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
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TLDR
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.Abstract:
In this study, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, the results of which showed that the convolutional neural network (CNN) deep learning algorithm could achieve the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. ABSTRACT The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and medical treatments in clinical settings. Although many conventional and molecular methods have been proven to be efficient and reliable, some of them suffer technical biases and limitations that require the development and application of novel and advanced techniques. Recently, due to its cost affordability, noninvasiveness, and label-free feature, Raman spectroscopy (RS) is emerging as a potential technique for fast bacterial detection. However, the method is still hampered by many technical issues, such as low signal intensity, poor reproducibility, and standard data set insufficiency, among others. Thus, it should be cautiously claimed that Raman spectroscopy could provide practical applications in real-world settings. In order to evaluate the implementation potentials of Raman spectroscopy in the identification of bacterial pathogens, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, 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. In summary, the SERS technique holds a promising potential for fast bacterial pathogen identification in clinical laboratories with the integration of machine learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples. IMPORTANCE In this study, we investigated 30 bacterial species belonging to 9 different bacterial genera that were isolated from clinical samples via surfaced enhanced Raman spectroscopy (SERS). A total of 17,149 SERS spectra were harvested from a Raman spectrometer and were further analyzed via machine learning approaches, the results of which showed that the convolutional neural network (CNN) deep learning algorithm could achieve the highest prediction accuracy for recognizing pathogenic bacteria at both the genus and species levels. Taken together, we concluded that the SERS technique held a promising potential for fast bacterial pathogen diagnosis in clinical laboratories with the integration of deep learning algorithms, which might be further developed and sharpened for the direct identification and prediction of bacterial pathogens from clinical samples.read more
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Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications.
Muhammad Hizbullahi Usman,Jia-Wei Tang,Feng-rong Li,Jin-Xin Lai,Qinghua Liu,Wei Li,Liang Wang +6 more
TL;DR: In this article , surface enhanced Raman spectroscopy (SERS) has been used for the identification of pathogenic bacteria in complex clinical settings, such as blood, urine and sputum.
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Microplastics and nanoplastics analysis: Options, imaging, advancements and challenges
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Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms
TL;DR: Wang et al. as mentioned in this paper used surface enhanced Raman spectroscopy (SERS) for classification of Shigella spp. and E. coli, which achieved the best performance and robustness in bacterial classification when compared with Random Forest (RF) and SVM.
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Simple and Rapid Discrimination of Methicillin-Resistant Staphylococcus aureus Based on Gram Staining and Machine Vision
TL;DR: In this article , a new simple, rapid identification method for MRSA using oxacillin sodium salt, a cell wall synthesis inhibitor, combined with Gram staining and machine vision (MV) analysis is presented.
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Determination of Shigella spp. via label-free SERS spectra coupled with deep learning
Jia-Wei Tang,Jingqiao Lyu,Jin-Xin Lai,Yang-Guang Du,Xin-Qiang Zhang,Yu-Dong Zhang,Bin Gu,Xiao Mei Zhang,Bing Gu,Liang Wang +9 more
TL;DR: In this paper , a novel method for rapid and accurate discrimination of Shigella spp. via label-free SERS coupling with multiscale deep learning method was developed.
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