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An T, Liang Z, Chen Z, Li G. Recent progress in online detection methods of bioaerosols. FUNDAMENTAL RESEARCH 2024; 4:442-454. [PMID: 38933213 PMCID: PMC10239662 DOI: 10.1016/j.fmre.2023.05.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/03/2023] [Accepted: 05/03/2023] [Indexed: 10/29/2023] Open
Abstract
The aerosol transmission of coronavirus disease in 2019, along with the spread of other respiratory diseases, caused significant loss of life and property; it impressed upon us the importance of real-time bioaerosol detection. The complexity, diversity, and large spatiotemporal variability of bioaerosols and their external/internal mixing with abiotic components pose challenges for effective online bioaerosol monitoring. Traditional methods focus on directly capturing bioaerosols before subsequent time-consuming laboratory analysis such as culture-based methods, preventing the high-resolution time-based characteristics necessary for an online approach. Through a comprehensive literature assessment, this review highlights and discusses the most commonly used real-time bioaerosol monitoring techniques and the associated commercially available monitors. Methods applied in online bioaerosol monitoring, including adenosine triphosphate bioluminescence, laser/light-induced fluorescence spectroscopy, Raman spectroscopy, and bioaerosol mass spectrometry are summarized. The working principles, characteristics, sensitivities, and efficiencies of these real-time detection methods are compared to understand their responses to known particle types and to contrast their differences. Approaches developed to analyze the substantial data sets obtained by these instruments and to overcome the limitations of current real-time bioaerosol monitoring technologies are also introduced. Finally, an outlook is proposed for future instrumentation indicating a need for highly revolutionized bioaerosol detection technologies.
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Affiliation(s)
- Taicheng An
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China
- Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Zhishu Liang
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China
- Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Zhen Chen
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China
| | - Guiying Li
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China
- Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
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2
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Bastos ML, Benevides CA, Zanchettin C, Menezes FD, Inácio CP, de Lima Neto RG, Filho JGAT, Neves RP, Almeida LM. Breaking barriers in Candida spp. detection with Electronic Noses and artificial intelligence. Sci Rep 2024; 14:956. [PMID: 38200060 PMCID: PMC10781724 DOI: 10.1038/s41598-023-50332-9] [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: 06/22/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
The timely and accurate diagnosis of candidemia, a severe bloodstream infection caused by Candida spp., remains challenging in clinical practice. Blood culture, the current gold standard technique, suffers from lengthy turnaround times and limited sensitivity. To address these limitations, we propose a novel approach utilizing an Electronic Nose (E-nose) combined with Time Series-based classification techniques to analyze and identify Candida spp. rapidly, using culture species of C. albicans, C.kodamaea ohmeri, C. glabrara, C. haemulonii, C. parapsilosis and C. krusei as control samples. This innovative method not only enhances diagnostic accuracy and reduces decision time for healthcare professionals in selecting appropriate treatments but also offers the potential for expanded usage and cost reduction due to the E-nose's low production costs. Our proof-of-concept experimental results, carried out with culture samples, demonstrate promising outcomes, with the Inception Time classifier achieving an impressive average accuracy of 97.46% during the test phase. This paper presents a groundbreaking advancement in the field, empowering medical practitioners with an efficient and reliable tool for early and precise identification of candidemia, ultimately leading to improved patient outcomes.
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Affiliation(s)
- Michael L Bastos
- Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil.
| | - Clayton A Benevides
- Comissão Nacional de Energia Nuclear, Centro Regional de Ciências Nucleares do Nordeste, Recife, PE, Brazil
| | - Cleber Zanchettin
- Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Frederico D Menezes
- Departamento de Mecânica, Instituto Federal de Pernambuco, Recife, PE, Brazil
| | - Cícero P Inácio
- Centro de Ciências Médicas, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | | | - José Gilson A T Filho
- Centro de Ciências Sociais e Aplicadas, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Rejane P Neves
- Centro de Ciências Médicas, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Leandro M Almeida
- Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil.
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Raslan MA, Raslan SA, Shehata EM, Mahmoud AS, Viana MVC, Aburjaile F, Barh D, Sabri NA, Azevedo V. Mass Spectrometry Applications to Study Human Microbiome. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1443:87-101. [PMID: 38409417 DOI: 10.1007/978-3-031-50624-6_5] [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: 02/28/2024]
Abstract
Microbiotas are an adaptable component of ecosystems, including human ecology. Microorganisms influence the chemistry of their specialized niche, such as the human gut, as well as the chemistry of distant surroundings, such as other areas of the body. Metabolomics based on mass spectrometry (MS) is one of the primary methods for detecting and identifying small compounds generated by the human microbiota, as well as understanding the functional significance of these microbial metabolites. This book chapter gives basic knowledge on the kinds of untargeted mass spectrometry as well as the data types that may be generated in the context of microbiome study. While data analysis remains a barrier, the emphasis is on data analysis methodologies and integrative analysis, particularly the integration of microbiome sequencing data. Mass spectrometry (MS)-based techniques have resurrected culture methods for studying the human gut microbiota, filling in the gaps left by high-throughput sequencing methods in terms of culturing minor populations.
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Affiliation(s)
| | | | | | - Amr S Mahmoud
- Department of Obstetrics and Gynecology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Marcus Vinicius Canário Viana
- Laboratório de Genética Celular e Molecular, Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Flávia Aburjaile
- Preventive Veterinary Medicine Departament, Veterinary School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Debmalya Barh
- Laboratório de Genética Celular e Molecular, Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Institute of Integrative Omics and Applied Biotechnology, Nonakuri, Purba Medinipur, West Bengal, India
| | - Nagwa A Sabri
- Department of Clinical Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
| | - Vasco Azevedo
- Laboratório de Genética Celular e Molecular, Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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4
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Visonà G, Duroux D, Miranda L, Sükei E, Li Y, Borgwardt K, Oliver C. Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information. Bioinformatics 2023; 39:btad717. [PMID: 38001023 PMCID: PMC10724849 DOI: 10.1093/bioinformatics/btad717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/08/2023] [Accepted: 11/23/2023] [Indexed: 11/26/2023] Open
Abstract
MOTIVATION Large-scale clinical proteomics datasets of infectious pathogens, combined with antimicrobial resistance outcomes, have recently opened the door for machine learning models which aim to improve clinical treatment by predicting resistance early. However, existing prediction frameworks typically train a separate model for each antimicrobial and species in order to predict a pathogen's resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability. RESULTS We demonstrate the effectiveness of multimodal learning over proteomic and chemical features by exploring two clinically relevant tasks for our proposed deep learning models: drug recommendation and generalized resistance prediction. By adopting this multi-view representation of the pathogenic samples and leveraging the scale of the available datasets, our models outperformed the previous single-drug and single-species predictive models by statistically significant margins. We extensively validated the multi-drug setting, highlighting the challenges in generalizing beyond the training data distribution, and quantitatively demonstrate how suitable representations of antimicrobial drugs constitute a crucial tool in the development of clinically relevant predictive models. AVAILABILITY AND IMPLEMENTATION The code used to produce the results presented in this article is available at https://github.com/BorgwardtLab/MultimodalAMR.
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Affiliation(s)
- Giovanni Visonà
- Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, Tübingen 72076, Germany
| | - Diane Duroux
- BIO3—GIGA-R Medical Genomics, University of Liège, Avenue de l’Hôpital 11, Liège 4000, Belgium
- ETH AI Center, ETH Zürich, Andreasstrasse 5, Zürich 8092, Switzerland
| | - Lucas Miranda
- Research Group Statistical Genetics, Max Planck Institute of Psychiatry, Kraepelinstraße 10, München 80804, Germany
| | - Emese Sükei
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés 28911, Spain
| | - Yiran Li
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland
| | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland
- Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
| | - Carlos Oliver
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland
- Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
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Sajjad B, Hussain S, Rasool K, Hassan M, Almomani F. Comprehensive insights into advances in ambient bioaerosols sampling, analysis and factors influencing bioaerosols composition. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 336:122473. [PMID: 37659632 DOI: 10.1016/j.envpol.2023.122473] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/20/2023] [Accepted: 08/27/2023] [Indexed: 09/04/2023]
Abstract
While the study of bioaerosols has a long history, it has garnered heightened interest in the past few years, focusing on both culture-dependent and independent sampling and analysis approaches. Observations have been made regarding the seasonal fluctuations in microbial communities and their connection to particular ambient atmospheric factors. The study of airborne microbial communities is important in public health and atmospheric processes. Nevertheless, the establishment of standardized protocols for evaluating airborne microbial communities and utilizing microbial taxonomy as a means to identify distinct bioaerosols sources and seasonal patterns remains relatively unexplored. This article discusses the challenges and limitations of ambient bioaerosols sampling and analysis, including the lack of standardized methods and the heterogeneity of sources. Future prospects in the field of bioaerosols, including the use of high-throughput sequencing technologies, omics studies, spectroscopy and fluorescence-based monitoring to provide comprehensive incite on metabolic capacity, and activity are also presented. Furthermore, the review highlights the factors that affect bioaerosols composition, including seasonality, atmospheric conditions, and pollution levels. Overall, this review provides a valuable resource for researchers, policymakers, and stakeholders interested in understanding and managing bioaerosols in various environments.
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Affiliation(s)
- Bilal Sajjad
- Qatar Environment and Energy Research Institute (QEERI), Hamad Bin Khalifa University, Qatar Foundation, P.O. Box 5825, Doha, Qatar; Department of Chemical Engineering, Qatar University, P. O. Box 2713, Doha, Qatar
| | - Sabir Hussain
- Department of Environmental Science, Institute of Space Technology, Islamabad, Pakistan
| | - Kashif Rasool
- Qatar Environment and Energy Research Institute (QEERI), Hamad Bin Khalifa University, Qatar Foundation, P.O. Box 5825, Doha, Qatar.
| | - Mujtaba Hassan
- Department of Environmental Science, Institute of Space Technology, Islamabad, Pakistan
| | - Fares Almomani
- Department of Chemical Engineering, Qatar University, P. O. Box 2713, Doha, Qatar
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da Silva RR, Tomachewski D, Karas LP, Galvão CW, da Rocha JCF, Miyoshi E, Etto RM. The new Ribopeaks (RPK-II): Updated and enlarged tool for bacterial classification based on r-protein m/z data. J Proteomics 2023; 289:105008. [PMID: 37775078 DOI: 10.1016/j.jprot.2023.105008] [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: 07/11/2023] [Revised: 08/29/2023] [Accepted: 09/20/2023] [Indexed: 10/01/2023]
Abstract
Ribopeaks is a rapid, sensitive, and economic web tool for bacterial identification based on m/z data from MALDI-TOF MS. To provide greater accuracy and robustness in the Ribopeaks analyzes we present an updated bacterial identification tool version, called Ribopeaks II (RPK-II). RPK-II contains a larger database, with r-protein data from fully sequenced bacterial genomes and optimized algorithms. Furthermore, this new version provides additional information about the identified bacterium, regarding antibiotic resistance.
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Affiliation(s)
- Renann Rodrigues da Silva
- Postgraduate Program in Agronomy, State University of Ponta Grossa, Paraná, Brazil; Microbial Molecular Biology Laboratory, State University of Ponta Grossa, Paraná, Brazil
| | - Douglas Tomachewski
- Postgraduate Program in Agronomy, State University of Ponta Grossa, Paraná, Brazil; Microbial Molecular Biology Laboratory, State University of Ponta Grossa, Paraná, Brazil
| | - Laís Priscila Karas
- Microbial Molecular Biology Laboratory, State University of Ponta Grossa, Paraná, Brazil; Postgraduate Program in Biomedical Sciences, State University of Ponta Grossa, Paraná, Brazil
| | - Carolina Weigert Galvão
- Postgraduate Program in Agronomy, State University of Ponta Grossa, Paraná, Brazil; Microbial Molecular Biology Laboratory, State University of Ponta Grossa, Paraná, Brazil
| | - José Carlos Ferreira da Rocha
- Microbial Molecular Biology Laboratory, State University of Ponta Grossa, Paraná, Brazil; Postgraduate Program in Applied Computing, State University of Ponta Grossa, Paraná, Brazil
| | - Edmar Miyoshi
- Microbial Molecular Biology Laboratory, State University of Ponta Grossa, Paraná, Brazil; Postgraduate Program in Biomedical Sciences, State University of Ponta Grossa, Paraná, Brazil
| | - Rafael Mazer Etto
- Postgraduate Program in Agronomy, State University of Ponta Grossa, Paraná, Brazil; Microbial Molecular Biology Laboratory, State University of Ponta Grossa, Paraná, Brazil; Postgraduate Program in Applied Computing, State University of Ponta Grossa, Paraná, Brazil.
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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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Sivanesan I, Gopal J, Hasan N, Muthu M. A systematic assessment of matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) application for rapid identification of pathogenic microbes that affect food crops: delivered and future deliverables. RSC Adv 2023; 13:17297-17314. [PMID: 37304772 PMCID: PMC10251190 DOI: 10.1039/d3ra01633a] [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: 03/13/2023] [Accepted: 05/20/2023] [Indexed: 06/13/2023] Open
Abstract
MALDI-TOF MS has decades of experience in the detection and identification of microbial pathogens. This has now become a valuable analytical tool when it comes to the identification and detection of clinical microbial pathogens. This review gives a brief synopsis of what has been achieved using MALDI-TOF MS in clinical microbiology. The major focus, however, is on summarizing and highlighting the effectiveness of MALDI-TOF MS as a novel tool for rapid identification of food crop microbial pathogens. The methods used and the sample preparation methodologies reported thus far have been highlighted and the challenges and gaps and recommendations for fine tuning the technique have been put forth. In an era where anything close to the health and welfare of humanity has been considered as the top priority, this review pitches on one such relevant research topics.
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Affiliation(s)
- Iyyakkannu Sivanesan
- Department of Bioresources and Food Science, Institute of Natural Science and Agriculture, Konkuk University 1 Hwayang-dong, Gwangjin-gu Seoul 05029 Korea
| | - Judy Gopal
- Department of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602105 Tamil Nadu India +91 44 2681 1009 +91 44 66726677
| | - Nazim Hasan
- Department of Chemistry, Faculty of Science, Jazan University P.O. Box 114 Jazan Saudi Arabia
| | - Manikandan Muthu
- Department of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602105 Tamil Nadu India +91 44 2681 1009 +91 44 66726677
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Hleba L, Hlebova M, Kovacikova E, Kovacik A. MALDI-TOF MS Indirect Beta-Lactamase Detection in Ampicillin-Resistant Haemophilus influenzae. Microorganisms 2023; 11:microorganisms11041018. [PMID: 37110441 PMCID: PMC10142446 DOI: 10.3390/microorganisms11041018] [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: 02/28/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
Rapid identification of beta-lactamase-producing strains of Haemophilus influenzae plays key role in diagnostics in clinical microbiology. Therefore, the aim of this study was the rapid determination of beta-lactamase's presence in H. influenzae isolates via indirect detection of degradation ampicillin products using MALDI-TOF MS. H. influenzae isolates were subjected to antibiotic resistance testing using disk diffusion and MIC methodologies. Beta-lactamase activity was tested using MALDI-TOF MS, and results were compared to spectral analysis of alkaline hydrolysis. Resistant and susceptible strains of H. influenzae were distinguished, and strains with a high MIC level were identified as beta-lactamase-producing. Results indicate that MALDI-TOF mass spectrometry is also suitable for the rapid identification of beta-lactamase-producing H. influenzae. This observation and confirmation can accelerate identification of beta-lactamase strains of H. influenzae in clinical microbiology, which can have an impact on health in general.
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Affiliation(s)
- Lukas Hleba
- Institute of Biotechnology, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture in Nitra, Tr. Andreja Hlinku 2, 949 76 Nitra, Slovakia
| | - Miroslava Hlebova
- Department of Biology, Faculty of Natural Sciences, University of Ss. Cyril and Methodius, Nám. J. Herdu 2, 917 01 Trnava, Slovakia
| | - Eva Kovacikova
- AgroBioTech Research Centre, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 949 76 Nitra, Slovakia
| | - Anton Kovacik
- Institute of Applied Biology, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 949 76 Nitra, Slovakia
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Isolation and Identification of a Bacillus sp. from Freshwater Sediment Displaying Potent Activity Against Bacteria and Phytopathogen Fungi. Curr Microbiol 2022; 79:398. [DOI: 10.1007/s00284-022-03090-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022]
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11
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Li D, Yi J, Han G, Qiao L. MALDI-TOF Mass Spectrometry in Clinical Analysis and Research. ACS MEASUREMENT SCIENCE AU 2022; 2:385-404. [PMID: 36785658 PMCID: PMC9885950 DOI: 10.1021/acsmeasuresciau.2c00019] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/15/2022] [Accepted: 07/15/2022] [Indexed: 05/04/2023]
Abstract
In the decade after being awarded the Nobel Prize in Chemistry in 2002, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used as an analytical chemistry tool for the detection of large and small molecules (e.g., polymers, proteins, peptides, nucleic acids, amino acids, lipids, etc.) and for clinical analysis and research (e.g., pathogen identification, genetic disorders screening, cancer diagnosis, etc.). In view of the fast development of MALDI-TOF MS in clinical usage, this review systematically summarizes the most important applications of MALDI-TOF MS in clinical analysis and research by analyzing MALDI TOF MS-related reviews collected in the Web of Science database. On the basis of the analysis of keyword co-occurrence of over 2000 review articles, four themes consisting of "pathogen identification", "disease diagnosis", "nucleic acids analysis", and "small molecules analysis" were found. For each theme, the review further outlined their application implications, analytical methods, and systems as well as limitations that need to be addressed. Overall, the review summarizes and elaborates on the clinical applications of MALDI-TOF MS, providing a comprehensive picture for researchers embarking on MALDI TOF MS-related clinical analysis and research.
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Nnachi RC, Sui N, Ke B, Luo Z, Bhalla N, He D, Yang Z. Biosensors for rapid detection of bacterial pathogens in water, food and environment. ENVIRONMENT INTERNATIONAL 2022; 166:107357. [PMID: 35777116 DOI: 10.1016/j.envint.2022.107357] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/10/2022] [Accepted: 06/14/2022] [Indexed: 06/15/2023]
Abstract
Conventional techniques (e.g., culture-based method) for bacterial detection typically require a central laboratory and well-trained technicians, which may take several hours or days. However, recent developments within various disciplines of science and engineering have led to a major paradigm shift in how microorganisms can be detected. The analytical sensors which are widely used for medical applications in the literature are being extended for rapid and on-site monitoring of the bacterial pathogens in food, water and the environment. Especially, within the low-resource settings such as low and middle-income countries, due to the advantages of low cost, rapidness and potential for field-testing, their use is indispensable for sustainable development of the regions. Within this context, this paper discusses analytical methods and biosensors which can be used to ensure food safety, water quality and environmental monitoring. In brief, most of our discussion is focused on various rapid sensors including biosensors and microfluidic chips. The analytical performances such as the sensitivity, specificity and usability of these sensors, as well as a brief comparison with the conventional techniques for bacteria detection, form the core part of the discussion. Furthermore, we provide a holistic viewpoint on how future research should focus on exploring the synergy of different sensing technologies by developing an integrated multiplexed, sensitive and accurate sensors that will enable rapid detection for food safety, water and environmental monitoring.
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Affiliation(s)
- Raphael Chukwuka Nnachi
- School of Water, Energy and Environment, Cranfield University, Milton Keynes MK43, 0AL, United Kingdom
| | - Ning Sui
- College of Materials Science and Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
| | - Bowen Ke
- Laboratory of Anesthesiology & Critical Care Medicine, Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan 61004, PR China
| | - Zhenhua Luo
- School of Water, Energy and Environment, Cranfield University, Milton Keynes MK43, 0AL, United Kingdom
| | - Nikhil Bhalla
- Nanotechnology and Integrated Bioengineering Centre (NIBEC), School of Engineering, Ulster University, Shore Road, BT37 0QB Jordanstown, Northern Ireland, United Kingdom; Healthcare Technology Hub, Ulster University, Jordanstown Shore Road, BT37 0QB, Northern Ireland, United Kingdom
| | - Daping He
- School of Science, Wuhan University of Technology, Wuhan 430070, China
| | - Zhugen Yang
- School of Water, Energy and Environment, Cranfield University, Milton Keynes MK43, 0AL, United Kingdom.
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Nehra M, Kumar V, Kumar R, Dilbaghi N, Kumar S. Current Scenario of Pathogen Detection Techniques in Agro-Food Sector. BIOSENSORS 2022; 12:bios12070489. [PMID: 35884292 PMCID: PMC9313409 DOI: 10.3390/bios12070489] [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: 05/11/2022] [Revised: 06/26/2022] [Accepted: 06/28/2022] [Indexed: 05/05/2023]
Abstract
Over the past-decade, agricultural products (such as vegetables and fruits) have been reported as the major vehicles for foodborne diseases, which are limiting food resources. The spread of infectious diseases due to foodborne pathogens poses a global threat to human health and the economy. The accurate and timely detection of infectious disease and of causative pathogens is crucial in the prevention and treatment of disease. Negligence in the detection of pathogenic substances can be catastrophic and lead to a pandemic. Despite the revolution in health diagnostics, much attention has been paid to the agro-food sector regarding the detection of food contaminants (such as pathogens). The conventional analytical techniques for pathogen detection are reliable and still in operation. However, laborious procedures and time-consuming detection via these approaches emphasize the need for simple, easy-to-use, and affordable detection techniques. The rapid detection of pathogens from food is essential to avoid the morbidity and mortality originating from the suboptimal nature of empiric pathogen treatment. This review critically discusses both the conventional and emerging bio-molecular approaches for pathogen detection in agro-food.
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Affiliation(s)
- Monika Nehra
- Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India; (M.N.); (V.K.); (N.D.)
- Department of Mechanical Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India;
| | - Virendra Kumar
- Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India; (M.N.); (V.K.); (N.D.)
| | - Rajesh Kumar
- Department of Mechanical Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India;
| | - Neeraj Dilbaghi
- Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India; (M.N.); (V.K.); (N.D.)
| | - Sandeep Kumar
- Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India; (M.N.); (V.K.); (N.D.)
- Correspondence:
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Metabolomics Research in Periodontal Disease by Mass Spectrometry. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27092864. [PMID: 35566216 PMCID: PMC9104832 DOI: 10.3390/molecules27092864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/24/2022] [Accepted: 04/27/2022] [Indexed: 11/20/2022]
Abstract
Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.
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MALDI-TOF Mass Spectrometry Analysis and Human Post-Mortem Microbial Community: A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19074354. [PMID: 35410034 PMCID: PMC8998342 DOI: 10.3390/ijerph19074354] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 02/04/2023]
Abstract
Introduction: The human post-mortem microbiome (HPM) plays a major role in the decomposition process. Successional changes in post-mortem bacterial communities have been recently demonstrated using high throughput metagenomic sequencing techniques, showing great potential as a post-mortem interval (PMI) predictor. The aim of this study is to verify the application of the mass spectrometry technique, better known as MALDI-TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry), as a cheap and quick method for microbe taxonomic identification and for studying the PM microbiome. Methods: The study was carried out on 18 human bodies, ranging from 4 months to 82 years old and with a PMI range from 24 h up to 15 days. The storage time interval in the coolers was included in the final PMI estimates. Using the PMI, the sample study was divided into three main groups: seven cases with a PMI < 72 h; six cases with a PMI of 72−168 h and five cases with a PMI > 168 h. For each body, microbiological swabs were sampled from five external anatomical sites (eyes, ears, nose, mouth, and rectum) and four internal organs (brain, spleen, liver, and heart). Results: The HPM became increasingly different from the starting communities over time in the internal organs as well as at skin sites; the HPM microbiome was mostly dominated by Firmicutes and Proteobacteria phyla; and a PM microbial turnover existed during decomposition, evolving with the PMI. Conclusions: MALDI-TOF is a promising method for PMI estimation, given its sample handling, good reproducibility, and high speed and throughput. Although several intrinsic and extrinsic factors can affect the structure of the HPM, MALDI-TOF can detect the overall microbial community turnover of most prevalent phyla during decomposition. Limitations are mainly related to its sensitivity due to the culture-dependent method and bias in the identification of new isolates.
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MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062814] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The term “proteome” refers to the total of all proteins expressed in an organism. The term “proteomics” refers to the field of research that includes not only information on the expression levels of individual proteins, but also their higher-order structures, intermolecular interactions, and post-translational modifications. The core technology, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), is available for protein analysis thanks to the work of Koichi Tanaka and John Fenn, who were awarded the Nobel Prize in Chemistry in 2002. The most successful proteome analysis in clinical practice is rapid microbial identification. This method determines the bacterial species by comparing the proteome profile of the bacteria obtained by matrix-assisted laser desorption ionization-time of flight MS (MALDI-TOF MS) with a database. MS is superior in simplicity, speed, and accuracy to classic speciation by staining and phenotyping. In clinical microbiology, MS has had a large impact on the diagnosis and treatment of infectious disease. Early diagnosis and treatment of infectious disease are important, and rapid identification by MALDI-TOF MS has made a major contribution to this field.
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Carbonero-Pacheco J, Moreno-García J, Moreno J, García-Martínez T, Mauricio JC. Revealing the Yeast Diversity of the Flor Biofilm Microbiota in Sherry Wines Through Internal Transcribed Spacer-Metabarcoding and Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry. Front Microbiol 2022; 12:825756. [PMID: 35222316 PMCID: PMC8864117 DOI: 10.3389/fmicb.2021.825756] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/22/2021] [Indexed: 01/04/2023] Open
Abstract
Flor yeast velum is a biofilm formed by certain yeast strains that distinguishes biologically aged wines such as Sherry wine from southern Spain from others. Although Saccharomyces cerevisiae is the most common species, 5.8 S-internal transcribed spacer (ITS) restriction fragment length polymorphism analyses have revealed the existence of non-Saccharomyces species. In order to uncover the flor microbiota diversity at a species level, we used ITS (internal transcribed spacer 1)-metabarcoding and matrix-assisted laser desorption/Ionization time of flight mass spectrometry techniques. Further, to enhance identification effectiveness, we performed an additional incubation stage in 1:1 wine:yeast extract peptone dextrose (YPD) before identification. Six species were identified: S. cerevisiae, Pichia manshurica, Pichia membranifaciens, Wickerhamomyces anomalus, Candida guillermondii, and Trichosporon asahii, two of which were discovered for the first time (C. guillermondii and Trichosporon ashaii) in Sherry wines. We analyzed wines where non-Saccharomyces yeasts were present or absent to see any potential link between the microbiota and the chemical profile. Only 2 significant volatile chemicals (out of 13 quantified), ethanol and ethyl lactate, and 2 enological parameters (out of 6 quantified), such as pH and titratable acidity, were found to differ in long-aged wines. Although results show a low impact where the non-Saccharomyces yeasts are present, these yeasts isolated from harsh environments (high ethanol and low nutrient availability) could have a potential industrial interest in fields such as food microbiology and biofuel production.
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Affiliation(s)
- Juan Carbonero-Pacheco
- Department of Agricultural Chemistry, Edaphology and Microbiology, Agrifood Campus of International Excellence CeiA3, University of Córdoba, Córdoba, Spain
| | - Jaime Moreno-García
- Department of Agricultural Chemistry, Edaphology and Microbiology, Agrifood Campus of International Excellence CeiA3, University of Córdoba, Córdoba, Spain
| | - Juan Moreno
- Department of Agricultural Chemistry, Edaphology and Microbiology, Agrifood Campus of International Excellence CeiA3, University of Córdoba, Córdoba, Spain
| | - Teresa García-Martínez
- Department of Agricultural Chemistry, Edaphology and Microbiology, Agrifood Campus of International Excellence CeiA3, University of Córdoba, Córdoba, Spain
| | - Juan Carlos Mauricio
- Department of Agricultural Chemistry, Edaphology and Microbiology, Agrifood Campus of International Excellence CeiA3, University of Córdoba, Córdoba, Spain
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Zhou B, Tong YK, Zhang R, Ye A. RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra. RSC Adv 2022; 12:26463-26469. [PMID: 36275115 PMCID: PMC9478993 DOI: 10.1039/d2ra03722j] [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: 06/16/2022] [Accepted: 08/25/2022] [Indexed: 11/21/2022] Open
Abstract
Raman spectroscopy combined convolutional neural network (CNN) enables rapid and accurate identification of the species of bacteria. However, the existing CNN requires a complex hyperparameters model design. Herein, we propose a new simple network architecture with less hyperparameter design and low computation cost, RamanNet, for rapid and accurate identifying of bacteria at the species level based on its Raman spectra. We verified that compared with the previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset and PKU-bacterial Raman spectral datasets, but using only about 1/45 and 1/297 network parameters, respectively. RamanNet achieved an average isolate-level accuracy of 84.7 ± 0.3%, antibiotic treatment identification accuracy of 97.1 ± 0.3%, and distinguished accuracy of 81.6 ± 0.9% for methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) on the Bacteria-ID dataset, respectively. Moreover, it achieved an average accuracy of 96.04% on the PKU-bacterial dataset. The RamanNet model benefited from fewer model parameters that can be quickly trained even using CPU. Therefore, our method has the potential to rapidly and accurately identify bacterial species based on their Raman spectra and can be easily extended to other classification tasks based on Raman spectra. We propose a novel CNN model named RamanNet for rapid and accurate identification of bacteria at the species-level based on Raman spectra. Compared to previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset.![]()
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Affiliation(s)
- Bo Zhou
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Yu-Kai Tong
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Ru Zhang
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Anpei Ye
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
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The Role of a Rapid Prevention of Ralstonia pickettii Growth during Dialysis in a Frail Patient. REPORTS 2021. [DOI: 10.3390/reports4040039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Ralstonia pickettii is an opportunistic bacillus found in Pseudomonas species, with the ability to induce systemic infections. We report the case of a 69-year-old man, with a clinical history of myeloma, Type IIdiabetes, renal failure (grade IV), and colon cancer, that developed a severe bacterial infection, with acute asthenia and a fever, that appeared at the end of dialysis. Using theMALDI-TOF technology, the bacillus Ralstonia pickettii was identified, and an antimicrobial treatment was quickly started with a rapid microbiological remission.
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