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Zhang Z, Li H, Huang L, Wang H, Niu H, Yang Z, Wang M. Rapid identification and quantitative analysis of malachite green in fish via SERS and 1D convolutional neural network. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124655. [PMID: 38885572 DOI: 10.1016/j.saa.2024.124655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/24/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024]
Abstract
Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues onfishscales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10-6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R2) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish.
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Affiliation(s)
- Zhaoyi Zhang
- School of Physical Science and Information Technology, Key Laboratory of Optical Communication Science and Technology of Shandong Province, Liaocheng University, Liaocheng 252000, PR China
| | - Hefu Li
- School of Physical Science and Information Technology, Key Laboratory of Optical Communication Science and Technology of Shandong Province, Liaocheng University, Liaocheng 252000, PR China.
| | - Lili Huang
- School of Physical Science and Information Technology, Key Laboratory of Optical Communication Science and Technology of Shandong Province, Liaocheng University, Liaocheng 252000, PR China
| | - Hongjun Wang
- School of Physical Science and Information Technology, Key Laboratory of Optical Communication Science and Technology of Shandong Province, Liaocheng University, Liaocheng 252000, PR China
| | - Huijuan Niu
- School of Physical Science and Information Technology, Key Laboratory of Optical Communication Science and Technology of Shandong Province, Liaocheng University, Liaocheng 252000, PR China
| | - Zhenshan Yang
- School of Physical Science and Information Technology, Key Laboratory of Optical Communication Science and Technology of Shandong Province, Liaocheng University, Liaocheng 252000, PR China
| | - Minghong Wang
- School of Physical Science and Information Technology, Key Laboratory of Optical Communication Science and Technology of Shandong Province, Liaocheng University, Liaocheng 252000, PR China.
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2
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Zhang Q, Lin Y, Lin D, Lin X, Liu M, Tao H, Wu J, Wang T, Wang C, Feng S. Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms. Talanta 2024; 275:126136. [PMID: 38692045 DOI: 10.1016/j.talanta.2024.126136] [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: 02/05/2024] [Revised: 04/06/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.
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Affiliation(s)
- Qiyi Zhang
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Yuxiang Lin
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China
| | - Duo Lin
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Xueliang Lin
- Fujian Provincial Key Laboratory for Advanced Micro-nano Photonics Technology and Devices, Quanzhou Normal University, Quanzhou, 362000, China
| | - Miaomiao Liu
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Hong Tao
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Jinxun Wu
- Department of Pathology, Fuzhou Lianjiang Country Hospital, Fuzhou, Fujian, 350500, China
| | - Tingyin Wang
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
| | - Chuan Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China.
| | - Shangyuan Feng
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
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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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Takallu S, Aiyelabegan HT, Zomorodi AR, Alexandrovna KV, Aflakian F, Asvar Z, Moradi F, Behbahani MR, Mirzaei E, Sarhadi F, Vakili-Ghartavol R. Nanotechnology improves the detection of bacteria: Recent advances and future perspectives. Heliyon 2024; 10:e32020. [PMID: 38868076 PMCID: PMC11167352 DOI: 10.1016/j.heliyon.2024.e32020] [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: 02/28/2024] [Revised: 04/23/2024] [Accepted: 05/27/2024] [Indexed: 06/14/2024] Open
Abstract
Nanotechnology has advanced significantly, particularly in biomedicine, showing promise for nanomaterial applications. Bacterial infections pose persistent public health challenges due to the lack of rapid pathogen detection methods, resulting in antibiotic overuse and bacterial resistance, threatening the human microbiome. Nanotechnology offers a solution through nanoparticle-based materials facilitating early bacterial detection and combating resistance. This study explores recent research on nanoparticle development for controlling microbial infections using various nanotechnology-driven detection methods. These approaches include Surface Plasmon Resonance (SPR) Sensors, Surface-Enhanced Raman Scattering (SERS) Sensors, Optoelectronic-based sensors, Bacteriophage-Based Sensors, and nanotechnology-based aptasensors. These technologies provide precise bacteria detection, enabling targeted treatment and infection prevention. Integrating nanoparticles into detection approaches holds promise for enhancing patient outcomes and mitigating harmful bacteria spread in healthcare settings.
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Affiliation(s)
- Sara Takallu
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Abolfazl Rafati Zomorodi
- Department of Bacteriology & Virology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Fatemeh Aflakian
- Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Zahra Asvar
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Farhad Moradi
- Department of Bacteriology & Virology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mahrokh Rajaee Behbahani
- Department of Bacteriology & Virology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Esmaeil Mirzaei
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Firoozeh Sarhadi
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Roghayyeh Vakili-Ghartavol
- Department of Medical Nanotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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6
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Ma WH, Chang CC, Lin TS, Chen YC. Distinguishing methicillin-resistant Staphylococcus aureus from methicillin-sensitive strains by combining Fe 3O 4 magnetic nanoparticle-based affinity mass spectrometry with a machine learning strategy. Mikrochim Acta 2024; 191:273. [PMID: 38635063 PMCID: PMC11026280 DOI: 10.1007/s00604-024-06342-z] [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: 02/06/2024] [Accepted: 03/30/2024] [Indexed: 04/19/2024]
Abstract
Pathogenic bacteria, including drug-resistant variants such as methicillin-resistant Staphylococcus aureus (MRSA), can cause severe infections in the human body. Early detection of MRSA is essential for clinical diagnosis and proper treatment, considering the distinct therapeutic strategies for methicillin-sensitive S. aureus (MSSA) and MRSA infections. However, the similarities between MRSA and MSSA properties present a challenge in promptly and accurately distinguishing between them. This work introduces an approach to differentiate MRSA from MSSA utilizing matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in conjunction with a neural network-based classification model. Four distinct strains of S. aureus were utilized, comprising three MSSA strains and one MRSA strain. The classification accuracy of our model ranges from ~ 92 to ~ 97% for each strain. We used deep SHapley Additive exPlanations to reveal the unique feature peaks for each bacterial strain. Furthermore, Fe3O4 MNPs were used as affinity probes for sample enrichment to eliminate the overnight culture and reduce the time in sample preparation. The limit of detection of the MNP-based affinity approach toward S. aureus combined with our machine learning strategy was as low as ~ 8 × 103 CFU mL-1. The feasibility of using the current approach for the identification of S. aureus in juice samples was also demonstrated.
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Affiliation(s)
- Wei-Hsiang Ma
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Che-Chia Chang
- Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
- Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan
| | - Te-Sheng Lin
- Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
- National Center for Theoretical Sciences, National Taiwan University, Taipei, 10617, Taiwan.
| | - Yu-Chie Chen
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
- International College of Semiconductor Technology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
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7
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Kang H, Lee J, Moon J, Lee T, Kim J, Jeong Y, Lim EK, Jung J, Jung Y, Lee SJ, Lee KG, Ryu S, Kang T. Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning-Based Classification of Raman Spectra. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2308317. [PMID: 38564785 DOI: 10.1002/smll.202308317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/14/2024] [Indexed: 04/04/2024]
Abstract
Proactive management of foodborne illness requires routine surveillance of foodborne pathogens, which requires developing simple, rapid, and sensitive detection methods. Here, a strategy is presented that enables the detection of multiple foodborne bacteria using a 3D nanostructure swab and deep learning-based Raman signal classification. The nanostructure swab efficiently captures foodborne pathogens, and the portable Raman instrument directly collects the Raman signals of captured bacteria. a deep learning algorithm has been demonstrated, 1D convolutional neural network with binary labeling, achieves superior performance in classifying individual bacterial species. This methodology has been extended to mixed bacterial populations, maintaining accuracy close to 100%. In addition, the gradient-weighted class activation mapping method is used to provide an investigation of the Raman bands for foodborne pathogens. For practical application, blind tests are conducted on contaminated kitchen utensils and foods. The proposed technique is validated by the successful detection of bacterial species from the contaminated surfaces. The use of a 3D nanostructure swab, portable Raman device, and deep learning-based classification provides a powerful tool for rapid identification (≈5 min) of foodborne bacterial species. The detection strategy shows significant potential for reliable food safety monitoring, making a meaningful contribution to public health and the food industry.
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Affiliation(s)
- Hyunju Kang
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Junhyeong Lee
- Department of Mechanical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jeong Moon
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, 06032, USA
| | - Taegu Lee
- Department of Mechanical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jueun Kim
- Department of Energy Resources and Chemical Engineering, Kangwon National University, 346 Jungang-ro, Samcheok, Gangwon-do, 25913, Republic of Korea
- Division of Nano-Bio Sensors/Chips Development, National NanoFab Center (NNFC), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Yeonwoo Jeong
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Eun-Kyung Lim
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- Department of Nanobiotechnology, KRIBB School of Biotechnology, University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, 34113, Republic of Korea
- School of Pharmacy, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Juyeon Jung
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- School of Pharmacy, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Yongwon Jung
- Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Seok Jae Lee
- Division of Nano-Bio Sensors/Chips Development, National NanoFab Center (NNFC), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Kyoung G Lee
- Division of Nano-Bio Sensors/Chips Development, National NanoFab Center (NNFC), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Seunghwa Ryu
- Department of Mechanical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Taejoon Kang
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
- School of Pharmacy, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Suwon, Gyeonggi-do, 16419, Republic of Korea
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8
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Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
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Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
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Mehmood N, Akram MW, Majeed MI, Nawaz H, Aslam MA, Naman A, Wasim M, Ghaffar U, Kamran A, Nadeem S, Kanwal N, Imran M. Surface-enhanced Raman spectroscopy for the characterization of bacterial pellets of Staphylococcus aureus infected by bacteriophage. RSC Adv 2024; 14:5425-5434. [PMID: 38348301 PMCID: PMC10859908 DOI: 10.1039/d3ra07575c] [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: 11/06/2023] [Accepted: 01/12/2024] [Indexed: 02/15/2024] Open
Abstract
Drug-resistant pathogenic bacteria are a major cause of infectious diseases in the world and they have become a major threat through the reduced efficacy of developed antibiotics. This issue can be addressed by using bacteriophages, which can kill lethal bacteria and prevent them from causing infections. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for studying the degradation of infectious bacteria by the interaction of bacteriophages to break the vicious cycle of drug-resistant bacteria and help to develop chemotherapy-independent remedial strategies. The phage (viruses)-sensitive Staphylococcus aureus (S. aureus) bacteria are exposed to bacteriophages (Siphoviridae family) in the time frame from 0 min (control) to 50 minutes with intervals of 5 minutes and characterized by SERS using silver nanoparticles as SERS substrate. This allows us to explore the effects of the bacteriophages against lethal bacteria (S. aureus) at different time intervals. The differentiating SERS bands are observed at 575 (C-C skeletal mode), 620 (phenylalanine), 649 (tyrosine, guanine (ring breathing)), 657 (guanine (COO deformation)), 728-735 (adenine, glycosidic ring mode), 796 (tyrosine (C-N stretching)), 957 (C-N stretching (amide lipopolysaccharides)), 1096 (PO2 (nucleic acid)), 1113 (phenylalanine), 1249 (CH2 of amide III, N-H bending and C-O stretching (amide III)), 1273 (CH2, N-H, C-N, amide III), 1331 (C-N stretching mode of adenine), 1373 (in nucleic acids (ring breathing modes of the DNA/RNA bases)) and 1454 cm-1 (CH2 deformation of saturated lipids), indicating the degradation of bacteria and replication of bacteriophages. Multivariate data analysis was performed by employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to study the biochemical differences in the S. aureus bacteria infected by the bacteriophage. The SERS spectral data sets were successfully differentiated by PLS-DA with 94.47% sensitivity, 98.61% specificity, 94.44% precision, 98.88% accuracy and 81.06% area under the curve (AUC), which shows that at 50 min interval S. aureus bacteria is degraded by the replicating bacteriophages.
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Affiliation(s)
- Nasir Mehmood
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Muhammad Waseem Akram
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Muhammad Aamir Aslam
- Institute of Microbiology, Faculty of Veterinary, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Abdul Naman
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Muhammad Wasim
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Usman Ghaffar
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Ali Kamran
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Sana Nadeem
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Naeema Kanwal
- Department of Chemistry, University of Agriculture Faisalabad Faisalabad (38000) Pakistan
| | - Muhammad Imran
- Department of Chemistry, Faculty of Science, King Khalid University P.O. Box 9004 Abha (61413) Saudi Arabia
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Akdeniz M, Al-Shaebi Z, Altunbek M, Bayraktar C, Kayabolen A, Bagci-Onder T, Aydin O. Characterization and discrimination of spike protein in SARS-CoV-2 virus-like particles via surface-enhanced Raman spectroscopy. Biotechnol J 2024; 19:e2300191. [PMID: 37750467 DOI: 10.1002/biot.202300191] [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: 04/30/2023] [Revised: 09/11/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023]
Abstract
Non-infectious virus-like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS-CoV-2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low-cost, and easy-to-use technique for both characterization and detection of S protein in the two VLPs, which are SARS-CoV-2 VLPs and HIV-based VLPs was achieved using surface-enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k-nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS-CoV-2 VLPs and HIV-based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.
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Affiliation(s)
- Munevver Akdeniz
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
| | - Zakarya Al-Shaebi
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
| | - Mine Altunbek
- Department of Chemical Engineering, University of Massachusetts, Lowell, Massachusetts, USA
| | - Canan Bayraktar
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
| | - Alisan Kayabolen
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
- McGovern Institute for Brain Research at MIT, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Tugba Bagci-Onder
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
| | - Omer Aydin
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
- Clinical Engineering Research and Implementation Center (ERKAM), Erciyes University, Kayseri, Turkey
- Nanotechnology Research and Application Center (ERNAM), Erciyes University, Kayseri, Turkey
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11
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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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12
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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: 0] [Impact Index Per Article: 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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13
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Jo T, Kim J, Bice P, Huynh K, Wang T, Arnold M, Meikle PJ, Giles C, Kaddurah-Daouk R, Saykin AJ, Nho K. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data. EBioMedicine 2023; 97:104820. [PMID: 37806288 PMCID: PMC10579282 DOI: 10.1016/j.ebiom.2023.104820] [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: 07/06/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics. METHODS The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification. FINDINGS The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6). INTERPRETATION Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation. FUNDING The specific funding of this article is provided in the acknowledgements section.
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Affiliation(s)
- Taeho Jo
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Junpyo Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Medical Research Institute, Sungkyunkwan University, School of Medicine, Seoul, South Korea
| | - Paula Bice
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27710, USA; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia; Monash University, Melbourne, VIC 3800, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27710, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, 27710, USA; Department of Medicine, Duke University, Durham, NC, 27710, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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14
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Barrera-Patiño CP, Soares JM, Branco KC, Inada NM, Bagnato VS. Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm. Antibiotics (Basel) 2023; 12:1502. [PMID: 37887203 PMCID: PMC10604181 DOI: 10.3390/antibiotics12101502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/23/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023] Open
Abstract
FTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacterial species have different molecular compositions, it will then result in unique FTIR spectra for each species and even bacterial strains. Having this important tool, here, we have developed a methodology aimed at refining the analysis and classification of the FTIR absorption spectra obtained from samples of Staphylococcus aureus, with the implementation of machine learning algorithms. In the first stage, the system conforming to four specified species groups, Control, Amoxicillin induced (AMO), Gentamicin induced (GEN), and Erythromycin induced (ERY), was analyzed. Then, in the second stage, five hidden samples were identified and correctly classified as with/without resistance to induced antibiotics. The total analyses were performed in three windows, Carbohydrates, Fatty Acids, and Proteins, of five hundred spectra. The protocol for acquiring the spectral data from the antibiotic-resistant bacteria via FTIR spectroscopy developed by Soares et al. was implemented here due to demonstrating high accuracy and sensitivity. The present study focuses on the prediction of antibiotic-induced samples through the implementation of the hierarchical cluster analysis (HCA), principal component analysis (PCA) algorithm, and calculation of confusion matrices (CMs) applied to the FTIR absorption spectra data. The data analysis process developed here has the main objective of obtaining knowledge about the intrinsic behavior of S. aureus samples within the analysis regions of the FTIR absorption spectra. The results yielded values with 0.7 to 1 accuracy and high values of sensitivity and specificity for the species identification in the CM calculations. Such results provide important information on antibiotic resistance in samples of S. aureus bacteria for potential application in the detection of antibiotic resistance in clinical use.
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Affiliation(s)
- Claudia P Barrera-Patiño
- São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil
| | - Jennifer M Soares
- São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil
| | - Kate C Branco
- São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil
| | - Natalia M Inada
- São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil
| | - Vanderlei Salvador Bagnato
- São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil
- Biomedical Engineering, Texas A&M University, 400 Bizzell St, College Station, TX 77843, USA
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15
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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: 0] [Impact Index Per Article: 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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16
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Qiu M, Tang L, Wang J, Xu Q, Zheng S, Weng S. SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network. Foods 2023; 12:3096. [PMID: 37628095 PMCID: PMC10453087 DOI: 10.3390/foods12163096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/12/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling β-cyclodextrin-modified gold nanoparticles (β-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (β-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 μg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the β-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field.
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Affiliation(s)
- Mengqing Qiu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (M.Q.); (Q.X.)
- Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
| | - Le Tang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; (L.T.); (J.W.)
| | - Jinghong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; (L.T.); (J.W.)
| | - Qingshan Xu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (M.Q.); (Q.X.)
| | - Shouguo Zheng
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; (M.Q.); (Q.X.)
- Anhui Institute of Innovation for Industrial Technology, Hefei 230088, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; (L.T.); (J.W.)
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17
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Gong L, Martinez O, Mesquita P, Kurtz K, Xu Y, Lin Y. A microfluidic approach for label-free identification of small-sized microplastics in seawater. Sci Rep 2023; 13:11011. [PMID: 37419935 PMCID: PMC10329028 DOI: 10.1038/s41598-023-37900-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023] Open
Abstract
Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. In this study, we introduce a novel microfluidic approach that simplifies the trapping and identification process of microplastics in surface seawater, eliminating the need for labeling. We examine various models, including support vector machine, random forest, convolutional neural network (CNN), and residual neural network (ResNet34), to assess their performance in identifying 11 common plastics. Our findings reveal that the CNN method outperforms the other models, achieving an impressive accuracy of 93% and a mean area under the curve of 98 ± 0.02%. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts.
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Affiliation(s)
- Liyuan Gong
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Omar Martinez
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Pedro Mesquita
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Kayla Kurtz
- Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, RI, USA
| | - Yang Xu
- Department of Computer Science, San Diego State University, San Diego, CA, USA
| | - Yang Lin
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA.
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18
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Jin L, Yang J, You G, Ge C, Cao Y, Shen S, Wang D, Hui Q. A characteristic bacterial SERS marker for direct identification of Salmonella in real samples assisted by a high-performance SERS chip and a selective culture medium. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 301:122941. [PMID: 37302194 DOI: 10.1016/j.saa.2023.122941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/19/2023] [Accepted: 05/27/2023] [Indexed: 06/13/2023]
Abstract
Salmonella should be absent in pharmaceutical preparations and foods according to the regulations. However, up to now, rapid and convenient identification of Salmonella is still full of challenge. Herein, we reported a label-free surface-enhanced Raman scattering (SERS) method for direct identification of Salmonella spiked in drug samples based on a characteristic bacterial SERS marker assisted by a high-performance SERS chip and a selective culture medium. The SERS chip being fabricated through in situ growth of bimetallic Au-Ag nanocomposites on silicon wafer within 2 h, featured a high SERS activity (EF > 107), good uniformity and batch-to-batch consistency (RSD < 10 %), and satisfactory chemical stability. The directly-visualized SERS marker at 1222 cm-1 originated from bacterial metabolite hypoxanthine was robust and exclusive for discrimination of Salmonella with other bacterial species. Moreover, the method was successfully used for direct discrimination of Salmonella in mixed pathogens by using a selective culture medium, and could identify Salmonella contaminant at ∼1 CFU spiked level in a real sample (Wenxin granule, a botanical drug) after 12 h of enrichment. The combined results showed that developed SERS method is practical and reliable, and could be a promising alternative for rapid identification of Salmonella contamination in pharmaceutical and foods industries.
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Affiliation(s)
- Lei Jin
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325000, China.
| | - Jinmei Yang
- School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325001, China
| | - Guohui You
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaojie Ge
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China
| | - Yanrong Cao
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China
| | - Siyuan Shen
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China
| | - Danyan Wang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China
| | - Qi Hui
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
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19
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Song Y, Park N, Jo DA, Kim J, Yong D, Song J, Park YM, Lee SJ, Kim YT, Im SG, Choi BG, Kang T, Lee KG. Polyaniline-based 3D network structure promotes entrapment and detection of drug-resistant bacteria. NANO CONVERGENCE 2023; 10:25. [PMID: 37243716 PMCID: PMC10224663 DOI: 10.1186/s40580-023-00370-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/07/2023] [Indexed: 05/29/2023]
Abstract
Sensitive and accurate capture, enrichment, and identification of drug-resistant bacteria on human skin are important for early-stage diagnosis and treatment of patients. Herein, we constructed a three-dimensional hierarchically structured polyaniline nanoweb (3D HPN) to capture, enrich, and detect drug-resistant bacteria on-site by rubbing infected skins. These unique hierarchical nanostructures enhance bacteria capture efficiency and help severely deform the surface of the bacteria entrapped on them. Therefore, 3D HPN significantly contributes to the effective and reliable recovery of drug-resistant bacteria from the infected skin and the prevention of potential secondary infection. The recovered bacteria were successfully identified by subsequent real-time polymerase chain reaction (PCR) analysis after the lysis process. The molecular analysis results based on a real-time PCR exhibit excellent sensitivity to detecting target bacteria of concentrations ranging from 102 to 107 CFU/mL without any fluorescent signal interruption. To confirm the field applicability of 3D HPN, it was tested with a drug-resistant model consisting of micropig skin similar to human skin and Klebsiella pneumoniae carbapenemase-producing carbapenem-resistant Enterobacteriaceae (KPC-CRE). The results show that the detection sensitivity of this assay is 102 CFU/mL. Therefore, 3D HPN can be extended to on-site pathogen detection systems, along with rapid molecular diagnostics through a simple method, to recover KPC-CRE from the skin.
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Affiliation(s)
- Younseong Song
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Nahyun Park
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Da Ae Jo
- Center for Nano Bio Development, National Nanofab Center (NNFC), Daejeon, 34141, Republic of Korea
| | - Jueun Kim
- Center for Nano Bio Development, National Nanofab Center (NNFC), Daejeon, 34141, Republic of Korea
| | - Dongeun Yong
- Department of Laboratory Medicine and Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea
| | - Jayeon Song
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea
| | - Yoo Min Park
- Center for Nano Bio Development, National Nanofab Center (NNFC), Daejeon, 34141, Republic of Korea
| | - Seok Jae Lee
- Center for Nano Bio Development, National Nanofab Center (NNFC), Daejeon, 34141, Republic of Korea
| | - Yong Tae Kim
- Department of Chemical Engineering & Biotechnology, Tech University of Korea, Siheung-Si, 15073, Republic of Korea
| | - Sung Gap Im
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Bong Gill Choi
- Department of Chemical Engineering, Kangwon National University, Samcheok, 25913, Republic of Korea.
| | - Taejoon Kang
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea.
- School of Pharmacy, Sungkyunkwan University (SKKU), Suwon-Si, 16419, Republic of Korea.
| | - Kyoung G Lee
- Center for Nano Bio Development, National Nanofab Center (NNFC), Daejeon, 34141, Republic of Korea.
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20
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Kumar AK, Jain S, Jain S, Ritam M, Xia Y, Chandra R. Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107421. [PMID: 36805280 DOI: 10.1016/j.cmpb.2023.107421] [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: 08/30/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES The use of machine learning methods for modelling bio-systems is becoming prominent which can further improve bio-medical technologies. Physics-informed neural networks (PINNs) can embed the knowledge of physical laws that govern a system during the model training process. PINNs utilise differential equations in the model which traditionally used numerical methods that are computationally complex. METHODS We integrate PINNs with an entangled ladder network for modelling respiratory systems by considering a lungs conduction zone to evaluate the respiratory impedance for different initial conditions. We evaluate the respiratory impedance for the inhalation phase of breathing for a symmetric model of the human lungs using entanglement and continued fractions. RESULTS We obtain the impedance of the conduction zone of the lungs pulmonary airways using PINNs for nine different combinations of velocity and pressure of inhalation. We compare the results from PINNs with the finite element method using the mean absolute error and root mean square error. The results show that the impedance obtained with PINNs contrasts with the conventional forced oscillation test used for deducing the respiratory impedance. The results show similarity with the impedance plots for different respiratory diseases. CONCLUSION We find a decrease in impedance when the velocity of breathing is lowered gradually by 20%. Hence, the methodology can be used to design smart ventilators to the improve flow of breathing.
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Affiliation(s)
- Amit Krishan Kumar
- Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam; State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Snigdha Jain
- Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - Shirin Jain
- Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - M Ritam
- Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - Yuanqing Xia
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Rohitash Chandra
- Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney, NSW 2052, Australia.
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21
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Lyu JW, Zhang XD, Tang JW, Zhao YH, Liu SL, Zhao Y, Zhang N, Wang D, Ye L, Chen XL, Wang L, Gu B. Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra. Microbiol Spectr 2023; 11:e0412622. [PMID: 36877048 PMCID: PMC10100812 DOI: 10.1128/spectrum.04126-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/20/2023] [Indexed: 03/07/2023] Open
Abstract
Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.
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Affiliation(s)
- Jing-Wen Lyu
- Department of Laboratory Medicine, School of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xue Di Zhang
- Department of Laboratory Medicine, School of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Laboratory Medicine, The Affiliated Xuzhou Infectious Diseases Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Jia-Wei Tang
- Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Jiangsu Province, Xuzhou, China
| | - Yun-Hu Zhao
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Su-Ling Liu
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Yue Zhao
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Ni Zhang
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Dan Wang
- Laboratory Medicine, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Long Ye
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xiao-Li Chen
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Bing Gu
- Department of Laboratory Medicine, School of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
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22
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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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23
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Beeram R, Vepa KR, Soma VR. Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques. BIOSENSORS 2023; 13:328. [PMID: 36979540 PMCID: PMC10046859 DOI: 10.3390/bios13030328] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.
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24
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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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25
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Ding Y, Sun Y, Liu C, Jiang Q, Chen F, Cao Y. SERS-Based Biosensors Combined with Machine Learning for Medical Application. ChemistryOpen 2023; 12:e202200192. [PMID: 36627171 PMCID: PMC9831797 DOI: 10.1002/open.202200192] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.
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Affiliation(s)
- Yan Ding
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Yang Sun
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Cheng Liu
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Qiao‐Yan Jiang
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Feng Chen
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Yue Cao
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
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26
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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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27
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Zhang XD, Gu B, Usman M, Tang JW, Li ZK, Zhang XQ, Yan JW, Wang L. Recent Progress in the Diagnosis of Staphylococcus in Clinical Settings. Infect Dis (Lond) 2022. [DOI: 10.5772/intechopen.108524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Staphylococci are mainly found on the skin or in the nose. These bacteria are typically friendly, causing no harm to healthy individuals or resulting in only minor issues that can go away on their own. However, under certain circumstances, staphylococcal bacteria could invade the bloodstream, affect the entire body, and lead to life-threatening problems like septic shock. In addition, antibiotic-resistant Staphylococcus is another issue because of its difficulty in the treatment of infections, such as the notorious methicillin-resistant Staphylococcus aureus (MRSA) which is resistant to most of the currently known antibiotics. Therefore, rapid and accurate diagnosis of Staphylococcus and characterization of the antibiotic resistance profiles are essential in clinical settings for efficient prevention, control, and treatment of the bacteria. This chapter highlights recent advances in the diagnosis of Staphylococci in clinical settings with a focus on the advanced technique of surface-enhanced Raman spectroscopy (SERS), which will provide a framework for the real-world applications of novel diagnostic techniques in medical laboratories via bench-top instruments and at the bedside through point-of-care devices.
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Popa SL, Pop C, Dita MO, Brata VD, Bolchis R, Czako Z, Saadani MM, Ismaiel A, Dumitrascu DI, Grad S, David L, Cismaru G, Padureanu AM. Deep Learning and Antibiotic Resistance. Antibiotics (Basel) 2022; 11:1674. [PMID: 36421316 PMCID: PMC9686762 DOI: 10.3390/antibiotics11111674] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 09/25/2023] Open
Abstract
Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.
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Affiliation(s)
- Stefan Lucian Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Cristina Pop
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Miruna Oana Dita
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Vlad Dumitru Brata
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Roxana Bolchis
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Zoltan Czako
- Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Mohamed Mehdi Saadani
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Simona Grad
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Liliana David
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Gabriel Cismaru
- Fifth Department of Internal Medicine, Cardiology Rehabilitation, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
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Hussain M, Zou J, Zhang H, Zhang R, Chen Z, Tang Y. Recent Progress in Spectroscopic Methods for the Detection of Foodborne Pathogenic Bacteria. BIOSENSORS 2022; 12:bios12100869. [PMID: 36291007 PMCID: PMC9599795 DOI: 10.3390/bios12100869] [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: 07/10/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 05/06/2023]
Abstract
Detection of foodborne pathogens at an early stage is very important to control food quality and improve medical response. Rapid detection of foodborne pathogens with high sensitivity and specificity is becoming an urgent requirement in health safety, medical diagnostics, environmental safety, and controlling food quality. Despite the existing bacterial detection methods being reliable and widely used, these methods are time-consuming, expensive, and cumbersome. Therefore, researchers are trying to find new methods by integrating spectroscopy techniques with artificial intelligence and advanced materials. Within this progress report, advances in the detection of foodborne pathogens using spectroscopy techniques are discussed. This paper presents an overview of the progress and application of spectroscopy techniques for the detection of foodborne pathogens, particularly new trends in the past few years, including surface-enhanced Raman spectroscopy, surface plasmon resonance, fluorescence spectroscopy, multiangle laser light scattering, and imaging analysis. In addition, the applications of artificial intelligence, microfluidics, smartphone-based techniques, and advanced materials related to spectroscopy for the detection of bacterial pathogens are discussed. Finally, we conclude and discuss possible research prospects in aspects of spectroscopy techniques for the identification and classification of pathogens.
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Affiliation(s)
- Mubashir Hussain
- School of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104, China
- Postdoctoral Innovation Practice, Shenzhen Polytechnic, Liuxian Avenue, Nanshan District, Shenzhen 518055, China
| | - Jun Zou
- School of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104, China
- Correspondence: (Z.J.); (T.Y.)
| | - He Zhang
- School of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104, China
| | - Ru Zhang
- School of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104, China
| | - Zhu Chen
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China
| | - Yongjun Tang
- Postdoctoral Innovation Practice, Shenzhen Polytechnic, Liuxian Avenue, Nanshan District, Shenzhen 518055, China
- Correspondence: (Z.J.); (T.Y.)
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30
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Exploring the identification of multiple bacteria on stainless steel using multi-scale spectral imaging from microscopic to macroscopic. Sci Rep 2022; 12:15412. [PMID: 36104368 PMCID: PMC9471055 DOI: 10.1038/s41598-022-19617-3] [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: 01/11/2022] [Accepted: 08/31/2022] [Indexed: 12/04/2022] Open
Abstract
This work investigates non-contact reflectance spectral imaging techniques, i.e. microscopic Fourier transform infrared (FTIR) imaging, macroscopic visible-near infrared (VNIR), and shortwave infrared (SWIR) spectral imaging, for the identification of bacteria on stainless steel. Spectral images of two Gram-positive (GP) bacteria (Bacillus subtilis (BS) and Lactobacillus plantarum (LP)), and three Gram-negative (GN) bacteria (Escherichia coli (EC), Cronobacter sakazakii (CS), and Pseudomonas fluorescens (PF)), were collected from dried suspensions of bacterial cells dropped onto stainless steel surfaces. Through the use of multiple independent biological replicates for model validation and testing, FTIR reflectance spectral imaging was found to provide excellent GP/GN classification accuracy (> 96%), while the fused VNIR-SWIR data yielded classification accuracy exceeding 80% when applied to the independent test sets. However, classification within gram type was far less reliable, with lower accuracies for classification within the GP (< 75%) and GN (≤ 51%) species when calibration models were applied to the independent test sets, underlining the importance of independent model validation when dealing with samples of high biological variability.
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31
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Benladghem Z, Seddiki SML, Dergal F, Mahdad YM, Aissaoui M, Choukchou-Braham N. Biofouling of reverse osmosis membranes: assessment by surface-enhanced Raman spectroscopy and microscopic imaging. BIOFOULING 2022; 38:852-864. [PMID: 36314078 DOI: 10.1080/08927014.2022.2139610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 10/18/2022] [Accepted: 10/19/2022] [Indexed: 05/26/2023]
Abstract
The decline in the performance of spiral-wound reverse osmosis (SWRO) membranes is frequently due to biofouling. This study focus on qualitative and quantitative diagnosis of SWRO membrane biofouling. Bacterial counts on the different surfaces of the fouled membranes were carried out. Surface enhanced Raman spectroscopy (SERS) was performed to highlight clogging materials as well as their natures and identity. The topography of the fouled membranes and the structures of biofilms were visualized by fluorescence microscopy (FM) and scanning electron microscopy (SEM). The results indicated the presence of bacteria in the different SWRO membrane areas. Those strongly adhered were significantly higher than those weakly. It varied between 26 × 105 and 262 × 105 CFU m-2. However, SERS mapping showed different fouling levels and the thickness of the fouling layer was 5 µm. Microscopic imaging revealed biotic and abiotic deposits. These data can together allow better management of the seawater desalination process.
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Affiliation(s)
- Zakaria Benladghem
- Antifungal Antibiotic: Physico-Chemical Synthesis and Biological Activity laboratory, Biology department, University of Tlemcen, Tlemcen, Algeria
| | - Sidi Mohammed Lahbib Seddiki
- Antifungal Antibiotic: Physico-Chemical Synthesis and Biological Activity laboratory, Biology department, University of Tlemcen, Tlemcen, Algeria
- Laboratory for Sustainable Management of Natural Resources in Arid and Semi-Arid Areas, University Center of Naâma, Naâma, Algeria
| | - Fayçal Dergal
- Scientific and Technical Research Center in Physico-Chemical Analysis, Tipaza, Algeria
- Laboratory of Catalysis and Synthesis in Organic Chemistry, Faculty of Sciences, University of Tlemcen, Algeria
| | - Yassine Moustafa Mahdad
- Laboratory for Sustainable Management of Natural Resources in Arid and Semi-Arid Areas, University Center of Naâma, Naâma, Algeria
- Department of Physiology, Physiopathology and Biochemistry of Nutrition, University of Tlemcen, Tlemcen, Algeria
| | - Mohammed Aissaoui
- Department of Biology, Faculty of Sciences and Technology, University of Tamanghasset, Tamanghasset, Algeria
| | - Noureddine Choukchou-Braham
- Laboratory of Catalysis and Synthesis in Organic Chemistry, Faculty of Sciences, University of Tlemcen, Algeria
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32
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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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33
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Nimitha N, Ezhumalai P, Chokkalingam A. An improved deep convolutional neural network architecture for chromosome abnormality detection using hybrid optimization model. Microsc Res Tech 2022; 85:3115-3129. [PMID: 35708217 DOI: 10.1002/jemt.24170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/25/2022] [Accepted: 04/19/2022] [Indexed: 11/07/2022]
Abstract
Chromosomes are thread-like structures located in the cell nucleus that contains the human body blueprint. Chromosome analysis is also known as karyotyping is the test taken to detect the abnormalities identified in the human chromosome. The two types of widely known chromosome abnormalities are structural and numerical abnormalities. Manual karyotyping is complex, time-consuming, and error-prone. To overcome these complexities, automated chromosome karyotype architecture is proposed using the deep convolutional neural network (DCNN) architecture. Training the DCNN architecture from scratch needs a huge dataset and to overcome this problem a generative adversarial networks is used to create adversarial samples that resemble the images in the actual dataset. The time-consuming hyperparameter tuning in the DCNN architecture is overcome using the hybrid moth-flame optimization integrated with the hill-climbing strategy (HMFOHC). The HMFOHC algorithm is mainly utilized in this article to minimize the huge number of parameters associated with the DCNN architecture. The efficiency of the proposed methodology is evaluated using two datasets namely the BioImLab chromosome dataset and hospital dataset. The proposed HMFOHC optimized DCNN architecture is mainly utilized for multiclass classification where it differentiates five numerical chromosome abnormalities, namely Trisomy 13, Trisomy 18, Trisomy 21, Trisomy XXY syndrome, and Monosomy X. The proposed model offers an accuracy, F1-score, and kappa coefficient value of 98.65%, 98.86%, and 0.9894, respectively. The results obtained show that the proposed model achieves higher classification accuracy when compared with the different state-of-art techniques such as deep learning, random forest, and CNN. The inference time of the proposed methodology is 12.5 s which is relatively lower than the state-of-art techniques. The proposed approach can help cytogenetics forensic experts make better decisions and save time by automating manual karyotyping.
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Affiliation(s)
- N Nimitha
- Department of ECE, RMK College of Engineering and Technology, Puduvoyal, India
| | - P Ezhumalai
- Department of Computer Science and Engineering, RMD Engineering College, Chennai, India
| | - Arun Chokkalingam
- Department of ECE, RMK College of Engineering and Technology, Puduvoyal, India
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34
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Saxena S, Punjabi K, Ahamad N, Singh S, Bendale P, Banerjee R. Nanotechnology Approaches for Rapid Detection and Theranostics of Antimicrobial Resistant Bacterial Infections. ACS Biomater Sci Eng 2022; 8:2232-2257. [PMID: 35546526 DOI: 10.1021/acsbiomaterials.1c01516] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
As declared by WHO, antimicrobial resistance (AMR) is a high priority issue with a pressing need to develop impactful technologies to curb it. The rampant and inappropriate use of antibiotics due to the lack of adequate and timely diagnosis is a leading cause behind AMR evolution. Unfortunately, populations with poor economic status and those residing in densely populated areas are the most affected ones, frequently leading to emergence of AMR pathogens. Classical approaches for AMR diagnostics like phenotypic methods, biochemical assays, and molecular techniques are cumbersome and resource-intensive and involve a long turnaround time to yield confirmatory results. In contrast, recent emergence of nanotechnology-assisted approaches helps to overcome challenges in classical approaches and offer simpler, more sensitive, faster, and more affordable solutions for AMR diagnostics. Nanomaterial platforms (metallic, quantum-dot, carbon-based, upconversion, etc.), nanoparticle-based rapid point-of-care platforms, nano-biosensors (optical, mechanical, electrochemical), microfluidic-assisted devices, and importantly, nanotheranostic devices for diagnostics with treatment of AMR infections are examples of rapidly growing nanotechnology approaches used for AMR management. This review comprehensively summarizes the past 10 years of research progress on nanotechnology approaches for AMR diagnostics and for estimating antimicrobial susceptibility against commonly used antibiotics. This review also highlights several bottlenecks in nanotechnology approaches that need to be addressed prior to considering their translation to clinics.
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Affiliation(s)
- Survanshu Saxena
- Nanomedicine Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Kapil Punjabi
- Nanomedicine Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Nadim Ahamad
- Nanomedicine Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Subhasini Singh
- Nanomedicine Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Prachi Bendale
- Nanomedicine Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Rinti Banerjee
- Nanomedicine Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India
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Pan SW, Lu HC, Lo JI, Ho LI, Tseng TR, Ho ML, Cheng BM. Using an ATR-FTIR Technique to Detect Pathogens in Patients with Urinary Tract Infections: A Pilot Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22103638. [PMID: 35632048 PMCID: PMC9147530 DOI: 10.3390/s22103638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/05/2022] [Accepted: 05/07/2022] [Indexed: 05/30/2023]
Abstract
Urinary tract infections (UTIs) are a leading hospital-acquired infection. Although timely detection of causative pathogens of UTIs is important, rapid and accurate measures assisting UTI diagnosis and bacterial determination are poorly developed. By reading infrared spectra of urine samples, Fourier-transform infrared spectroscopy (FTIR) may help detect urine compounds, but its role in UTI diagnosis remains uncertain. In this pilot study, we proposed a characterization method in attenuated total reflection (ATR)-FTIR spectra to evaluate urine samples and assessed the correlation between ATR-FTIR patterns, UTI diagnosis, and causative pathogens. We enrolled patients with a catheter-associated UTI in a subacute-care unit and non-UTI controls (total n = 18), and used urine culture to confirm the causative pathogens of the UTIs. In the ATR-FTIR analysis, the spectral variation between the UTI group and non-UTI, as well as that between various pathogens, was found in a range of 1800-900 cm-1, referring to the presence of specific constituents of the bacterial cell wall. The results indicated that the relative ratios between different area zones of vibration, as well as multivariate analysis, can be used as a clue to discriminate between UTI and non-UTI, as well as different causative pathogens of UTIs. This warrants a further large-scale study to validate the findings of this pilot research.
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Affiliation(s)
- Sheng-Wei Pan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (S.-W.P.); (L.-I.H.)
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 12304, Taiwan
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Hsiao-Chi Lu
- Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Sec. 3, Chung-Yang Rd., Hualien City 97002, Taiwan; (H.-C.L.); (J.-I.L.)
| | - Jen-Iu Lo
- Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Sec. 3, Chung-Yang Rd., Hualien City 97002, Taiwan; (H.-C.L.); (J.-I.L.)
| | - Li-Ing Ho
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (S.-W.P.); (L.-I.H.)
| | - Ton-Rong Tseng
- Mastek Technologies, Inc., 4F-4, No. 13, Wuquan 1st Rd., Xinzhuang, New Taipei City 24892, Taiwan;
| | - Mei-Lin Ho
- Department of Chemistry, Soochow University, No. 70, LinShih Rd., Shih-Lin, Taipei 11102, Taiwan
| | - Bing-Ming Cheng
- Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Sec. 3, Chung-Yang Rd., Hualien City 97002, Taiwan; (H.-C.L.); (J.-I.L.)
- Office of Research and Development, Tzu Chi University of Science and Technology, No. 880, Sec. 2, Chien-kuo Rd., Hualien City 97005, Taiwan
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36
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Liu W, Wei L, Wang D, Zhu C, Huang Y, Gong Z, Tang C, Fan M. Phenotyping Bacteria through a Black-Box Approach: Amplifying Surface-Enhanced Raman Spectroscopy Spectral Differences among Bacteria by Inputting Appropriate Environmental Stress. Anal Chem 2022; 94:6791-6798. [PMID: 35476403 DOI: 10.1021/acs.analchem.2c00502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Surface-enhanced Raman spectroscopy (SERS) stands out in the field of microbial analysis due to its rich molecular information, fast analysis speed, and high sensitivity. However, achieving strain-level differentiation is still challenging because numerous bacterial species inevitably have very similar SERS profiles. Here, a method inspired by the black-box theory was proposed to boost the spectral differences, where the undifferentiated bacteria was considered as a type of black-box, external environmental stress was used as the input, and the SERS spectra of bacteria exposed to the same stress was output. For proof of the concept, three types of environmental stress were explored, i.e., ethanol, ultraviolet light (UV), and ultrasound. Enterococcus faecalis (E. faecalis) and three types of Escherichia coli (E. coli) were all subjected to the stimuli (stress) before SERS measurement. Then the collected spectra were processed only by simple principal component analysis (PCA) to achieve differentiation. The results showed that appropriate stress was beneficial to increase the differences in bacterial SERS spectra. When sonication at 490 W for 60 s was used as the input, the optimal differentiation of bacteria at the species (E. faecalis and E. coli) and strain-level (three E. coli) can be achieved.
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Affiliation(s)
- Wen Liu
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Linbo Wei
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Dongmei Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Chengye Zhu
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Yuting Huang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Zhengjun Gong
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Changyu Tang
- Chengdu Development Center of Science and Technology, China Academy of Engineering Physics, Chengdu 610200, China
| | - Meikun Fan
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
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37
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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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38
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Akdeniz M, Uysal Ciloglu F, Tunc CU, Yilmaz U, Kanarya D, Atalay P, Aydin O. Investigation of mammalian cells expressing SARS-CoV-2 proteins by surface-enhanced Raman scattering and multivariate analysis. Analyst 2022; 147:1213-1221. [PMID: 35212693 DOI: 10.1039/d1an01989a] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
COVID-19 has caused millions of cases and deaths all over the world since late 2019. Rapid detection of the virus is crucial for controlling its spread through a population. COVID-19 is currently detected by nucleic acid-based tests and serological tests. However, these methods have limitations such as the requirement of high-cost reagents, false negative results and being time consuming. Surface-enhanced Raman scattering (SERS), which is a powerful technique that enhances the Raman signals of molecules using plasmonic nanostructures, can overcome these disadvantages. In this study, we developed a virus-infected cell model and analyzed this model by SERS combined with Principal Component Analysis (PCA). HEK293 cells were transfected with plasmids encoding the nucleocapsid (N), membrane (M) and envelope (E) proteins of SARS-CoV-2 via polyethyleneimine (PEI). Non-plasmid transfected HEK293 cells were used as the control group. Cellular uptake was optimized with green fluorescence protein (GFP) plasmids and evaluated by fluorescence microscopy and flow cytometry. The transfection efficiency was found to be around 60%. The expression of M, N, and E proteins was demonstrated by western blotting. The SERS spectra of the total proteins of transfected cells were obtained using a gold nanoparticle-based SERS substrate. Proteins of the transfected cells have peak positions at 646, 680, 713, 768, 780, 953, 1014, 1046, 1213, 1243, 1424, 2102, and 2124 cm-1. To reveal spectral differences between plasmid transfected cells and non-transfected control cells, PCA was applied to the spectra. The results demonstrated that SERS coupled with PCA might be a favorable and reliable way to develop a rapid, low-cost, and promising technique for the detection of COVID-19.
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Affiliation(s)
- Munevver Akdeniz
- Department of Biomedical Engineering, Erciyes University, Kayseri 38039, Turkey. .,NanoThera Lab, ERFARMA-Drug Application and Research Center, Erciyes University, 38039, Kayseri, Turkey.,ERNAM-Nanotechnology Research and Application Center, Erciyes University, Kayseri 38039, Turkey
| | - Fatma Uysal Ciloglu
- Department of Biomedical Engineering, Erciyes University, Kayseri 38039, Turkey. .,NanoThera Lab, ERFARMA-Drug Application and Research Center, Erciyes University, 38039, Kayseri, Turkey
| | - Cansu Umran Tunc
- Department of Biomedical Engineering, Erciyes University, Kayseri 38039, Turkey. .,NanoThera Lab, ERFARMA-Drug Application and Research Center, Erciyes University, 38039, Kayseri, Turkey
| | - Ummugulsum Yilmaz
- Department of Biomedical Engineering, Erciyes University, Kayseri 38039, Turkey. .,NanoThera Lab, ERFARMA-Drug Application and Research Center, Erciyes University, 38039, Kayseri, Turkey
| | - Dilek Kanarya
- Department of Biomedical Engineering, Erciyes University, Kayseri 38039, Turkey. .,NanoThera Lab, ERFARMA-Drug Application and Research Center, Erciyes University, 38039, Kayseri, Turkey.,ERNAM-Nanotechnology Research and Application Center, Erciyes University, Kayseri 38039, Turkey
| | - Pinar Atalay
- NanoThera Lab, ERFARMA-Drug Application and Research Center, Erciyes University, 38039, Kayseri, Turkey.,Department of Basic Sciences, Faculty of Pharmacy, Erciyes University, Kayseri 38040, Turkey
| | - Omer Aydin
- Department of Biomedical Engineering, Erciyes University, Kayseri 38039, Turkey. .,NanoThera Lab, ERFARMA-Drug Application and Research Center, Erciyes University, 38039, Kayseri, Turkey.,ERNAM-Nanotechnology Research and Application Center, Erciyes University, Kayseri 38039, Turkey.,ERKAM-Clinical Engineering Research and Implementation Center, Erciyes University, Kayseri 38030, Turkey
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Arslan AH, Ciloglu FU, Yilmaz U, Simsek E, Aydin O. Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120475. [PMID: 34653850 DOI: 10.1016/j.saa.2021.120475] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 09/17/2021] [Accepted: 10/04/2021] [Indexed: 05/24/2023]
Abstract
Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.
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Affiliation(s)
- Afra Hacer Arslan
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
| | | | - Ummugulsum Yilmaz
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
| | - Emrah Simsek
- Preclinical Sciences, Faculty of Veterinary Medicine, Erciyes University, Kayseri, Turkey
| | - Omer Aydin
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey; ERNAM-Nanotechnology Research and Application Center, Erciyes University, Kayseri, Turkey; ERKAM-Clinical Engineering Research and Application Center, Erciyes University, Kayseri, Turkey.
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40
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New Antibacterial Secondary Metabolites from a Marine-Derived Talaromyces sp. Strain BTBU20213036. Antibiotics (Basel) 2022; 11:antibiotics11020222. [PMID: 35203824 PMCID: PMC8868179 DOI: 10.3390/antibiotics11020222] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 11/16/2022] Open
Abstract
New polyketide-derived oligophenalenone dimers, bacillisporins K and L (1 and 2) and xanthoradone dimer rugulosin D (3), together with four known compounds, bacillisporin B (4), macrosporusone D (5), rugulosin A and penicillide (6 and 7), were isolated from the marine-derived fungus Talaromyces sp. BTBU20213036. Their structures were determined by detailed analysis of HRESIMS, 1D and 2D NMR data, and the absolute configurations were determined on the basis of calculated and experimental electronic circular dichroism (ECD). The antibacterial and antifungal activities of these compounds were tested against Gram-positive—Staphylococcus aureus, Gram-negative—Escherichia coli, and fungal strain—Candida albicans. These compounds showed potential inhibitory effects against S. aureus with minimum inhibitory concentrations ranging from 0.195 to 100 µg/mL.
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41
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Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12010208. [PMID: 35054375 PMCID: PMC8774325 DOI: 10.3390/diagnostics12010208] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 11/17/2022] Open
Abstract
Staphylococcus aureus is an opportunistic pathogen responsible for a wide range of infections in humans, such as skin and soft tissue infections, pneumonia, food poisoning or sepsis. Historically, S. aureus was able to rapidly adapt to anti-staphylococcal antibiotics and become resistant to several classes of antibiotics. Today, methicillin-resistant S. aureus (MRSA) is a multidrug-resistant pathogen and is one of the most common bacteria responsible for hospital-acquired infections and outbreaks, in community settings as well. The rapid and accurate diagnosis of antimicrobial resistance in S. aureus is crucial to the early initiation of directed antibiotic therapy and to improve clinical outcomes for patients. In this narrative review, I provide an overview of recent phenotypic and molecular diagnostic methods for antimicrobial resistance detection in S. aureus, with a particular focus on MRSA detection. I consider methods for resistance detection in both clinical samples and isolated S. aureus cultures, along with a brief discussion of the advantages and the challenges of implementing such methods in routine diagnostics.
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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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Park S, Lee J, Khan S, Wahab A, Kim M. SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network. BIOSENSORS 2021; 11:bios11120490. [PMID: 34940246 PMCID: PMC8699110 DOI: 10.3390/bios11120490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 05/10/2023]
Abstract
Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.
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Affiliation(s)
- Seongyong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; (S.P.); (S.K.)
| | - Jaeseok Lee
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea;
- Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
| | - Shujaat Khan
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; (S.P.); (S.K.)
| | - Abdul Wahab
- Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;
| | - Minseok Kim
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea;
- Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
- Correspondence:
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