AIMC Topic: Pseudomonas aeruginosa
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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...
Predicting drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra.
mSystems
Aug 16, 2024
Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited....
VacSol-ML(ESKAPE) Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.
Vaccine
Aug 9, 2024
The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensiv...
Nonionic surfactant Tween 80-facilitated bacterial transport in porous media: A nonmonotonic concentration-dependent performance, mechanism, and machine learning prediction.
Environmental research
Mar 16, 2024
The surfactant-enhanced bioremediation (SEBR) of organic-contaminated soil is a promising soil remediation technology, in which surfactants not only mobilize pollutants, but also alter the mobility of bacteria. However, the bacterial response and und...
Machine learning identification of Pseudomonas aeruginosa strains from colony image data.
PLoS computational biology
Dec 13, 2023
When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have u...
Rapid Discrimination of ST175 Isolates Involved in a Nosocomial Outbreak Using MALDI-TOF Mass Spectrometry and FTIR Spectroscopy Coupled with Machine Learning.
Transboundary and emerging diseases
Sep 7, 2023
The goal of this study was to evaluate matrix-assisted laser desorption ionization-iime of flight mass spectrometry (MALDI-TOF MS) and Fourier-transform infrared spectroscopy (FTIR-S) as diagnostic alternatives to DNA-based methods for the detection ...
The Microorganism Detection System (SDM) for microbiological control of cosmetic products.
Annals of agricultural and environmental medicine : AAEM
Dec 16, 2021
The Microorganism Detection System (SDM) is a new solution using artificial intelligence, unique on the international scale, to correctly identify and count microorganisms, with particular emphasis on specificlisted microorganisms (Document of Standa...
Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.
International journal of molecular sciences
Dec 2, 2021
The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic ...
Decreased neutrophil-mediated bacterial killing in COVID-19 patients.
Scandinavian journal of immunology
Jun 11, 2021
The coronavirus disease COVID-19 was first described in December 2019. The peripheral blood of COVID-19 patients have increased numbers of neutrophils which are important in controlling the bacterial infections observed in COVID-19. We sought to eval...
A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach.
Bio Systems
Mar 20, 2021
Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response t...
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