AIMC Topic: Microbial Sensitivity Tests

Clear Filters Showing 51 to 60 of 289 articles

Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools.

International journal of antimicrobial agents Dec 5, 2024
BACKGROUND: Given the rising number of multidrug-resistant (MDR) bacteria, there is a need to design synthetic antimicrobial peptides (AMPs) that are highly active, non-hemolytic, and highly soluble. Machine learning tools allow the straightforward i...
Humans Machine Learning Inhibitory Concentration 50 Erythrocytes Tryptophan Hemolysis Antimicrobial Peptides Methicillin-Resistant Staphylococcus aureus Antimicrobial Cationic Peptides CHO Cells Solubility Anti-Bacterial Agents Animals Microbial Sensitivity Tests
View on PubMed DOI

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BMC infectious diseases Dec 5, 2024
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...
Middle Aged Microbial Sensitivity Tests Uganda Polymorphism, Single Nucleotide Female Humans Young Adult Machine Learning Adult Mycobacterium tuberculosis Drug Resistance, Bacterial Male Isoniazid Rifampin Streptomycin Antitubercular Agents South Africa Tuberculosis, Multidrug-Resistant Whole Genome Sequencing
View on PubMed DOI

Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant from whole genome sequencing and gene expression.

Antimicrobial agents and chemotherapy Nov 14, 2024
Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susce...
Humans Neural Networks, Computer Anti-Bacterial Agents Phenotype Acinetobacter baumannii Phylogeny Genotype Microbial Sensitivity Tests Multilocus Sequence Typing Whole Genome Sequencing Drug Resistance, Multiple, Bacterial Genome, Bacterial Acinetobacter Infections
View on PubMed DOI

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.

Scientific reports Nov 7, 2024
Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the dis...
Mycobacterium tuberculosis Genome, Bacterial Machine Learning Drug Resistance, Bacterial Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial Mutation Antitubercular Agents Tuberculosis, Multidrug-Resistant Whole Genome Sequencing Humans
View on PubMed DOI

Application of Raman spectroscopy and machine learning for identification and characterization.

Applied and environmental microbiology Oct 29, 2024
UNLABELLED: an emerging fungal pathogen characterized by multidrug resistance and high-mortality nosocomial infections, poses a serious global health threat. However, the precise and rapid identification and characterization of remain a challenge. ...
Machine Learning Microbial Sensitivity Tests Spectrum Analysis, Raman Candida Humans Amphotericin B Candida auris Candidiasis Antifungal Agents Fluconazole Drug Resistance, Fungal
View on PubMed DOI

Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests.

PeerJ Oct 9, 2024
BACKGROUND: Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pat...
Drug Resistance, Bacterial Microbial Sensitivity Tests Bacteria Humans Whole Genome Sequencing Machine Learning Anti-Bacterial Agents
View on PubMed DOI

Proof of concept study on early forecasting of antimicrobial resistance in hospitalized patients using machine learning and simple bacterial ecology data.

Scientific reports Sep 30, 2024
Antibiotic resistance in bacterial pathogens is a major threat to global health, exacerbated by the misuse of antibiotics. In hospital practice, results of bacterial cultures and antibiograms can take several days. Meanwhile, prescribing an empirical...
Anti-Bacterial Agents Drug Resistance, Bacterial Hospitalization Microbial Sensitivity Tests Humans Neural Networks, Computer Machine Learning Proof of Concept Study Bacteria Bacterial Infections Bayes Theorem France
View on PubMed DOI

Recursive dynamics of GspE through machine learning enabled identification of inhibitors.

Computational biology and chemistry Sep 28, 2024
Type II secretion System has been increasingly recognized as a key driver of virulence in many pathogenic bacteria including Achromobacter xylosoxidans. ATPase GspE is the powerhouse of the T2SS. It powers the entire secretion process by binding with...
Machine Learning Anti-Bacterial Agents Molecular Docking Simulation Molecular Structure Microbial Sensitivity Tests Molecular Dynamics Simulation Enzyme Inhibitors Adenosine Triphosphatases
View on PubMed DOI

Artificial intelligence-clinical decision support system for enhanced infectious disease management: Accelerating ceftazidime-avibactam resistance detection in Klebsiella pneumoniae.

Journal of infection and public health Sep 10, 2024
BACKGROUND: Effective and rapid diagnostic strategies are required to manage antibiotic resistance in Klebsiella pneumonia (KP). This study aimed to design an artificial intelligence-clinical decision support system (AI-CDSS) using matrix-assisted la...
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Azabicyclo Compounds Humans Ceftazidime Artificial Intelligence Decision Support Systems, Clinical Machine Learning Klebsiella pneumoniae Klebsiella Infections Anti-Bacterial Agents Drug Combinations Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial
View on PubMed DOI

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...
Humans Machine Learning Oxacillin Anti-Bacterial Agents Drug Resistance, Bacterial Databases, Factual Genetic Markers Staphylococcal Infections Microbial Sensitivity Tests Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Staphylococcus aureus Penicillin-Binding Proteins Taiwan Bacterial Proteins
View on PubMed DOI
Popular Topics
Recent Journals