AIMC Topic: Drug Resistance, Multiple, Bacterial

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Antimicrobial Activity of Tea and Agarwood Leaf Extracts Against Multidrug-Resistant Microbes.

BioMed research international Dec 19, 2024
Emerging multidrug-resistant (MDR) strains are the main challenges to the progression of new drug discovery. To diminish infectious disease-causing pathogens, new antibiotics are required while the drying pipeline of potent antibiotics is adding to t...
Thymelaeaceae Drug Resistance, Multiple, Bacterial Camellia sinensis Tea Bacteria Anti-Bacterial Agents Plant Extracts Flavonoids Anti-Infective Agents Plant Leaves Phytochemicals Microbial Sensitivity Tests Phenols
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Integrating machine learning and multitargeted drug design to combat antimicrobial resistance: a systematic review.

Journal of drug targeting Nov 26, 2024
Antimicrobial resistance (AMR) is a critical global health challenge, undermining the efficacy of antimicrobial drugs against microorganisms like bacteria, fungi and viruses. Multidrug resistance (MDR) arises when microorganisms become resistant to m...
Enterobacteriaceae Anti-Bacterial Agents Drug Design Humans Drug Resistance, Multiple, Bacterial Machine Learning
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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...
Genotype Whole Genome Sequencing Acinetobacter Infections Acinetobacter baumannii Multilocus Sequence Typing Genome, Bacterial Humans Neural Networks, Computer Phenotype Anti-Bacterial Agents Phylogeny Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial
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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...
Humans Machine Learning Drug Resistance, Bacterial Tuberculosis, Multidrug-Resistant Whole Genome Sequencing Mycobacterium tuberculosis Genome, Bacterial Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial Mutation Antitubercular Agents
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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...
Artificial Intelligence Machine Learning Anti-Bacterial Agents Microbial Sensitivity Tests Humans Drug Combinations Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Azabicyclo Compounds Ceftazidime Drug Resistance, Multiple, Bacterial Decision Support Systems, Clinical Klebsiella pneumoniae Klebsiella Infections
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A deep learning-driven discovery of berberine derivatives as novel antibacterial against multidrug-resistant Helicobacter pylori.

Signal transduction and targeted therapy Jul 8, 2024
Helicobacter pylori (H. pylori) is currently recognized as the primary carcinogenic pathogen associated with gastric tumorigenesis, and its high prevalence and resistance make it difficult to tackle. A graph neural network-based deep learning model, ...
Humans Anti-Bacterial Agents Animals Berberine Omeprazole Clarithromycin Helicobacter Infections Helicobacter pylori Deep Learning Amoxicillin Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial
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Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

Antimicrobial resistance and infection control Jul 6, 2024
BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO p...
Middle Aged Humans Drug Resistance, Multiple, Bacterial Machine Learning ROC Curve Aged Anti-Bacterial Agents Cross Infection Adult Intensive Care Units Male Electronic Health Records Female
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An exploration of descriptive machine learning approaches for antimicrobial resistance: Multidrug resistance patterns in Salmonella enterica.

Preventive veterinary medicine Jul 2, 2024
Salmonellosis is one of the most common foodborne diseases worldwide, with the ability to infect humans and animals. Antimicrobial resistance (AMR) and, particularly, multidrug resistance (MDR) among Salmonella enterica poses a risk to human health. ...
Salmonella enterica Salmonella Infections, Animal Machine Learning Anti-Bacterial Agents Animals Drug Resistance, Multiple, Bacterial Cattle Cattle Diseases United States
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Innovative strategies against superbugs: Developing an AI-CDSS for precise Stenotrophomonas maltophilia treatment.

Journal of global antimicrobial resistance Jun 22, 2024
OBJECTIVES: The World Health Organization named Stenotrophomonas maltophilia (SM) a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 h, including 72 h for bacterial growth...
Gram-Negative Bacterial Infections Levofloxacin Trimethoprim, Sulfamethoxazole Drug Combination Stenotrophomonas maltophilia Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial Decision Support Systems, Clinical Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Artificial Intelligence Machine Learning Humans Anti-Bacterial Agents
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Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant Gram-negative infection.

International journal of antimicrobial agents Apr 19, 2024
OBJECTIVES: Colistin-induced nephrotoxicity prolongs hospitalisation and increases mortality. The study aimed to construct machine learning models to predict colistin-induced nephrotoxicity in patients with multidrug-resistant Gram-negative infection...
Retrospective Studies ROC Curve Humans Electronic Health Records Adult Colistin Machine Learning Female Male Aged Middle Aged Gram-Negative Bacterial Infections Drug Resistance, Multiple, Bacterial Algorithms Anti-Bacterial Agents
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