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Drug Resistance, Bacterial

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A historical, economic, and technical-scientific approach to the current crisis in the development of antibacterial drugs: Promising role of antibacterial peptides in this scenario.

Microbial pathogenesis
The emergence of antibiotic resistance (AMR) is a global public health problem. According to estimates, drug-resistant bacteria infect 2 million patients and perish 23,000 annually. To overcome this problem, antimicrobial peptides became a potential ...

Computational biology: Role and scope in taming antimicrobial resistance.

Indian journal of medical microbiology
BACKGROUND: Infectious diseases pose many challenges due to increasing threat of antimicrobial resistance, which necessitates continuous research to develop novel strategies for development of new molecules with antibacterial activity. In the era of ...

Using machine learning techniques to predict antimicrobial resistance in stone disease patients.

World journal of urology
PURPOSE: Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predictin...

Strengths and caveats of identifying resistance genes from whole genome sequencing data.

Expert review of anti-infective therapy
INTRODUCTION: Antimicrobial resistance (AMR) continues to present major challenges to modern healthcare. Recent advances in whole-genome sequencing (WGS) have made the rapid molecular characterization of AMR a realistic possibility for diagnostic lab...

Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.

International journal of molecular sciences
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 ...

Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis.

Analytical and bioanalytical chemistry
The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the ant...

A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction.

IEEE/ACM transactions on computational biology and bioinformatics
Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to whi...

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.

Genome medicine
BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly...

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility.

mSphere
antibiotic susceptibility testing often fails to accurately predict drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investiga...