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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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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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53
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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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54
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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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56
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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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57
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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: 3] [Impact Index Per Article: 3.0] [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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58
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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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59
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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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61
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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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62
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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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63
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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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64
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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 EngineeringUniversity of Science and Technology BeijingBeijing100083China
- CAS Key Laboratory of Nanosystem and Hierarchical FabricationNational Center for Nanoscience and TechnologyBeijing100190China
| | - Yang Gao
- CAS Key Laboratory of Nanosystem and Hierarchical FabricationNational Center for Nanoscience and TechnologyBeijing100190China
| | - Yanhong Chang
- Department of Environmental Science and EngineeringUniversity of Science and Technology BeijingBeijing100083China
| | - Jiajie Peng
- School of Computer ScienceNorthwestern Polytechnical UniversityXi'an710072China
| | - Yadong Yu
- College of Biotechnology and Pharmaceutical EngineeringNanjing Tech UniversityNanjing211816China
| | - Bin Wang
- CAS Key Laboratory of Nanosystem and Hierarchical FabricationNational Center for Nanoscience and TechnologyBeijing100190China
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65
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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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66
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Ramalingam M, Jaisankar A, Cheng L, Krishnan S, Lan L, Hassan A, Sasmazel HT, Kaji H, Deigner HP, Pedraz JL, Kim HW, Shi Z, Marrazza G. Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses. DISCOVER NANO 2023; 18:58. [PMID: 37032711 PMCID: PMC10066940 DOI: 10.1186/s11671-023-03842-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023]
Abstract
Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.
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Affiliation(s)
- Murugan Ramalingam
- School of Basic Medical Sciences, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, 610106 China
- Institute of Tissue Regeneration Engineering, Dankook University, Cheonan, 31116 Republic of Korea
- Department of Nanobiomedical Science, Dankook University, Cheonan, 31116 Republic of Korea
- BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan, 31116 Republic of Korea
- Mechanobiology Dental Medicine Research Center, Dankook University, Cheonan, 31116 Republic of Korea
- UCL Eastman-Korea Dental Medicine Innovation Centre, Dankook University, Cheonan, 31116 South Korea
- Department of Metallurgical and Materials Engineering, Faculty of Engineering, Atilim University, 06836 Ankara, Turkey
| | - Abinaya Jaisankar
- Centre for Biomaterials, Cellular and Molecular Theranostics, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014 India
| | - Lijia Cheng
- School of Basic Medical Sciences, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, 610106 China
| | - Sasirekha Krishnan
- Centre for Biomaterials, Cellular and Molecular Theranostics, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014 India
| | - Liang Lan
- School of Basic Medical Sciences, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, 610106 China
| | - Anwarul Hassan
- Department of Mechanical and Industrial Engineering, Biomedical Research Center, Qatar University, 2713, Doha, Qatar
| | - Hilal Turkoglu Sasmazel
- Department of Metallurgical and Materials Engineering, Faculty of Engineering, Atilim University, 06836 Ankara, Turkey
| | - Hirokazu Kaji
- Department of Biomechanics, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo, 101-0062 Japan
| | - Hans-Peter Deigner
- Institute of Precision Medicine, Medical and Life Sciences Faculty, Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Jose Luis Pedraz
- NanoBioCel Group, Laboratory of Pharmaceutics, School of Pharmacy, University of the Basque Country, 01006 Vitoria-Gasteiz, Spain
- Biomedical Research Networking Centre in Bioengineering, Biomaterials and Nanomedicine, 28029 Madrid, Spain
| | - Hae-Won Kim
- Institute of Tissue Regeneration Engineering, Dankook University, Cheonan, 31116 Republic of Korea
- Department of Nanobiomedical Science, Dankook University, Cheonan, 31116 Republic of Korea
- BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan, 31116 Republic of Korea
- Mechanobiology Dental Medicine Research Center, Dankook University, Cheonan, 31116 Republic of Korea
- UCL Eastman-Korea Dental Medicine Innovation Centre, Dankook University, Cheonan, 31116 South Korea
| | - Zheng Shi
- School of Basic Medical Sciences, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, 610106 China
| | - Giovanna Marrazza
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Florence, Italy
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67
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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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68
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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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70
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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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71
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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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72
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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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73
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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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74
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Du M, Ren Z, Li Q, Pu Q, Li X, Qiu Y, Li Y. Reduced bacterial resistance antibiotics with improved microbiota tolerance in human intestinal: Molecular design and mechanism analysis. JOURNAL OF HAZARDOUS MATERIALS 2023; 460:132368. [PMID: 37619278 DOI: 10.1016/j.jhazmat.2023.132368] [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: 06/21/2023] [Revised: 08/17/2023] [Accepted: 08/20/2023] [Indexed: 08/26/2023]
Abstract
Antibiotic selectivity and bacterial resistance are critical global public health issues. We constructed a multi-class machine learning model to study antibiotic effects on human intestinal microbiota abundance and identified key features. Binding energies of β-lactam antibiotics with Escherichia coli PBP3 mutant protein were calculated, and a 2D-QSAR model for bacterial resistance was established. Sensitivity analysis identified key features affecting bacterial resistance. By coupling key features from the machine learning model and 2D-QSAR model, we designed ten flucloxacillin (FLU) substitutes that improved intestinal microbiota tolerance and reduced antibiotic bacterial resistance. Concurrently, the substitutes exhibited superior degradability in soil, aquatic environments, and under photolytic conditions, coupled with a reduced environmental toxicity compared to the FLU. Evaluations under combined medication revealed significant improvements in functionality and bacterial resistance for 80% of FLU substitutes, with 50% showing more than a twofold increase. Mechanistic analysis demonstrated enhanced binding to target proteins and increased biodegradability for FLU substitutes due to more concentrated surface charges. Reduced solvent hindrance and increased cell membrane permeability of FLU substitutes, mainly due to enhanced interactions with phospholipid bilayers, contributed to their functional selectivity. This study aims to address poor antibiotic selectivity and strong bacterial resistance, providing guidance for designing antibiotic substitutes.
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Affiliation(s)
- Meijin Du
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Zhixing Ren
- College of Forestry, Northeast Forestry University, Harbin 150040, China
| | - Qing Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Qikun Pu
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Xinao Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Youli Qiu
- School of Chemical Safety, North China Institute of Science and Technology, Yanjiao 065201, China.
| | - Yu Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
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75
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Betts R, Dierking I. Machine learning classification of polar sub-phases in liquid crystal MHPOBC. SOFT MATTER 2023; 19:7502-7512. [PMID: 37646209 DOI: 10.1039/d3sm00902e] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Experimental polarising microscopy texture images of the fluid smectic phases and sub-phases of the classic liquid crystal MHPOBC were classified as paraelectric (SmA*), ferroelectric (SmC*), ferrielectric (SmC1/3*), and antiferroelectric (SmCA*) using convolutional neural networks, CNNs. Two neural network architectures were tested, a sequential convolutional neural network with varying numbers of layers and a simplified inception model with varying number of inception blocks. Both models are successful in binary classifications between different phases as well as classification between all four phases. Optimised architectures for the multi-phase classification achieved accuracies of (84 ± 2)% and (93 ± 1)% for sequential convolutional and inception networks, respectively. The results of this study contribute to the understanding of how CNNs may be used in classifying liquid crystal phases. Especially the inception model is of sufficient accuracy to allow automated characterization of liquid crystal phase sequences and thus opens a path towards an additional method to determine the phases of novel liquid crystals for applications in electro-optics, photonics or sensors. The outlined procedure of supervised machine learning can be applied to practically all liquid crystal phases and materials, provided the infrastructure of training data and computational power is provided.
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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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76
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Campuzano S, Pingarrón JM. Electrochemical Affinity Biosensors: Pervasive Devices with Exciting Alliances and Horizons Ahead. ACS Sens 2023; 8:3276-3293. [PMID: 37534629 PMCID: PMC10521145 DOI: 10.1021/acssensors.3c01172] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/25/2023] [Indexed: 08/04/2023]
Abstract
Electrochemical affinity biosensors are evolving at breakneck speed, strengthening and colonizing more and more niches and drawing unimaginable roadmaps that increasingly make them protagonists of our daily lives. They achieve this by combining their intrinsic attributes with those acquired by leveraging the significant advances that occurred in (nano)materials technology, bio(nano)materials and nature-inspired receptors, gene editing and amplification technologies, and signal detection and processing techniques. The aim of this Perspective is to provide, with the support of recent representative and illustrative literature, an updated and critical view of the repertoire of opportunities, innovations, and applications offered by electrochemical affinity biosensors fueled by the key alliances indicated. In addition, the imminent challenges that these biodevices must face and the new directions in which they are envisioned as key players are discussed.
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Affiliation(s)
- Susana Campuzano
- Departamento de Química Analítica,
Facultad de Ciencias Químicas, Universidad
Complutense de Madrid, 28040 Madrid, España
| | - José M. Pingarrón
- Departamento de Química Analítica,
Facultad de Ciencias Químicas, Universidad
Complutense de Madrid, 28040 Madrid, España
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77
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Jobst S, Recum P, Écija-Arenas Á, Moser E, Bierl R, Hirsch T. Semi-Selective Array for the Classification of Purines with Surface Plasmon Resonance Imaging and Deep Learning Data Analysis. ACS Sens 2023; 8:3530-3537. [PMID: 37505186 DOI: 10.1021/acssensors.3c01114] [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] [Indexed: 07/29/2023]
Abstract
In process analytics or environmental monitoring, the real-time recording of the composition of complex samples over a long period of time presents a great challenge. Promising solutions are label-free techniques such as surface plasmon resonance (SPR) spectroscopy. They are, however, often limited due to poor reversibility of analyte binding. In this work, we introduce how SPR imaging in combination with a semi-selective functional surface and smart data analysis can identify small and chemically similar molecules. Our sensor uses individual functional spots made from different ratios of graphene oxide and reduced graphene oxide, which generate a unique signal pattern depending on the analyte due to different binding affinities. These patterns allow four purine bases to be distinguished after classification using a convolutional neural network (CNN) at concentrations as low as 50 μM. The validation and test set classification accuracies were constant across multiple measurements on multiple sensors using a standard CNN, which promises to serve as a future method for developing online sensors in complex mixtures.
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Affiliation(s)
- Simon Jobst
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany
- Sensorik-ApplikationsZentrum (SappZ), Regensburg University of Applied Sciences, 93053 Regensburg, Germany
| | - Patrick Recum
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany
| | - Ángela Écija-Arenas
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany
| | - Elisabeth Moser
- Sensorik-ApplikationsZentrum (SappZ), Regensburg University of Applied Sciences, 93053 Regensburg, Germany
| | - Rudolf Bierl
- Sensorik-ApplikationsZentrum (SappZ), Regensburg University of Applied Sciences, 93053 Regensburg, Germany
| | - Thomas Hirsch
- Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, 93053 Regensburg, Germany
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78
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Choi HK, Choi JH, Yoon J. An Updated Review on Electrochemical Nanobiosensors for Neurotransmitter Detection. BIOSENSORS 2023; 13:892. [PMID: 37754127 PMCID: PMC10526534 DOI: 10.3390/bios13090892] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 09/28/2023]
Abstract
Neurotransmitters are chemical compounds released by nerve cells, including neurons, astrocytes, and oligodendrocytes, that play an essential role in the transmission of signals in living organisms, particularly in the central nervous system, and they also perform roles in realizing the function and maintaining the state of each organ in the body. The dysregulation of neurotransmitters can cause neurological disorders. This highlights the significance of precise neurotransmitter monitoring to allow early diagnosis and treatment. This review provides a complete multidisciplinary examination of electrochemical biosensors integrating nanomaterials and nanotechnologies in order to achieve the accurate detection and monitoring of neurotransmitters. We introduce extensively researched neurotransmitters and their respective functions in biological beings. Subsequently, electrochemical biosensors are classified based on methodologies employed for direct detection, encompassing the recently documented cell-based electrochemical monitoring systems. These methods involve the detection of neurotransmitters in neuronal cells in vitro, the identification of neurotransmitters emitted by stem cells, and the in vivo monitoring of neurotransmitters. The incorporation of nanomaterials and nanotechnologies into electrochemical biosensors has the potential to assist in the timely detection and management of neurological disorders. This study provides significant insights for researchers and clinicians regarding precise neurotransmitter monitoring and its implications regarding numerous biological applications.
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Affiliation(s)
- Hye Kyu Choi
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA;
| | - Jin-Ha Choi
- School of Chemical Engineering, Clean Energy Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Jinho Yoon
- Department of Biomedical-Chemical Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea
- Department of Biotechnology, The Catholic University of Korea, Bucheon 14662, Republic of Korea
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79
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Qian H, McLamore E, Bliznyuk N. Machine Learning for Improved Detection of Pathogenic E. coli in Hydroponic Irrigation Water Using Impedimetric Aptasensors: A Comparative Study. ACS OMEGA 2023; 8:34171-34179. [PMID: 37744804 PMCID: PMC10515366 DOI: 10.1021/acsomega.3c05797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023]
Abstract
Reuse of alternative water sources for irrigation (e.g., untreated surface water) is a sustainable approach that has the potential to reduce water gaps, while increasing food production. However, when growing fresh produce, this practice increases the risk of bacterial contamination. Thus, rapid and accurate identification of pathogenic organisms such as Shiga-toxin producing Escherichia coli (STEC) is crucial for resource management when using alternative water(s). Although many biosensors exist for monitoring pathogens in food systems, there is an urgent need for data analysis methodologies that can be applied to accurately predict bacteria concentrations in complex matrices such as untreated surface water. In this work, we applied an impedimetric electrochemical aptasensor based on gold interdigitated electrodes for measuring E. coliO157:H7 in surface water for hydroponic lettuce irrigation. We developed a statistical machine-learning (SML) framework for assessing different existing SML methods to predict the E. coliO157:H7 concentration. In this study, three classes of statistical models were evaluated for optimizing prediction accuracy. The SML framework developed here facilitates selection of the most appropriate analytical approach for a given application. In the case of E. coliO157:H7 prediction in untreated surface water, selection of the optimum SML technique led to a reduction of test set RMSE by at least 20% when compared with the classic analytical technique. The statistical framework and code (open source) include a portfolio of SML models, an approach which can be used by other researchers using electrochemical biosensors to measure pathogens in hydroponic irrigation water for rapid decision support.
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Affiliation(s)
- Hanyu Qian
- Department
of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida 32611, United States
| | - Eric McLamore
- Department
of Agricultural Sciences, College of Agriculture, Forestry and Life
Sciences, Clemson University, Clemson, South Carolina 29634, United States
| | - Nikolay Bliznyuk
- Department
of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida 32611, United States
- Departments
of Statistics, Biostatistics and Electrical & Computer Engineering, University of Florida, Gainesville, Florida 32611, United States
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80
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Kokabi M, Tahir MN, Singh D, Javanmard M. Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis. BIOSENSORS 2023; 13:884. [PMID: 37754118 PMCID: PMC10526782 DOI: 10.3390/bios13090884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.
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Affiliation(s)
| | | | | | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA; (M.K.); (M.N.T.); (D.S.)
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81
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Lv S, Wang X, Wang G, Yang W, Cheng K. Efficient evaluation of photodynamic therapy on tumor based on deep learning. Photodiagnosis Photodyn Ther 2023; 43:103658. [PMID: 37339692 DOI: 10.1016/j.pdpdt.2023.103658] [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: 04/03/2023] [Revised: 05/30/2023] [Accepted: 06/16/2023] [Indexed: 06/22/2023]
Abstract
Photodynamic therapy (PDT) is a non-invasive treatment method for treating tumors. Under laser irradiation, photosensitizers in tumor tissues generate biotoxic reactive oxygen, which can kill tumor cells. The traditional live/dead staining method of evaluating the cell mortality caused by PDT mainly depends on manual counting, which is time-consuming and relies on dye quality. In this paper, we have constructed a dataset of cells after PDT treatment and trained the cell detection model YOLOv3 to count both the dead and live cells. YOLO is a real time AI object detection algorithm. The achieved results demonstrate that the proposed method has a good performance in cell detection, with a mean average precision (mAP) of 94% for live cells and 71.3% for dead cells. This approach can efficiently evaluate the effectiveness of PDT treatment, thus speeding up treatment development effectively.
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Affiliation(s)
- Shuangshuang Lv
- College of Electronic Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road. Haidian Dist, Beijing 100876, China
| | - Xiaohui Wang
- College of Electronic Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road. Haidian Dist, Beijing 100876, China.
| | - Guisheng Wang
- Department of Radiology, the Third medical centre, Chinese PLA General Hospital, No. 69, Yongding Road, Haidian Dist, Beijing 100039, China
| | - Wei Yang
- College of Electronic Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road. Haidian Dist, Beijing 100876, China
| | - Kun Cheng
- College of Electronic Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road. Haidian Dist, Beijing 100876, China.
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82
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Yogev D, Goldberg T, Arami A, Tejman-Yarden S, Winkler TE, Maoz BM. Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges. APL Bioeng 2023; 7:031506. [PMID: 37781727 PMCID: PMC10539032 DOI: 10.1063/5.0152290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.
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Affiliation(s)
| | | | | | | | | | - Ben M. Maoz
- Authors to whom correspondence should be addressed: and
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83
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Aggarwal M, Sahoo P, Saha S, Das P. Machine Learning-Mediated Ultrasensitive Detection of Citrinin and Associated Mycotoxins in Real Food Samples Discerned from a Photoluminescent Carbon Dot Barcode Array. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:12849-12858. [PMID: 37584518 DOI: 10.1021/acs.jafc.3c04846] [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: 08/17/2023]
Abstract
Economically viable remote sensing of foodborne contaminants using minimalistic chemical reagents and simultaneous automation calls for a concrete integration of a chemical detection strategy with artificial intelligence. In a first of its kind, we report the ultrasensitive detection of citrinin and associated mycotoxins like aflatoxin B1 and ochratoxin A using an Alizarin Red S (ARS) and cystamine-derived carbon dot (CD) that aptly amalgamate with machine learning algorithms for automation. The photoluminescence response of the CD as a function of various solvents and pH is used to generate array channels that are further modulated in the presence of the mycotoxins whose digital images were acquired to determine pixelation, essentially creating a barcode. The barcode was fed to machine learning algorithms that actualize and intertwine convoluted databases, demonstrating Extreme Gradient Boosting (XGBoost) as the optimized model out of eight algorithms tested. Spiked samples of wheat, rice, gram, maize, coffee, and milk were used to evaluate the testing model where an exemplary accuracy of 100% even at 10 pmol of mycotoxin concentration was achieved. Most importantly, the coexistence of mycotoxins could also be detected through the CD array and XGBoost synergy hinting toward a broader scope of the developed methodology for smart detection of foodborne contaminants.
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Affiliation(s)
- Maansi Aggarwal
- Department of Chemistry, Indian Institute of Technology Patna, Patna 801103, Bihar, India
| | - Pranab Sahoo
- Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna 801103, Bihar, India
| | - Sriparna Saha
- Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna 801103, Bihar, India
| | - Prolay Das
- Department of Chemistry, Indian Institute of Technology Patna, Patna 801103, Bihar, India
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84
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Lu N, Chen J, Rao Z, Guo B, Xu Y. Recent Advances of Biosensors for Detection of Multiple Antibiotics. BIOSENSORS 2023; 13:850. [PMID: 37754084 PMCID: PMC10526323 DOI: 10.3390/bios13090850] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 09/28/2023]
Abstract
The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.
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Affiliation(s)
| | | | | | | | - Ying Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
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85
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Fang G, Hasi W, Sha X, Cao G, Han S, Wu J, Lin X, Bao Z. Interfacial Self-Assembly of Surfactant-Free Au Nanoparticles as a Clean Surface-Enhanced Raman Scattering Substrate for Quantitative Detection of As 5+ in Combination with Convolutional Neural Networks. J Phys Chem Lett 2023; 14:7290-7298. [PMID: 37560985 DOI: 10.1021/acs.jpclett.3c01969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Surface-enhanced Raman scattering (SERS) is a highly sensitive tool in the field of environmental testing. However, the detection and accurate quantification of weakly adsorbed molecules (such as heavy metal ions) remain a challenge. Herein, we combine clean SERS substrates capable of capturing heavy metal ions with convolutional neural network (CNN) algorithm models for quantitative detection of heavy metal ions in solution. The SERS substrate consists of surfactant-free Au nanoparticles (NPs) and l-cysteine molecules. As plasmonic nanobuilt blocks, surfactant-free Au NPs without physical or chemical barriers are more accessible to target molecules. The amino and carboxyl groups in the l-cysteine molecule can chelate As5+ ions. The CNN algorithm model is applied to quantify and predict the concentration of As5+ ions in samples. The results demonstrated that this strategy allows for fast and accurate prediction of As5+ ion concentrations, and the determination coefficient between the predicted and actual values is as high as 0.991.
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Affiliation(s)
- Guoqiang Fang
- National Key Laboratory of Science and Technology on Tuneable Laser, Harbin Institute of Technology, Harbin, 150080, China
- Key Laboratory of New Energy and Rare Earth Resource Utilization of State Ethnic Affairs Commission, Key Laboratory of Photosensitive Materials & Devices of Liaoning Province, School of Physics and Materials Engineering, Dalian Minzu University, Dalian 116600, China
| | - Wuliji Hasi
- National Key Laboratory of Science and Technology on Tuneable Laser, Harbin Institute of Technology, Harbin, 150080, China
| | - Xuanyu Sha
- National Key Laboratory of Science and Technology on Tuneable Laser, Harbin Institute of Technology, Harbin, 150080, China
| | - Guangxu Cao
- Research Center for Space Control and Inertial Technology, Harbin Institute of Technology, Harbin, 150080, P. R. China
| | - Siqingaowa Han
- Department of Combination of Mongolian Medicine and Western Medicine Stomatology, Affiliated Hospital of Inner Mongolia University for the Nationalities, Tongliao 028043, China
| | - Jinlei Wu
- Key Laboratory of New Energy and Rare Earth Resource Utilization of State Ethnic Affairs Commission, Key Laboratory of Photosensitive Materials & Devices of Liaoning Province, School of Physics and Materials Engineering, Dalian Minzu University, Dalian 116600, China
| | - Xiang Lin
- Key Laboratory of New Energy and Rare Earth Resource Utilization of State Ethnic Affairs Commission, Key Laboratory of Photosensitive Materials & Devices of Liaoning Province, School of Physics and Materials Engineering, Dalian Minzu University, Dalian 116600, China
| | - Zhouzhou Bao
- Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
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86
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Yi J, Wisuthiphaet N, Raja P, Nitin N, Earles JM. AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water. WATER RESEARCH 2023; 242:120258. [PMID: 37390659 DOI: 10.1016/j.watres.2023.120258] [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: 03/21/2023] [Revised: 06/17/2023] [Accepted: 06/20/2023] [Indexed: 07/02/2023]
Abstract
Rapid pathogen detection in food and agricultural water is essential for ensuring food safety and public health. However, complex and noisy environmental background matrices delay the identification of pathogens and require highly trained personnel. Here, we present an AI-biosensing framework for accelerated and automated pathogen detection in various water samples, from liquid food to agricultural water. A deep learning model was used to identify and quantify target bacteria based on their microscopic patterns generated by specific interactions with bacteriophages. The model was trained on augmented datasets to maximize data efficiency, using input images of selected bacterial species, and then fine-tuned on a mixed culture. Model inference was performed on real-world water samples containing environmental noises unseen during model training. Overall, our AI model trained solely on lab-cultured bacteria achieved rapid (< 5.5 h) prediction with 80-100% accuracy on the real-world water samples, demonstrating its ability to generalize to unseen data. Our study highlights the potential applications in microbial water quality monitoring during food and agricultural processes.
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Affiliation(s)
- Jiyoon Yi
- Department of Biological & Agricultural Engineering, University of California, Davis, CA 95616, United States of America; Department of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, MI 48824, United States of America
| | - Nicharee Wisuthiphaet
- Department of Food Science & Technology, University of California, Davis, CA 95616, United States of America; Department of Biotechnology, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, 10800, Thailand
| | - Pranav Raja
- Department of Biological & Agricultural Engineering, University of California, Davis, CA 95616, United States of America
| | - Nitin Nitin
- Department of Biological & Agricultural Engineering, University of California, Davis, CA 95616, United States of America; Department of Food Science & Technology, University of California, Davis, CA 95616, United States of America
| | - J Mason Earles
- Department of Biological & Agricultural Engineering, University of California, Davis, CA 95616, United States of America; Department of Viticulture & Enology, University of California, Davis, CA 95616, United States of America.
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87
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Zhang S. Editorial: Current development on wearable biosensors towards biomedical applications. Front Bioeng Biotechnol 2023; 11:1264337. [PMID: 37614631 PMCID: PMC10442947 DOI: 10.3389/fbioe.2023.1264337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/04/2023] [Indexed: 08/25/2023] Open
Affiliation(s)
- Sheng Zhang
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
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88
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Georgas A, Georgas K, Hristoforou E. Advancements in SARS-CoV-2 Testing: Enhancing Accessibility through Machine Learning-Enhanced Biosensors. MICROMACHINES 2023; 14:1518. [PMID: 37630054 PMCID: PMC10456522 DOI: 10.3390/mi14081518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023]
Abstract
The COVID-19 pandemic highlighted the importance of widespread testing for SARS-CoV-2, leading to the development of various new testing methods. However, traditional invasive sampling methods can be uncomfortable and even painful, creating barriers to testing accessibility. In this article, we explore how machine learning-enhanced biosensors can enable non-invasive sampling for SARS-CoV-2 testing, revolutionizing the way we detect and monitor the virus. By detecting and measuring specific biomarkers in body fluids or other samples, these biosensors can provide accurate and accessible testing options that do not require invasive procedures. We provide examples of how these biosensors can be used for non-invasive SARS-CoV-2 testing, such as saliva-based testing. We also discuss the potential impact of non-invasive testing on accessibility and accuracy of testing. Finally, we discuss potential limitations or biases associated with the machine learning algorithms used to improve the biosensors and explore future directions in the field of machine learning-enhanced biosensors for SARS-CoV-2 testing, considering their potential impact on global healthcare and disease control.
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Affiliation(s)
- Antonios Georgas
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece; (K.G.); (E.H.)
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89
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Hu J, Safir F, Chang K, Dagli S, Balch HB, Abendroth JM, Dixon J, Moradifar P, Dolia V, Sahoo MK, Pinsky BA, Jeffrey SS, Lawrence M, Dionne JA. Rapid genetic screening with high quality factor metasurfaces. Nat Commun 2023; 14:4486. [PMID: 37495593 PMCID: PMC10372074 DOI: 10.1038/s41467-023-39721-w] [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: 10/11/2021] [Accepted: 06/20/2023] [Indexed: 07/28/2023] Open
Abstract
Genetic analysis methods are foundational to advancing personalized medicine, accelerating disease diagnostics, and monitoring the health of organisms and ecosystems. Current nucleic acid technologies such as polymerase chain reaction (PCR) and next-generation sequencing (NGS) rely on sample amplification and can suffer from inhibition. Here, we introduce a label-free genetic screening platform based on high quality (high-Q) factor silicon nanoantennas functionalized with nucleic acid fragments. Each high-Q nanoantenna exhibits average resonant quality factors of 2,200 in physiological buffer. We quantitatively detect two gene fragments, SARS-CoV-2 envelope (E) and open reading frame 1b (ORF1b), with high-specificity via DNA hybridization. We also demonstrate femtomolar sensitivity in buffer and nanomolar sensitivity in spiked nasopharyngeal eluates within 5 minutes. Nanoantennas are patterned at densities of 160,000 devices per cm2, enabling future work on highly-multiplexed detection. Combined with advances in complex sample processing, our work provides a foundation for rapid, compact, and amplification-free molecular assays.
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Affiliation(s)
- Jack Hu
- Department of Materials Science and Engineering, Stanford University, 496 Lomita Mall, Stanford, CA, 94305, USA.
| | - Fareeha Safir
- Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, CA, 94305, USA
| | - Kai Chang
- Department of Electrical Engineering, Stanford University, 350 Jane Stanford Way, Stanford, CA, 94305, USA
| | - Sahil Dagli
- Department of Materials Science and Engineering, Stanford University, 496 Lomita Mall, Stanford, CA, 94305, USA
| | - Halleh B Balch
- Department of Materials Science and Engineering, Stanford University, 496 Lomita Mall, Stanford, CA, 94305, USA
| | - John M Abendroth
- Laboratory for Solid State Physics, ETH Zürich, CH-8093, Zürich, Switzerland
| | - Jefferson Dixon
- Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, CA, 94305, USA
| | - Parivash Moradifar
- Department of Materials Science and Engineering, Stanford University, 496 Lomita Mall, Stanford, CA, 94305, USA
| | - Varun Dolia
- Department of Materials Science and Engineering, Stanford University, 496 Lomita Mall, Stanford, CA, 94305, USA
| | - Malaya K Sahoo
- Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Benjamin A Pinsky
- Department of Pathology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA
| | - Stefanie S Jeffrey
- Department of Surgery, Stanford University School of Medicine, 1201 Welch Road, Stanford, CA, 94305, USA
| | - Mark Lawrence
- Department of Electrical & Systems Engineering, Washington University in St. Louis, 1 Brookings Drive, St. Louis, MO, 63130, USA.
| | - Jennifer A Dionne
- Department of Materials Science and Engineering, Stanford University, 496 Lomita Mall, Stanford, CA, 94305, USA.
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90
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Azeem M, Javaid S, Khalil RA, Fahim H, Althobaiti T, Alsharif N, Saeed N. Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges. Bioengineering (Basel) 2023; 10:850. [PMID: 37508877 PMCID: PMC10416184 DOI: 10.3390/bioengineering10070850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/09/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.
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Affiliation(s)
- Muhammad Azeem
- School of Science, Engineering & Environment, University of Salford, Manchester M5 4WT, UK;
| | - Shumaila Javaid
- Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China; (S.J.); (H.F.)
| | - Ruhul Amin Khalil
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan;
- Department of Electrical and Communication Engineering, United Arab Emirates University (UAEU), Al-Ain 15551, United Arab Emirates
| | - Hamza Fahim
- Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China; (S.J.); (H.F.)
| | - Turke Althobaiti
- Department of Computer Science, Faculty of Science, Northern Border University, Arar 73222, Saudi Arabia;
| | - Nasser Alsharif
- Department of Administrative and Financial Sciences, Ranyah University Collage, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Nasir Saeed
- Department of Electrical and Communication Engineering, United Arab Emirates University (UAEU), Al-Ain 15551, United Arab Emirates
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91
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Ferguson C, Zhang Y, Palego C, Cheng X. Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5990. [PMID: 37447838 DOI: 10.3390/s23135990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/17/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.
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Affiliation(s)
- Caroline Ferguson
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Cristiano Palego
- Department of Computer Science and Electronic Engineering, Bangor University, Bangor LL57 2DG, UK
| | - Xuanhong Cheng
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
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92
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Rahn KL, Peramune U, Zhang T, Anand RK. Label-Free Electrochemical Methods for Disease Detection. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:49-69. [PMID: 36854209 DOI: 10.1146/annurev-anchem-091622-085754] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Label-free electrochemical biosensing leverages the advantages of label-free techniques, low cost, and fewer user steps, with the sensitivity and portability of electrochemical analysis. In this review, we identify four label-free electrochemical biosensing mechanisms: (a) blocking the electrode surface, (b) allowing greater access to the electrode surface, (c) changing the intercalation or electrostatic affinity of a redox probe to a biorecognition unit, and (d) modulating ion or electron transport properties due to conformational and surface charge changes. Each mechanism is described, recent advancements are summarized, and relative advantages and disadvantages of the techniques are discussed. Furthermore, two avenues for gaining further diagnostic information from label-free electrochemical biosensors, through multiplex analysis and incorporating machine learning, are examined.
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Affiliation(s)
- Kira L Rahn
- Department of Chemistry, Colorado State University, Fort Collins, Colorado, USA
- Department of Chemistry, Iowa State University, Ames, Iowa, USA;
| | - Umesha Peramune
- Department of Chemistry, Iowa State University, Ames, Iowa, USA;
| | - Tianyi Zhang
- Department of Chemistry, Iowa State University, Ames, Iowa, USA;
| | - Robbyn K Anand
- Department of Chemistry, Iowa State University, Ames, Iowa, USA;
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93
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Nguyen DD, Lee S, Kim I. Recent Advances in Metaphotonic Biosensors. BIOSENSORS 2023; 13:631. [PMID: 37366996 PMCID: PMC10296124 DOI: 10.3390/bios13060631] [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: 04/29/2023] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023]
Abstract
Metaphotonic devices, which enable light manipulation at a subwavelength scale and enhance light-matter interactions, have been emerging as a critical pillar in biosensing. Researchers have been attracted to metaphotonic biosensors, as they solve the limitations of the existing bioanalytical techniques, including the sensitivity, selectivity, and detection limit. Here, we briefly introduce types of metasurfaces utilized in various metaphotonic biomolecular sensing domains such as refractometry, surface-enhanced fluorescence, vibrational spectroscopy, and chiral sensing. Further, we list the prevalent working mechanisms of those metaphotonic bio-detection schemes. Furthermore, we summarize the recent progress in chip integration for metaphotonic biosensing to enable innovative point-of-care devices in healthcare. Finally, we discuss the impediments in metaphotonic biosensing, such as its cost effectiveness and treatment for intricate biospecimens, and present a prospect for potential directions for materializing these device strategies, significantly influencing clinical diagnostics in health and safety.
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Affiliation(s)
- Dang Du Nguyen
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Seho Lee
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Inki Kim
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea
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94
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Li S, Zhang H, Zhu M, Kuang Z, Li X, Xu F, Miao S, Zhang Z, Lou X, Li H, Xia F. Electrochemical Biosensors for Whole Blood Analysis: Recent Progress, Challenges, and Future Perspectives. Chem Rev 2023. [PMID: 37262362 DOI: 10.1021/acs.chemrev.1c00759] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Whole blood, as one of the most significant biological fluids, provides critical information for health management and disease monitoring. Over the past 10 years, advances in nanotechnology, microfluidics, and biomarker research have spurred the development of powerful miniaturized diagnostic systems for whole blood testing toward the goal of disease monitoring and treatment. Among the techniques employed for whole-blood diagnostics, electrochemical biosensors, as known to be rapid, sensitive, capable of miniaturization, reagentless and washing free, become a class of emerging technology to achieve the target detection specifically and directly in complex media, e.g., whole blood or even in the living body. Here we are aiming to provide a comprehensive review to summarize advances over the past decade in the development of electrochemical sensors for whole blood analysis. Further, we address the remaining challenges and opportunities to integrate electrochemical sensing platforms.
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Affiliation(s)
- Shaoguang Li
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Hongyuan Zhang
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Man Zhu
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Zhujun Kuang
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Xun Li
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Fan Xu
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Siyuan Miao
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Zishuo Zhang
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Xiaoding Lou
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Hui Li
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
| | - Fan Xia
- State Key Laboratory of Biogeology and Environmental Geology, Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, China
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95
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Yang K, Chen Y, Yan S, Yang W. Nanostructured surface plasmon resonance sensors: Toward narrow linewidths. Heliyon 2023; 9:e16598. [PMID: 37292265 PMCID: PMC10245261 DOI: 10.1016/j.heliyon.2023.e16598] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/10/2023] Open
Abstract
Surface plasmon resonance sensors have found wide applications in optical sensing field due to their excellent sensitivity to the slight refractive index change of surrounding medium. However, the intrinsically high optical losses in metals make it nontrivial to obtain narrow resonance spectra, which greatly limits the performance of surface plasmon resonance sensors. This review first introduces the influence factors of plasmon linewidths of metallic nanostructures. Then, various approaches to achieve narrow resonance linewidths are summarized, including the fabrication of nanostructured surface plasmon resonance sensors supporting surface lattice resonance/plasmonic Fano resonance or coupling with a photonic cavity, the preparation of surface plasmon resonance sensors with ultra-narrow resonators, as well as strategies such as platform-induced modification, alternating different dielectric layers, and the coupling with whispering-gallery-modes. Lastly, the applications and some existing challenges of surface plasmon resonance sensors are discussed. This review aims to provide guidance for the further development of nanostructured surface plasmon resonance sensors.
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Affiliation(s)
- Kang Yang
- School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou, 434023, China
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Yan Chen
- School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou, 434023, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Wenxing Yang
- School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou, 434023, China
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96
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Tseng YM, Chen KL, Chao PH, Han YY, Huang NT. Deep Learning-Assisted Surface-Enhanced Raman Scattering for Rapid Bacterial Identification. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37216401 DOI: 10.1021/acsami.3c03212] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Bloodstream infection (BSI) is characterized by the presence of viable microorganisms in the bloodstream and may induce systemic immune responses. Early and appropriate antibiotic usage is crucial to effectively treating BSI. However, conventional culture-based microbiological diagnostics are time-consuming and cannot provide timely bacterial identification for subsequent antimicrobial susceptibility test (AST) and clinical decision-making. To address this issue, modern microbiological diagnostics have been developed, such as surface-enhanced Raman scattering (SERS), which is a sensitive, label-free, and quick bacterial detection method measuring specific bacterial metabolites. In this study, we aim to integrate a new deep learning (DL) method, Vision Transformer (ViT), with bacterial SERS spectral analysis to build the SERS-DL model for rapid identification of Gram type, species, and resistant strains. To demonstrate the feasibility of our approach, we used 11,774 SERS spectra obtained directly from eight common bacterial species in clinical blood samples without artificial introduction as the training dataset for the SERS-DL model. Our results showed that ViT achieved excellent identification accuracy of 99.30% for Gram type and 97.56% for species. Moreover, we employed transfer learning by using the Gram-positive species identifier as a pre-trained model to perform the antibiotic-resistant strain task. The identification accuracy of methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) can reach 98.5% with only 200-dataset requirement. In summary, our SERS-DL model has great potential to provide a quick clinical reference to determine the bacterial Gram type, species, and even resistant strains, which can guide early antibiotic usage in BSI.
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Affiliation(s)
- Yi-Ming Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
| | - Ko-Lun Chen
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, Taiwan, 106319
| | - Po-Hsuan Chao
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
| | - Yin-Yi Han
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan, 100229
| | - Nien-Tsu Huang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, 106319
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97
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Flynn CD, Chang D, Mahmud A, Yousefi H, Das J, Riordan KT, Sargent EH, Kelley SO. Biomolecular sensors for advanced physiological monitoring. NATURE REVIEWS BIOENGINEERING 2023; 1:1-16. [PMID: 37359771 PMCID: PMC10173248 DOI: 10.1038/s44222-023-00067-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/06/2023] [Indexed: 06/28/2023]
Abstract
Body-based biomolecular sensing systems, including wearable, implantable and consumable sensors allow comprehensive health-related monitoring. Glucose sensors have long dominated wearable bioanalysis applications owing to their robust continuous detection of glucose, which has not yet been achieved for other biomarkers. However, access to diverse biological fluids and the development of reagentless sensing approaches may enable the design of body-based sensing systems for various analytes. Importantly, enhancing the selectivity and sensitivity of biomolecular sensors is essential for biomarker detection in complex physiological conditions. In this Review, we discuss approaches for the signal amplification of biomolecular sensors, including techniques to overcome Debye and mass transport limitations, and selectivity improvement, such as the integration of artificial affinity recognition elements. We highlight reagentless sensing approaches that can enable sequential real-time measurements, for example, the implementation of thin-film transistors in wearable devices. In addition to sensor construction, careful consideration of physical, psychological and security concerns related to body-based sensor integration is required to ensure that the transition from the laboratory to the human body is as seamless as possible.
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Affiliation(s)
- Connor D. Flynn
- Department of Chemistry, Faculty of Arts & Science, University of Toronto, Toronto, ON Canada
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL USA
| | - Dingran Chang
- Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON Canada
| | - Alam Mahmud
- The Edward S. Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON Canada
| | - Hanie Yousefi
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL USA
| | - Jagotamoy Das
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL USA
| | - Kimberly T. Riordan
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL USA
| | - Edward H. Sargent
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL USA
- The Edward S. Rogers Sr Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON Canada
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL USA
| | - Shana O. Kelley
- Department of Chemistry, Faculty of Arts & Science, University of Toronto, Toronto, ON Canada
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL USA
- Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON Canada
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL USA
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Evanston, IL USA
- International Institute for Nanotechnology, Northwestern University, Evanston, IL USA
- Chan Zuckerberg Biohub Chicago, Chicago, IL USA
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98
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Arya SS, Dias SB, Jelinek HF, Hadjileontiadis LJ, Pappa AM. The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics? Biosens Bioelectron 2023; 235:115387. [PMID: 37229842 DOI: 10.1016/j.bios.2023.115387] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/11/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023]
Abstract
Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.
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Affiliation(s)
- Sagar S Arya
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Interdisciplinary Center for Human Performance, Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal.
| | - Herbert F Jelinek
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR, 54124, Thessaloniki, Greece
| | - Anna-Maria Pappa
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates; Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge, UK.
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99
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Zhu Y, Li J, Kim J, Li S, Zhao Y, Bahari J, Eliahoo P, Li G, Kawakita S, Haghniaz R, Gao X, Falcone N, Ermis M, Kang H, Liu H, Kim H, Tabish T, Yu H, Li B, Akbari M, Emaminejad S, Khademhosseini A. Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare. Biomaterials 2023; 296:122075. [PMID: 36931103 PMCID: PMC10085866 DOI: 10.1016/j.biomaterials.2023.122075] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/09/2023]
Abstract
Skin-interfaced electronics (skintronics) have received considerable attention due to their thinness, skin-like mechanical softness, excellent conformability, and multifunctional integration. Current advancements in skintronics have enabled health monitoring and digital medicine. Particularly, skintronics offer a personalized platform for early-stage disease diagnosis and treatment. In this comprehensive review, we discuss (1) the state-of-the-art skintronic devices, (2) material selections and platform considerations of future skintronics toward intelligent healthcare, (3) device fabrication and system integrations of skintronics, (4) an overview of the skintronic platform for personalized healthcare applications, including biosensing as well as wound healing, sleep monitoring, the assessment of SARS-CoV-2, and the augmented reality-/virtual reality-enhanced human-machine interfaces, and (5) current challenges and future opportunities of skintronics and their potentials in clinical translation and commercialization. The field of skintronics will not only minimize physical and physiological mismatches with the skin but also shift the paradigm in intelligent and personalized healthcare and offer unprecedented promise to revolutionize conventional medical practices.
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Affiliation(s)
- Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States.
| | - Jinghang Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Jinjoo Kim
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Shaopei Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Yichao Zhao
- Interconnected and Integrated Bioelectronics Lab, Department of Electrical and Computer Engineering, and Materials Science and Engineering, University of California, Los Angeles, CA, 90095, United States
| | - Jamal Bahari
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Payam Eliahoo
- Biomedical Engineering Department, University of Southern California, Los Angeles, CA, 90007, United States
| | - Guanghui Li
- The Centre of Nanoscale Science and Technology and Key Laboratory of Functional Polymer Materials, Institute of Polymer Chemistry, College of Chemistry, Nankai University, Tianjin, 300071, China; Renewable Energy Conversion and Storage Center (RECAST), Nankai University, Tianjin, 300071, China
| | - Satoru Kawakita
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Reihaneh Haghniaz
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Xiaoxiang Gao
- Department of Nanoengineering, University of California, San Diego, La Jolla, CA, 92093, United States
| | - Natashya Falcone
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Menekse Ermis
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Heemin Kang
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Hao Liu
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China
| | - HanJun Kim
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; College of Pharmacy, Korea University, Sejong, 30019, Republic of Korea
| | - Tanveer Tabish
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Haidong Yu
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Bingbing Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; Department of Manufacturing Systems Engineering and Management, California State University, Northridge, CA, 91330, United States
| | - Mohsen Akbari
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; Laboratory for Innovation in Microengineering (LiME), Department of Mechanical Engineering, Center for Biomedical Research, University of Victoria, Victoria, BC V8P 2C5, Canada
| | - Sam Emaminejad
- Interconnected and Integrated Bioelectronics Lab, Department of Electrical and Computer Engineering, and Materials Science and Engineering, University of California, Los Angeles, CA, 90095, United States
| | - Ali Khademhosseini
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States.
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100
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Parihar A, Yadav S, Sadique MA, Ranjan P, Kumar N, Singhal A, Khare V, Khan R, Natarajan S, Srivastava AK. Internet-of-medical-things integrated point-of-care biosensing devices for infectious diseases: Toward better preparedness for futuristic pandemics. Bioeng Transl Med 2023; 8:e10481. [PMID: 37206204 PMCID: PMC10189496 DOI: 10.1002/btm2.10481] [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: 09/12/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/04/2023] Open
Abstract
Microbial pathogens have threatened the world due to their pathogenicity and ability to spread in communities. The conventional laboratory-based diagnostics of microbes such as bacteria and viruses need bulky expensive experimental instruments and skilled personnel which limits their usage in resource-limited settings. The biosensors-based point-of-care (POC) diagnostics have shown huge potential to detect microbial pathogens in a faster, cost-effective, and user-friendly manner. The use of various transducers such as electrochemical and optical along with microfluidic integrated biosensors further enhances the sensitivity and selectivity of detection. Additionally, microfluidic-based biosensors offer the advantages of multiplexed detection of analyte and the ability to deal with nanoliters volume of fluid in an integrated portable platform. In the present review, we discussed the design and fabrication of POCT devices for the detection of microbial pathogens which include bacteria, viruses, fungi, and parasites. The electrochemical techniques and current advances in this field in terms of integrated electrochemical platforms that include mainly microfluidic- based approaches and smartphone and Internet-of-things (IoT) and Internet-of-Medical-Things (IoMT) integrated systems have been highlighted. Further, the availability of commercial biosensors for the detection of microbial pathogens will be briefed. In the end, the challenges while fabrication of POC biosensors and expected future advances in the field of biosensing have been discussed. The integrated biosensor-based platforms with the IoT/IoMT usually collect the data to track the community spread of infectious diseases which would be beneficial in terms of better preparedness for current and futuristic pandemics and is expected to prevent social and economic losses.
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Affiliation(s)
- Arpana Parihar
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
| | - Shalu Yadav
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
| | - Mohd Abubakar Sadique
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
| | - Pushpesh Ranjan
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
| | - Neeraj Kumar
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
| | - Ayushi Singhal
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
| | - Vedika Khare
- School of Nanotechnology, UTD, RGPV CampusBhopalMadhya PradeshIndia
| | - Raju Khan
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
| | - Sathish Natarajan
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
| | - Avanish K. Srivastava
- Industrial Waste Utilization, Nano and Biomaterials, CSIR‐Advanced Materials and Processes Research Institute (AMPRI)BhopalMadhya PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadIndia
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