AIMC Topic: Drug Resistance, Bacterial

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Prediction of protein structural changes mediated by NS-SNPs in antibiotic resistance determinants in Streptococcus pneumoniae.

Archives of microbiology
Streptococcus pneumoniae (S. pneumoniae) is a gram-positive bacterium, which is a human pathogen that colonises the human nasopharyngeal region. The evolution of its resistance to many antibiotics has become a major clinical and public health problem...

Hospital acquired drug resistant pathogens infections in patients with viral respiratory tract infections: a retrospective study.

BMC infectious diseases
BACKGROUND: Viral respiratory infections (VRTIs) caused by influenza (Flu) and COVID-19 pose significant global health challenges. Clinical outcomes are further exacerbated by infections with hospital acquired drug resistant pathogens (DRPs).

Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning.

Scientific reports
Antimicrobial resistance is a growing global health threat, and artificial intelligence offers a promising avenue for developing advanced tools to address this challenge. In this study, we applied various machine learning techniques to predict bacter...

LC-MS/MS metabolomics unravels the resistant phenotype of carbapenemase-producing Enterobacterales.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: The degree of antimicrobial resistance demonstrated by carbapenemase-producing Enterobacterales (CPE) represents a growing public health challenge. Conventional methods for detecting CPE involve culture-based techniques with lengthy inc...

Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.

BMC genomic data
BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine lear...

Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for via metagenomic sequencing.

Microbiology spectrum
Antimicrobial resistance (AMR) represents a critical global health challenge, demanding rapid and accurate antimicrobial susceptibility testing (AST) to guide timely treatments. Traditional culture-based AST methods are slow, while existing whole-gen...

Machine Learning and DIA Proteomics Reveal New Insights into Carbapenem Resistance Mechanisms in .

Journal of proteome research
The emergence of Carbapenem-resistant (CRKP) represents a major public health concern, primarily driven by its ability to evade a wide range of antibiotics. Despite extensive genomic studies, proteomic insights into antibiotic resistance mechanisms ...

Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry.

Scientific reports
Antibiotic resistance in Burkholderia pseudomallei (Bp) is a growing public health concern requiring urgent attention. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid bacterial identi...

1,3-Thiazole nucleus as promising molecular platform against antimicrobial resistance: a recent overview in drug discovery.

European journal of medicinal chemistry
Antimicrobial resistance (AMR) has emerged as one of the foremost public health threats of the 21st century. The progressive decline in the efficacy of conventional antibiotics, combined with a scarcity of new drug classes approved in recent decades,...

NLP-like deep learning aided in identification and validation of thiosulfinate tolerance clusters in diverse bacteria.

mSphere
Allicin tolerance () clusters in phytopathogenic bacteria, which provide resistance to thiosulfinates like allicin, are challenging to find using conventional approaches due to their varied architecture and the paradox of being vertically maintained ...