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Sun W, Nan J, Xu H, Wang L, Niu J, Zhang J, Yang B. Neural Network Enables High Accuracy for Hepatitis B Surface Antigen Detection with a Plasmonic Platform. NANO LETTERS 2024; 24:8784-8792. [PMID: 38975746 DOI: 10.1021/acs.nanolett.4c02860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
The detection of hepatitis B surface antigen (HBsAg) is critical in diagnosing hepatitis B virus (HBV) infection. However, existing clinical detection technologies inevitably cause certain inaccuracies, leading to delayed or unwarranted treatment. Here, we introduce a label-free plasmonic biosensing method based on the thickness-sensitive plasmonic coupling, combined with supervised deep learning (DL) using neural networks. The strategy of utilizing neural networks to process output data can reduce the limit of detection (LOD) of the sensor and significantly improve the accuracy (from 93.1%-97.4% to 99%-99.6%). Compared with widely used emerging clinical technologies, our platform achieves accurate decisions with higher sensitivity in a short assay time (∼30 min). The integration of DL models considerably simplifies the readout procedure, resulting in a substantial decrease in processing time. Our findings offer a promising avenue for developing high-precision molecular detection tools for point-of-care (POC) applications.
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Affiliation(s)
- Weihong Sun
- Joint Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State Key Laboratory of Supramolecular Structure and Materials, Center for Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Jingjie Nan
- Joint Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State Key Laboratory of Supramolecular Structure and Materials, Center for Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Hongqin Xu
- Department of Hepatology, Center of Infectious Diseases and Pathogen Biology, The First Hospital of Jilin University, Changchun 130021, P. R. China
| | - Lei Wang
- Joint Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State Key Laboratory of Supramolecular Structure and Materials, Center for Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Junqi Niu
- Department of Hepatology, Center of Infectious Diseases and Pathogen Biology, The First Hospital of Jilin University, Changchun 130021, P. R. China
| | - Junhu Zhang
- Joint Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State Key Laboratory of Supramolecular Structure and Materials, Center for Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Bai Yang
- Joint Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State Key Laboratory of Supramolecular Structure and Materials, Center for Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
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Singh R, Ryu J, Hyoung Lee W, Kang JH, Park S, Kim K. Wastewater-borne viruses and bacteria, surveillance and biosensors at the interface of academia and field deployment. Crit Rev Biotechnol 2024:1-21. [PMID: 38973015 DOI: 10.1080/07388551.2024.2354709] [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: 07/11/2023] [Accepted: 04/28/2024] [Indexed: 07/09/2024]
Abstract
Wastewater is a complex, but an ideal, matrix for disease monitoring and surveillance as it represents the entire load of enteric pathogens from a local catchment area. It captures both clinical and community disease burdens. Global interest in wastewater surveillance has been growing rapidly for infectious diseases monitoring and for providing an early warning of potential outbreaks. Although molecular detection methods show high sensitivity and specificity in pathogen monitoring from wastewater, they are strongly limited by challenges, including expensive laboratory settings and prolonged sample processing and analysis. Alternatively, biosensors exhibit a wide range of practical utility in real-time monitoring of biological and chemical markers. However, field deployment of biosensors is primarily challenged by prolonged sample processing and pathogen concentration steps due to complex wastewater matrices. This review summarizes the role of wastewater surveillance and provides an overview of infectious viral and bacterial pathogens with cutting-edge technologies for their detection. It emphasizes the practical utility of biosensors in pathogen monitoring and the major bottlenecks for wastewater surveillance of pathogens, and overcoming approaches to field deployment of biosensors for real-time pathogen detection. Furthermore, the promising potential of novel machine learning algorithms to resolve uncertainties in wastewater data is discussed.
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Affiliation(s)
- Rajendra Singh
- Department of Biological and Environmental Science, Dongguk University, Goyang, Gyeonggi-do, South Korea
| | - Jaewon Ryu
- Department of Biological and Environmental Science, Dongguk University, Goyang, Gyeonggi-do, South Korea
| | - Woo Hyoung Lee
- Department of Civil, Environmental, and Construction Engineering, University of Central FL, Orlando, FL, USA
| | - Joo-Hyon Kang
- Department of Civil and Environmental Engineering, Dongguk University-Seoul, Seoul, South Korea
| | - Sanghwa Park
- Bacteria Research Team, Freshwater Bacteria Research Department, Nakdonggang National Institute of Biological Resources (NNIBR), Sangju-si, South Korea
| | - Keugtae Kim
- Department of Biological and Environmental Science, Dongguk University, Goyang, Gyeonggi-do, South Korea
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Elashnikov R, Khrystonko O, Trelin A, Kuchař M, Švorčík V, Lyutakov O. Label-free SERS-ML detection of cocaine trace in human blood plasma. JOURNAL OF HAZARDOUS MATERIALS 2024; 472:134525. [PMID: 38743978 DOI: 10.1016/j.jhazmat.2024.134525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/22/2024] [Accepted: 05/01/2024] [Indexed: 05/16/2024]
Abstract
The widespread consumption of cocaine poses a significant threat to modern society. The most effective way to combat this problem is to control the distribution of cocaine, based on its accurate and sensitive detection. Here, we proposed the detection of cocaine in human blood plasma using a combination of surface enhanced Raman spectroscopy and machine learning (SERS-ML). To demonstrate the efficacy of our proposed approach, cocaine was added into blood plasma at various concentrations and drop-deposited onto a specially prepared disposable SERS substrate. SERS substrates were created by deposition of metal nanoclusters on electrospun polymer nanofibers. Subsequently, SERS spectra were measured and as could be expected, the manual distinguishing of cocaine from the spectra proved unfeasible, as its signal was masked by the background signal from blood plasma molecules. To overcome this issue, a database of SERS spectra of cocaine in blood plasma was collected and used for ML training and validation. After training, the reliability of proposed approach was tested on independently prepared samples, with unknown for SERS-ML cocaine presence or absence. As a result, the possibility of rapid determination of cocaine in blood plasma with a probability above 99.5% for cocaine concentrations up to 10-14 M was confirmed. Therefore, it is evident that the proposed approach has the ability to detect trace amounts of cocaine in bioliquids in an express and simple manner.
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Affiliation(s)
- Roman Elashnikov
- Department of Solid State Engineering, University of Chemistry and Technology, 16628 Prague, Czech Republic
| | - Olena Khrystonko
- Department of Solid State Engineering, University of Chemistry and Technology, 16628 Prague, Czech Republic
| | - Andrii Trelin
- Department of Solid State Engineering, University of Chemistry and Technology, 16628 Prague, Czech Republic
| | - Martin Kuchař
- Forensic Laboratory of Biologically Active Substances, Department of Chemistry of Natural Compounds, University of Chemistry and Technology Prague, Prague, Czech Republic
| | - Václav Švorčík
- Department of Solid State Engineering, University of Chemistry and Technology, 16628 Prague, Czech Republic
| | - Oleksiy Lyutakov
- Department of Solid State Engineering, University of Chemistry and Technology, 16628 Prague, Czech Republic.
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Tumrani SH, Soomro RA, Thabet HK, Karakuş S, El-Bahy ZM, Küçükdeniz T, Khoso S. Au-decorated Ti 3C 2T x/porous carbon immunoplatform for ECM1 breast cancer biomarker detection with machine learning computation for predictive accuracy. Talanta 2024; 278:126507. [PMID: 38968654 DOI: 10.1016/j.talanta.2024.126507] [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: 03/07/2024] [Revised: 06/10/2024] [Accepted: 07/01/2024] [Indexed: 07/07/2024]
Abstract
Electrochemical immunosensors, surpassing conventional diagnostics, exhibit significant potential for cancer biomarker detection. However, achieving a delicate balance between signal sensitivity and operational stability, especially at the heterostructure interface, is crucial for practical immunosensors. Herein, porous carbon (PC) integration with Ti3C2Tx-MXene (MX) and gold nanoparticles (Au NPs) constructs a versatile immunosensing platform for detecting extracellular matrix protein-1 (ECM1), a breast cancer-associated biomarker. The inclusion of PC provided robust structural support, enhancing electrolytic diffusion with an expansive surface area while synergistically facilitating charge transfer with Ti3C2Tx. The biosensor optimized with 1.0 mg PC demonstrates a robust electrochemical redox response to the surface-bound thionine (th) redox probe, utilizing an inhibition-based strategy for ECM1 detection. The robust antibody-antigen interactions across the PC-integrated Ti3C2Tx-Au NPs platform (MX-Au-C-1) enabled robust ECM1 detection within 0.1-7.5 nM, with a low limit of detection (LOD) of 0.012 nM. The constructed biosensor shows improved operational stability with a 98.6 % current retention over 1 h, surpassing MXene-integrated (MX-Au) and pristine Au NPs (63.2 % and 44.3 %, respectively) electrodes. Moreover, the successful adaptation of the artificial neural network (ANN) model for predictive analysis of the generated DPV data further validates the accuracy of the biosensor, promising its future application in AI-powered remote health monitoring.
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Affiliation(s)
- Sadam Hussain Tumrani
- Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, PR China; The Key Laboratory of Water and Sediment Sciences, Ministry of Education, School of Environment, Beijing Normal University, Beijing, 100875, PR China
| | - Razium Ali Soomro
- State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Electrochemical Process and Technology for Materials, Beijing University of Chemical Technology, Beijing, 100029, PR China
| | - Hamdy Khamees Thabet
- Department of Chemistry, College of Sciences and Arts , Northern Border University, Rafha, 91911, Saudi Arabia
| | - Selcan Karakuş
- Department of Chemistry, Faculty of Engineering, Istanbul University-Cerrahpaşa, Istanbul, 34320, Turkey; Health Biotechnology Center for Excellence Joint Practice and Research (SABIOTEK), Istanbul University-Cerrahpaşa, Istanbul, Türkiye.
| | - Zeinhom M El-Bahy
- Department of Chemistry, Faculty of Science, Al-Azhar University, Nasr City, 11884, Cairo, Egypt.
| | - Tarık Küçükdeniz
- Department of Industrial Engineering, Faculty of Engineering, Istanbul University-Cerrahpaşa, Istanbul, 34320, Turkey
| | - Salim Khoso
- Department of Civil Engineering, The University of Toledo, 2801 Bancroft St, Toledo, OH, 43606, USA
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Haghayegh F, Norouziazad A, Haghani E, Feygin AA, Rahimi RH, Ghavamabadi HA, Sadighbayan D, Madhoun F, Papagelis M, Felfeli T, Salahandish R. Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400595. [PMID: 38958517 DOI: 10.1002/advs.202400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
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Affiliation(s)
- Fatemeh Haghayegh
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Alireza Norouziazad
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Elnaz Haghani
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Ariel Avraham Feygin
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Reza Hamed Rahimi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Hamidreza Akbari Ghavamabadi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Deniz Sadighbayan
- Department of Biology, Faculty of Science, York University, Toronto, ON, M3J 1P3, Canada
| | - Faress Madhoun
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Manos Papagelis
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Tina Felfeli
- Department of Ophthalmology and Vision Sciences, University of Toronto, Ontario, M5T 3A9, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Ontario, M5T 3M6, Canada
| | - Razieh Salahandish
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
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Moulahoum H, Ghorbanizamani F. Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence. Food Chem 2024; 445:138800. [PMID: 38382253 DOI: 10.1016/j.foodchem.2024.138800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/12/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.
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Affiliation(s)
- Hichem Moulahoum
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
| | - Faezeh Ghorbanizamani
- Department of Biochemistry, Faculty of Science, Ege University, 35100-Bornova, Izmir, Turkey.
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Vo TS, Hoang T, Vo TTBC, Jeon B, Nguyen VH, Kim K. Recent Trends of Bioanalytical Sensors with Smart Health Monitoring Systems: From Materials to Applications. Adv Healthc Mater 2024; 13:e2303923. [PMID: 38573175 DOI: 10.1002/adhm.202303923] [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: 11/09/2023] [Revised: 03/09/2024] [Indexed: 04/05/2024]
Abstract
Smart biosensors attract significant interest due to real-time monitoring of user health status, where bioanalytical electronic devices designed to detect various activities and biomarkers in the human body have potential applications in physical sign monitoring and health care. Bioelectronics can be well integrated by output signals with wireless communication modules for transferring data to portable devices used as smart biosensors in performing real-time diagnosis and analysis. In this review, the scientific keys of biosensing devices and the current trends in the field of smart biosensors, (functional materials, technological approaches, sensing mechanisms, main roles, potential applications and challenges in health monitoring) will be summarized. Recent advances in the design and manufacturing of bioanalytical sensors with smarter capabilities and enhanced reliability indicate a forthcoming expansion of these smart devices from laboratory to clinical analysis. Therefore, a general description of functional materials and technological approaches used in bioelectronics will be presented after the sections of scientific keys to bioanalytical sensors. A careful introduction to the established systems of smart monitoring and prediction analysis using bioelectronics, regarding the integration of machine-learning-based basic algorithms, will be discussed. Afterward, applications and challenges in development using these smart bioelectronics in biological, clinical, and medical diagnostics will also be analyzed. Finally, the review will conclude with outlooks of smart biosensing devices assisted by machine learning algorithms, wireless communications, or smartphone-based systems on current trends and challenges for future works in wearable health monitoring.
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Affiliation(s)
- Thi Sinh Vo
- School of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Trung Hoang
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, South Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Tran Thi Bich Chau Vo
- Faculty of Industrial Management, College of Engineering, Can Tho University, Can Tho, 900000, Vietnam
| | - Byounghyun Jeon
- School of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
| | - Vu Hoang Nguyen
- Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC, 3800, Australia
| | - Kyunghoon Kim
- School of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
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Rodoplu Solovchuk D. Advances in AI-assisted biochip technology for biomedicine. Biomed Pharmacother 2024; 177:116997. [PMID: 38943990 DOI: 10.1016/j.biopha.2024.116997] [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: 04/24/2024] [Revised: 06/13/2024] [Accepted: 06/15/2024] [Indexed: 07/01/2024] Open
Abstract
The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.
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Affiliation(s)
- Didem Rodoplu Solovchuk
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan.
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Roy A, Zenker S, Jain S, Afshari R, Oz Y, Zheng Y, Annabi N. A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2404225. [PMID: 38970527 DOI: 10.1002/adma.202404225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/05/2024] [Indexed: 07/08/2024]
Abstract
Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), β-cyclodextrin (β-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27 kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensures high stretchability and flexibility. Combining pAAm with β-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.
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Affiliation(s)
- Arpita Roy
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Shea Zenker
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Saumya Jain
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Ronak Afshari
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yavuz Oz
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yuting Zheng
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Nasim Annabi
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
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10
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Duan S, Cai T, Liu F, Li Y, Yuan H, Yuan W, Huang K, Hoettges K, Chen M, Lim EG, Zhao C, Song P. Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease. Anal Chim Acta 2024; 1308:342575. [PMID: 38740448 DOI: 10.1016/j.aca.2024.342575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/25/2024] [Accepted: 04/02/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection. RESULTS This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1-42 (Aβ 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. SIGNIFICANCE This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1-42, particularly in areas with low resources and limited communication infrastructure.
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Affiliation(s)
- Sixuan Duan
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK; Key Laboratory of Bionic Engineering, Jilin University, 5988 Renmin Street, Changchun, 130022, China
| | - Tianyu Cai
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China
| | - Fuyuan Liu
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Yifan Li
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Hang Yuan
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China
| | - Wenwen Yuan
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No.28 Xianning West Road, Xi'an, 710079, China
| | - Kaizhu Huang
- Department of Electrical and Computer Engineering, Duke Kunshan University, 8 Duke Avenue, Kunshan, 215316, China
| | - Kai Hoettges
- Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Min Chen
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Eng Gee Lim
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Chun Zhao
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK
| | - Pengfei Song
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 111 Ren'ai Road, Suzhou, 215000, China; Department of Electrical and Electronic Engineering, University of Liverpool, Foundation Building, Brownlow Hill, Liverpool, L69 7ZX, UK.
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11
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Jena MK, Mittal S, Pathak B. Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap. ACS APPLIED MATERIALS & INTERFACES 2024; 16:29891-29901. [PMID: 38818926 DOI: 10.1021/acsami.4c04858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
DNA sequencing with the quantum tunneling technique heralds a paradigm shift in genetic analysis, promising rapid and accurate identification for diverging applications ranging from personalized medicine to security issues. However, the widespread distribution of molecular conductance, conduction orbital alignment for resonant transport, and decoding crisscrossing conductance signals of isomorphic nucleotides have been persistent experimental hurdles for swift and precise identification. Herein, we have reported a machine learning (ML)-driven quantum tunneling study with solid-state model nanogap to determine nucleotides at single-base resolution. The optimized ML basecaller has demonstrated a high predictive basecalling accuracy of all four nucleotides from seven distinct data pools, each containing complex transmission readouts of their different dynamic conformations. ML classification of quaternary, ternary, and binary nucleotide combinations is also performed with high precision, sensitivity, and F1 score. ML explainability unravels the evidence of how extracted normalized features within overlapped nucleotide signals contribute to classification improvement. Moreover, electronic fingerprints, conductance sensitivity, and current readout analysis of nucleotides have promised practical applicability with significant sensitivity and distinguishability. Through this ML approach, our study pushes the boundaries of quantum sequencing by highlighting the effectiveness of single nucleotide basecalling with promising implications for advancing genomics and molecular diagnostics.
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Affiliation(s)
- Milan Kumar Jena
- Department of Chemistry, Indian Institute of Technology (IIT) Indore, Indore Madhya Pradesh 453552, India
| | - Sneha Mittal
- Department of Chemistry, Indian Institute of Technology (IIT) Indore, Indore Madhya Pradesh 453552, India
| | - Biswarup Pathak
- Department of Chemistry, Indian Institute of Technology (IIT) Indore, Indore Madhya Pradesh 453552, India
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12
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Lee S, Dang H, Moon JI, Kim K, Joung Y, Park S, Yu Q, Chen J, Lu M, Chen L, Joo SW, Choo J. SERS-based microdevices for use as in vitro diagnostic biosensors. Chem Soc Rev 2024; 53:5394-5427. [PMID: 38597213 DOI: 10.1039/d3cs01055d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.
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Affiliation(s)
- Sungwoon Lee
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Hajun Dang
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Joung-Il Moon
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Kihyun Kim
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Younju Joung
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Sohyun Park
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Qian Yu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Jiadong Chen
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Mengdan Lu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Lingxin Chen
- School of Pharmacy, Binzhou Medical University, Yantai, 264003, China
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Yantai 264003, China.
| | - Sang-Woo Joo
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul 06978, South Korea.
| | - Jaebum Choo
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
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13
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Betts R, Dierking I. Possibilities and limitations of convolutional neural network machine learning architectures in the characterisation of achiral orthogonal smectic liquid crystals. SOFT MATTER 2024; 20:4226-4236. [PMID: 38745467 DOI: 10.1039/d4sm00295d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Machine learning is becoming a valuable tool in the characterisation and property prediction of liquid crystals. It is thus worthwhile to be aware of the possibilities but also the limitations of current machine learning algorithms. In this study we investigated a phase sequence of isotropic - fluid smecticA - hexatic smectic B - soft crystal CrE - crystalline. This is a sequence of transitions between orthogonal phases, which are expected to be difficult to distinguish, because of only minute changes in order. As expected, strong first order transitions such as the liquid to liquid crystal transition and the crystallisation can be distinguished with high accuracy. It is shown that also the hexatic SmB to soft crystal CrE transition is clearly characterised, which represents the transition from short- to long-range order. Limitations of convolutional neural networks can be observed for the fluid to hexatic SmA to SmB transition, where both phases exhibit short-range ordering.
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Affiliation(s)
- Rebecca Betts
- Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UK.
| | - Ingo Dierking
- Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UK.
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14
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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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15
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Sankar K, Kuzmanović U, Schaus SE, Galagan JE, Grinstaff MW. Strategy, Design, and Fabrication of Electrochemical Biosensors: A Tutorial. ACS Sens 2024; 9:2254-2274. [PMID: 38636962 DOI: 10.1021/acssensors.4c00043] [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: 04/20/2024]
Abstract
Advanced healthcare requires novel technologies capable of real-time sensing to monitor acute and long-term health. The challenge relies on converting a real-time quantitative biological and chemical signal into a desired measurable output. Given the success in detecting glucose and the commercialization of glucometers, electrochemical biosensors continue to be a mainstay of academic and industrial research activities. Despite the wealth of literature on electrochemical biosensors, reports are often specific to a particular application (e.g., pathogens, cancer markers, glucose, etc.), and most fail to convey the underlying strategy and design, and if it is transferable to detection of a different analyte. Here we present a tutorial review for those entering this research area that summarizes the basic electrochemical techniques utilized as well as discusses the designs and optimization strategies employed to improve sensitivity and maximize signal output.
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16
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Kokabi M, Tayyab M, Rather GM, Pournadali Khamseh A, Cheng D, DeMauro EP, Javanmard M. Integrating optical and electrical sensing with machine learning for advanced particle characterization. Biomed Microdevices 2024; 26:25. [PMID: 38780704 PMCID: PMC11116188 DOI: 10.1007/s10544-024-00707-0] [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] [Accepted: 04/15/2024] [Indexed: 05/25/2024]
Abstract
Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properties. In this study, we investigated the integration of electrical and optical features through a multimodal approach for particle classification. Machine learning classifier algorithms were applied to evaluate the impact of combining these measurements. Our results demonstrate the superiority of the multimodal approach over analyzing electrical or optical features independently. We achieved an average test accuracy of 94.9% by integrating both modalities, compared to 66.4% for electrical features alone and 90.7% for optical features alone. This highlights the complementary nature of electrical and optical information and its potential for enhancing classification performance. By leveraging electrical sensing and optical imaging techniques, our multimodal approach provides deeper insights into particle properties and offers a more comprehensive understanding of complex biological systems.
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Affiliation(s)
- Mahtab Kokabi
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Muhammad Tayyab
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Gulam M Rather
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, 08901, USA
| | | | - Daniel Cheng
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Edward P DeMauro
- Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
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17
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Ma W, Li M, Chu Z, Chen H. Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:3289. [PMID: 38894082 PMCID: PMC11174864 DOI: 10.3390/s24113289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/17/2024] [Accepted: 05/19/2024] [Indexed: 06/21/2024]
Abstract
Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.
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Affiliation(s)
- Wenming Ma
- School of Computer and Control Engineering, Yantai University, Yantai 264005, China; (M.L.); (Z.C.); (H.C.)
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18
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Yang ZH, Huang LF, Wang YS, Chang CC. Turn-off enzyme activity of histidine-rich peptides for the detection of lysozyme. Mikrochim Acta 2024; 191:307. [PMID: 38713296 DOI: 10.1007/s00604-024-06388-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/25/2024] [Indexed: 05/08/2024]
Abstract
An assay that integrates histidine-rich peptides (HisRPs) with high-affinity aptamers was developed enabling the specific and sensitive determination of the target lysozyme. The enzyme-like activity of HisRP is inhibited by its interaction with a target recognized by an aptamer. In the presence of the target, lysozyme molecules progressively assemble on the surface of HisRP in a concentration-dependent manner, resulting in the gradual suppression of enzyme-like activity. This inhibition of HisRP's enzyme-like activity can be visually observed through color changes in the reaction product or quantified using UV-visible absorption spectroscopy. Under optimal conditions, the proposed colorimetric assay for lysozyme had a detection limit as low as 1 nM and exhibited excellent selectivity against other nonspecific interferents. Furthermore, subsequent research validated the practical applicability of the developed colorimetric approach to saliva samples, indicating that the assay holds significant potential for the detection of lysozymes in samples derived from humans.
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Affiliation(s)
- Zu-Han Yang
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, 333, Taiwan
| | - Ling-Fang Huang
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, 333, Taiwan
- Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, 333, Taiwan
| | - Yi-Shan Wang
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, 333, Taiwan
- Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, 333, Taiwan
| | - Chia-Chen Chang
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, 333, Taiwan.
- Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, 333, Taiwan.
- Kidney Research Center, Department of Nephrology, Linkou Chang Gung Memorial Hospital, Taoyuan, 333, Taiwan.
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19
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Mikata Y, Tosaka N, Yasuda S, Sakurai Y, Shoji S, Konno H, Matsuo T. Cd 2+-Specific Fluorescence Response of Methoxy-Substituted N, N-Bis(2-quinolylmethyl)-2-methoxyaniline Derivatives. Inorg Chem 2024; 63:8026-8037. [PMID: 38651295 DOI: 10.1021/acs.inorgchem.3c04395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
The N3O1 tetradentate ligand, TriMeOBQMOA (N,N-bis(5,6,7-trimethoxy-2-quinolylmethyl)-2-methoxyaniline), was developed as a Cd2+-specific fluorescent sensor. The structure of TriMeOBQMOA is half of TriMeOBAPTQ (N,N,N',N'-tetrakis(5,6,7-trimethoxy-2-quinolylmethyl)-1,2-bis(2-aminophenoxy)ethane), which is a tetrakisquinoline derivative of the well-known calcium chelator BAPTA (1,2-bis(2-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid). The fluorescent Cd2+ selectivity of TriMeOBAPTQ (IZn/ICd = 5.3% in the presence of 3 equiv of metal ions in MeOH-HEPES buffer (9:1)) comes from the formation of fluorescent dinuclear cadmium (M2L) and nonfluorescent OH-bridged dizinc ((μ-OH)M2L) complexes. TriMeOBQMOA also exhibits excellent Cd2+ specificity in fluorescence enhancement (IZn/ICd = 2.3% in the presence of 5 equiv of metal ions in DMF-HEPES buffer (1:1, HEPES 50 mM, KCl 0.1 M, pH = 7.5)) via substantial formation of a highly fluorescent bis(μ-chloro)dinuclear cadmium complex ([Cd2(μ-Cl)2L2]2+), which is in equilibrium with the mononuclear Cd2+ complex ([CdLCl]+), and extremely poor stability of the TriMeOBQMOA-Zn2+ complex. The all-nitrogen derivatives of BQMOA and BAPTQ, namely, N,N-BQDMPHEN (N,N-bis(2-quinolylmethyl)-N',N'-dimethyl-1,2-phenylenediamine) and BPDTQ (N,N,N',N'-tetrakis(2-quinolylmethyl)-2,2'-(N,N'-dimethylethylenediamino)dianiline), respectively, and their methoxy-substituted derivatives were also prepared, and the fluorescent metal ion sensing properties are discussed.
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Affiliation(s)
- Yuji Mikata
- Laboratory for Molecular & Functional Design, Department of Engineering, Nara Women's University, Nara 630-8506, Japan
- KYOUSEI Science Center, Nara Women's University, Nara 630-8506, Japan
- Department of Chemistry, Biology, and Environmental Science, Faculty of Science, Nara Women's University, Nara 630-8506, Japan
- Cooperative Major in Human Centered Engineering, Nara Women's University, Nara 630-8506, Japan
| | - Nao Tosaka
- Department of Chemistry, Biology, and Environmental Science, Faculty of Science, Nara Women's University, Nara 630-8506, Japan
| | - Saori Yasuda
- Cooperative Major in Human Centered Engineering, Nara Women's University, Nara 630-8506, Japan
| | - Yui Sakurai
- Department of Chemistry, Biology, and Environmental Science, Faculty of Science, Nara Women's University, Nara 630-8506, Japan
| | - Sunao Shoji
- Laboratory for Molecular & Functional Design, Department of Engineering, Nara Women's University, Nara 630-8506, Japan
- Cooperative Major in Human Centered Engineering, Nara Women's University, Nara 630-8506, Japan
| | - Hideo Konno
- National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki 305-8565, Japan
| | - Takashi Matsuo
- Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Takayama, Ikoma, Nara 630-0192, Japan
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20
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Liu Y, Zhou X, Wang T, Luo A, Jia Z, Pan X, Cai W, Sun M, Wang X, Wen Z, Zhou G. Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor. Biotechnol Bioeng 2024; 121:1583-1595. [PMID: 38247359 DOI: 10.1002/bit.28661] [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: 10/16/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024]
Abstract
As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.
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Affiliation(s)
- Yuan Liu
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Xiaotian Zhou
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Teng Wang
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - An Luo
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Zhaojun Jia
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Xingquan Pan
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Weiqi Cai
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Mengge Sun
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Xuezhong Wang
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Zhenguo Wen
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Guangzheng Zhou
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
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21
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Li X, Zhu H, Gu B, Yao C, Gu Y, Xu W, Zhang J, He J, Liu X, Li D. Advancing Intelligent Organ-on-a-Chip Systems with Comprehensive In Situ Bioanalysis. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305268. [PMID: 37688520 DOI: 10.1002/adma.202305268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/03/2023] [Indexed: 09/11/2023]
Abstract
In vitro models are essential to a broad range of biomedical research, such as pathological studies, drug development, and personalized medicine. As a potentially transformative paradigm for 3D in vitro models, organ-on-a-chip (OOC) technology has been extensively developed to recapitulate sophisticated architectures and dynamic microenvironments of human organs by applying the principles of life sciences and leveraging micro- and nanoscale engineering capabilities. A pivotal function of OOC devices is to support multifaceted and timely characterization of cultured cells and their microenvironments. However, in-depth analysis of OOC models typically requires biomedical assay procedures that are labor-intensive and interruptive. Herein, the latest advances toward intelligent OOC (iOOC) systems, where sensors integrated with OOC devices continuously report cellular and microenvironmental information for comprehensive in situ bioanalysis, are examined. It is proposed that the multimodal data in iOOC systems can support closed-loop control of the in vitro models and offer holistic biomedical insights for diverse applications. Essential techniques for establishing iOOC systems are surveyed, encompassing in situ sensing, data processing, and dynamic modulation. Eventually, the future development of iOOC systems featuring cross-disciplinary strategies is discussed.
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Affiliation(s)
- Xiao Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Hui Zhu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Bingsong Gu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Cong Yao
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yuyang Gu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Wangkai Xu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jia Zhang
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jiankang He
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xinyu Liu
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada
| | - Dichen Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
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22
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Zhang Q, Ren T, Cao K, Xu Z. Advances of machine learning-assisted small extracellular vesicles detection strategy. Biosens Bioelectron 2024; 251:116076. [PMID: 38340580 DOI: 10.1016/j.bios.2024.116076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.
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Affiliation(s)
- Qi Zhang
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Tingju Ren
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Ke Cao
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Zhangrun Xu
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China.
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23
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Deng S, Men X, Hu M, Liang X, Dai Y, Zhan Z, Huang Z, Chen H, Dong Z. Ratiometric fluorescence sensing NADH using AIE-dots transducers at the point of care. Biosens Bioelectron 2024; 250:116082. [PMID: 38308942 DOI: 10.1016/j.bios.2024.116082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/13/2024] [Accepted: 01/26/2024] [Indexed: 02/05/2024]
Abstract
Reduced nicotinamide adenine dinucleotide (NADH) has a strong impact on physiological metabolism, and its concentration is related to metabolic and neurodegenerative diseases. A more reliable and accurate detection method for NADH quantitation is needed for early disease diagnosis and point-of-care testing. Aggregation-induced emission (AIE) materials are widely used to improve the sensitivity in analytes assays due to their anti-aggregation-caused quenching property. Here we developed TPA-BQD-Py AIE-dots transducers and evaluated its performance in NADH detection. The NADH concentration-dependent ratiometric sensing was based on electron transfer from TPA-BQD-Py AIE-dots to NADH with variable fluorescence intensity at 584 nm and 470 nm, resulting in high sensitivity (limit of detection at 110 nM), photostability, selectivity, and a rapid and reversible response. We further developed the application of TPA-BQD-Py AIE-dots transducers in in vivo NADH imaging using a smartphone and digital camera, respectively, demonstrating the potential for NADH point-of-care testing.
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Affiliation(s)
- Sile Deng
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China
| | - Xiaoju Men
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China; Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, College of Pharmacy, Changsha Medical University, Changsha, 410219, China
| | - Muhua Hu
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China
| | - Xiao Liang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China
| | - Yujuan Dai
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China
| | - Zhengkun Zhan
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China
| | - Zhongchao Huang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China
| | - Haobin Chen
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China; Furong Laboratory, Changsha, Hunan, China.
| | - Zhuxin Dong
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha, 410013, China; Furong Laboratory, Changsha, Hunan, China.
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24
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Nandipati M, Fatoki O, Desai S. Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review. MATERIALS (BASEL, SWITZERLAND) 2024; 17:1621. [PMID: 38612135 PMCID: PMC11012965 DOI: 10.3390/ma17071621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/05/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024]
Abstract
Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.
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Affiliation(s)
- Mutha Nandipati
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
| | - Olukayode Fatoki
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
| | - Salil Desai
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
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25
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Xu X, Wang X, Ding Y, Zhou X, Ding Y. Integration of lanthanide MOFs/methylcellulose-based fluorescent sensor arrays and deep learning for fish freshness monitoring. Int J Biol Macromol 2024; 265:131011. [PMID: 38518947 DOI: 10.1016/j.ijbiomac.2024.131011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/03/2024] [Accepted: 03/18/2024] [Indexed: 03/24/2024]
Abstract
Preserving fish meat poses a significant challenge due to its high protein and low fat content. This study introduces a novel approach that utilizes a common type of lanthanide metal-organic frameworks (Ln-MOFs), EuMOFs, in combination with 5-fluorescein isothiocyanate (FITC) and methylcellulose (MC) to develop fluorescent sensor arrays for real-time monitoring the freshness of fish meat. The EuMOF-FITC/MC fluorescence films were characterized with excellent fluorescence response, ideal morphology, good mechanical properties, and improved hydrophobicity. The efficacy of the fluorescence sensor array was evaluated by testing various concentrations of spoilage gases (such as ammonia, dimethylamine, and trimethylamine) within a 20-min timeframe using a smartphone-based camera obscura device. This sensor array enables the real-time monitoring of fish freshness, with the ability to preliminarily identify the freshness status of mackerel meat with the naked eye. Furthermore, the study employed four convolutional neural network (CNN) models to enhance the performance of freshness assessment, all of which achieved accuracy levels exceeding 93 %. Notably, the ResNext-101 model demonstrated a particularly high accuracy of 98.97 %. These results highlight the potential of the EuMOF-based fluorescence sensor array, in conjunction with the CNN model, as a reliable and accurate method for real-time monitoring the freshness of fish meat.
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Affiliation(s)
- Xia Xu
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China; Zhejiang Key Laboratory of Green, Low-carbon and Efficient Development of Marine Fishery Resources, Hangzhou 310014, PR China; National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou 310014, PR China.
| | - Xinyu Wang
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Yicheng Ding
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China
| | - Xuxia Zhou
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China; Zhejiang Key Laboratory of Green, Low-carbon and Efficient Development of Marine Fishery Resources, Hangzhou 310014, PR China; National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou 310014, PR China
| | - Yuting Ding
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, PR China; Zhejiang Key Laboratory of Green, Low-carbon and Efficient Development of Marine Fishery Resources, Hangzhou 310014, PR China; National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou 310014, PR China
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26
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Dai C, Xiong H, He R, Zhu C, Li P, Guo M, Gou J, Mei M, Kong D, Li Q, Wee ATS, Fang X, Kong J, Liu Y, Wei D. Electro-Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker Detection. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312540. [PMID: 38288781 DOI: 10.1002/adma.202312540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/13/2024] [Indexed: 02/06/2024]
Abstract
On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests (POCTs) within a few minutes. Here, this work develops a dual-mode multiclassification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%-93% accuracy of rapid point-of-care testing technologies at 100% statistical power (>150 clinical tests). Moreover, the diagnosis time is 5 min without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare.
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Affiliation(s)
- Changhao Dai
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, 200433, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, 200433, China
| | - Huiwen Xiong
- Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Rui He
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou, 73000, China
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Chenxin Zhu
- Institute of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Pintao Li
- Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Mingquan Guo
- Department of Laboratory Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Jian Gou
- Department of Physics, National University of Singapore, Singapore, 117542, Singapore
| | - Miaomiao Mei
- Yizheng Hospital of Traditional Chinese Medicine, Yangzhou, 211400, China
| | - Derong Kong
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, 200433, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, 200433, China
| | - Qiang Li
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Andrew Thye Shen Wee
- Department of Physics, National University of Singapore, Singapore, 117542, Singapore
| | - Xueen Fang
- Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Jilie Kong
- Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Yunqi Liu
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, 200433, China
| | - Dacheng Wei
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, 200433, China
- Laboratory of Molecular Materials and Devices, Fudan University, Shanghai, 200433, China
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27
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Liu H, Chen L, Shen Y, Fan L, Zhang J, Zhu H, Shi Y, Yan S. Advances in selenium from materials to applications. NANOTECHNOLOGY 2024; 35:242003. [PMID: 38471145 DOI: 10.1088/1361-6528/ad32d3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 03/11/2024] [Indexed: 03/14/2024]
Abstract
Over the past few decades, single-element semiconductors have received a great deal of attention due to their unique light-sensitive and heat-sensitive properties, which are of great application and research significance. As one promising material, selenium, being a typical semiconductor, has attracted significant attention from researchers due to its unique properties including high optical conductivity, anisotropic, thermal conductivity, and so on. To promote the application of selenium nanomaterials in various fields, numerous studies over the past few decades have successfully synthesized selenium nanomaterials in various morphologies using a wide range of physical and chemical methods. In this paper, we review and summarise the different methods of synthesis of various morphologies of selenium nanomaterials and discuss the applications of different nanostructures of selenium nanomaterials in optoelectronic devices, chemical sensors, and biomedical applications. Finally, we discuss possible challenges for selenium nanodevices and provide an outlook on the future applications of selenium nanomaterials.
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Affiliation(s)
- Hao Liu
- School of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
| | - Liping Chen
- School of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
| | - Yunkun Shen
- College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
| | - Li Fan
- School of Materials Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
| | - Jiawei Zhang
- School of Materials Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
| | - Hongliang Zhu
- School of Materials Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
| | - Yi Shi
- National Laboratory of Solid State Microstructures School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, People's Republic of China
| | - Shancheng Yan
- School of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
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28
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Chen Z, Wang W, Tian H, Yu W, Niu Y, Zheng X, Liu S, Wang L, Huang Y. Wearable intelligent sweat platform for SERS-AI diagnosis of gout. LAB ON A CHIP 2024; 24:1996-2004. [PMID: 38373026 DOI: 10.1039/d3lc01094e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
For the past few years, sweat analysis for health monitoring has attracted increasing attention benefiting from wearable technology. In related research, the sensitive detection of uric acid (UA) in sweat with complex composition based on surface-enhanced Raman spectroscopy (SERS) for the diagnosis of gout is still a significant challenge. Herein, we report a visualized and intelligent wearable sweat platform for SERS detection of UA in sweat. In this wearable platform, the spiral channel consisted of colorimetric paper with Ag nanowires (AgNWs) that could capture sweat for SERS measurement. With the help of photos from a smartphone, the pH value and volume of sweat could be quantified intelligently based on the image recognition technique. To diagnose gout, SERS spectra of human sweat with UA are collected in this wearable intelligent platform and analyzed by artificial intelligence (AI) algorithms. The results indicate that the artificial neural network (ANN) algorithm exhibits good identification of gout with high accuracy at 97%. Our work demonstrates that SERS-AI in a wearable intelligent sweat platform could be a feasible strategy for diagnosis of gout, which expands research on sweat analysis for comfortable and noninvasive health monitoring.
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Affiliation(s)
- Zhaoxian Chen
- Chongqing Key Laboratory of Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing, 400044, China.
| | - Wei Wang
- Chongqing Key Laboratory of Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing, 400044, China.
| | - Hao Tian
- Chongqing Key Laboratory of Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing, 400044, China.
| | - Wenrou Yu
- Chongqing Key Laboratory of Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing, 400044, China.
| | - Yu Niu
- Chongqing Key Laboratory of Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing, 400044, China.
| | - Xueli Zheng
- Chongqing Key Laboratory of Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing, 400044, China.
| | - Shihong Liu
- Chongqing University Cancer Hospital, Department of Palliative care, Department of Geriatric Oncology, Chongqing, China
| | - Li Wang
- Key Laboratory of Optoelectronic Technology and Systems (Ministry of Education), College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Yingzhou Huang
- Chongqing Key Laboratory of Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing, 400044, China.
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29
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Raju C, Elpa DP, Urban PL. Automation and Computerization of (Bio)sensing Systems. ACS Sens 2024; 9:1033-1048. [PMID: 38363106 PMCID: PMC10964247 DOI: 10.1021/acssensors.3c01887] [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: 09/08/2023] [Revised: 12/21/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024]
Abstract
Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.
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Affiliation(s)
- Chamarthi
Maheswar Raju
- Department of Chemistry, National
Tsing Hua University 101, Section 2, Kuang-Fu Rd., Hsinchu 300044, Taiwan
| | - Decibel P. Elpa
- Department of Chemistry, National
Tsing Hua University 101, Section 2, Kuang-Fu Rd., Hsinchu 300044, Taiwan
| | - Pawel L. Urban
- Department of Chemistry, National
Tsing Hua University 101, Section 2, Kuang-Fu Rd., Hsinchu 300044, Taiwan
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30
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Wei Y, Abbasi SMT, Mehmood N, Li L, Qu F, Cheng G, Hu D, Ho YP, Yuan W, Ho HP. Deep-qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors. SMALL METHODS 2024; 8:e2301293. [PMID: 38010980 DOI: 10.1002/smtd.202301293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/12/2023] [Indexed: 11/29/2023]
Abstract
Absolute quantification of biological samples provides precise numerical expression levels, enhancing accuracy, and performance for rare templates. Current methodologies, however, face challenges-flow cytometers are costly and complex, whereas fluorescence imaging, relying on software or manual counting, is time-consuming and error-prone. It is presented that Deep-qGFP, a deep learning-aided pipeline for the automated detection and classification of green fluorescent protein (GFP) labeled microreactors, enables real-time absolute quantification. This approach achieves an accuracy of 96.23% and accurately measures the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, providing precise template concentrations. Deep-qGFP demonstrates remarkable speed, quantifying over 2000 microreactors across ten images in just 2.5 seconds, with a dynamic range of 56.52-1569.43 copies µL-1 . The method demonstrates impressive generalization capabilities, successfully applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based applications. Notably, Deep-qGFP is the first all-in-one image analysis algorithm successfully implemented in droplet digital polymerase chain reaction (PCR), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without requiring transfer learning, modifications, or retraining. This makes Deep-qGFP readily applicable in biomedical laboratories and holds potential for broader clinical applications.
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Affiliation(s)
- Yuanyuan Wei
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Syed Muhammad Tariq Abbasi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Nawaz Mehmood
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Luoquan Li
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Fuyang Qu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Guangyao Cheng
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Dehua Hu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Yi-Ping Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
- Centre for Biomaterials, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
- Hong Kong Branch of CAS Center for Excellence in Animal Evolution and Genetics, Shatin, Hong Kong SAR, 999 077, China
- State Key Laboratory of Marine Pollution, City University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Wu Yuan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Ho-Pui Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
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Gradisteanu Pircalabioru G, Raileanu M, Dionisie MV, Lixandru-Petre IO, Iliescu C. Fast detection of bacterial gut pathogens on miniaturized devices: an overview. Expert Rev Mol Diagn 2024; 24:201-218. [PMID: 38347807 DOI: 10.1080/14737159.2024.2316756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 02/06/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION Gut microbes pose challenges like colon inflammation, deadly diarrhea, antimicrobial resistance dissemination, and chronic disease onset. Development of early, rapid and specific diagnosis tools is essential for improving infection control. Point-of-care testing (POCT) systems offer rapid, sensitive, low-cost and sample-to-answer methods for microbe detection from various clinical and environmental samples, bringing the advantages of portability, automation, and simple operation. AREAS COVERED Rapid detection of gut microbes can be done using a wide array of techniques including biosensors, immunological assays, electrochemical impedance spectroscopy, mass spectrometry and molecular biology. Inclusion of Internet of Things, machine learning, and smartphone-based point-of-care applications is an important aspect of POCT. In this review, the authors discuss various fast diagnostic platforms for gut pathogens and their main challenges. EXPERT OPINION Developing effective assays for microbe detection can be complex. Assay design must consider factors like target selection, real-time and multiplex detection, sample type, reagent stability and storage, primer/probe design, and optimizing reaction conditions for accuracy and sensitivity. Mitigating these challenges requires interdisciplinary collaboration among scientists, clinicians, engineers, and industry partners. Future efforts are essential to enhance sensitivity, specificity, and versatility of POCT systems for gut microbe detection and quantification, advancing infectious disease diagnostics and management.
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Affiliation(s)
- Gratiela Gradisteanu Pircalabioru
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
- Division of Earth, Environmental and Life Sciences, The Research Institute of University of Bucharest (ICUB), Bucharest, Romania
- Academy of Romanian Scientists, Bucharest, Romania
| | - Mina Raileanu
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
- Department of Life and Environmental Physics, Horia Hulubei National Institute of Physics and Nuclear Engineering, Magurele, Romania
| | - Mihai Viorel Dionisie
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
| | - Irina-Oana Lixandru-Petre
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
| | - Ciprian Iliescu
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
- Academy of Romanian Scientists, Bucharest, Romania
- Microsystems in Biomedical and Environmental Applications, National Research and Development Institute for Microtechnology, Bucharest, Romania
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Zhang T, Zhao M, Zhai M, Wang L, Ma X, Liao S, Wang X, Liu Y, Chen D. Improving the Resolution of Flexible Large-Area Tactile Sensors through Machine-Learning Perception. ACS APPLIED MATERIALS & INTERFACES 2024; 16:11013-11025. [PMID: 38353218 DOI: 10.1021/acsami.3c17880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Industrial robots are the main piece of equipment of intelligent manufacturing, and array-type tactile sensors are considered to be the core devices for their active sensing and understanding of the production environment. A great challenge for existing array-type tactile sensors is the wiring of sensing units in a limited area, the contradiction between a small number of sensing units and high resolution, and the deviation of the overall output pattern due to the difference in the performance of each sensing unit itself. Inspired by the human somatosensory processing hierarchy, we combine tactile sensors with artificial intelligence algorithms to simplify the sensor architecture while achieving tactile resolution capabilities far greater than the number of signal channels. The prepared 8-electrode carbon-based conductive network achieves high-precision identification of 32 regions with 97% classification accuracy assisted by a quadratic discriminant analysis algorithm. Notably, the output of the sensor remains unchanged after 13,000 cycles at 60 kPa, indicating its excellent durability performance. Moreover, the large-area skin-like continuous conductive network is simple to fabricate, cost-effective, and can be easily scaled up/down depending on the application. This work may address the increasing need for simple fabrication, rapid integration, and adaptable geometry tactile sensors for use in industrial robots.
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Affiliation(s)
- Tong Zhang
- College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China
| | - Minghui Zhao
- College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China
| | - Mingxuan Zhai
- College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China
| | - Lisha Wang
- Laser Institute, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, Shandong 266000, China
| | - Xingyu Ma
- College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China
| | - Shengmei Liao
- College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China
| | - Xiaona Wang
- College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China
| | - Yijian Liu
- College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China
| | - Da Chen
- College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China
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Liu X, Jiang S, Zhang T, Xu Z, Liu L, Zhang Z, Pan S, Li Y. "Magnet" Based on Activated Silver Nanoparticles Adsorbed Bacteria to Predict Refractory Apical Periodontitis Via Surface-Enhanced Raman Scattering. ACS APPLIED MATERIALS & INTERFACES 2024; 16:8499-8508. [PMID: 38335515 DOI: 10.1021/acsami.3c16677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
Refractory apical periodontitis (RAP) is an endodontic apical inflammatory disease caused by Enterococcus faecalis (E. faecalis). Bacterial detection using surface-enhanced Raman scattering (SERS) technology is a hot research topic, but the specific and direct detection of oral bacteria is a challenge, especially in real clinical samples. In this paper, we develop a novel SERS-based green platform for label-free detection of oral bacteria. The platform was built on silver nanoparticles with a two-step enhancement way using NaBH4 and sodium (Na+) to form "hot spots," which resulted in an enhanced SERS fingerprint of E. faecalis with fast, quantitative, lower-limit, reproducibility, and stability. In combination with machine learning, four different oral bacteria (E. faecalis, Porphyromonas gingivalis, Streptococcus mutans, and Escherichia coli) could be intelligently distinguished. The unlabeled detection method emphasized the specificity of E. faecalis in simulated saliva, serum, and even real samples from patients with clinical root periapical disease. In addition, the assay has been shown to be environmentally friendly and without secondary contamination through antimicrobial assays. The proposed label-free, rapid, safe, and green SERS detection strategy for oral bacteria provided an innovative solution for the early diagnosis and prevention of RAP and other perioral diseases.
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Affiliation(s)
- Xin Liu
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Shen Jiang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Ting Zhang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Inorganic Chemistry and Physical Chemistry, College of Pharmacy, Harbin Medical University, Heilongjiang 150081, P. R. China
| | - Ziming Xu
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Ling Liu
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Zhe Zhang
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- College of Public Health, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
| | - Shuang Pan
- The First Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
- Department of Endodontics, School of Stomatology, Harbin Medical University, Harbin, Heilongjiang 150001, P. R. China
| | - Yang Li
- College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Center for Innovative Technology of Pharmaceutical Analysis, Harbin Medical University, Harbin, Heilongjiang 150081, P. R. China
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine University of Oulu, 2125B, Aapistie 5A, Oulu 90220, Finland
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Zhang J, Srivatsa P, Ahmadzai FH, Liu Y, Song X, Karpatne A, Kong ZJ, Johnson BN. Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning. Biosens Bioelectron 2024; 246:115829. [PMID: 38008059 DOI: 10.1016/j.bios.2023.115829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/14/2023] [Accepted: 11/08/2023] [Indexed: 11/28/2023]
Abstract
False results and time delay are longstanding challenges in biosensing. While classification models and deep learning may provide new opportunities for improving biosensor performance, such as measurement confidence and speed, it remains a challenge to ensure that predictions are explainable and consistent with domain knowledge. Here, we show that consistency of deep learning classification model predictions with domain knowledge in biosensing can be achieved by cost function supervision and enables rapid and accurate biosensing using the biosensor dynamic response. The impact and utility of the methodology were validated by rapid and accurate quantification of microRNA (let-7a) across the nanomolar (nM) to femtomolar (fM) concentration range using the dynamic response of cantilever biosensors. Data augmentation and cost function supervision based on the consistency of model predictions and experimental observations with the theory of surface-based biosensors improved the F1 score, precision, and recall of a recurrent neural network (RNN) classifier by an average of 13.8%. The theory-guided RNN (TGRNN) classifier enabled quantification of target analyte concentration and false results with an average prediction accuracy, precision, and recall of 98.5% using the initial transient or entire dynamic response, which is indicative of high prediction accuracy and low probability of false-negative and false-positive results. Classification scores were used to establish new relationships among biosensor performance characteristics (e.g., measurement confidence) and design parameters (e.g., inputs and hyperparameters of classification models and data acquisition parameters) that may be used for characterizing biosensor performance.
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Affiliation(s)
- Junru Zhang
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Purna Srivatsa
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Fazel Haq Ahmadzai
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Yang Liu
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA; School of Neuroscience, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Xuerui Song
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Anuj Karpatne
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Zhenyu James Kong
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Blake N Johnson
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24061, USA; School of Neuroscience, Virginia Tech, Blacksburg, VA, 24061, USA; Department of Materials Science and Engineering, Virginia Tech, Blacksburg, VA, 24061, USA; Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
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Xie M, Zhu Y, Li Z, Yan Y, Liu Y, Wu W, Zhang T, Li Z, Wang H. Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis. Talanta 2024; 268:125281. [PMID: 37832450 DOI: 10.1016/j.talanta.2023.125281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/09/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023]
Abstract
Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.
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Affiliation(s)
- Maomei Xie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yiting Zhu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zhiyao Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yueling Yan
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yidan Liu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Wenbo Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Tong Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
| | - Haixia Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
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Paul AA, Aladese AD, Marks RS. Additive Manufacturing Applications in Biosensors Technologies. BIOSENSORS 2024; 14:60. [PMID: 38391979 PMCID: PMC10887193 DOI: 10.3390/bios14020060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/24/2024]
Abstract
Three-dimensional (3D) printing technology, also known as additive manufacturing (AM), has emerged as an attractive state-of-the-art tool for precisely fabricating functional materials with complex geometries, championing several advancements in tissue engineering, regenerative medicine, and therapeutics. However, this technology has an untapped potential for biotechnological applications, such as sensor and biosensor development. By exploring these avenues, the scope of 3D printing technology can be expanded and pave the way for groundbreaking innovations in the biotechnology field. Indeed, new printing materials and printers would offer new possibilities for seamlessly incorporating biological functionalities within the growing 3D scaffolds. Herein, we review the additive manufacturing applications in biosensor technologies with a particular emphasis on extrusion-based 3D printing modalities. We highlight the application of natural, synthetic, and composite biomaterials as 3D-printed soft hydrogels. Emphasis is placed on the approach by which the sensing molecules are introduced during the fabrication process. Finally, future perspectives are provided.
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Affiliation(s)
- Abraham Abbey Paul
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Be’er Sheva 84105, Israel;
| | - Adedamola D. Aladese
- Department of Physics and Material Science, University of Memphis, Memphis, TN 38152, USA;
| | - Robert S. Marks
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Be’er Sheva 84105, Israel;
- Ilse Katz Centre for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Be’er Sheva 84105, Israel
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37
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Zhao Y, Kumar A, Yang Y. Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates. Chem Soc Rev 2024; 53:1004-1057. [PMID: 38116610 DOI: 10.1039/d3cs00540b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Recently, there has been an exponential growth in the number of publications focusing on surface-enhanced Raman scattering (SERS), primarily driven by advancements in nanotechnology and the increasing demand for chemical and biological detection. While many of these publications have focused on the development of new substrates and detection-based applications, there is a noticeable lack of attention given to various practical issues related to SERS measurements and detection. This review aims to fill this gap by utilizing silver nanorod (AgNR) SERS substrates fabricated through the oblique angle deposition method as an illustrative example. The review highlights and addresses a range of practical issues associated with SERS measurements and detection. These include the optimization of SERS substrates in terms of morphology and structural design, considerations for measurement configurations such as polarization and the incident angle of the excitation laser, and exploration of enhancement mechanisms encompassing both intrinsic properties induced by the structure and materials, as well as extrinsic factors arising from wetting/dewetting phenomena and analyte size. The manufacturing and storage aspects of SERS substrates, including scalable fabrication techniques, contamination control, cleaning procedures, and appropriate storage methods, are also discussed. Furthermore, the review delves into device design considerations, such as well arrays, flow cells, and fiber probes, and explores various sample preparation methods such as drop-cast and immersion. Measurement issues, including the effect of excitation laser wavelength and power, as well as the influence of buffer, are thoroughly examined. Additionally, the review discusses spectral analysis techniques, encompassing baseline removal, chemometric analysis, and machine learning approaches. The wide range of AgNR-based applications of SERS, across various fields, is also explored. Throughout the comprehensive review, key lessons learned from collective findings are outlined and analyzed, particularly in the context of detailed SERS measurements and standardization. The review also provides insights into future challenges and perspectives in the field of SERS. It is our hope that this comprehensive review will serve as a valuable reference for researchers seeking to embark on in-depth studies and applications involving their own SERS substrates.
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Affiliation(s)
- Yiping Zhao
- Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
| | - Amit Kumar
- Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
| | - Yanjun Yang
- School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
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Qin J, Tian X, Liu S, Yang Z, Shi D, Xu S, Zhang Y. Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy. Talanta 2024; 267:125080. [PMID: 37678002 DOI: 10.1016/j.talanta.2023.125080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/05/2023] [Accepted: 08/13/2023] [Indexed: 09/09/2023]
Abstract
The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.
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Affiliation(s)
- Jingwang Qin
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, PR China; Department of Translational Medicine, Xiamen Institute of Rare Earth Materials, Haixi Institute, Chinese Academy of Sciences, Xiamen, 361021, PR China
| | - Xiangdong Tian
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, PR China; Department of Translational Medicine, Xiamen Institute of Rare Earth Materials, Haixi Institute, Chinese Academy of Sciences, Xiamen, 361021, PR China
| | - Siying Liu
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, PR China; Department of Translational Medicine, Xiamen Institute of Rare Earth Materials, Haixi Institute, Chinese Academy of Sciences, Xiamen, 361021, PR China; University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhengxia Yang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, PR China; Department of Translational Medicine, Xiamen Institute of Rare Earth Materials, Haixi Institute, Chinese Academy of Sciences, Xiamen, 361021, PR China; University of the Chinese Academy of Sciences, Beijing, 100049, China
| | - Dawei Shi
- National Institutes for Food and Drug Control, Beijing, 100050, China.
| | - Sihong Xu
- National Institutes for Food and Drug Control, Beijing, 100050, China.
| | - Yun Zhang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, PR China; Department of Translational Medicine, Xiamen Institute of Rare Earth Materials, Haixi Institute, Chinese Academy of Sciences, Xiamen, 361021, PR China; University of the Chinese Academy of Sciences, Beijing, 100049, China.
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Lee K, Ha SM, Gurudatt NG, Heo W, Hyun KA, Kim J, Jung HI. Machine learning-powered electrochemical aptasensor for simultaneous monitoring of di(2-ethylhexyl) phthalate and bisphenol A in variable pH environments. JOURNAL OF HAZARDOUS MATERIALS 2024; 462:132775. [PMID: 37865074 DOI: 10.1016/j.jhazmat.2023.132775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/19/2023] [Accepted: 10/11/2023] [Indexed: 10/23/2023]
Abstract
Plastic waste is a pernicious environmental pollutant that threatens ecosystems and human health by releasing contaminants including di(2-ethylhexyl) phthalate (DEHP) and bisphenol A (BPA). Therefore, a machine-learning (ML)-powered electrochemical aptasensor was developed in this study for simultaneously detecting DEHP and BPA in river waters, particularly to minimize the electrochemical signal errors caused by varying pH levels. The aptasensor leverages a straightforward and effective surface modification strategy featuring gold nanoflowers to achieve low detection limits for DEHP and BPA (0.58 and 0.59 pg/mL, respectively), excellent specificity, and stability. The least-squares boosting (LSBoost) algorithm was introduced to reliably monitor the targets regardless of pH; it employs a layer that adjusts the number of multi-indexes and the parallel learning structure of an ensemble model to accurately predict concentrations by preventing overfitting and enhancing the learning effect. The ML-powered aptasensor successfully detected targets in 12 river sites with diverse pH values, exhibiting higher accuracy and reliability. To our knowledge, the platform proposed in this study is the first attempt to utilize ML for the simultaneous assessment of DEHP and BPA. This breakthrough allows for comprehensive investigations into the effects of contamination originating from diverse plastics by eliminating external interferent-caused influences.
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Affiliation(s)
- Kyungyeon Lee
- Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea; Department of Medical Engineering, College of Medicine, Yonsei University, Seoul 03722, Republic of Korea
| | - Seong Min Ha
- Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - N G Gurudatt
- Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Woong Heo
- Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Kyung-A Hyun
- Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea; Korea Electronics Technology Institute (KETI), 25 Saenari-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, 13509, Republic of Korea
| | - Jayoung Kim
- Department of Medical Engineering, College of Medicine, Yonsei University, Seoul 03722, Republic of Korea
| | - Hyo-Il Jung
- Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea; The DABOM Inc., Seoul, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
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40
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Chen Y, Wang Y, Zhang Y, Wang X, Zhang C, Cheng N. Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0. Foods 2024; 13:235. [PMID: 38254535 PMCID: PMC10815208 DOI: 10.3390/foods13020235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/07/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Food safety is closely related to human health. However, the regulation and testing processes for food safety are intricate and resource-intensive. Therefore, it is necessary to address food safety risks using a combination of deep learning, the Internet of Things, smartphones, quick response codes, smart packaging, and other smart technologies. Intelligent designs that combine digital systems and advanced functionalities with biosensors hold great promise for revolutionizing current food safety practices. This review introduces the concept of Food Safety 4.0, and discusses the impact of intelligent biosensors, which offer attractive smarter solutions, including real-time monitoring, predictive analytics, enhanced traceability, and consumer empowerment, helping improve risk management and ensure the highest standards of food safety.
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Affiliation(s)
- Yuehua Chen
- School of Electrical and Information, Northeast Agricultural University, Harbin 150030, China;
| | - Yicheng Wang
- School of Food Science, Northeast Agricultural University, Harbin 150030, China;
| | - Yiran Zhang
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China; (Y.Z.); (C.Z.)
| | - Xin Wang
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China; (Y.Z.); (C.Z.)
| | - Chen Zhang
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China; (Y.Z.); (C.Z.)
| | - Nan Cheng
- College of Food Science & Nutritional Engineering, China Agricultural University, Beijing 100083, China; (Y.Z.); (C.Z.)
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Liu L, Du K. A perspective on computer vision in biosensing. BIOMICROFLUIDICS 2024; 18:011301. [PMID: 38223547 PMCID: PMC10787640 DOI: 10.1063/5.0185732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/26/2023] [Indexed: 01/16/2024]
Abstract
Computer vision has become a powerful tool in the field of biosensing, aiding in the development of innovative and precise systems for the analysis and interpretation of biological data. This interdisciplinary approach harnesses the capabilities of computer vision algorithms and techniques to extract valuable information from various biosensing applications, including medical diagnostics, environmental monitoring, and food health. Despite years of development, there is still significant room for improvement in this area. In this perspective, we outline how computer vision is applied to raw sensor data in biosensors and its advantages to biosensing applications. We then discuss ongoing research and developments in the field and subsequently explore the challenges and opportunities that computer vision faces in biosensor applications. We also suggest directions for future work, ultimately underscoring the significant impact of computer vision on advancing biosensing technologies and their applications.
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Affiliation(s)
- Li Liu
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
| | - Ke Du
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
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42
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Huang J, Gao Y, Chang Y, Peng J, Yu Y, Wang B. Machine Learning in Bioelectrocatalysis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306583. [PMID: 37946709 PMCID: PMC10787072 DOI: 10.1002/advs.202306583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Indexed: 11/12/2023]
Abstract
At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.
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Affiliation(s)
- Jiamin Huang
- Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Yang Gao
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Yanhong Chang
- Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yadong Yu
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Bin Wang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
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Moro G, Fratte CD, Normanno N, Polo F, Cinti S. Point-of-Care Testing for the Detection of MicroRNAs: Towards Liquid Biopsy on a Chip. Angew Chem Int Ed Engl 2023; 62:e202309135. [PMID: 37672490 DOI: 10.1002/anie.202309135] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/23/2023] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Point-of-care (PoC) testing is revolutionizing the healthcare sector improving patient care in daily hospital practice and allowing reaching even remote geographical areas. In the frame of cancer management, the design and validation of PoC enabling the non-invasive, rapid detection of cancer markers is urgently required to implement liquid biopsy in clinical practice. Therefore, focusing on stable blood-based markers with high-specificity, such as microRNAs, is of crucial importance. In this work, we highlight the potential impact of circulating microRNAs detection on cancer management and the crucial role of PoC testing devices, especially for low-income countries. A detailed discussion about the challenges that should be faced to promote the technological transfer and clinical use of these tools has been added, to provide the readers with a complete overview of potentialities and current limitations.
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Affiliation(s)
- Giulia Moro
- Department of Pharmacy, University of Naples Federico II, Via Montesano 9, 80131, Naples, Italy
| | - Chiara Dalle Fratte
- Department of Medical Biotechnology and Translational Medicine, Postgraduate School of Clinical Pharmacology and Toxicology, University of Milan "Statale", Via Vanvitelli 32, 20133, Milan, Italy
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori (IRCCS), Fondazione Pascale, Via Mariano Semmola, 53, 80131, Naples, Italy
| | - Federico Polo
- Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172, Venice, Italy
- European Centre for Living Technology (ECLT), Ca' Foscari University of Venice Ca' Bottacin, 30124, Venice, Italy
| | - Stefano Cinti
- Department of Pharmacy, University of Naples Federico II, Via Montesano 9, 80131, Naples, Italy
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Zhang J, Srivatsa P, Ahmadzai FH, Liu Y, Song X, Karpatne A, Kong Z, Johnson BN. Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning. ACS Sens 2023; 8:4079-4090. [PMID: 37931911 PMCID: PMC10683760 DOI: 10.1021/acssensors.3c01258] [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: 06/22/2023] [Accepted: 09/29/2023] [Indexed: 11/08/2023]
Abstract
Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensing can complement and improve the biosensor accuracy and speed relative to the performance achieved by traditional regression analysis of a standard curve based on the biosensor steady-state response. The methodology was validated by rapid and accurate quantification of microRNA across the nanomolar to femtomolar range using the dynamic response of cantilever biosensors. Theory-guided feature engineering improved the performance and efficiency of several classification models relative to the performance achieved using traditional feature engineering methods (TSFRESH). In addition to the entire dynamic response, the technique enabled rapid and accurate quantification of the target analyte concentration and false-positive and false-negative results using the initial transient response, thereby reducing the required data acquisition time (i.e., time delay). We show that model explainability can be achieved by combining theory-guided feature engineering and feature importance analysis. The performance of multiple classifiers using both TSFRESH- and theory-based features from the biosensor's initial transient response was similar to that achieved using the entire dynamic response with data augmentation. We also show that the methodology can guide design of experiments for high-performance biosensing applications, specifically, the selection of data acquisition parameters (e.g., time) based on potential application-dependent performance thresholds. This work provides an example of the opportunities for improving biosensor performance, such as reducing biosensor false results and time delay, using explainable machine learning models supervised by domain knowledge in biosensing.
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Affiliation(s)
- Junru Zhang
- Grado
Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Purna Srivatsa
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Fazel Haq Ahmadzai
- Grado
Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Yang Liu
- Grado
Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- School
of Neuroscience, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Xuerui Song
- Grado
Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Anuj Karpatne
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Zhenyu Kong
- Grado
Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Blake N. Johnson
- Grado
Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- School
of Neuroscience, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department
of Materials Science and Engineering, Virginia
Tech, Blacksburg, Virginia 24061, United States
- Department
of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
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45
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Ranjbar S, Salavati AH, Ashari Astani N, Naseri N, Davar N, Ejtehadi MR. Electrochromic Sensor Augmented with Machine Learning for Enzyme-Free Analysis of Antioxidants. ACS Sens 2023; 8:4281-4292. [PMID: 37963856 DOI: 10.1021/acssensors.3c01637] [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: 11/16/2023]
Abstract
Our study presents an electrochromic sensor that operates without the need for enzymes or multiple oxidant reagents. This sensor is augmented with machine learning algorithms, enabling the identification, classification, and prediction of six different antioxidants with high accuracy. We utilized polyaniline (PANI), Prussian blue (PB), and copper-Prussian blue analogues (Cu-PBA) at their respective oxidation states as electrochromic materials (ECMs). By designing three readout channels with these materials, we were able to achieve visual detection of antioxidants without relying on traditional "lock and key" specific interactions. Our sensing approach is based on the direct electrochemical reactions between oxidized electrochromic materials (ECMsox) as electron acceptors and various antioxidants, which act as electron donors. This interaction generates unique fingerprint patterns by switching the ECMsox to reduced electrochromic materials (ECMsred), causing their colors to change. Through the application of density functional theory (DFT), we demonstrated the molecular-level basis for the distinct multicolor patterns. Additionally, machine learning algorithms were employed to correlate the optical patterns with RGB data, enabling complex data analysis and the prediction of unknown samples. To demonstrate the practical applications of our design, we successfully used the EC sensor to diagnose antioxidants in serum samples, indicating its potential for the on-site monitoring of antioxidant-related diseases. This advancement holds promise for various applications, including the real-time monitoring of antioxidant levels in biological samples, the early diagnosis of antioxidant-related diseases, and personalized medicine. Furthermore, the success of our electrochromic sensor design highlights the potential for exploring similar strategies in the development of sensors for diverse analytes, showcasing the versatility and adaptability of this approach.
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Affiliation(s)
- Saba Ranjbar
- Department of Physics, Sharif University of Technology, Tehran 11365-9161, Iran
- Department of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 14965/161, Iran
| | - Amir Hesam Salavati
- Tehran Institute of Advanced Studies (TeIAS), Khatam University, Tehran 1991633357, Iran
| | - Negar Ashari Astani
- Departments of Physics and Energy Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran
| | - Naimeh Naseri
- Department of Physics, Sharif University of Technology, Tehran 11365-9161, Iran
- Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia
- ARC Research Hub for Advanced Manufacturing with Two-dimensional Materials (AM2D), Monash University, Clayton, VIC 3800, Australia
| | - Navid Davar
- Departments of Physics and Energy Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran
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Esmaeili F, Cassie E, Nguyen HPT, Plank NOV, Unsworth CP, Wang A. Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest. Bioengineering (Basel) 2023; 10:1348. [PMID: 38135939 PMCID: PMC10740562 DOI: 10.3390/bioengineering10121348] [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: 10/12/2023] [Revised: 11/14/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023] Open
Abstract
Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 μM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.
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Affiliation(s)
- Fatemeh Esmaeili
- Department of Engineering Science, University of Auckland, Auckland 1010, New Zealand; (F.E.); (C.P.U.)
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Victoria University of Wellington, Wellington 6021, New Zealand; (E.C.); (H.P.T.N.); (N.O.V.P.)
| | - Erica Cassie
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Victoria University of Wellington, Wellington 6021, New Zealand; (E.C.); (H.P.T.N.); (N.O.V.P.)
- School of Chemical and Physical Sciences, Victoria University of Wellington, Wellington 6021, New Zealand
| | - Hong Phan T. Nguyen
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Victoria University of Wellington, Wellington 6021, New Zealand; (E.C.); (H.P.T.N.); (N.O.V.P.)
- School of Chemical and Physical Sciences, Victoria University of Wellington, Wellington 6021, New Zealand
| | - Natalie O. V. Plank
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Victoria University of Wellington, Wellington 6021, New Zealand; (E.C.); (H.P.T.N.); (N.O.V.P.)
- School of Chemical and Physical Sciences, Victoria University of Wellington, Wellington 6021, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, University of Auckland, Auckland 1010, New Zealand; (F.E.); (C.P.U.)
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Victoria University of Wellington, Wellington 6021, New Zealand; (E.C.); (H.P.T.N.); (N.O.V.P.)
| | - Alan Wang
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
- Center for Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1010, New Zealand
- Centre for Brain Research, University of Auckland, Auckland 1010, New Zealand
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Yigci D, Atçeken N, Yetisen AK, Tasoglu S. Loop-Mediated Isothermal Amplification-Integrated CRISPR Methods for Infectious Disease Diagnosis at Point of Care. ACS OMEGA 2023; 8:43357-43373. [PMID: 38027359 PMCID: PMC10666231 DOI: 10.1021/acsomega.3c04422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 12/01/2023]
Abstract
Infectious diseases continue to pose an imminent threat to global public health, leading to high numbers of deaths every year and disproportionately impacting developing countries where access to healthcare is limited. Biological, environmental, and social phenomena, including climate change, globalization, increased population density, and social inequity, contribute to the emergence of novel communicable diseases. Rapid and accurate diagnoses of infectious diseases are essential to preventing the transmission of infectious diseases. Although some commonly used diagnostic technologies provide highly sensitive and specific measurements, limitations including the requirement for complex equipment/infrastructure and refrigeration, the need for trained personnel, long sample processing times, and high cost remain unresolved. To ensure global access to affordable diagnostic methods, loop-mediated isothermal amplification (LAMP) integrated clustered regularly interspaced short palindromic repeat (CRISPR) based pathogen detection has emerged as a promising technology. Here, LAMP-integrated CRISPR-based nucleic acid detection methods are discussed in point-of-care (PoC) pathogen detection platforms, and current limitations and future directions are also identified.
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Affiliation(s)
- Defne Yigci
- School
of Medicine, Koç University, Istanbul 34450, Turkey
| | - Nazente Atçeken
- Koç
University Translational Medicine Research Center (KUTTAM), Koç University, Istanbul 34450, Turkey
| | - Ali K. Yetisen
- Department
of Chemical Engineering, Imperial College
London, London SW7 2AZ, U.K.
| | - Savas Tasoglu
- Koç
University Translational Medicine Research Center (KUTTAM), Koç University, Istanbul 34450, Turkey
- Boğaziçi
Institute of Biomedical Engineering, Boğaziçi
University, Istanbul 34684, Turkey
- Koç
University Arçelik Research Center for Creative Industries
(KUAR), Koç University, Istanbul 34450, Turkey
- Physical
Intelligence Department, Max Planck Institute
for Intelligent Systems, Stuttgart 70569, Germany
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Sun T, Feng B, Huo J, Xiao Y, Wang W, Peng J, Li Z, Du C, Wang W, Zou G, Liu L. Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses. NANO-MICRO LETTERS 2023; 16:14. [PMID: 37955844 PMCID: PMC10643743 DOI: 10.1007/s40820-023-01235-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/24/2023] [Indexed: 11/14/2023]
Abstract
The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.
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Affiliation(s)
- Tianming Sun
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
- College of Materials Science and Engineering, Shanxi Province, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China
| | - Bin Feng
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Jinpeng Huo
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Yu Xiao
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Wengan Wang
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Jin Peng
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Zehua Li
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Chengjie Du
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Wenxian Wang
- College of Materials Science and Engineering, Shanxi Province, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China.
| | - Guisheng Zou
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China.
| | - Lei Liu
- Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education of PR China, Tsinghua University, Beijing, 100084, People's Republic of China.
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Huang J, Cheddah S, Ma Y, Wang Y. Highly-accurate solvent identification using dynamic evaporation reflection spectra from an inverse opal sensor combined with a deep learning model. NANOSCALE 2023; 15:17422-17433. [PMID: 37855430 DOI: 10.1039/d3nr02807k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Developing a low-cost, rapid, and highly accurate method for detecting solvents with similar structures and properties is highly demanded. In recent years, methods based on dynamic reflection spectroscopy have been developed to distinguish isomers and homologues. However, these methods heavily rely on responsive photonic crystals that can interact intricately with the solvent. In this work, we propose a deep learning approach for direct solvent identification from dynamic evaporative reflection spectra (DERS) obtained on a simple inverse opal (IO) sensor. The sensor was prepared using co-assembly and sacrificial template methods. Then, a dataset was constructed with 985 DERS obtained from 14 different solvents. Different classical machine learning and deep learning algorithms were employed for classifying these DERS. The results showed that ResNet18-CBAM, an improved convolutional neural network, outperformed all other algorithms, achieving 97.7 ± 0.9% on the 5-fold cross-validation set and 100% accuracy on the test set. This strategy presents not only a simple, efficient, and repeatable method for solvent detection but also, more importantly, by integrating the deep learning model, it allows an automatic, rapid, and accurate analysis of DERS data without the need for human intervention. It holds great application prospects in the field of solvent detection.
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Affiliation(s)
- Jin Huang
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Soumia Cheddah
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Yinjie Ma
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Yan Wang
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
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50
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Wang F, Su Q, Li C. Identification of cuproptosis-related asthma diagnostic genes by WGCNA analysis and machine learning. J Asthma 2023; 60:2052-2063. [PMID: 37289763 DOI: 10.1080/02770903.2023.2213334] [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: 03/26/2023] [Accepted: 05/08/2023] [Indexed: 06/10/2023]
Abstract
OBJECTIVE Cuproptosis is the latest novel form of cell death. However, the relationship between asthma and cuproptosis is not fully understood. METHODS In this study, we screened differentially expressed cuproptosis-related genes from the Gene Expression Omnibus (GEO) database and performed immune infiltration analysis. Subsequently, patients with asthma were typed and analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was performed to calculate the module-trait correlations, and the hub genes of the intersection were taken to construct machine learning (XGB, SVM, RF, GLM). Finally, we used TGF-β to establish a BEAS-2B asthma model to observe the expression levels of hub genes. RESULTS Six cuproptosis-related genes were obtained. Immune-infiltration analysis shows that cuproptosis-related genes are associated with a variety of biological functions. We classified asthma patients into two subtypes based on the expression of cuproptosis-related genes and found significant Gene Ontology (GO) and immune function differences between the different subtypes. WGCNA selected 2 significant modules associated with disease features and typing. Finally, we identified TRIM25, DYSF, NCF4, ABTB1, CXCR1 as asthma biomarkers by taking the intersection of the hub genes of the 2 modules and constructing a 5-genes signature, which nomograph, decision curve analysis (DCA) and calibration curves, receiver operating characteristic curve (ROC) showed high efficiency in diagnosing the probability of survival of asthma patients. Finally, in vitro experiments have shown that DYSF and CXCR1 expression is up expressed in asthma. CONCLUSIONS Our study provides further directions for studying the molecular mechanism of asthma.
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Affiliation(s)
- Fangwei Wang
- Department of Respiratory Medicine, the First Affiliated Hospital of Guangxi Medical University, Nan'ning, China
| | - Qisheng Su
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Nan'ning, China
| | - Chaoqian Li
- Department of Respiratory Medicine, the First Affiliated Hospital of Guangxi Medical University, Nan'ning, China
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