Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings.
Liang Wang,Wei Liu,Jia-Wei Tang,Jun-Jiao Wang,Qing-Hua Liu,Peng-Bo Wen,Mengmeng Wang,Ya-Cheng Pan,Bing Gu,Xiao Zhang +9 more
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TLDR
In this article, a review of recent studies of Raman spectroscopy in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.Abstract:
Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multi-drug resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that Raman spectroscopy may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and non-invasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of Raman spectroscopy in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.read more
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Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species.
Jia-Wei Tang,Qing-Hua Liu,Xiao-Cong Yin,Ya-Cheng Pan,Peng-Bo Wen,Xin Liu,Xing-Xing Kang,Bing Gu,Bing Gu,Zuobin Zhu,Liang Wang +10 more
TL;DR: In this paper, the authors compared three unsupervised machine learning methods and 10 supervised machine learning algorithms, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphilicococcus species in order to test the capacity of different machine learning method for bacterial rapid differentiation and accurate prediction.
Journal ArticleDOI
Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study
Wei Wei Liu,Jia-Wei Tang,Jingqiao Lyu,Jun-Jiao Wang,Ya-Cheng Pan,Xin-Yi Shi,Qinghua Liu,Xiao Mei Zhang,Bing Gu,Liang Wang +9 more
TL;DR: Wang et al. as discussed by the authors used surface-enhanced Raman spectroscopy (SERS) to detect carbapenem-sensitive Klebsiella pneumoniae (CSKP) from clinical samples.
Journal ArticleDOI
Rapid Discrimination of Clinically Important Pathogens Through Machine Learning Analysis of Surface Enhanced Raman Spectra
Jia-Wei Tang,Jia-Qi Li,Xi Yin,Wenting Xu,Ya-Cheng Pan,Qinghua Liu,Bing Gu,Xiao Mei Zhang,Liang Wang +8 more
TL;DR: Machine learning methods can be potentially applied to classify and predict bacterial pathogens via Raman spectra at general level through machine learning algorithms in order to discriminate bacterial pathogens quickly and accurately.
Journal ArticleDOI
Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning
B. Thomsen,Jesper H. Christensen,Olga Rodenko,Iskander Usenov,Rasmus Birkholm Grønnemose,Thomas Emil Andersen,M. Th. Lassen +6 more
TL;DR: In this article , a spectral transformer model for hyper-spectral Raman images of bacteria was implemented for fast identification of minimally prepared bacteria phenotypes and the distinctions of methicillin-resistant (MR) from methicill-susceptible (MS) bacteria.
Journal ArticleDOI
Identification of Bacterial Pathogens at Genus and Species Levels through Combination of Raman Spectrometry and Deep-Learning Algorithms
Liang Wang,Jia-Wei Tang,Feng-rong Li,Muhammad Usman,Chang-Yu Wu,Qinghua Liu,Haiquan Kang,Wei Li,Bing Gu +8 more
TL;DR: Wang et al. as discussed by the authors investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS), which showed that a convolutional neural network (CNN) deep learning algorithm achieved the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels.
References
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Classification and identification of pigmented cocci bacteria relevant to the soil environment via Raman spectroscopy
TL;DR: The results demonstrate the potential of Raman spectroscopy as a minimally invasive taxonomic tool to identify pigmented cocci soil bacteria at a single-cell level.
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A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species.
Amira A. Moawad,Anja Silge,Thomas Bocklitz,Thomas Bocklitz,Katja Fischer,Petra Rösch,Uwe Roesler,Mandy C. Elschner,Jürgen Popp,Jürgen Popp,Heinrich Neubauer +10 more
TL;DR: The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay.
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Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features.
Fernanda SantAna de Siqueira e Oliveira,Adriano Moraes da Silva,Marcos Tadeu Tavares Pacheco,Hector Enrique Giana,Landulfo Silveira +4 more
TL;DR: Raman spectroscopy could be a promising technique to identify spectral differences related to the biochemical content of pathogenic microorganisms and to provide a faster diagnosis of infectious diseases.
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Diagnosis of Bacterial Pathogens in the Urine of Urinary-Tract-Infection Patients Using Surface-Enhanced Raman Spectroscopy
Ni Tien,Tzu-Hsien Lin,Zen-Chao Hung,Hsiu-Shen Lin,I-Kuan Wang,Hung-Chih Chen,Hung-Chih Chen,Chiz-Tzung Chang +7 more
TL;DR: SERS can be used in the diagnosis of urinary tract infection with the aid of the recognition software and PCA and, among them, 93 can be successfully identified by using SERS without sample concentration.
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ELISA is superior to bacterial culture and agglutination test in the diagnosis of brucellosis in an endemic area in China
TL;DR: It is concluded that ELISA assay is the most sensitive and specific method to detect Brucellosis in Chinese population and should be used as a routine lab test when Bru cellosis is suspected in clinical practice.