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Detection and Identification of the Stages of DH5-Alpha Escherichia coli Biofilm Formation on Metal by Using an Artificial Intelligence System

Published online by Cambridge University Press:  04 August 2021

Pei-Chun Wong
Affiliation:
Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan Orthopedics Research Center, Taipei Medical University Hospital, Taipei, Taiwan
Tai-En Fan
Affiliation:
Department Electrical Engineering, Yuan-Ze University, No. 135, Yuandong Road, Zhongli District, Taoyuan City 320070, Taiwan (R.O.C.)
Yu-Lin Lee
Affiliation:
Department Electrical Engineering, Yuan-Ze University, No. 135, Yuandong Road, Zhongli District, Taoyuan City 320070, Taiwan (R.O.C.)
Chun-Yu Lai
Affiliation:
Department Electrical Engineering, Yuan-Ze University, No. 135, Yuandong Road, Zhongli District, Taoyuan City 320070, Taiwan (R.O.C.)
Jia-Lin Wu
Affiliation:
Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan Orthopedics Research Center, Taipei Medical University Hospital, Taipei, Taiwan Centers for Regional Anesthesia and Pain Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
Ling-Hua Chang*
Affiliation:
Department Electrical Engineering, Yuan-Ze University, No. 135, Yuandong Road, Zhongli District, Taoyuan City 320070, Taiwan (R.O.C.)
Tai-Yuan Su*
Affiliation:
Department Electrical Engineering, Yuan-Ze University, No. 135, Yuandong Road, Zhongli District, Taoyuan City 320070, Taiwan (R.O.C.)
*
*Corresponding authors: Ling-Hua Chang, E-mail: iamjaung@ee.yzu.edu.tw; Tai-Yuan Su, E-mail: sutai.tw@gmail.com
*Corresponding authors: Ling-Hua Chang, E-mail: iamjaung@ee.yzu.edu.tw; Tai-Yuan Su, E-mail: sutai.tw@gmail.com

Abstract

In clinical environments, orthopedic implants are associated with a risk of infection during implantation. However, the growth paths of bacteria on metal, which is nontransparent, are difficult to observe. In this study, we visualized the DH5-alpha Escherichia coli bacterial growth path on the surface of magnesium by using scanning electron microscope (SEM) images and constructed a convolutional neural network-based artificial intelligence (AI) system to identify metal surfaces, bacteria, and its generated products to grade the growth stage of the bacteria implanted on the magnesium. The detection result of the E. coli growth stage by the AI system was close to that manually marked by experts, and it may greatly accelerate the investigation of the bacterial growth process in various types of metallic material.

Type
Biological Applications
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

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References

Asahi, Y, Miura, J, Tsuda, T, Kuwabata, S, Tsunashima, K, Noiri, Y, Sakata, T, Ebisu, S & Hayashi, M (2015). Simple observation of Streptococcus mutans biofilm by scanning electron microscopy using ionic liquids. AMB Express 5(1), 6.CrossRefGoogle ScholarPubMed
Chudzik, P, Majumdar, S, Calivá, F, Al-Diri, B & Hunter, A (2018). Microaneurysm detection using fully convolutional neural networks. Comput Methods Programs Biomed 158, 185192.CrossRefGoogle ScholarPubMed
Costerton, JW, Stewart, PS & Greenberg, EP (1999). Bacterial biofilms: A common cause of persistent infections. Science 284, 13181322.CrossRefGoogle ScholarPubMed
Glorot, X, Bordes, A & Bengio, Y (2011). Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323.Google Scholar
Gomes, LC & Mergulhao, FJ (2017). SEM analysis of surface impact on biofilm antibiotic treatment. Scanning 2017, 2960194.CrossRefGoogle ScholarPubMed
Guo, Y, Budak, Ü & Şengür, A (2018). A novel retinal vessel detection approach based on multiple deep convolution neural networks. Comput Methods Programs Biomed 167, 4348.CrossRefGoogle ScholarPubMed
He, K, Gkioxari, G, Dollár, P & Girshick, R (2017). Mask r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969.CrossRefGoogle Scholar
Hinton, GE, Osindero, S & Teh, Y-W (2006). A fast learning algorithm for deep belief nets. Neural Comput 18(7), 15271554.CrossRefGoogle ScholarPubMed
Krizhevsky, A, Sutskever, I & Hinton, GE (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pp. 1097–1105.Google Scholar
Li, M, Huang, R, Zhou, X, Zhang, K, Zheng, X & Gregory, RL (2014). Effect of nicotine on dual-species biofilms of Streptococcus mutans and Streptococcus sanguinis. FEMS Microbiol Lett 350(2), 125132.CrossRefGoogle ScholarPubMed
Liu, S, Wei, Y, Zhou, X, Zhang, K, Peng, X, Ren, B, Chen, V, Cheng, L & Li, M (2018). Function of alanine racemase in the physiological activity and cariogenicity of Streptococcus mutans. Sci Rep 8(1), 5984.CrossRefGoogle ScholarPubMed
Liu, Y, Kamesh, AC, Xiao, Y, Sun, V, Hayes, M, Daniell, H & Koo, H (2016). Topical delivery of low-cost protein drug candidates made in chloroplasts for biofilm disruption and uptake by oral epithelial cells. Biomaterials 105, 156166.CrossRefGoogle ScholarPubMed
Maunders, E & Welch, M (2017). Matrix exopolysaccharides; The sticky side of biofilm formation. FEMS Microbiol Lett 364, 110.CrossRefGoogle ScholarPubMed
McCutcheon, J & Southam, G (2018). Advanced biofilm staining techniques for TEM and SEM in geomicrobiology: Implications for visualizing EPS architecture, mineral nucleation, and microfossil generation. Chem Geol 498, 115127.CrossRefGoogle Scholar
Nithya, C, Gnanalakshmi, B & Pandian, SK (2011). Assessment and characterization of heavy metal resistance in Palk Bay sediment bacteria. Mar Environ Res 71(4), 283294.CrossRefGoogle ScholarPubMed
Perdomo, O, Rios, H, Rodríguez, F, Otálora, S, Meriaudeau, F, Müller, H & González, FA (2019). Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Comput Methods Programs Biomed 178, 181189.CrossRefGoogle ScholarPubMed
Prasad, K, Bazaka, O, Chua, M, Rochford, M, Fedrick, L, Spoor, J, Symes, R, Tieppo, M, Collins, C, Cao, A, Markwell, D, Ostrikov, KK & Bazaka, K (2017). Metallic biomaterials: Current challenges and opportunities. Materials (Basel) 10(8), 884.CrossRefGoogle ScholarPubMed
Schlafer, S & Meyer, RL (2017). Confocal microscopy imaging of the biofilm matrix. J Microbiol Methods 138, 5059.CrossRefGoogle ScholarPubMed
Stokes, DJ (2008). Principles and Practice of Variable Pressure/Environmental Scanning Electron Microscopy (VP-ESEM). Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Su, TY, Liu, ZY & Chen, DY (2018). Tear film break-up time measurement using deep convolutional neural networks for screening dry eye disease. IEEE Sensors J 18(16), 68576862.CrossRefGoogle Scholar
Su, T-Y, Ting, P-J, Chang, S-W & Chen, D-Y (2019). Superficial punctate keratitis grading for dry eye screening using deep convolutional neural networks. IEEE Sensors J 20, 16721678.CrossRefGoogle Scholar
Toyofuku, M, Inaba, T, Kiyokawa, T, Obana, N, Yawata, Y & Nomura, N (2016). Environmental factors that shape biofilm formation. Biosci Biotechnol Biochem 80, 712.CrossRefGoogle ScholarPubMed
Villa, F, Pitts, B, Lauchnor, E, Cappitelli, F & Stewart, PS (2015). Development of a laboratory model of a phototroph-heterotroph mixed-species biofilm at the stone/air interface. Front Microbiol 6, 1251.CrossRefGoogle Scholar

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Detection and Identification of the Stages of DH5-Alpha Escherichia coli Biofilm Formation on Metal by Using an Artificial Intelligence System
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