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Rapid and accurate classification and discrimination of bacteria is an important task and has been highlighted recently for rapid diagnostics using real-time results. Coupled with a recent report by Jim O'Neill [] that if left unaddressed antimicrobial resistance (AMR) in bacteria could kill 10 million people per year by 2050, which would surpass current cancer mortality, this further highlights the need for unequivocal identification of microorganisms. Whilst traditional microbiological testing has offered insights into the characterisation and identification of a wide range of bacteria, these approaches have proven to be laborious and time-consuming and are not really fit for purpose, considering the modern day speed and volume of international travel and the opportunities it creates for the spread of pathogens globally. To overcome these disadvantages, modern analytical methods, such as mass spectrometry (MS) and vibrational spectroscopy, that analyse the whole organism, have emerged as essential alternative approaches. Currently within clinical microbiology laboratories, matrix assisted laser desorption ionisation (MALDI)-MS is the method of choice for bacterial identification. This is largely down to its robust analysis as it largely measures the ribosomes which are always present irrespective of how the bacteria are cultured. However, MALDI-MS requires large amounts of biomass and infrared spectroscopy and Raman spectroscopy are attractive alternatives as these physicochemical bioanalytical techniques have the advantages of being rapid, reliable and cost-effective for analysing various types of bacterial samples, even at the single cell level. In this review, we discuss the fundamental applications, advantages and disadvantages of modern analytical techniques used for bacterial characterisation, classification and identification.
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http://dx.doi.org/10.1039/d0an01482f | DOI Listing |
Appl Environ Microbiol
September 2025
DGIMI, Université de Montpellier, INRAE, Montpellier, France.
is an entomopathogenic bacterium involved in a mutualistic relationship with nematodes. produces a multitude of specialized metabolites by non-ribosomal peptide synthetase (NRPS) pathways to mediate bacterium-nematode-insect interactions. PAX cyclolipopeptides are a family of NRP-type molecules whose ecological role remains poorly understood.
View Article and Find Full Text PDFBrain Behav
September 2025
Department of Neurosurgery, First Medical Center of the Chinese PLA General Hospital, Beijing, People's Republic of China.
Background: The gut microbiota plays a crucial role in the development of glioma. With the evolution of artificial intelligence technology, applying AI to analyze the vast amount of data from the gut microbiome indicates the potential that artificial intelligence and computational biology hold in transforming medical diagnostics and personalized medicine.
Methods: We conducted metagenomic sequencing on stool samples from 42 patients diagnosed with glioma after operation and 30 non-intracranial tumor patients and developed a Gradient Boosting Machine (GBM) machine learning model to predict the glioma patients based on the gut microbiome data.
Food Res Int
November 2025
Department of Agricultural, Forest and Food Sciences, University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy; Interdepartmental Centre for Grapevines and Wine Sciences, University of Turin, Corso Enotria 2/C, 12051 Alba, Italy. Electronic address:
Microorganisms colonizing grapevines possess diverse functional capabilities that influence the health, growth, productivity and, consequently, wine quality. In this study, spatial and temporal dynamics of the microbiome of Vitis vinifera cv. Barbera grapevine were determined by shotgun sequencing.
View Article and Find Full Text PDFJ Int Med Res
September 2025
Department of Hematology, Guangzhou Eighth People's Hospital, Guangzhou Medical University, China.
ObjectiveAccurate prognostication is crucial for managing human immunodeficiency virus (HIV)-associated cutaneous T-cell lymphoma. In this study, we aimed to develop an improved machine learning-based prognostic model for predicting the 5-year survival rates in HIV-associated cutaneous T-cell lymphoma patients.MethodsWe derived and tested machine learning models using algorithms including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest.
View Article and Find Full Text PDFCell Mol Biol (Noisy-le-grand)
September 2025
Department of Dentistry, Al-Esraa University, Baghdad, Iraq.
Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and is often associated with poor oral health. Cytokines play a central role in RA immunopathogenesis. This case-control study investigated the involvement of salivary interleukin-17A (IL-17A) and interleukin-18 (IL-18) in RA patients in relation to oral health status.
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