AIMC Topic: Staphylococcus aureus
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High-throughput, rapid, and non-destructive detection of common foodborne pathogens via hyperspectral imaging coupled with deep neural networks and support vector machines.
Food research international (Ottawa, Ont.)
Jan 7, 2025
Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major causes of gastrointestinal diseases worldwide and frequently contaminate dairy products. Compared to nucleic acid detection and MALDI-TOF MS, hyperspec...
Optimizing the production and efficacy of antimicrobial bioactive compounds from in combating multi-drug-resistant pathogens.
Frontiers in cellular and infection microbiology
Jan 6, 2025
BACKGROUND: The rise of antibiotic-resistant pathogens has intensified the search for novel antimicrobial agents. This study aimed to isolate from local soil samples and evaluate its antimicrobial properties, along with optimizing the production of ...
Drug Resistance, Multiple, Bacterial
Anti-Bacterial Agents
Bacillus subtilis
Soil Microbiology
DNA, Bacterial
Spectroscopy, Fourier Transform Infrared
Anti-Infective Agents
RNA, Ribosomal, 16S
Culture Media
Escherichia coli
Microbial Sensitivity Tests
Candida albicans
Streptomyces
Staphylococcus aureus
High-content imaging and deep learning-driven detection of infectious bacteria in wounds.
Bioprocess and biosystems engineering
Dec 2, 2024
Fast and accurate detection of infectious bacteria in wounds is crucial for effective clinical treatment. However, traditional methods take over 24 h to yield results, which is inadequate for urgent clinical needs. Here, we introduce a deep learning-...
Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning.
Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi
Nov 29, 2024
BACKGROUND: Rapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based methods are time-consuming, while MALDI-TOF MS is expensive. The Raman spectroscopy, due to its relati...
Antimicrobial efficacy of an experimental UV-C robot in controlled conditions and in a real hospital scenario.
The Journal of hospital infection
Nov 19, 2024
BACKGROUND: Among no-touch automated disinfection devices, ultraviolet-C (UV-C) radiation has been proven to be one of the most effective against a broad spectrum of micro-organisms causing healthcare-associated infections.
Application of artificial neural network for the mechano-bactericidal effect of bioinspired nanopatterned surfaces.
European biophysics journal : EBJ
Oct 7, 2024
This study aimed to calculate the effect of nanopatterns' peak sharpness, width, and spacing parameters on P. aeruginosa and S. aureus cell walls by artificial neural network and finite element analysis. Elastic and creep deformation models of bacter...
Accurate prediction of antimicrobial resistance and genetic marker of Staphylococcus aureus clinical isolates using MALDI-TOF MS and machine learning - across DRIAMS and Taiwan database.
International journal of antimicrobial agents
Sep 6, 2024
BACKGROUND: The use of matrix-assisted laser desorption/ionisation-time-of-flight mass spectra (MALDI-TOF MS) with machine learning (ML) has been explored for predicting antimicrobial resistance. This study evaluates the effectiveness of MALDI-TOF MS...
Genetic Markers
Staphylococcal Infections
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Penicillin-Binding Proteins
Oxacillin
Anti-Bacterial Agents
Microbial Sensitivity Tests
Drug Resistance, Bacterial
Staphylococcus aureus
Humans
Databases, Factual
Taiwan
Machine Learning
Bacterial Proteins
Integrated epigenomic exposure signature discovery.
Epigenomics
Sep 3, 2024
The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis. Here we developed and implemented a machine learning algorithm, the exposure signature discove...
Discovery of AMPs from random peptides via deep learning-based model and biological activity validation.
European journal of medicinal chemistry
Aug 26, 2024
The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent ...
Machine Learning-Driven Discovery and Evaluation of Antimicrobial Peptides from Mucus Proteome.
Marine drugs
Aug 26, 2024
Marine antimicrobial peptides (AMPs) represent a promising source for combating infections, especially against antibiotic-resistant pathogens and traditionally challenging infections. However, traditional drug discovery methods face challenges such a...
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