physics, University of São Paulo (USP), São Carlos, SP Brazil
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