1
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Sun Z, Wang Z, Jiang M. RamanCluster: A deep clustering-based framework for unsupervised Raman spectral identification of pathogenic bacteria. Talanta 2024; 275:126076. [PMID: 38663070 DOI: 10.1016/j.talanta.2024.126076] [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: 11/23/2023] [Revised: 04/03/2024] [Accepted: 04/06/2024] [Indexed: 05/30/2024]
Abstract
Raman spectroscopy serves as a powerful and reliable tool for the characterization of pathogenic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenic bacteria has become paramount for expediting disease diagnosis. However, the development of prevailing supervised artificial intelligence algorithms is still constrained by costly and limited well-annotated Raman spectroscopy datasets. Furthermore, tackling various high-dimensional and intricate Raman spectra of pathogenic bacteria in the absence of annotations remains a formidable challenge. In this paper, we propose a concise and efficient deep clustering-based framework (RamanCluster) to achieve accurate and robust unsupervised Raman spectral identification of pathogenic bacteria without the need for any annotated data. RamanCluster is composed of a novel representation learning module and a machine learning-based clustering module, systematically enabling the extraction of robust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria. The extensive experimental results show that RamanCluster has achieved high accuracy on both Bacteria-4 and Bacteria-6, with ACC values of 77 % and 74.1 %, NMI values of 75 % and 73 %, as well as AMI values of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of-the-art methods, RamanCluster exhibits the superior accuracy on handling various complicated pathogenic bacterial Raman spectroscopy datasets, including situations with strong noise and a wide variety of pathogenic bacterial species. Additionally, RamanCluster also demonstrates commendable robustness in these challenging scenarios. In short, RamanCluster has a promising prospect in accelerating the development of low-cost and widely applicable disease diagnosis in clinical medicine.
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Affiliation(s)
- Zhijian Sun
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhuo Wang
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China.
| | - Mingqi Jiang
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China
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2
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Chisanga M, Masson JF. Machine Learning-Driven SERS Nanoendoscopy and Optophysiology. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2024; 17:313-338. [PMID: 38701442 DOI: 10.1146/annurev-anchem-061622-012448] [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: 05/05/2024]
Abstract
A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.
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Affiliation(s)
- Malama Chisanga
- Département de Chimie, Institut Courtois, Quebec Center for Advanced Materials, Regroupement Québécois sur les Matériaux de Pointe, and Centre Interdisciplinaire de Recherche sur le Cerveau et l'Apprentissage, Université de Montréal, Montréal, Québec, Canada;
| | - Jean-Francois Masson
- Département de Chimie, Institut Courtois, Quebec Center for Advanced Materials, Regroupement Québécois sur les Matériaux de Pointe, and Centre Interdisciplinaire de Recherche sur le Cerveau et l'Apprentissage, Université de Montréal, Montréal, Québec, Canada;
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3
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Mou JY, Usman M, Tang JW, Yuan Q, Ma ZW, Wen XR, Liu Z, Wang L. Pseudo-Siamese network combined with label-free Raman spectroscopy for the quantification of mixed trace amounts of antibiotics in human milk: A feasibility study. Food Chem X 2024; 22:101507. [PMID: 38855098 PMCID: PMC11157215 DOI: 10.1016/j.fochx.2024.101507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/16/2024] [Accepted: 05/22/2024] [Indexed: 06/11/2024] Open
Abstract
The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.
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Affiliation(s)
- Jing-Yi Mou
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
- Department of Clinical Medicine, School of the 1 Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Muhammad Usman
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Jia-Wei Tang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Quan Yuan
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Zhang-Wen Ma
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Department of Pharmaceutical Analysis, School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Xin-Ru Wen
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Zhao Liu
- Department of Clinical Medicine, School of the 1 Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
- Division of Microbiology and Immunology, School of Biomedical Sciences, The University of Western Australia, Crawley, Western Australia, Australia
- School of Agriculture and Food Sustainability, University of Queensland, Brisbane, Queensland, Australia
- Center for Precision Health, School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia
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4
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Chen X, Shen J, Liu C, Shi X, Feng W, Sun H, Zhang W, Zhang S, Jiao Y, Chen J, Hao K, Gao Q, Li Y, Hong W, Wang P, Feng L, Yue S. Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification. Anal Chem 2024; 96:6158-6169. [PMID: 38602477 DOI: 10.1021/acs.analchem.3c04930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.
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Affiliation(s)
- Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Beijing 100191, China
| | - Jianghao Shen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chang Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiaoyu Shi
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Weichen Feng
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Hongyi Sun
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Weifeng Zhang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Shengpai Zhang
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Yuqing Jiao
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Jing Chen
- Su Zhou Surgi-Master High Tech Co., Ltd., Zhangjiagang, Suzhou 215626, China
| | - Kun Hao
- Research and Development Center, Beijing Yaogen Biotechnology Co., Ltd., Beijing 102600, China
| | - Qi Gao
- Research and Development Center, Beijing Yaogen Biotechnology Co., Ltd., Beijing 102600, China
| | - Yitong Li
- Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100021, China
| | - Weili Hong
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Pu Wang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Limin Feng
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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5
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Dong H, Lin J, Tao Y, Jia Y, Sun L, Li WJ, Sun H. AI-enhanced biomedical micro/nanorobots in microfluidics. LAB ON A CHIP 2024; 24:1419-1440. [PMID: 38174821 DOI: 10.1039/d3lc00909b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.
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Affiliation(s)
- Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Jiawen Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
| | - Yihui Tao
- Department of Automation Control and System Engineering, University of Sheffield, Sheffield, UK
| | - Yuan Jia
- Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China
| | - Lining Sun
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
| | - Wen Jung Li
- Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China
| | - Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
- School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
- Research Center of Aerospace Mechanism and Control, Harbin Institute of Technology, Harbin, China
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6
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Nguyen T, Jeong S, Kang SK, Han SW, Nguyen TMT, Lee S, Jung YJ, Kim YH, Park S, Bak GH, Ko YC, Choi EJ, Kim HY, Oh JW. 3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer. ACS Sens 2024; 9:699-707. [PMID: 38294962 PMCID: PMC10897927 DOI: 10.1021/acssensors.3c01938] [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: 09/14/2023] [Revised: 01/09/2024] [Accepted: 01/12/2024] [Indexed: 02/02/2024]
Abstract
The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.
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Affiliation(s)
- Thanh
Mien Nguyen
- Bio-IT
Fusion Technology Research Institute, Pusan
National University, Busan 46241, Republic
of Korea
| | - SinSung Jeong
- Telecommunication
System Technology, College of Engineering, Korea University, Seoul 02841, Republic
of Korea
| | - Seok Kyung Kang
- Department
of Surgery, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan 49241, Republic of Korea
| | - Seung-Wook Han
- Department
of Nano Fusion Technology, Pusan National
University, Busan 46214, Republic of Korea
| | - Thu M. T. Nguyen
- Department
of Nano Fusion Technology, Pusan National
University, Busan 46214, Republic of Korea
| | - Seungju Lee
- Department
of Surgery, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan 49241, Republic of Korea
| | - Youn Joo Jung
- Department
of Surgery, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan 49241, Republic of Korea
| | - You Hwan Kim
- Department
of Nano Fusion Technology, Pusan National
University, Busan 46214, Republic of Korea
| | - Sunwoo Park
- Department
of Nano Fusion Technology, Pusan National
University, Busan 46214, Republic of Korea
| | - Gyeong-Ha Bak
- Department
of Nano Fusion Technology, Pusan National
University, Busan 46214, Republic of Korea
| | - Young-Chai Ko
- School
of Electrical and Computer Engineering, Korea University, Seoul 02841, Republic
of Korea
| | - Eun-Jung Choi
- Bio-IT
Fusion Technology Research Institute, Pusan
National University, Busan 46241, Republic
of Korea
| | - Hyun Yul Kim
- Department
of Surgery, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan 49241, Republic of Korea
| | - Jin-Woo Oh
- Bio-IT
Fusion Technology Research Institute, Pusan
National University, Busan 46241, Republic
of Korea
- Department
of Nano Fusion Technology, Pusan National
University, Busan 46214, Republic of Korea
- Department
of Nanoenergy Engineering and Research Center for Energy Convergence
Technology, Pusan National University, Busan 46214, Republic of Korea
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7
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Zhang J, Wang M, Xiao J, Wang M, Liu Y, Gao X. Metabolism-Triggered Plasmonic Nanosensor for Bacterial Detection and Antimicrobial Susceptibility Testing of Clinical Isolates. ACS Sens 2024; 9:379-387. [PMID: 38175523 DOI: 10.1021/acssensors.3c02144] [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: 01/05/2024]
Abstract
Antimicrobial resistance (AMR) is predicted to become the leading cause of death worldwide in the coming decades. Rapid and on-site antibiotic susceptibility testing (AST) is crucial for guiding appropriate antibiotic choices to combat AMR. With this in mind, we have designed a simple and efficient plasmonic nanosensor consisting of Cu2+ and cysteine-modified AuNP (Au/Cys) that utilizes the metabolic activity of bacteria toward Cu2+ for bacterial detection and AST. When Cu2+ is present, it induces the aggregation of Au/Cys. However, in the presence of bacteria, Cu2+ is metabolized to varying extents, resulting in distinct levels of aggregation. Moreover, the metabolic activity of bacteria can be influenced by their antibiotic susceptibility, allowing us to differentiate between susceptible and resistant strains through direct color changes from the Cu2+-Au/Cys platform over approximately 3 h. These color changes can be easily detected using naked-eye observation, smartphone analysis, or absorption readout. We have validated the platform using four clinical isolates and six types of antibiotics, demonstrating a clinical sensitivity and specificity of 95.8%. Given its simplicity, low cost, high speed, and high accuracy, the plasmonic nanosensor holds great potential for point-of-care detection of antibiotic susceptibility across various settings.
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Affiliation(s)
- Jing Zhang
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Mengna Wang
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Jinru Xiao
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Mengqi Wang
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Yaqing Liu
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Xia Gao
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
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8
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Bi X, Lin L, Chen Z, Ye J. Artificial Intelligence for Surface-Enhanced Raman Spectroscopy. SMALL METHODS 2024; 8:e2301243. [PMID: 37888799 DOI: 10.1002/smtd.202301243] [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: 09/15/2023] [Revised: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Zhou Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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9
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Plou J, Valera PS, García I, Vila-Liarte D, Renero-Lecuna C, Ruiz-Cabello J, Carracedo A, Liz-Marzán LM. Machine Learning-Assisted High-Throughput SERS Classification of Cell Secretomes. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207658. [PMID: 37046181 DOI: 10.1002/smll.202207658] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/25/2023] [Indexed: 06/19/2023]
Abstract
During the response to different stress conditions, damaged cells react in multiple ways, including the release of a diverse cocktail of metabolites. Moreover, secretomes from dying cells can contribute to the effectiveness of anticancer therapies and can be exploited as predictive biomarkers. The nature of the stress and the resulting intracellular responses are key determinants of the secretome composition, but monitoring such processes remains technically arduous. Hence, there is growing interest in developing tools for noninvasive secretome screening. In this regard, it has been previously shown that the relative concentrations of relevant metabolites can be traced by surface-enhanced Raman scattering (SERS), thereby allowing label-free biofluid interrogation. However, conventional SERS approaches are insufficient to tackle the requirements imposed by high-throughput modalities, namely fast data acquisition and automatized analysis. Therefore, machine learning methods were implemented to identify cell secretome variations while extracting standard features for cell death classification. To this end, ad hoc microfluidic chips were devised, to readily conduct SERS measurements through a prototype relying on capillary pumps made of filter paper, which eventually would function as the SERS substrates. The developed strategy may pave the way toward a faster implementation of SERS into cell secretome classification, which can be extended even to laboratories lacking highly specialized facilities.
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Affiliation(s)
- Javier Plou
- CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain
| | - Pablo S Valera
- CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain
- CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Derio, 48160, Spain
- Department of Applied Chemistry, University of the Basque Country, Donostia, 20018, Spain
| | - Isabel García
- CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain
| | - David Vila-Liarte
- CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain
| | - Carlos Renero-Lecuna
- CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain
| | - Jesús Ruiz-Cabello
- CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain
- Biomedical Research Networking Center in Respiratory Diseases (CIBERES), Madrid, 28029, Spain
- Universidad Complutense de Madrid, Madrid, 28040, Spain
| | - Arkaitz Carracedo
- CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Derio, 48160, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain
- Biomedical Research Networking Center in Cancer (CIBERONC), Derio, 48160, Spain
- Translational Prostate Cancer Research Lab, CIC bioGUNE-Basurto, Biocruces Bizkaia Health Research Institute, Derio, 48160, Spain
| | - Luis M Liz-Marzán
- CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain
- Cinbio, Universidade de Vigo, Vigo, 36310, Spain
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10
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Zagajewski A, Turner P, Feehily C, El Sayyed H, Andersson M, Barrett L, Oakley S, Stracy M, Crook D, Nellåker C, Stoesser N, Kapanidis AN. Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli. Commun Biol 2023; 6:1164. [PMID: 37964031 PMCID: PMC10645916 DOI: 10.1038/s42003-023-05524-4] [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: 02/02/2023] [Accepted: 10/30/2023] [Indexed: 11/16/2023] Open
Abstract
The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current culture-based turnaround times for bacterial identification in clinical samples and antimicrobial susceptibility testing (AST) are typically 18-24 h. We present a novel proof-of-concept methodological advance in susceptibility testing based on the deep-learning of single-cell specific morphological phenotypes directly associated with antimicrobial susceptibility in Escherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells for a lab-reference fully susceptible E. coli strain, across four antibiotics (ciprofloxacin, gentamicin, rifampicin and co-amoxiclav). For ciprofloxacin, we demonstrate our models reveal significant (p < 0.001) differences between bacterial cell populations affected and unaffected by antibiotic treatment, and show that given treatment with a fixed concentration of 10 mg/L over 30 min these phenotypic effects correlate with clinical susceptibility defined by established clinical breakpoints. Deploying our approach on cell populations from six E. coli strains obtained from human bloodstream infections with varying degrees of ciprofloxacin resistance and treated with a range of ciprofloxacin concentrations, we show single-cell phenotyping has the potential to provide equivalent information to growth-based AST assays, but in as little as 30 min.
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Affiliation(s)
- Alexander Zagajewski
- Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Piers Turner
- Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Conor Feehily
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Hafez El Sayyed
- Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Monique Andersson
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Department of Microbiology and Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Lucinda Barrett
- Department of Microbiology and Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Sarah Oakley
- Department of Microbiology and Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Mathew Stracy
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford, OX1 3RE, UK
| | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Department of Microbiology and Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Christoffer Nellåker
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Big Data Institute, Oxford, OX3 7LF, UK.
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
- Department of Microbiology and Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK.
| | - Achillefs N Kapanidis
- Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
- Kavli Institute for Nanoscience Discovery, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.
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11
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Wei H, Smith JP. Modernized Machine Learning Approach to Illuminate Enzyme Immobilization for Biocatalysis. ACS CENTRAL SCIENCE 2023; 9:1913-1926. [PMID: 37901174 PMCID: PMC10604017 DOI: 10.1021/acscentsci.3c00757] [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] [Indexed: 10/31/2023]
Abstract
Biocatalysis is an established technology with significant application in the pharmaceutical industry. Immobilization of enzymes offers significant benefits for commercial and practical purposes to enhance the stability and recyclability of biocatalysts. Determination of the spatial and chemical distributions of immobilized enzymes on solid support materials is essential for an optimal catalytic performance. However, current analytical methodologies often fall short of rapidly identifying and characterizing immobilized enzyme systems. Herein, we present a new analytical methodology that combines non-negative matrix factorization (NMF)-an unsupervised machine learning tool-with Raman hyperspectral imaging to simultaneously resolve the spatial and spectral characteristics of all individual species involved in enzyme immobilization. Our novel approach facilitates the determination of the optimal NMF model using new data-driven, quantitative selection criteria that fully resolve all chemical species present, offering a robust methodology for analyzing immobilized enzymes. Specifically, we demonstrate the ability of NMF with Raman hyperspectral imaging to resolve the spatial and spectral profiles of an engineered pantothenate kinase immobilized on two different commercial microporous resins. Our results demonstrate that this approach can accurately identify and spatially resolve all species within this enzyme immobilization process. To the best of our knowledge, this is the first report of NMF within hyperspectral imaging for enzyme immobilization analysis, and as such, our methodology can now provide a new powerful tool to streamline biocatalytic process development within the pharmaceutical industry.
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Affiliation(s)
- Hong Wei
- Process Research & Development,
MRL, Merck & Co., Inc., West Point, Pennsylvania 19486, United States
| | - Joseph P. Smith
- Process Research & Development,
MRL, Merck & Co., Inc., West Point, Pennsylvania 19486, United States
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12
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Wang W, Vikesland PJ. Metabolite-Mediated Bacterial Antibiotic Resistance Revealed by Surface-Enhanced Raman Spectroscopy. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:13375-13383. [PMID: 37624741 DOI: 10.1021/acs.est.3c04001] [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: 08/27/2023]
Abstract
A prompt on-site, real-time method to detect bacterial antibiotic resistance is crucial for controlling the spread of resistance. Herein, we report the use of surface-enhanced Raman spectroscopy (SERS) for the monitoring of bioactive metabolites produced by ampicillin-resistant Pseudomonas aeruginosa strains and identification of mechanisms underlying antibiotic resistance. The results indicate that the blue-green pigment pyocyanin (PYO) dominates the metabolite signals and is significantly enhanced upon exposure to subminimal inhibitory concentrations of ampicillin. PYO accumulates during exponential growth and subsequently either diffuses into the culture medium or is consumed in response to nutrient deprivation. The SERS spectra further reveal that the production of some intermediate substances such as polysaccharides and amino acids is minimally impacted and that nutrient consumption remains consistent. Moreover, the intensity changes and peak shifts observed in the SERS spectra of non-PYO-producing ampicillin-susceptible Escherichia coli demonstrate that exogenously added PYO enhances E. coli tolerance to ampicillin to some extent. These results indicate that PYO mediates antibiotic resistance not only in the parent species but also in cocultured bacterial strains. The metabolic SERS signal provides new insight regarding antibiotic resistance with promising applications for both environmental monitoring and rapid clinical detection.
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Affiliation(s)
- Wei Wang
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, United States
| | - Peter J Vikesland
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
- Virginia Tech Institute of Critical Technology and Applied Science (ICTAS) Sustainable Nanotechnology Center (VTSuN), Blacksburg, Virginia 24061, United States
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13
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Kaushal S, Priyadarshi N, Garg P, Singhal NK, Lim DK. Nano-Biotechnology for Bacteria Identification and Potent Anti-bacterial Properties: A Review of Current State of the Art. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2529. [PMID: 37764558 PMCID: PMC10536455 DOI: 10.3390/nano13182529] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 08/26/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023]
Abstract
Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed.
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Affiliation(s)
- Shimayali Kaushal
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
| | - Nitesh Priyadarshi
- National Agri-Food Biotechnology Institute (NABI), Sector-81, Mohali 140306, India; (N.P.); (P.G.)
| | - Priyanka Garg
- National Agri-Food Biotechnology Institute (NABI), Sector-81, Mohali 140306, India; (N.P.); (P.G.)
| | - Nitin Kumar Singhal
- National Agri-Food Biotechnology Institute (NABI), Sector-81, Mohali 140306, India; (N.P.); (P.G.)
| | - Dong-Kwon Lim
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
- Department of Integrative Energy Engineering, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
- Brain Science Institute, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea
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14
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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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15
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Liu Y, Qin Z, Zhou J, Jia X, Li H, Wang X, Chen Y, Sun Z, He X, Li H, Wang G, Chang H. Nano-biosensor for SARS-CoV-2/COVID-19 detection: methods, mechanism and interface design. RSC Adv 2023; 13:17883-17906. [PMID: 37323463 PMCID: PMC10262965 DOI: 10.1039/d3ra02560h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/26/2023] [Indexed: 06/17/2023] Open
Abstract
The epidemic of coronavirus disease 2019 (COVID-19) was a huge disaster to human society. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to COVID-19, has resulted in a large number of deaths. Even though the reverse transcription-polymerase chain reaction (RT-PCR) is the most efficient method for the detection of SARS-CoV-2, the disadvantages (such as long detection time, professional operators, expensive instruments, and laboratory equipment) limit its application. In this review, the different kinds of nano-biosensors based on surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), field-effect transistor (FET), fluorescence methods, and electrochemical methods are summarized, starting with a concise description of their sensing mechanism. The different bioprobes (such as ACE2, S protein-antibody, IgG antibody, IgM antibody, and SARS-CoV-2 DNA probes) with different bio-principles are introduced. The key structural components of the biosensors are briefly introduced to give readers an understanding of the principles behind the testing methods. In particular, SARS-CoV-2-related RNA mutation detection and its challenges are also briefly described. We hope that this review will encourage readers with different research backgrounds to design SARS-CoV-2 nano-biosensors with high selectivity and sensitivity.
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Affiliation(s)
- Yansheng Liu
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
- Quantum-Nano Matter and Device Lab, State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan 430074 Hubei China
| | - Zhenle Qin
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Jin Zhou
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Xiaobo Jia
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Hongli Li
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Xiaohong Wang
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Yating Chen
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Zijun Sun
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Xiong He
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Hongda Li
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
- Quantum-Nano Matter and Device Lab, State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan 430074 Hubei China
| | - Guofu Wang
- School of Electronic Engineering, Guangxi University of Science and Technology Liuzhou 545616 Guangxi China
| | - Haixin Chang
- Quantum-Nano Matter and Device Lab, State Key Laboratory of Material Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan 430074 Hubei China
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16
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Zhou Y, Lu Y, Liu Y, Hu X, Chen H. Current strategies of plasmonic nanoparticles assisted surface-enhanced Raman scattering toward biosensor studies. Biosens Bioelectron 2023; 228:115231. [PMID: 36934607 DOI: 10.1016/j.bios.2023.115231] [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: 07/26/2022] [Revised: 02/21/2023] [Accepted: 03/12/2023] [Indexed: 03/15/2023]
Abstract
With the progressive nanofabrication technology, plasmonic nanoparticles (PNPs) have been increasingly deployed in the field of biosensing. PNPs have favorable biocompatibility, conductivity, and tunable optical properties. In addition, the localized surface plasmon resonance (LSPR) of PNPs plays a vital role in surface-enhanced Raman scattering (SERS). PNPs-based SERS biosensing enables wide-ranging applications for sensitive detection and high spatial and temporal resolution imaging. Numerous reviews of PNPs in the field of SERS biosensing highlight the fabrication or applications in one or more fields. However, the specific strategies for the SERS biosensor construction had not been summarized systematically. Thus, this work offers a comprehensive overview of SERS enhancement strategies based on PNPs, with a focus on SERS label-free detection along with label detection sensing construction, as well as its challenges and future trends.
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Affiliation(s)
- Yangyang Zhou
- School of Medicine, Shanghai University, Shanghai, 200444, PR China; School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, PR China
| | - Yongkai Lu
- School of Life Sciences, Shanghai University, Shanghai, 200444, PR China
| | - Yawen Liu
- School of Medicine, Shanghai University, Shanghai, 200444, PR China; School of Environmental and Chemical Engineering, Shanghai University, Shanghai, 200444, PR China
| | - Xiaojun Hu
- School of Life Sciences, Shanghai University, Shanghai, 200444, PR China
| | - Hongxia Chen
- School of Life Sciences, Shanghai University, Shanghai, 200444, PR China.
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17
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Zhao Y, Zhang Z, Ning Y, Miao P, Li Z, Wang H. Simultaneous quantitative analysis of Escherichia coli, Staphylococcus aureus and Salmonella typhimurium using surface-enhanced Raman spectroscopy coupled with partial least squares regression and artificial neural networks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 293:122510. [PMID: 36812753 DOI: 10.1016/j.saa.2023.122510] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Simultaneous detection of mixed bacteria accurately and sensitively is a major challenge in microbial quality control field. In this study, we proposed a label-free SERS technique coupled with partial least squares regression (PLSR) and artificial neural networks (ANNs) for quantitative analysis of Escherichia coli, Staphylococcus aureus and Salmonella typhimurium simultaneously. SERS-active and reproducible Raman spectra can be acquired directly upon the bacteria and Au@Ag@SiO2 nanoparticle composites on the surface of gold foil substrates. After applying different preprocessing models, SERS-PLSR and SERS-ANNs quantitative analysis models were developed to map SERS spectra of concentrations of the Escherichia coli, Staphylococcus aureus and Salmonella typhimurium, respectively. Both models achieved high prediction accuracy and low prediction error, while the performance of SERS-ANNs model in both quality of fit (R2 > 0.95) and accuracy of predictions (RMSE < 0.06) was superior to SERS-PLSR model. Therefore, it is feasible to develop simultaneous quantitative analysis of mixed pathogenic bacteria by proposed SERS methodology.
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Affiliation(s)
- Yuwen Zhao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin 301617, China
| | - Zeshuai Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin 301617, China
| | - Ying Ning
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin 301617, China
| | - Peiqi Miao
- Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300392, 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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18
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Huang YH, Wei H, Santiago PJ, Thrift WJ, Ragan R, Jiang S. Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:4880-4891. [PMID: 36934344 PMCID: PMC10061928 DOI: 10.1021/acs.est.3c00027] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/27/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring.
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Affiliation(s)
- Yen-Hsiang Huang
- Department
of Civil and Environmental Engineering, University of California, Irvine, Irvine, California 92697, United States
| | - Hong Wei
- Department
of Materials Science and Engineering, University
of California, Irvine, Irvine, California 92697, United States
| | - Peter J. Santiago
- Department
of Materials Science and Engineering, University
of California, Irvine, Irvine, California 92697, United States
| | - William John Thrift
- Department
of Materials Science and Engineering, University
of California, Irvine, Irvine, California 92697, United States
| | - Regina Ragan
- Department
of Materials Science and Engineering, University
of California, Irvine, Irvine, California 92697, United States
| | - Sunny Jiang
- Department
of Civil and Environmental Engineering, University of California, Irvine, Irvine, California 92697, United States
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19
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Liu L, Ma W, Wang X, Li S. Recent Progress of Surface-Enhanced Raman Spectroscopy for Bacteria Detection. BIOSENSORS 2023; 13:350. [PMID: 36979564 PMCID: PMC10046079 DOI: 10.3390/bios13030350] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
There are various pathogenic bacteria in the surrounding living environment, which not only pose a great threat to human health but also bring huge losses to economic development. Conventional methods for bacteria detection are usually time-consuming, complicated and labor-intensive, and cannot meet the growing demands for on-site and rapid analyses. Sensitive, rapid and effective methods for pathogenic bacteria detection are necessary for environmental monitoring, food safety and infectious bacteria diagnosis. Recently, benefiting from its advantages of rapidity and high sensitivity, surface-enhanced Raman spectroscopy (SERS) has attracted significant attention in the field of bacteria detection and identification as well as drug susceptibility testing. Here, we comprehensively reviewed the latest advances in SERS technology in the field of bacteria analysis. Firstly, the mechanism of SERS detection and the fabrication of the SERS substrate were briefly introduced. Secondly, the label-free SERS applied for the identification of bacteria species was summarized in detail. Thirdly, various SERS tags for the high-sensitivity detection of bacteria were also discussed. Moreover, we emphasized the application prospects of microfluidic SERS chips in antimicrobial susceptibility testing (AST). In the end, we gave an outlook on the future development and trends of SERS in point-of-care diagnoses of bacterial infections.
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Affiliation(s)
- Lulu Liu
- College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
| | - Wenrui Ma
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
- Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Xiang Wang
- Department of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China
| | - Shunbo Li
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
- Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
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20
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Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends. Anal Bioanal Chem 2023:10.1007/s00216-023-04620-y. [PMID: 36864313 PMCID: PMC9981450 DOI: 10.1007/s00216-023-04620-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/20/2023] [Indexed: 03/04/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.
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21
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Masson JF, Biggins JS, Ringe E. Machine learning for nanoplasmonics. NATURE NANOTECHNOLOGY 2023; 18:111-123. [PMID: 36702956 DOI: 10.1038/s41565-022-01284-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 10/27/2022] [Indexed: 06/18/2023]
Abstract
Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data.
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Affiliation(s)
- Jean-Francois Masson
- Département de chimie, Quebec Center for Advanced Materials, Regroupement québécois sur les matériaux de pointe, and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage, Université de Montréal, Montréal, Quebec, Canada.
| | - John S Biggins
- Engineering Department, University of Cambridge, Cambridge, UK.
| | - Emilie Ringe
- Department of Material Science and Metallurgy, University of Cambridge, Cambridge, UK.
- Department of Earth Science, University of Cambridge, Cambridge, UK.
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22
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Zhou H, Xu L, Ren Z, Zhu J, Lee C. Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics. NANOSCALE ADVANCES 2023; 5:538-570. [PMID: 36756499 PMCID: PMC9890940 DOI: 10.1039/d2na00608a] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/06/2022] [Indexed: 06/17/2023]
Abstract
The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.
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Affiliation(s)
- Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Liangge Xu
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Zhihao Ren
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Jiaqi Zhu
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- NUS Suzhou Research Institute (NUSRI) Suzhou 215123 China
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23
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Dina NE, Tahir MA, Bajwa SZ, Amin I, Valev VK, Zhang L. SERS-based antibiotic susceptibility testing: Towards point-of-care clinical diagnosis. Biosens Bioelectron 2023; 219:114843. [PMID: 36327563 DOI: 10.1016/j.bios.2022.114843] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 08/09/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022]
Abstract
Emerging antibiotic resistant bacteria constitute one of the biggest threats to public health. Surface-enhanced Raman scattering (SERS) is highly promising for detecting such bacteria and for antibiotic susceptibility testing (AST). SERS is fast, non-destructive (can probe living cells) and it is technologically flexible (readily integrated with robotics and machine learning algorithms). However, in order to integrate into efficient point-of-care (PoC) devices and to effectively replace the current culture-based methods, it needs to overcome the challenges of reliability, cost and complexity. Recently, significant progress has been made with the emergence of both new questions and new promising directions of research and technological development. This article brings together insights from several representative SERS-based AST studies and approaches oriented towards clinical PoC biosensing. It aims to serve as a reference source that can guide progress towards PoC routines for identifying antibiotic resistant pathogens. In turn, such identification would help to trace the origin of sporadic infections, in order to prevent outbreaks and to design effective medical treatment and preventive procedures.
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Affiliation(s)
- Nicoleta Elena Dina
- Department of Molecular and Biomolecular Department, National Institute for Research and Development of Isotopic and Molecular Technologies, 400293, Cluj-Napoca, Romania.
| | - Muhammad Ali Tahir
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai, 200433, People's Republic of China
| | - Sadia Z Bajwa
- National Institute for Biotechnology and Genetic Engineering (NIBGE), P.O. Box No. 577, Jhang Road, 38000, Faisalabad, Pakistan
| | - Imran Amin
- National Institute for Biotechnology and Genetic Engineering (NIBGE), P.O. Box No. 577, Jhang Road, 38000, Faisalabad, Pakistan
| | - Ventsislav K Valev
- Centre for Photonics and Photonic Materials, Department of Physics, University of Bath, Bath, BA2 7AY, United Kingdom; Centre for Therapeutic Innovation, University of Bath, Bath, United Kingdom; Centre for Nanoscience and Nanotechnology, University of Bath, Bath, United Kingdom.
| | - Liwu Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai, 200433, People's Republic of China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, People's Republic of China.
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24
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Machine learning-assisted optical nano-sensor arrays in microorganism analysis. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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25
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Chen X, Shu W, Zhao L, Wan J. Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis. VIEW 2022. [DOI: 10.1002/viw.20220038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Xiaonan Chen
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Weikang Shu
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Liang Zhao
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
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26
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Yuan K, Jurado-Sánchez B, Escarpa A. Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review. J Nanobiotechnology 2022; 20:537. [PMID: 36544151 PMCID: PMC9771791 DOI: 10.1186/s12951-022-01711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/15/2022] [Indexed: 12/24/2022] Open
Abstract
Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.
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Affiliation(s)
- Kaisong Yuan
- Bio-Analytical Laboratory, Shantou University Medical College, No. 22, Xinling Road, Shantou, 515041, China
- Department of Analytical Chemistry, Physical Chemistry, and Chemical Engineering, University of Alcala, Alcala de Henares, 28802, Madrid, Spain
| | - Beatriz Jurado-Sánchez
- Department of Analytical Chemistry, Physical Chemistry, and Chemical Engineering, University of Alcala, Alcala de Henares, 28802, Madrid, Spain
- Chemical Research Institute "Andrés M. del Río", University of Alcala, Alcala de Henares, 28802, Madrid, Spain
| | - Alberto Escarpa
- Department of Analytical Chemistry, Physical Chemistry, and Chemical Engineering, University of Alcala, Alcala de Henares, 28802, Madrid, Spain
- Chemical Research Institute "Andrés M. del Río", University of Alcala, Alcala de Henares, 28802, Madrid, Spain
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27
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Hwang CH, Lee S, Lee S, Kim H, Kang T, Lee D, Jeong KH. Highly Adsorptive Au-TiO 2 Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols. ACS APPLIED MATERIALS & INTERFACES 2022; 14:54550-54557. [PMID: 36448483 PMCID: PMC9718102 DOI: 10.1021/acsami.2c16446] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Human respiratory aerosols contain diverse potential biomarkers for early disease diagnosis. Here, we report the direct and label-free detection of SARS-CoV-2 in respiratory aerosols using a highly adsorptive Au-TiO2 nanocomposite SERS face mask and an ablation-assisted autoencoder. The Au-TiO2 SERS face mask continuously preconcentrates and efficiently captures the oronasal aerosols, which substantially enhances the SERS signal intensities by 47% compared to simple Au nanoislands. The ultrasensitive Au-TiO2 nanocomposites also demonstrate the successful detection of SARS-CoV-2 spike proteins in artificial respiratory aerosols at a 100 pM concentration level. The deep learning-based autoencoder, followed by the partial ablation of nondiscriminant SERS features of spike proteins, allows a quantitative assay of the 101-104 pfu/mL SARS-CoV-2 lysates (comparable to 19-29 PCR cyclic threshold from COVID-19 patients) in aerosols with an accuracy of over 98%. The Au-TiO2 SERS face mask provides a platform for breath biopsy for the detection of various biomarkers in respiratory aerosols.
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Affiliation(s)
- Charles
S. H. Hwang
- Department
of Bio and Brain Engineering, Korea Advanced
Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
- KAIST
Institute for Health Science and Technology (KIHST), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro,
Yuseong-gu, Daejeon 34141, Korea
| | - Sangyeon Lee
- Department
of Bio and Brain Engineering, Korea Advanced
Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Sejin Lee
- Department
of Bio and Brain Engineering, Korea Advanced
Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
- KAIST
Institute for Health Science and Technology (KIHST), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro,
Yuseong-gu, Daejeon 34141, Korea
| | - Hanjin Kim
- Department
of Bio and Brain Engineering, Korea Advanced
Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Taejoon Kang
- Bionanotechnology
Research Center, Korea Research Institute
of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon 34141, Korea
- School
of Pharmacy, Sungkyunkwan University, Suwon 16419, Korea
| | - Doheon Lee
- Department
of Bio and Brain Engineering, Korea Advanced
Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Ki-Hun Jeong
- Department
of Bio and Brain Engineering, Korea Advanced
Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
- KAIST
Institute for Health Science and Technology (KIHST), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro,
Yuseong-gu, Daejeon 34141, Korea
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28
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Wang J, Hui P, Zhang X, Cai X, Lian J, Liu X, Lu X, Chen W. Rapid Antimicrobial Susceptibility Testing Based on a Bio-Inspired Chemiluminescence Sensor. Anal Chem 2022; 94:17240-17247. [PMID: 36459659 DOI: 10.1021/acs.analchem.2c04020] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Indiscriminate usage of antibiotics has caused accelerating growth and global expansion of antimicrobial resistance. Therefore, rapid antimicrobial susceptibility testing (AST) for guiding antibiotic prescription and preventing the spread of antimicrobial resistance is in urgent need. Phenotypic AST is the clinical gold standard method; however, no phenotypic AST has realized a colony-to-answer at about 1 h by utilizing the chemiluminescence sensor to detect the enzyme expressed by bacteria. Inspired by the bubble formation in the mixture of Escherichia coli and H2O2, we demonstrate a strategy based on the chemiluminescence sensor for rapid AST. Compared with the gold standard methods, the values of AUC are 0.960 for E. coli and 0.950 for Staphylococcus aureus, close to 1, indicating superb diagnostic performance as an AST method. The whole process from colonies to answer is 55 min for E. coli and 70 min for S. aureus. The chemiluminescence readout is based on the common equipment in the laboratory of the hospital, which is conducive to follow-up clinical promotion. Our sensor promises great potential in rapid AST, facilitating antimicrobial stewardship.
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Affiliation(s)
- Jidong Wang
- Medical Research Center, Huazhong University of Science and Technology Union Shenzhen Hospital, the 6th Affiliated Hospital, Shenzhen University Health Science Center, Shenzhen 518052, P. R. China
| | - Ping Hui
- School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, P. R. China
| | - Xinyu Zhang
- School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, P. R. China
| | - Xiaoqing Cai
- School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, P. R. China
| | - Jie Lian
- Medical Research Center, Huazhong University of Science and Technology Union Shenzhen Hospital, the 6th Affiliated Hospital, Shenzhen University Health Science Center, Shenzhen 518052, P. R. China
| | - Xiaolei Liu
- School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, P. R. China
| | - Xi Lu
- School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, P. R. China
| | - Wenwen Chen
- School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, P. R. China
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29
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Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
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30
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Tetz G, Tetz V. Overcoming Antibiotic Resistance with Novel Paradigms of Antibiotic Selection. Microorganisms 2022; 10:2383. [PMID: 36557636 PMCID: PMC9781420 DOI: 10.3390/microorganisms10122383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/04/2022] Open
Abstract
Conventional antimicrobial susceptibility tests, including phenotypic and genotypic methods, are insufficiently accurate and frequently fail to identify effective antibiotics. These methods predominantly select therapies based on the antibiotic response of only the lead bacterial pathogen within pure bacterial culture. However, this neglects the fact that, in the majority of human infections, the lead bacterial pathogens are present as a part of multispecies communities that modulate the response of these lead pathogens to antibiotics and that multiple pathogens can contribute to the infection simultaneously. This discrepancy is a major cause of the failure of antimicrobial susceptibility tests to detect antibiotics that are effective in vivo. This review article provides a comprehensive overview of the factors that are missed by conventional antimicrobial susceptibility tests and it explains how accounting for these methods can aid the development of novel diagnostic approaches.
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Affiliation(s)
- George Tetz
- Human Microbiology Institute, New York, NY 100141, USA
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31
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Balytskyi Y, Bendesky J, Paul T, Hagen GM, McNear K. Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach. Anal Chem 2022; 94:15297-15306. [PMID: 36279588 PMCID: PMC9728505 DOI: 10.1021/acs.analchem.2c02666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were encountered during the training phase. That is, species that are known to the neural network. However, in real-world settings, such as in clinical applications, there will always be substances whose spectra have not yet been taken. When the neural network encounters these new species during the testing phase, the number of false positives becomes uncontrollable, limiting the usefulness of these techniques, especially in public safety applications. To overcome these barriers, we implemented the recently introduced Entropic Open Set and Objectosphere loss functions. To demonstrate the efficacy and efficiency of this approach, we compiled a database of hyperspectral Raman images of 40 chemical species separating them into three class categorizations. The known class consisted of 20 biologically relevant species comprising amino acids, the ignored class was 10 "irrelevant" species comprising bio-related chemicals, and the never seen before class was 10 various chemical species that the neural network had not seen before. We show that this approach not only enables the network to effectively separate the unknown species while preserving high accuracy on the known ones and reducing false positives but also performs better than the current gold standards in machine learning techniques. This opens the door to using Raman spectroscopy, combined with our novel machine learning algorithm, in a variety of practical applications. Availability and implementation: freely available on the web at https://github.com/BalytskyiJaroslaw/RamanOpenSet.git.
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Affiliation(s)
- Yaroslav Balytskyi
- Department of Physics and Energy Science, University of Colorado, Colorado Springs, Colorado 80918, United States
- UCCS BioFrontiers Center, University of Colorado, Colorado Springs, Colorado 80918, United States
| | - Justin Bendesky
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Tristan Paul
- Department of Physics and Energy Science, University of Colorado, Colorado Springs, Colorado 80918, United States
- UCCS BioFrontiers Center, University of Colorado, Colorado Springs, Colorado 80918, United States
| | - Guy M Hagen
- UCCS BioFrontiers Center, University of Colorado, Colorado Springs, Colorado 80918, United States
| | - Kelly McNear
- UCCS BioFrontiers Center, University of Colorado, Colorado Springs, Colorado 80918, United States
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32
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Kraka E, Quintano M, La Force HW, Antonio JJ, Freindorf M. The Local Vibrational Mode Theory and Its Place in the Vibrational Spectroscopy Arena. J Phys Chem A 2022; 126:8781-8798. [DOI: 10.1021/acs.jpca.2c05962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Mateus Quintano
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Hunter W. La Force
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Juliana J. Antonio
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, Texas75275-0314, United States
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33
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Garg A, Mejia E, Nam W, Vikesland P, Zhou W. Biomimetic Transparent Nanoplasmonic Meshes by Reverse-Nanoimprinting for Bio-Interfaced Spatiotemporal Multimodal SERS Bioanalysis. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204517. [PMID: 36161480 DOI: 10.1002/smll.202204517] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Indexed: 06/16/2023]
Abstract
Multicellular systems, such as microbial biofilms and cancerous tumors, feature complex biological activities coordinated by cellular interactions mediated via different signaling and regulatory pathways, which are intrinsically heterogeneous, dynamic, and adaptive. However, due to their invasiveness or their inability to interface with native cellular networks, standard bioanalysis methods do not allow in situ spatiotemporal biochemical monitoring of multicellular systems to capture holistic spatiotemporal pictures of systems-level biology. Here, a high-throughput reverse nanoimprint lithography approach is reported to create biomimetic transparent nanoplasmonic microporous mesh (BTNMM) devices with ultrathin flexible microporous structures for spatiotemporal multimodal surface-enhanced Raman spectroscopy (SERS) measurements at the bio-interface. It is demonstrated that BTNMMs, supporting uniform and ultrasensitive SERS hotspots, can simultaneously enable spatiotemporal multimodal SERS measurements for targeted pH sensing and non-targeted molecular detection to resolve the diffusion dynamics for pH, adenine, and Rhodamine 6G molecules in agarose gel. Moreover, it is demonstrated that BTNMMs can act as multifunctional bio-interfaced SERS sensors to conduct in situ spatiotemporal pH mapping and molecular profiling of Escherichia coli biofilms. It is envisioned that the ultrasensitive multimodal SERS capability, transport permeability, and biomechanical compatibility of the BTNMMs can open exciting avenues for bio-interfaced multifunctional sensing applications both in vitro and in vivo.
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Affiliation(s)
- Aditya Garg
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Elieser Mejia
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Wonil Nam
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Peter Vikesland
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Wei Zhou
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
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34
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Fang G, Lin X, Liang X, Wu J, Xu W, Hasi W, Dong B. Machine Learning-Driven 3D Plasmonic Cavity-in-Cavity Surface-Enhanced Raman Scattering Platform with Triple Synergistic Enhancement Toward Label-Free Detection of Antibiotics in Milk. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2204588. [PMID: 36161767 DOI: 10.1002/smll.202204588] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/03/2022] [Indexed: 06/16/2023]
Abstract
The surface-enhanced Raman scattering (SERS) technique with ultrahigh sensitivity has gained attention to meet the increasing demands for food safety analysis. The integration of machine learning and SERS facilitates the practical applicability of sensing devices. In this study, a machine learning-driven 3D plasmonic cavity-in-cavity (CIC) SERS platform is proposed for sensitive and quantitative detection of antibiotics. The platform is prepared by transferring truncated concave nanocubes (NCs) to an obconical-shaped template surface. Owing to the triple synergistic enhancement effect, the highly ordered 3D CIC arrays improve the simulated electromagnetic field intensity and experimental SERS activity, demonstrating a 33.1-fold enhancement compared to a typical system consisting of Au NCs deposited on a flat substrate. The integration of machine learning and Raman spectroscopy eliminates subjective judgments on the concentration of detectors using a single feature peak and achieves accurate identification. The machine learning-driven CIC SERS platform is capable of detecting ampicillin traces in milk with a detection limit of 0.1 ppm, facilitating quantitative analysis of different concentrations of ampicillin. Therefore, the proposed platform has potential applications in food safety monitoring, health care, and environmental sampling.
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Affiliation(s)
- Guoqiang Fang
- Key Laboratory of New Energy and Rare Earth Resource Utilization of State Ethnic Affairs Commission, Key Laboratory of Photosensitive Materials and Devices of Liaoning Province, School of Physics and Materials Engineering, Dalian Minzu University, Dalian, 116600, China
- National Key Laboratory of Science and Technology on Tuneable Laser, Harbin Institute of Technology, Harbin, 150080, China
| | - Xiang Lin
- Key Laboratory of New Energy and Rare Earth Resource Utilization of State Ethnic Affairs Commission, Key Laboratory of Photosensitive Materials and Devices of Liaoning Province, School of Physics and Materials Engineering, Dalian Minzu University, Dalian, 116600, China
| | - Xiu Liang
- Advanced Materials Institute, Shandong Academy of Sciences Qilu University of Technology, Jinan, 250014, China
| | - Jinlei Wu
- Key Laboratory of New Energy and Rare Earth Resource Utilization of State Ethnic Affairs Commission, Key Laboratory of Photosensitive Materials and Devices of Liaoning Province, School of Physics and Materials Engineering, Dalian Minzu University, Dalian, 116600, China
| | - Wen Xu
- Key Laboratory of New Energy and Rare Earth Resource Utilization of State Ethnic Affairs Commission, Key Laboratory of Photosensitive Materials and 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
| | - Bin Dong
- Key Laboratory of New Energy and Rare Earth Resource Utilization of State Ethnic Affairs Commission, Key Laboratory of Photosensitive Materials and Devices of Liaoning Province, School of Physics and Materials Engineering, Dalian Minzu University, Dalian, 116600, China
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35
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Zhang W, Sun H, He S, Chen X, Yao L, Zhou L, Wang Y, Wang P, Hong W. Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine. Front Microbiol 2022; 13:874966. [PMID: 36090077 PMCID: PMC9449455 DOI: 10.3389/fmicb.2022.874966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 08/05/2022] [Indexed: 11/23/2022] Open
Abstract
Rapid identification and antimicrobial susceptibility testing (AST) of bacteria are key interventions to curb the spread and emergence of antimicrobial resistance. The current gold standard identification and AST methods provide comprehensive diagnostic information but often take 3 to 5 days. Here, a compound Raman microscopy (CRM), which integrates Raman spectroscopy and stimulated Raman scattering microscopy in one system, is presented and demonstrated for rapid identification and AST of pathogens in urine. We generated an extensive bacterial Raman spectral dataset and applied deep learning to identify common clinical bacterial pathogens. In addition, we employed stimulated Raman scattering microscopy to quantify bacterial metabolic activity to determine their antimicrobial susceptibility. For proof-of-concept, we demonstrated an integrated assay to diagnose urinary tract infection pathogens, S. aureus and E. coli. Notably, the CRM system has the unique ability to provide Gram-staining classification and AST results within ~3 h directly from urine samples and shows great potential for clinical applications.
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Affiliation(s)
- Weifeng Zhang
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hongyi Sun
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Shipei He
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Xun Chen
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- School of Engineering Medicine, Beihang University, Beijing, China
| | - Lin Yao
- Department of Urology, Peking University First Hospital, Beijing, China
- Lin Yao,
| | - Liqun Zhou
- Department of Urology, Peking University First Hospital, Beijing, China
| | - Yi Wang
- Department of Clinical Laboratory, China Rehabilitation Research Center, Capital Medical University, Beijing, China
| | - Pu Wang
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- *Correspondence: Pu Wang,
| | - Weili Hong
- Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Weili Hong,
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36
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Al-Shaebi Z, Uysal Ciloglu F, Nasser M, Aydin O. Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms. ACS OMEGA 2022; 7:29443-29451. [PMID: 36033656 PMCID: PMC9404519 DOI: 10.1021/acsomega.2c03856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.
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Affiliation(s)
- Zakarya Al-Shaebi
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkey
- NanoThera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkey
| | - Fatma Uysal Ciloglu
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkey
- NanoThera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkey
| | - Mohammed Nasser
- Department
of Geomatics Engineering, Erciyes University, 38039 Kayseri, Turkey
| | - Omer Aydin
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkey
- NanoThera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkey
- Clinical
Engineering Research and Implementation Center, (ERKAM), Erciyes University, 38030 Kayseri, Turkey
- Nanotechnology
Research and Application Center (ERNAM), Erciyes University, 38039 Kayseri, Turkey
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37
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Ciloglu FU, Hora M, Gundogdu A, Kahraman M, Tokmakci M, Aydin O. SERS-based sensor with a machine learning based effective feature extraction technique for fast detection of colistin-resistant Klebsiella pneumoniae. Anal Chim Acta 2022; 1221:340094. [DOI: 10.1016/j.aca.2022.340094] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 06/12/2022] [Accepted: 06/13/2022] [Indexed: 11/01/2022]
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38
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Luo SH, Wang WL, Zhou ZF, Xie Y, Ren B, Liu GK, Tian ZQ. Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS. Anal Chem 2022; 94:10151-10158. [PMID: 35794045 DOI: 10.1021/acs.analchem.2c01450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.
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Affiliation(s)
- Si-Heng Luo
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.,State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Wei-Li Wang
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Zhi-Fan Zhou
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Yi Xie
- Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen, Fujian 361005, China.,Shenzhen Research Institute of Xiamen University, Xiamen University, Shenzhen 518000, China
| | - Bin Ren
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Zhong-Qun Tian
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
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39
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Li Z, Jiang Y, Tang S, Zou H, Wang W, Qi G, Zhang H, Jin K, Wang Y, Chen H, Zhang L, Qu X. 2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification. Mikrochim Acta 2022; 189:273. [PMID: 35792975 PMCID: PMC9259531 DOI: 10.1007/s00604-022-05368-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/04/2022] [Indexed: 11/28/2022]
Abstract
An integrated custom cross-response sensing array has been developed combining the algorithm module’s visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 105 ~ 108 CFU/mL for Escherichia coli, 102 ~ 107 CFU/mL for E. coli-β, 103 ~ 108 CFU/mL for Staphylococcus aureus, 103 ~ 107 CFU/mL for MRSA, 102 ~ 108 CFU/mL for Pseudomonas aeruginosa, 103 ~ 108 CFU/mL for Enterococcus faecalis, 102 ~ 108 CFU/mL for Klebsiella pneumoniae, and 103 ~ 108 CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification.
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Affiliation(s)
- Zhijun Li
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Yizhou Jiang
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Shihuan Tang
- Department of Clinical Laboratory, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518017, Guangdong, China
| | - Haixia Zou
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Wentao Wang
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Guangpei Qi
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Hongbo Zhang
- Pharmaceutical Sciences Laboratory, Åbo Akademi University, 20520, Turku, Finland.
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland.
| | - Kun Jin
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Yuhe Wang
- School of Petroleum Engineering, State Key Laboratory of Heavy Oil Processing, China University of Petroleum (East China), Qingdao, 266580, China
| | - Hong Chen
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
| | - Liyuan Zhang
- School of Petroleum Engineering, State Key Laboratory of Heavy Oil Processing, China University of Petroleum (East China), Qingdao, 266580, China.
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
| | - Xiangmeng Qu
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China.
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40
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Zahn J, Germond A, Lundgren AY, Cicerone MT. Discriminating cell line specific features of antibiotic-resistant strains of Escherichia coli from Raman spectra via machine learning analysis. JOURNAL OF BIOPHOTONICS 2022; 15:e202100274. [PMID: 35238159 PMCID: PMC9262779 DOI: 10.1002/jbio.202100274] [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: 08/31/2021] [Revised: 02/02/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
While Raman spectroscopy can provide label-free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain-specific (and function-specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model.
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Affiliation(s)
- Jessica Zahn
- Department of Chemistry and Biochemistry, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA 30332, USA
| | - Arno Germond
- INRAE, UR 370 Qualité des Produits Animaux (QuaPA) Équipe Imagerie & Transferts (IT), 63122 Saint-Gènes-Champanelle, France
| | - Alice Y Lundgren
- Department of Mathematics, Brigham Young University, 275 TMCB, Provo, UT 84602, USA
| | - Marcus T Cicerone
- Department of Chemistry and Biochemistry, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA 30332, USA
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41
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Usefulness of Automatic Hyperparameter Optimization in Developing Radiation Emulator in a Numerical Weather Prediction Model. ATMOSPHERE 2022. [DOI: 10.3390/atmos13050721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
To improve the forecasting accuracy of a radiation emulator in a weather prediction model over the Korean peninsula, the learning rate used in neural network training was automatically optimized using the Sherpa. The Sherpa experiment results were compared with two control simulation results using learning rates of 0.0001 and 1 for different batch sizes (full to 500). In the offline evaluation, the Sherpa results showed significant improvements in predicting longwave/shortwave heating rates and fluxes compared to the lowest learning rate results, whereas the improvements compared to the highest learning rate were relatively small because the optimized values by the Sherpa were 0.4756–0.6656. The online evaluation results over one month, which were linked with the weather prediction model, demonstrated the usefulness of Sherpa on a universal performance for the radiation emulator. In particular, at the full batch size, Sherpa contributed to reducing the one-week forecast errors for longwave/shortwave fluxes, skin temperature, and precipitation by 39–125%, 137–159%, and 24–26%, respectively, compared with the two control simulations. Considering the widespread use of parallel learning based on full batch, Sherpa can contribute to producing robust results regardless of batch sizes used in neural network training for developing radiation emulators.
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42
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He C, Zhu S, Wu X, Zhou J, Chen Y, Qian X, Ye J. Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning. ACS OMEGA 2022; 7:10458-10468. [PMID: 35382336 PMCID: PMC8973095 DOI: 10.1021/acsomega.1c07263] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 03/09/2022] [Indexed: 05/04/2023]
Abstract
Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been demonstrated to be a powerful tool for tumor identification. However, the analysis and classification of Raman spectra for biological samples with complex compositions are still challenges. In addition, the signal-to-noise ratio of the spectra also influences the accuracy of the classification. Herein, we applied the variational autoencoder (VAE) to Raman spectra for downscaling and noise reduction simultaneously. We validated the performance of the VAE algorithm at the cellular and tissue levels. VAE successfully downscaled high-dimensional Raman spectral data to two-dimensional (2D) data for three subtypes of non-small cell lung cancer cells and two subtypes of kidney cancer tissues. Gaussian naïve bayes was applied to subtype discrimination with the 2D data after VAE encoding at both the cellular and tissue levels, significantly outperforming the discrimination results using original spectra. Therefore, the analysis of Raman spectroscopy based on VAE and machine learning has great potential for rapid diagnosis of tumor subtypes.
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Affiliation(s)
- Chang He
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Shuo Zhu
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Xiaorong Wu
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Jiale Zhou
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Yonghui Chen
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Xiaohua Qian
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Jian Ye
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
- Shanghai
Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of
Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
- Institute
of Medical Robotics, Shanghai Jiao Tong
University, Shanghai 200240, P.R. China
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43
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Feng Y, Chen M, Wei X, Zhu H, Zhang J, Zhang Y, Xue L, Huang L, Chen G, Chen M, Ding Y, Wu Q. Pseudotargeted Metabolomic Fingerprinting and Deep Learning for Identification and Visualization of Common Pathogens. Front Microbiol 2022; 13:830832. [PMID: 35359729 PMCID: PMC8960985 DOI: 10.3389/fmicb.2022.830832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
Matrix-assisted laser desorption/ionization time-of-flight mass (MALDI-TOF) spectrometry fingerprinting has reduced turnaround times, costs, and labor as conventional procedures in various laboratories. However, some species strains with high genetic correlation have not been directly distinguished using conventional standard procedures. Metabolomes can identify these strains by amplifying the minor differences because they are directly related to the phenotype. The pseudotargeted metabolomics method has the advantages of both non-targeted and targeted metabolomics. It can provide a new semi-quantitative fingerprinting with high coverage. We combined this pseudotargeted metabolomic fingerprinting with deep learning technology for the identification and visualization of the pathogen. A variational autoencoder framework was performed to identify and classify pathogenic bacteria and achieve their visualization, with prediction accuracy exceeding 99%. Therefore, this technology will be a powerful tool for rapidly and accurately identifying pathogens.
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Affiliation(s)
- Ying Feng
- Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Moutong Chen
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Xianhu Wei
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Honghui Zhu
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Jumei Zhang
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Youxiong Zhang
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Liang Xue
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Lanyan Huang
- Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Guoyang Chen
- Guangzhou Institute of Chemistry, Chinese Academy of Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Minling Chen
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Yu Ding
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- Department of Food Science and Technology, Institute of Food Safety and Nutrition, Jinan University, Guangzhou, China
- *Correspondence: Yu Ding,
| | - Qingping Wu
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, Ministry of Agricultural and Rural Affairs, Key Laboratory of Agricultural Microbiomics and Precision Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- Qingping Wu,
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44
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A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing. PHOTONICS 2022. [DOI: 10.3390/photonics9030133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Antimicrobial resistance (AMR) is a global medical threat that seriously endangers human health. Rapid bacterial identification and antimicrobial susceptibility testing (AST) are key interventions to combat the spread and emergence of AMR. Although current clinical bacterial identification and AST provide comprehensive information, they are labor-intensive, complex, inaccurate, and slow (requiring several days, depending on the growth of pathogenic bacteria). Recently, Raman-based identification and AST technologies have played an increasingly important role in fighting AMR. This review summarizes major Raman-based techniques for bacterial identification and AST, including spontaneous Raman scattering, surface-enhanced Raman scattering (SERS), and coherent Raman scattering (CRS) imaging. Then, we discuss recent developments in rapid identification and AST methods based on Raman technology. Finally, we highlight the major challenges and potential future efforts to improve clinical outcomes through rapid bacterial identification and AST.
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45
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Plou J, Valera PS, García I, de Albuquerque CDL, Carracedo A, Liz-Marzán LM. Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine. ACS PHOTONICS 2022; 9:333-350. [PMID: 35211644 PMCID: PMC8855429 DOI: 10.1021/acsphotonics.1c01934] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 05/14/2023]
Abstract
Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.
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Affiliation(s)
- Javier Plou
- CIC
biomaGUNE, Basque Research
and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain
- Biomedical
Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine
(CIBER-BBN), 20014 Donostia-San Sebastián, Spain
- CIC
bioGUNE, Basque Research and Technology
Alliance (BRTA), 48160 Derio, Spain
| | - Pablo S. Valera
- CIC
biomaGUNE, Basque Research
and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain
- CIC
bioGUNE, Basque Research and Technology
Alliance (BRTA), 48160 Derio, Spain
| | - Isabel García
- CIC
biomaGUNE, Basque Research
and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain
- Biomedical
Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine
(CIBER-BBN), 20014 Donostia-San Sebastián, Spain
| | | | - Arkaitz Carracedo
- CIC
bioGUNE, Basque Research and Technology
Alliance (BRTA), 48160 Derio, Spain
- Biomedical
Research Networking Center in Cancer (CIBERONC), 48160, Derio, Spain
- Ikerbasque,
Basque Foundation for Science, 48009 Bilbao, Spain
- Translational
Prostate Cancer Research Lab, CIC bioGUNE-Basurto, Biocruces Bizkaia Health Research Institute, 48160 Derio, Spain
| | - Luis M. Liz-Marzán
- CIC
biomaGUNE, Basque Research
and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain
- Biomedical
Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine
(CIBER-BBN), 20014 Donostia-San Sebastián, Spain
- Ikerbasque,
Basque Foundation for Science, 48009 Bilbao, Spain
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46
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Gukowsky JC, Yang T, He L. Assessment of three SERS approaches for studying E. Coli O157:H7 susceptibility to ampicillin. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 264:120239. [PMID: 34358786 DOI: 10.1016/j.saa.2021.120239] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 07/14/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
Antibiotic resistant bacteria pose an increasing threat to global public health, and it is essential that effective detection methods for identifying these organisms. This study assesses the ability of three different analytical approaches that were developed using surface-enhanced Raman spectroscopy (SERS) to differentiate between antibiotic sensitive and resistant bacteria based on their responses to ampicillin exposure, using Escherichia coli O157:H7 as a model bacterium. The approaches tested in this study included a conventional SERS approach of mixing a droplet of bacterial culture with gold nanoparticles, extracellular matrix analysis, and in situ mapping of bacterial cells on a filter membrane. All three of the SERS techniques were able to differentiate between the sensitive and resistant bacterial strains based on peak intensity changes associated with compounds released by the bacteria in response to antibiotic exposure, with extracellular matrix analysis and filter mapping both observed to be more effective than the conventional approach. However, there were significant differences between the spectra obtained from the different techniques and the potential advantages and disadvantages of each approach should be considered when used in the future. This study shows that SERS can be an effective technique for rapid and efficient assessment of ampicillin sensitivity in E. coli, and more work should be done to explore these analytical approaches with other types of bacterial samples.
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Affiliation(s)
- Joshua C Gukowsky
- Department of Food Science, University of Massachusetts Amherst. 240 Chenoweth Laboratory, 102 Holdsworth Way, Amherst, MA 01003, USA
| | - Tianxi Yang
- Department of Food Science, University of Massachusetts Amherst. 240 Chenoweth Laboratory, 102 Holdsworth Way, Amherst, MA 01003, USA
| | - Lili He
- Department of Food Science, University of Massachusetts Amherst. 240 Chenoweth Laboratory, 102 Holdsworth Way, Amherst, MA 01003, USA.
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47
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Hassanain WA, Johnson CL, Faulds K, Graham D, Keegan N. Recent advances in antibiotic resistance diagnosis using SERS: focus on the “ Big 5” challenges. Analyst 2022; 147:4674-4700. [DOI: 10.1039/d2an00703g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
SERS for antibiotic resistance diagnosis.
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Affiliation(s)
- Waleed A. Hassanain
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, Glasgow, G1 1RD, UK
| | - Christopher L. Johnson
- Translational and Clinical Research Institute, Newcastle University, Newcastle-Upon-Tyne, NE2 4HH, UK
| | - Karen Faulds
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, Glasgow, G1 1RD, UK
| | - Duncan Graham
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, Glasgow, G1 1RD, UK
| | - Neil Keegan
- Translational and Clinical Research Institute, Newcastle University, Newcastle-Upon-Tyne, NE2 4HH, UK
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48
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Datar R, Orenga S, Pogorelcnik R, Rochas O, Simner PJ, van Belkum A. Recent Advances in Rapid Antimicrobial Susceptibility Testing. Clin Chem 2021; 68:91-98. [DOI: 10.1093/clinchem/hvab207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/17/2021] [Indexed: 12/30/2022]
Abstract
Abstract
Background
Antimicrobial susceptibility testing (AST) is classically performed using growth-based techniques that essentially require viable bacterial matter to become visible to the naked eye or a sophisticated densitometer.
Content
Technologies based on the measurement of bacterial density in suspension have evolved marginally in accuracy and rapidity over the 20th century, but assays expanded for new combinations of bacteria and antimicrobials have been automated, and made amenable to high-throughput turn-around. Over the past 25 years, elevated AST rapidity has been provided by nucleic acid-mediated amplification technologies, proteomic and other “omic” methodologies, and the use of next-generation sequencing. In rare cases, AST at the level of single-cell visualization was developed. This has not yet led to major changes in routine high-throughput clinical microbiological detection of antimicrobial resistance.
Summary
We here present a review of the new generation of methods and describe what is still urgently needed for their implementation in day-to-day management of the treatment of infectious diseases.
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Affiliation(s)
- Rucha Datar
- bioMérieux, Microbiology Research, La Balme Les Grottes, France
| | - Sylvain Orenga
- bioMérieux, Microbiology Research, La Balme Les Grottes, France
| | | | - Olivier Rochas
- bioMérieux, Corporate Business Development, Marcy l'Etoile, France
| | - Patricia J Simner
- Department of Pathology, Bacteriology, Division of Medical Microbiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alex van Belkum
- bioMérieux, Open Innovation and Partnerships, La Balme Les Grottes, France
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49
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Dong W, Li Z, Wen W, Liu B, Wen G. Novel CdS/MOF Cathodic Photoelectrochemical (PEC) Platform for the Detection of Doxorubicin Hydrochloride and Gentamicin Sulfate. ACS APPLIED MATERIALS & INTERFACES 2021; 13:57497-57504. [PMID: 34807581 DOI: 10.1021/acsami.1c19481] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Nanomaterial selection is critical for photoelectrochemical (PEC) sensing. In this report, a novel cathodic photoelectrochemical (PEC) strategy was proposed for the detection of doxorubicin hydrochloride (Dox) and gentamicin sulfate (CN). The photocathode was synthesized by noncovalently coupling cadmium sulfide (CdS) to the porphyrin-derived metal-organic framework (CdS@PCN-224). This type of assembly created a pleasant interface for the combination of doxorubicin hydrochloride and gentamicin sulfate, resulting in a good CdS@PCN-224 donor-acceptor system. When compared to a single optoelectronic material, its photocurrent is enhanced by unprecedented nine times. This research could pave the way for the realization of PCN-224's enormous potential in PEC sensing.
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Affiliation(s)
- Wenxia Dong
- Institute of Environmental Science, School of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, China
| | - Zhongping Li
- Institute of Environmental Science, School of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, China
| | - Wen Wen
- Institute of Environmental Science, School of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, China
| | - Bin Liu
- Institute of Molecular Science, Key Laboratory of Chemical Biology and Molecular Engineering of the Education Ministry, Shanxi University, Taiyuan 030006, Shanxi, China
| | - Guangming Wen
- Institute of Environmental Science, School of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, China
- School of Chemistry and Chemical Engineering, Jinzhong University, Jinzhong 030619, China
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50
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Fu Q, Zhang Y, Wang P, Pi J, Qiu X, Guo Z, Huang Y, Zhao Y, Li S, Xu J. Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis. Anal Bioanal Chem 2021; 413:7401-7410. [PMID: 34673992 DOI: 10.1007/s00216-021-03691-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/23/2021] [Accepted: 09/23/2021] [Indexed: 11/24/2022]
Abstract
The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.
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Affiliation(s)
- Qiuyue Fu
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Yanjiao Zhang
- School of Basic Medicine, Guangdong Medical University, Dongguan, 523808, China
| | - Peng Wang
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Jiang Pi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Xun Qiu
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Zhusheng Guo
- Donghua Hospital Laboratory Department, Dongguan, 523808, Guangdong, China
| | - Ya Huang
- Donghua Hospital Laboratory Department, Dongguan, 523808, Guangdong, China
| | - Yi Zhao
- Guangdong Provincial Key Laboratory of Molecular Diagnosis, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Shaoxin Li
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
| | - Junfa Xu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
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