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Статті в журналах з теми "Bacterial classification"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Krieg, Noel R. "Bacterial classification: an overview." Canadian Journal of Microbiology 34, no. 4 (1988): 536–40. http://dx.doi.org/10.1139/m88-091.

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Classification of bacteria evolved from limited subjective groupings to general, more objective arrangements based on overall phenotypic similarities. However, classifications based on phenotypic characters lack stability, whereas those based on genetic relatedness tend to be stable. DNA–DNA hybridization has proven to be extremely useful in resolving taxonomic problems at the species level. Broad relationships among bacteria have been identified by comparing ribosomal RNA cistrons; however, many groups based on ribosomal RNA analysis are not easily definable in terms of phenotypic similaritie

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2

Hokkinen, Lauri, Artturi Kesti, Jaakko Lepomäki, et al. "Differential mobility spectrometry classification of bacteria." Future Microbiology 15, no. 4 (2020): 233–40. http://dx.doi.org/10.2217/fmb-2019-0192.

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Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost–effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-va

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3

Brenner, Don J. "Phylogenetic classification of bacteria and recent developments in bacterial classification and nomenclature." Clinical Microbiology Newsletter 10, no. 20 (1988): 153–56. http://dx.doi.org/10.1016/0196-4399(88)90030-x.

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4

Tanko, Sani Muhammed, Muhammad Sani, and Abubakar Ahmad. "Enhancing Bacteria Classification using Image Processing and Convolutional Neural Network." Journal of Basics and Applied Sciences Research 2, no. 1 (2024): 156–61. http://dx.doi.org/10.33003/jobasr-2024-v2i1-42.

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Bacteria classification plays a vital role in the medical field, facilitating the diagnosis and treatment of various diseases. Traditionally, clinical specialists have relied on conventional techniques for classification, which lack predictive capabilities. Manual classification of bacteria is a laborious and time-consuming task that demands significant human effort. However, advancements in technology have opened possibilities for microorganism classification through the utilization of novel machine learning algorithms. This research explores the integration of Convolutional Neural Networks (

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5

Haddad, Mohammed F., Basima A. Abdullah, Hassan AA AlObeidi, Ali M. Saadi, and Mustafa F. Haddad. "Antibiotic classification, mechanisms, and indications: A review." International Journal of Medical and All Body Health Research 5, no. 3 (2024): 39–46. http://dx.doi.org/10.54660/ijmbhr.2024.5.3.39-46.

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An antibiotic was originally a material created by one microorganism that selectively inhibits another’s development. Since then, synthetic antibiotics have been developed that perform comparable tasks, typically chemically similar to natural antibiotics. There are no effects of antibiotics on viral infections. Antibiotics are used to treat bacterial infections in humans and animals. Several proposed classifications of antibiotics including chemical structure, mode of action, or organism of inhibitory activity. Most of the classification systems include two primary categories, the first catego

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6

Ananya, Das Rojina Khatun Sudeshna Sengupta Malavika Bhattacharya*. "Advances in Bacterial Classification: From Phenotypic Traits to Genomic Signatures." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 1293–306. https://doi.org/10.5281/zenodo.15367325.

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Our knowledge of the biology of bacterial systems and classification methods has greatly increased as a result of recent developments in bacterial classification using cell imaging. In other instances, imaging has fueled fascinating developments in bacterial cell biology that have resulted in a finer knowledge of the mechanisms behind protein localization and cell growth. Geometric elements taken from digital microscopic pictures have been used to create automated systems for the identification and classification of bacterial cells. The current study's goal is to create an automated system for

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7

Wonohadidjojo, Daniel Martomanggolo. "Classification of Bacterial Images using Transfer Learning, Optimized Training and Resnet-50." Eduvest - Journal Of Universal Studies 2, no. 2 (2022): 297–305. http://dx.doi.org/10.36418/edv.v2i2.352.

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Bacterial image analysis using traditional laboratory methods encounters bacterial recognition errors and requires extra experience and long processing time. Therefore, the automated classification technique of bacterial images is more useful than traditional visual observations for biologists because of their accurate classification, low cost, and fast diagnosis. In this study, a method to classify bacteria images by implementing the CNN deep learning method using Transfer Learning is proposed. This trained ResNet-50 is implemented as the CNN architecture. In the training of the classificatio

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8

Wonohadidjojo, Daniel Martomanggolo. "Classification of Bacterial Images using Transfer Learning, Optimized Training and Resnet-50." Eduvest - Journal of Universal Studies 2, no. 2 (2022): 297–305. http://dx.doi.org/10.59188/eduvest.v2i2.352.

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Анотація:

Bacterial image analysis using traditional laboratory methods encounters bacterial recognition errors and requires extra experience and long processing time. Therefore, the automated classification technique of bacterial images is more useful than traditional visual observations for biologists because of their accurate classification, low cost, and fast diagnosis. In this study, a method to classify bacteria images by implementing the CNN deep learning method using Transfer Learning is proposed. This trained ResNet-50 is implemented as the CNN architecture. In the training of the classificatio

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9

Timofeeva, Anna M., Maria R. Galyamova, and Sergey E. Sedykh. "Bacterial Siderophores: Classification, Biosynthesis, Perspectives of Use in Agriculture." Plants 11, no. 22 (2022): 3065. http://dx.doi.org/10.3390/plants11223065.

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Siderophores are synthesized and secreted by many bacteria, yeasts, fungi, and plants for Fe (III) chelation. A variety of plant-growth-promoting bacteria (PGPB) colonize the rhizosphere and contribute to iron assimilation by plants. These microorganisms possess mechanisms to produce Fe ions under iron-deficient conditions. Under appropriate conditions, they synthesize and release siderophores, thereby increasing and regulating iron bioavailability. This review focuses on various bacterial strains that positively affect plant growth and development through synthesizing siderophores. Here we di

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10

Sohn, Miryeong, David S. Himmelsbach, Franklin E. Barton, and Paula J. Fedorka-Cray. "Fluorescence Spectroscopy for Rapid Detection and Classification of Bacterial Pathogens." Applied Spectroscopy 63, no. 11 (2009): 1251–55. http://dx.doi.org/10.1366/000370209789806993.

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This study deals with the rapid detection and differentiation of Escherichia coli, Salmonella, and Campylobacter, which are the most commonly identified commensal and pathogenic bacteria in foods, using fluorescence spectroscopy and multivariate analysis. Each bacterial sample cultured under controlled conditions was diluted in physiologic saline for analysis. Fluorescence spectra were collected over a range of 200–700 nm with 0.5 nm intervals on the PerkinElmer Fluorescence Spectrometer. The synchronous scan technique was employed to find the optimum excitation (λex) and emission (λem) wavele

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11

Hermanns, Thomas, and Kay Hofmann. "Bacterial DUBs: deubiquitination beyond the seven classes." Biochemical Society Transactions 47, no. 6 (2019): 1857–66. http://dx.doi.org/10.1042/bst20190526.

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Protein ubiquitination is a posttranslational modification that regulates many aspects of cellular life, including proteostasis, vesicular trafficking, DNA repair and NF-κB activation. By directly targeting intracellular bacteria or bacteria-containing vacuoles to the lysosome, ubiquitination is also an important component of cell-autonomous immunity. Not surprisingly, several pathogenic bacteria encode deubiquitinases (DUBs) and use them as secreted effectors that prevent ubiquitination of bacterial components. A systematic overview of known bacterial DUBs, including their cleavage specificit

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12

Sørensen, Patricia E., Duncan Y. K. Ng, Luc Duchateau, Hanne Ingmer, An Garmyn, and Patrick Butaye. "Classification of In Vitro Phage–Host Population Growth Dynamics." Microorganisms 9, no. 12 (2021): 2470. http://dx.doi.org/10.3390/microorganisms9122470.

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The therapeutic use of bacteriophages (phage therapy) represents a promising alternative to antibiotics to control bacterial pathogens. However, the understanding of the phage–bacterium interactions and population dynamics seems essential for successful phage therapy implementation. Here, we investigated the effect of three factors: phage species (18 lytic E. coli-infecting phages); bacterial strain (10 APEC strains); and multiplicity of infection (MOI) (MOI 10, 1, and 0.1) on the bacterial growth dynamics. All factors had a significant effect, but the phage appeared to be the most important.

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13

Zakhia, Frédéric, and Philippe de Lajudie. "La taxonomie bactérienne moderne : revue des techniques — application à la caractérisation des bactéries nodulant les légumineuses (BNL)." Canadian Journal of Microbiology 52, no. 3 (2006): 169–81. http://dx.doi.org/10.1139/w05-092.

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Taxonomy is the science that studies the relationships between organisms. It comprises classification, nomenclature, and identification. Modern bacterial taxonomy is polyphasic. This means that it is based on several molecular techniques, each one retrieving the information at different cellular levels (proteins, fatty acids, DNA...). The obtained results are combined and analysed to reach a "consensus taxonomy" of a microorganism. Until 1970, a small number of classification techniques were available for microbiologists (mainly phenotypic characterization was performed: a legume species nodul

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14

Abougarair, Ahmed Jaber, Abdulhamid A. Oun, Salah I. Sawan, and Alfian Ma’arif. "Deep Learning-Based Automated Approach for Classifying Bacterial Images." International Journal of Robotics and Control Systems 4, no. 2 (2024): 849–76. http://dx.doi.org/10.31763/ijrcs.v4i2.1423.

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Identifying and classifying bacterial species from microscopic images is crucial for medical applications like prevention, diagnosis, and treatment. However, because of their diversity and variability in appearance, manually classifying bacteria is difficult and time-consuming. This work suggests employing deep learning architecture to automatically categorize bacterial species in order to overcome these difficulties and raise the accuracy of bacterial species recognition. We have evaluated our suggested approach using the Digital Images of Bacteria Species (DIBaS), a publicly accessible resou

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15

Madaki, Khadija Aminu, and Halima Musa Rabi’u. "Biosurfactant: Bacterial Production, Properties, Classification and Applications." Dutse Journal of Pure and Applied Sciences 11, no. 1b (2025): 17–25. https://doi.org/10.4314/dujopas.v11i1b.3.

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Анотація:

Biosurfactants (BS) are amphiphilic substances that are made by microbes like bacteria which due to their accumulation at the interface of immiscible liquids (water and oil) lower surface and interfacial tension. These compounds are environmentally benign substitutes for synthetic surfactants because of their biodegradability, low toxicity, and efficacy in harsh environmental settings. Exploring the taxonomy, characteristics, synthesis, and uses of bacteria that produce biosurfactants is the goal of this review. Based on their chemical makeup and molecular weight, they are classified including

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16

K. PRIYADARSINI, HARSHITHA THOKALA, and PHANEENDRA VATTIKUNTA. "Classification of gram-positive and gram-negative bacteria using Few-shot learning algorithm." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 516–23. http://dx.doi.org/10.30574/wjarr.2024.22.2.1427.

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Our research introduces a novel approach to classify bacteria as Gram-positive or Gram-negative using few-shot learning. We employ deep neural networks, specifically Prototypical Networks, to learn distinctive features from bacterial images, enabling accurate classification even with limited data. Experimental results on diverse datasets demonstrate the model's effectiveness and potential for real-world applications in microbiology and healthcare. We also address interpretability, ethics, and data privacy, making it a valuable tool for bacterial classification and diagnostics.

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17

K., PRIYADARSINI, THOKALA HARSHITHA, and VATTIKUNTA PHANEENDRA. "Classification of gram-positive and gram-negative bacteria using Few-shot learning algorithm." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 516–23. https://doi.org/10.5281/zenodo.14555072.

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Анотація:

Our research introduces a novel approach to classify bacteria as Gram-positive or Gram-negative using few-shot learning. We employ deep neural networks, specifically Prototypical Networks, to learn distinctive features from bacterial images, enabling accurate classification even with limited data. Experimental results on diverse datasets demonstrate the model's effectiveness and potential for real-world applications in microbiology and healthcare. We also address interpretability, ethics, and data privacy, making it a valuable tool for bacterial classification and diagnostics.

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18

Mihaylova-Garnizova, Raynichka, Slavena Davidova, Yordan Hodzhev, and Galina Satchanska. "Antimicrobial Peptides Derived from Bacteria: Classification, Sources, and Mechanism of Action against Multidrug-Resistant Bacteria." International Journal of Molecular Sciences 25, no. 19 (2024): 10788. http://dx.doi.org/10.3390/ijms251910788.

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Antimicrobial peptides (AMPs) are short, usually cationic peptides with an amphiphilic structure, which allows them to easily bind and interact with the cellular membranes of viruses, bacteria, fungi, and other pathogens. Bacterial AMPs, or bacteriocins, can be produced from Gram-negative and Gram-positive bacteria via ribosomal synthesis to eliminate competing organisms. Bacterial AMPs are vital in addressing the increasing antibiotic resistance of various pathogens, potentially serving as an alternative to ineffective antibiotics. Bacteriocins have a narrow spectrum of action, making them hi

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19

Paul, Bobby, Gunjan Dixit, Thokur Sreepathy Murali, and Kapaettu Satyamoorthy. "Genome-based taxonomic classification." Genome 62, no. 2 (2019): 45–52. http://dx.doi.org/10.1139/gen-2018-0072.

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Bacterial populations are routinely characterized based on microscopic examination, colony formation, and biochemical tests. However, in the recent past, bacterial identification, classification, and nomenclature have been strongly influenced by genome sequence information. Advances in bioinformatics and growth in genome databases has placed genome-based metadata analysis in the hands of researchers who will require taxonomic experience to resolve intricacies. To achieve this, different tools are now available to quantitatively measure genome relatedness within members of the same species, and

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20

Fitri, Zilvanhisna Emka, and Arizal Mujibtamana Nanda Imron. "Implementation of Channel Area Thresholding in Early Detection System of Acute Respiratory Infection (ARI)." Indonesian Applied Physics Letters 5, no. 1 (2024): 14–22. http://dx.doi.org/10.20473/iapl.v5i1.55626.

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Acute respiratory infections (ARI) are infectious diseases that affect both children and adults, particularly in the context of climate change. Bacteria are one of the causes of ARI. According to the government, the discovery of the bacteria that cause ARI is an indicator of successful management of infectious diseases. The current obstacle is the limited number of medical analysts, which results in longer microscopic examination times and requires a high level of objectivity. Therefore, a system for the early detection of ARI-causing bacteria was developed using digital image processing techn

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21

Wang, Hua, Fei-Fei Han, Jian-Xun Wen, et al. "Accuracy of the Sysmex UF-5000 analyzer for urinary tract infection screening and pathogen classification." PLOS ONE 18, no. 2 (2023): e0281118. http://dx.doi.org/10.1371/journal.pone.0281118.

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The screening performance of urine flow cytometry parameters (e.g., white blood cell and bacteria) for urinary tract infection (UTI) has been widely recognized. The majority of previous studies, however, investigated the screening performance of Sysmex UF-1000i urine flow cytometer. This study aimed to investigate the screening performance of Sysmex UF-5000 analyzer, a third-generation urinary flow cytometer, for UTI and its novel parameter named Gram flag for discriminating gram-positive and negative pathogens. Urine specimens sent to the clinical microbiology laboratory of our hospital for b

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22

Mai, Duc-Tho, and Koichiro Ishibashi. "Small-Scale Depthwise Separable Convolutional Neural Networks for Bacteria Classification." Electronics 10, no. 23 (2021): 3005. http://dx.doi.org/10.3390/electronics10233005.

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Bacterial recognition and classification play a vital role in diagnosing disease by determining the presence of large bacteria in the specimens and the symptoms. Artificial intelligence and computer vision widely applied in the medical domain enable improving accuracy and reducing the bacterial recognition and classification time, which aids in making clinical decisions and choosing the proper treatment. This paper aims to provide an approach of 33 bacteria strains’ automated classification from the Digital Images of Bacteria Species (DIBaS) dataset based on small-scale depthwise separable con

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23

Treguier, Sylvain, Christel Couderc, Marjorie Audonnet, et al. "Identification of lactic acid bacteria and rhizobacteria by ultraviolet-visible-near infrared spectroscopy and multivariate classification." Journal of Near Infrared Spectroscopy 29, no. 5 (2021): 278–88. http://dx.doi.org/10.1177/09670335211035992.

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The biological processes of interest to agro-industry involve numerous bacterial species. Lactic acid bacteria produce metabolites capable of fermenting food products and modifying their organoleptic properties, and plant-growth-promoting rhizobacteria can act as biofertilizers, biostimulants, or biocontrol agents in agriculture. The protocol of conventional techniques for bacterial identification, currently based on genotyping and phenotyping, require specific sample preparation and destruction. The work presented herein details a method for rapid identification of lactic acid bacteria and rh

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24

Treguier, Sylvain, Christel Couderc, Marjorie Audonnet, et al. "Identification of lactic acid bacteria and rhizobacteria by ultraviolet-visible-near infrared spectroscopy and multivariate classification." Journal of Near Infrared Spectroscopy 29, no. 5 (2021): 278–88. http://dx.doi.org/10.1177/09670335211035992.

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Анотація:

The biological processes of interest to agro-industry involve numerous bacterial species. Lactic acid bacteria produce metabolites capable of fermenting food products and modifying their organoleptic properties, and plant-growth-promoting rhizobacteria can act as biofertilizers, biostimulants, or biocontrol agents in agriculture. The protocol of conventional techniques for bacterial identification, currently based on genotyping and phenotyping, require specific sample preparation and destruction. The work presented herein details a method for rapid identification of lactic acid bacteria and rh

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25

Li, Hui, Peter Torab, Kathleen E. Mach, et al. "Adaptable microfluidic system for single-cell pathogen classification and antimicrobial susceptibility testing." Proceedings of the National Academy of Sciences 116, no. 21 (2019): 10270–79. http://dx.doi.org/10.1073/pnas.1819569116.

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Infectious diseases caused by bacterial pathogens remain one of the most common causes of morbidity and mortality worldwide. Rapid microbiological analysis is required for prompt treatment of bacterial infections and to facilitate antibiotic stewardship. This study reports an adaptable microfluidic system for rapid pathogen classification and antimicrobial susceptibility testing (AST) at the single-cell level. By incorporating tunable microfluidic valves along with real-time optical detection, bacteria can be trapped and classified according to their physical shape and size for pathogen classi

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26

ARPIGNY, Jean Louis, and Karl-Erich JAEGER. "Bacterial lipolytic enzymes: classification and properties." Biochemical Journal 343, no. 1 (1999): 177–83. http://dx.doi.org/10.1042/bj3430177.

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Knowledge of bacterial lipolytic enzymes is increasing at a rapid and exciting rate. To obtain an overview of this industrially very important class of enzymes and their characteristics, we have collected and classified the information available from protein and nucleotide databases. Here we propose an updated and extensive classification of bacterial esterases and lipases based mainly on a comparison of their amino acid sequences and some fundamental biological properties. These new insights result in the identification of eight different families with the largest being further divided into s

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27

ARPIGNY, Jean Louis, and Karl-Erich JAEGER. "Bacterial lipolytic enzymes: classification and properties." Biochemical Journal 343, no. 1 (1999): 177. http://dx.doi.org/10.1042/0264-6021:3430177.

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28

Couturier, M., F. Bex, P. L. Bergquist, and W. K. Maas. "Identification and classification of bacterial plasmids." Microbiological Reviews 52, no. 3 (1988): 375–95. http://dx.doi.org/10.1128/mmbr.52.3.375-395.1988.

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Couturier, M., F. Bex, P. L. Bergquist, and W. K. Maas. "Identification and classification of bacterial plasmids." Microbiological Reviews 52, no. 3 (1988): 375–95. http://dx.doi.org/10.1128/mr.52.3.375-395.1988.

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30

de las Rivas, Blanca, Héctor Rodríguez, Juan Anguita, and Rosario Muñoz. "Bacterial tannases: classification and biochemical properties." Applied Microbiology and Biotechnology 103, no. 2 (2018): 603–23. http://dx.doi.org/10.1007/s00253-018-9519-y.

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31

Liu, Bo, Dandan Zheng, Siyu Zhou, Lihong Chen, and Jian Yang. "VFDB 2022: a general classification scheme for bacterial virulence factors." Nucleic Acids Research 50, no. D1 (2021): D912—D917. http://dx.doi.org/10.1093/nar/gkab1107.

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Abstract The virulence factor database (VFDB, http://www.mgc.ac.cn/VFs/) is dedicated to presenting a comprehensive knowledge base and a versatile analysis platform for bacterial virulence factors (VFs). Recent developments in sequencing technologies have led to increasing demands to analyze potential VFs within microbiome data that always consist of many different bacteria. Nevertheless, the current classification of VFs from various pathogens is based on different schemes, which create a chaotic situation and form a barrier for the easy application of the VFDB dataset for future panbacterial

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32

Kastanos, Evdokia, Alexandros Kyriakides, Katerina Hadjigeorgiou, and Costas Pitris. "A Novel Method for Bacterial UTI Diagnosis Using Raman Spectroscopy." International Journal of Spectroscopy 2012 (January 12, 2012): 1–13. http://dx.doi.org/10.1155/2012/195317.

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The current state of the art on bacterial classification using Raman and Surface Enhanced Raman Spectroscopy (SERS) for the purpose of developing a rapid and more accurate method for urinary tract infection (UTI) diagnosis is presented. SERS, an enhanced version of Raman offering much increased sensitivity, provides complex biochemical information which, in conjunction with advanced analysis and classification techniques, can become a valuable diagnostic tool. The variety of metal substrates used for SERS, including silver and gold colloids, as well as nanostructured metal surfaces, is reviewe

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33

Rahmawati, Diana, Mutiara Puspa Putri I, Miftachul Ulum, and Koko Joni. "Identification and Classification of Pathogenic Bacteria Using the K-Nearest Neighbor Method." JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) 5, no. 1 (2021): 60–70. http://dx.doi.org/10.21070/jeeeu.v5i1.1221.

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Анотація:

Bacteria are a group of living things or organisms that do not have a core covering. In the grouping, some bacteria are pathogenic. With a microscopic size, many pathogenic bacteria are found around and spread through the food eaten or by touching objects around them, then cause diseases such as diarrhea, vomiting, and others. As a more effective effort to help the government and society prevent disease caused by pathogenic bacteria, a system for the identification and classification of pathogenic bacteria K-Nearest Neighbor was created. This system uses a biological microscope that is attache

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34

Buzalewicz, Igor, Agnieszka Suchwałko, Magdalena Karwańska, Alina Wieliczko, and Halina Podbielska. "Development of the Correction Algorithm to Limit the Deformation of Bacterial Colonies Diffraction Patterns Caused by Misalignment and Its Impact on the Bacteria Identification in the Proposed Optical Biosensor." Sensors 20, no. 20 (2020): 5797. http://dx.doi.org/10.3390/s20205797.

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Recently proposed methods of bacteria identification in optical biosensors based on the phenomenon of light diffraction on macro-colonies offer over 98% classification accuracy. However, such high accuracy relies on the comparable and repeatable spatial intensity distribution of diffraction patterns. Therefore, it is essential to eliminate all non-species/strain-dependent factors affecting the diffraction patterns. In this study, the impact of the bacterial colony and illuminating beam misalignment on the variation of classification features extracted from diffraction patterns was examined. It

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35

Staley, James T. "The bacterial species dilemma and the genomic–phylogenetic species concept." Philosophical Transactions of the Royal Society B: Biological Sciences 361, no. 1475 (2006): 1899–909. http://dx.doi.org/10.1098/rstb.2006.1914.

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The number of species of Bacteria and Archaea ( ca 5000) is surprisingly small considering their early evolution, genetic diversity and residence in all ecosystems. The bacterial species definition accounts in part for the small number of named species. The primary procedures required to identify new species of Bacteria and Archaea are DNA–DNA hybridization and phenotypic characterization. Recently, 16S rRNA gene sequencing and phylogenetic analysis have been applied to bacterial taxonomy. Although 16S phylogeny is arguably excellent for classification of Bacteria and Archaea from the Domain l

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36

Hallström, Erik, Vinodh Kandavalli, Petter Ranefall, Johan Elf, and Carolina Wählby. "Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy." PLOS Computational Biology 19, no. 11 (2023): e1011181. http://dx.doi.org/10.1371/journal.pcbi.1011181.

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Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determi

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37

Saida, H., N. Ytow, and H. Seki. "Photometric Application of the Gram Stain Method To Characterize Natural Bacterial Populations in Aquatic Environments." Applied and Environmental Microbiology 64, no. 2 (1998): 742–47. http://dx.doi.org/10.1128/aem.64.2.742-747.1998.

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ABSTRACT The Gram stain method was applied to the photometric characterization of aquatic bacterial populations with a charge-coupled device camera and an image analyzer. Escherichia coli andBacillus subtilis were used as standards of typical gram-negative and gram-positive bacteria, respectively. A mounting agent to obtain clear images of Gram-stained bacteria on Nuclepore membrane filters was developed. The bacterial stainability by the Gram stain was indicated by the Gram stain index (GSI), which was applicable not only to the dichotomous classification of bacteria but also to the character

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38

Fitri, Zilvanhisna Emka, Lalitya Nindita Sahenda, Pramuditha Shinta Dewi Puspitasari, Prawidya Destarianto, Dyah Laksito Rukmi, and Arizal Mujibtamala Nanda Imron. "The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 12, no. 2 (2021): 91. http://dx.doi.org/10.24843/lkjiti.2021.v12.i02.p03.

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Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion

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39

Ahmed, Shwan, Jawaher Albahri, Sahand Shams, et al. "Rapid Classification and Differentiation of Sepsis-Related Pathogens Using FT-IR Spectroscopy." Microorganisms 12, no. 7 (2024): 1415. http://dx.doi.org/10.3390/microorganisms12071415.

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Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for

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40

Young, J. M., C. T. Bull, S. H. De Boer, et al. "Classification, Nomenclature, and Plant Pathogenic Bacteria - A Clarification." Phytopathology® 91, no. 7 (2001): 617–20. http://dx.doi.org/10.1094/phyto.2001.91.7.617.

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In a recent Letter to the Editor of Phytopathology, proposals were made for endorsement and for rejection of selected names of plant pathogenic Pseudomonas spp. and Xanthomonas spp. We believe that support for, and rejection of, several names was based on misconceptions concerning the Approved Lists of Bacterial Names and entails misinterpretations of several Rules of the International Code of Nomenclature of Bacteria. This letter aims to clarify those misconceptions and misinterpretations.

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41

Greenman, Noah, Sayf Al-Deen Hassouneh, Latifa S. Abdelli, Catherine Johnston, and Taj Azarian. "Improving Bacterial Metagenomic Research through Long-Read Sequencing." Microorganisms 12, no. 5 (2024): 935. http://dx.doi.org/10.3390/microorganisms12050935.

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Metagenomic sequencing analysis is central to investigating microbial communities in clinical and environmental studies. Short-read sequencing remains the primary approach for metagenomic research; however, long-read sequencing may offer advantages of improved metagenomic assembly and resolved taxonomic identification. To compare the relative performance for metagenomic studies, we simulated short- and long-read datasets using increasingly complex metagenomes comprising 10, 20, and 50 microbial taxa. Additionally, we used an empirical dataset of paired short- and long-read data generated from

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42

Seo, Youngwook, Bosoon Park, Seung-Chul Yoon, Kurt C. Lawrence, and Gary R. Gamble. "Morphological Image Analysis for Foodborne Bacteria Classification." Transactions of the ASABE 61, no. 1 (2018): 5–13. http://dx.doi.org/10.13031/trans.11800.

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Abstract. The hyperspectral imaging methods used previously for analyzing food quality and safety focused on spectral data analysis to elucidate the spectral characteristics relevant to the quality and safety of food and agricultural commodities. However, the use of spatial information, including physical size, geometric characteristics, orientation, shape, color, and texture, in hyperspectral imaging analysis of food safety and quality has been limited. In this study, image processing techniques were employed for extracting information related to the morphological features of fifteen differen

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43

Akbar, Son Ali, Kawarul Hawari Ghazali, Habsah Hasan, Zeehaida Mohamed, Wahyu Sapto Aji, and Anton Yudhana. "Rapid bacterial colony classification using deep learning." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 352. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp352-361.

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Bacterial colonies infection is one of the causes of bloodstream disease, and it can be a fatality. Therefore, medical diagnoses require fast identification and classification of organisms. Artificial Intelligence with deep learning (DL) can now be developed as a rapid bacterial classification. The research aims to combine deep learning and support vector machines (SVM). The ResNet-101 model of the DL algorithm extracted the image’s features using transfer learning then classified by the SVM classifier. According to the experimental results, this model had 99.61% accuracy, 99.58% recall, 99.58

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44

NYEO, SU-LONG, I.-CHING YANG, and CHI-HAO WU. "SPECTRAL CLASSIFICATION OF ARCHAEAL AND BACTERIAL GENOMES." Journal of Biological Systems 10, no. 03 (2002): 233–41. http://dx.doi.org/10.1142/s0218339002000561.

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The power spectra of the nucleotides in the coding and noncoding sequences of the complete genomes of twenty-two archaea and bacteria are obtained. According to the intensities at the periodicity of 3 bp in the spectra, it is observed that the genomic sequences may be classified into three types. Moreover, the spectra generally have a small but broad peak in the 10–11 bp periodicities. For the archaea, the peak is seen to locate preferably at about 10 bp periodicity, while for the bacteria, it tends to locate at about 11 bp. These features suggest that the DNA sequences of archaea generally ha

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45

Barash, Eran, Neta Sal-Man, Sivan Sabato, and Michal Ziv-Ukelson. "BacPaCS—Bacterial Pathogenicity Classification via Sparse-SVM." Bioinformatics 35, no. 12 (2018): 2001–8. http://dx.doi.org/10.1093/bioinformatics/bty928.

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46

Dubuisson, Marie-Pierre, Anil K. Jain, and Mahendra K. Jain. "Segmentation and classification of bacterial culture images." Journal of Microbiological Methods 19, no. 4 (1994): 279–95. http://dx.doi.org/10.1016/0167-7012(94)90031-0.

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47

Zieliński, Bartosz, Anna Plichta, Krzysztof Misztal, Przemysław Spurek, Monika Brzychczy-Włoch, and Dorota Ochońska. "Deep learning approach to bacterial colony classification." PLOS ONE 12, no. 9 (2017): e0184554. http://dx.doi.org/10.1371/journal.pone.0184554.

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48

Grimont, Patrick A. D. "Use of DNA reassociation in bacterial classification." Canadian Journal of Microbiology 34, no. 4 (1988): 541–46. http://dx.doi.org/10.1139/m88-092.

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The reassociation properties of DNA provide invaluable taxonomic tools. Different methods may give different reassociation values. However, the thermal stability of reassociated DNA strands (a measurement that seems independent of method) is useful in delineating genomic species. Although many phenotypically defined species have been confirmed by DNA reassociation, some medically important genomic species previously had been split into several nomenspecies on the basis of a few characteristics whereas some environmental genomic species had been lumped into unidentifiable aggregates. It might t

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49

Akbar, Son Ali, Kawarul Hawari Ghazali, Habsah Hasan, Zeehaida Mohamed, Wahyu Sapto Aji, and Anton Yudhana. "Rapid bacterial colony classification using deep learning." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 352–61. https://doi.org/10.11591/ijeecs.v26.i1.pp352-361.

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Анотація:

Bacterial colonies infection is one of the causes of bloodstream disease, and it can be a fatality. Therefore, medical diagnoses require fast identification and classification of organisms. Artificial Intelligence with deep learning (DL) can now be developed as a rapid bacterial classification. The research aims to combine deep learning and support vector machines (SVM). The ResNet-101 model of the DL algorithm extracted the image’s features using transfer learning then classified by the SVM classifier. According to the experimental results, this model had 99.61% accuracy, 99.58% recall,

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50

Shifa, Begum, Begum Tofa, Rahman Naziza, and A. Khan Ruhul. "A review on antibiotic resistance and way of combating antimicrobial resistance." GSC Biological and Pharmaceutical Sciences 14, no. 2 (2021): 087–91. https://doi.org/10.5281/zenodo.4605427.

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Antibiotics are widely used most effective medication since the twentieth century against bacterial infections (Tetanus, Strep Throat, Urinary Tract Infections, etc.) and thus save one’s life. Before 20th-century infectious disease played the main role in the death. Thus, antibiotics opened a revolutionary era in the field of medication. These cannot fight against viral infections. Antibiotics are also known as an antibacterial that kill or slow down bacterial growth and prohibit the bacteria to harm. Resistance comes as a curse with antibiotics that occurs when bacteria change in some w

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