AIMC Topic: Drug Resistance, Bacterial

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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...

Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability.

Scientific reports
The prevalence and spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global public health problem. This study aims to identify the risk factors of CRPA infection and construct a machine learning model to provide a prediction tool for ...

Barriers to the widespread adoption of diagnostic artificial intelligence for preventing antimicrobial resistance.

Scientific reports
Currently, antimicrobial resistance (AMR) poses a major public health challenge. The emergence of AMR, which significantly threatens public health, is primarily due to the overuse of antimicrobial agents. This study explored the possibility that the ...

Antimicrobial resistance: Linking molecular mechanisms to public health impact.

SLAS discovery : advancing life sciences R & D
BACKGROUND: Antimicrobial resistance (AMR) develops into a worldwide health emergency through genetic and biochemical adaptations which enable microorganisms to resist antimicrobial treatment. β-lactamases (blaNDM, blaKPC) and efflux pumps (MexAB-Opr...

Febrile neutropenia management in high-risk neutropenic patients: a narrative review on antibiotic prophylaxis and empirical treatment.

Expert review of anti-infective therapy
INTRODUCTION: Although febrile neutropenia (FN) remains a major cause of morbidity and mortality in patients with hematologic malignancies and hematopoietic stem cell transplant (HSCT) recipients, the increasing prevalence of antimicrobial resistance...

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.

Scientific reports
Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, an...

Assessment for antibiotic resistance in : A practical and interpretable machine learning model based on genome-wide genetic variation.

Virulence
() antibiotic resistance poses a global health threat. Accurate identification of antibiotic resistant strains is essential for the control of infection. In the present study, our goal is to leverage the whole-genome data of to develop practical an...

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial is...

DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.

Microbiome
BACKGROUND: Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen resistance to antimicrobial substances, posing a threat of fatal infections and limiting the benefi...

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

Biochemistry
The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target...