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Tago H, Maeda Y, Tanaka Y, Kohketsu H, Lim TK, Harada M, Yoshino T, Matsunaga T, Tanaka T. Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification. Biosens Bioelectron 2024; 249:116006. [PMID: 38199081 DOI: 10.1016/j.bios.2024.116006] [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: 10/13/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 01/12/2024]
Abstract
The rapid identification of pathogenic bacteria is crucial across various industries, including food or beverage manufacturing. Bacterial microcolony image-based classification has emerged as a promising approach to expedite identification, automate inspections, and reduce costs. However, conventional imaging methods have significant practical limitations, namely low throughput caused by the limited imaging range and slow imaging speed. To address these challenges, we developed an imaging system based on a line image sensor for rapid and wide-field imaging compared to existing colony imaging methods. This system can image a standard Petri dish (92 mm in diameter) completely within 22 s, successfully acquiring bacterial microcolony images. This process yielded a set of discrimination parameters termed as colony fingerprints, which were employed for machine learning. We demonstrated the performance of our system by identifying Staphylococcus aureus in food products using a machine learning model trained on a colony fingerprint dataset of 15 species from 9 genera, including foodborne pathogens. While conventional mass spectrometry-based methods require 24 h of incubation, our colony fingerprinting approach achieved 96% accuracy in just 10 h of incubation. Line image sensor offer high imaging speeds and scalability, allowing for swift and straightforward microbiological testing, eliminating the need for specialized expertise and overcoming the limitations of conventional methods. This innovation marks a transformative shift in industrial applications.
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Affiliation(s)
- Hikaru Tago
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan
| | - Yoshiaki Maeda
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan; Institute of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki, 305-8572, Japan
| | - Yusuke Tanaka
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan
| | - Hiroya Kohketsu
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan
| | - Tae-Kyu Lim
- Malcom Co., Ltd., 4-15-10, Honmachi, Shibuya-ku, Tokyo, 151-0071, Japan
| | - Manabu Harada
- Malcom Co., Ltd., 4-15-10, Honmachi, Shibuya-ku, Tokyo, 151-0071, Japan
| | - Tomoko Yoshino
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan
| | - Tadashi Matsunaga
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan
| | - Tsuyoshi Tanaka
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan.
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Nuankaew S, Boonyuen N, Thumanu K, Pornputtapong N. Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy. Heliyon 2024; 10:e26812. [PMID: 38439823 PMCID: PMC10909729 DOI: 10.1016/j.heliyon.2024.e26812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 02/01/2024] [Accepted: 02/20/2024] [Indexed: 03/06/2024] Open
Abstract
Aspergillus section Nigri (black aspergilli) fungi are economically important food spoilage agents. Some species in this section also produce harmful mycotoxins in food. However, it is remarkably difficult to identify this fungal group at the species level using morphological and chemical characteristics. The molecular approach for classification is preferable; however, it is time-consuming, making it inappropriate for rapid testing of large numbers of samples. To address this, we explored synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) as a rapid method for obtaining data suitable for species classification. SR-FTIR data were obtained from the mycelia/conidia of 22 black aspergilli species. The Convolutional Neural Network (CNN) approach, a supervised deep learning algorithm, was used with SR-FTIR data to classify black aspergilli at the species level. A subset of the data was used to train the CNN model, and the model classification performance was evaluated using the validation data subsets. The model demonstrated a 95.97% accuracy in species classification on the testing (blind) data subset. The technique presented herein could be an alternative method for identifying problematic black aspergilli in the food industry.
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Affiliation(s)
- Salilaporn Nuankaew
- Department of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani, 12120, Thailand
| | - Nattawut Boonyuen
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani, 12120, Thailand
| | - Kanjana Thumanu
- Synchrotron Light Research Institute (SLRI), Nakhon Ratchasima, 30000, Thailand
| | - Natapol Pornputtapong
- Department of Biochemistry and Microbiology, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand
- Center of Excellence in DNA Barcoding of Thai Medicinal Plants, Chulalongkorn University, Bangkok, 10330, Thailand
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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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Fabrication and application of three-dimensional nanocomposites modified electrodes for evaluating the aging process of Huangjiu (Chinese rice wine). Food Chem 2022; 372:131158. [PMID: 34601421 DOI: 10.1016/j.foodchem.2021.131158] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 08/08/2021] [Accepted: 09/14/2021] [Indexed: 02/08/2023]
Abstract
In this study, three modified glassy carbon electrodes based on three-dimensional conducting polymer nanocomposites (TDCPNs) were fabricated for evaluating the aging process of Huangjiu (Chinese rice wines). The electrochemical activity and experimental conditions of the TDCPNs modified electrodes were investigated by cyclic voltammetry, the aging information obtained by the modified electrodes were optimized by variance inflation factor (VIF). Principal components analysis (PCA), locally linear embedding (LLE), and locality preserving projection (LPP, which presented the best classification result) based on the optimized data were applied to classify the wine samples. Then, the dimensionality reduction data of PCA, LLE, and LPP were used as input variables of the logistic regression and extreme learning machine (ELM) for evaluating the aging process of Huangjiu, and the LLE-ELM method exhibited the best prediction results. These results demonstrated that the TDCPNs modified electrodes presented the potential for the quality analysis of food and beverages.
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Recent Advances in Applications of Support Vector Machines in Fungal Biology. Fungal Biol 2022. [DOI: 10.1007/978-3-030-83749-5_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Maeda Y, Yoshino T, Kogiso A, Negishi R, Takabayashi T, Tago H, Lim TK, Harada M, Matsunaga T, Tanaka T. Lensless imaging-based discrimination between tumour cells and blood cells towards circulating tumour cell cultivation. Analyst 2021; 146:7327-7335. [PMID: 34766603 DOI: 10.1039/d1an01414e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Circulating tumour cells (CTCs) are recognized as important markers for cancer research. Nonetheless, the extreme rarity of CTCs in blood samples limits their availability for multiple characterization. The cultivation of CTCs is still technically challenging due to the lack of information of CTC proliferation, and it is difficult for conventional microscopy to monitor CTC cultivation owing to low throughput. In addition, for precise monitoring, CTCs need to be distinguished from the blood cells which co-exist with CTCs. Lensless imaging is an emerging technique to visualize micro-objects over a wide field of view, and has been applied for various cytometry analyses including blood tests. However, discrimination between tumour cells and blood cells was not well studied. In this study, we evaluated the potential of the lensless imaging system as a tool for monitoring CTC cultivation. Cell division of model tumour cells was examined using the lensless imaging system composed of a simple setup. Subsequently, we confirmed that tumour cells, JM cells (model lymphocytes), and erythrocytes exhibited cell line-specific patterns on the lensless images. After several discriminative parameters were extracted, discrimination between the tumour cells and other blood cells was demonstrated based on linear discriminant analysis. We also combined the highly efficient CTC recovery device, termed microcavity array, with the lensless-imaging to demonstrate recovery, monitoring and discrimination of the tumour cells spiked into whole blood samples. This study indicates that lensless imaging can be a powerful tool to investigate CTC proliferation and cultivation.
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Affiliation(s)
- Yoshiaki Maeda
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo, 184-8588, Japan.
| | - Tomoko Yoshino
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo, 184-8588, Japan.
| | - Atsushi Kogiso
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo, 184-8588, Japan.
| | - Ryo Negishi
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo, 184-8588, Japan.
| | - Tomohiro Takabayashi
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo, 184-8588, Japan.
| | - Hikaru Tago
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo, 184-8588, Japan.
| | - Tae-Kyu Lim
- Malcom Co., Ltd, 4-15-10, Honmachi, Shibuya-ku, Tokyo, 151-0071, Japan
| | - Manabu Harada
- Malcom Co., Ltd, 4-15-10, Honmachi, Shibuya-ku, Tokyo, 151-0071, Japan
| | - Tadashi Matsunaga
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo, 184-8588, Japan. .,Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 2-15, Natsushima-cho, Yokosuka, Kanagawa, 237-0061, Japan
| | - Tsuyoshi Tanaka
- Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Naka-cho, Koganei, Tokyo, 184-8588, Japan.
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