AIMC Topic: Anti-Bacterial Agents

Clear Filters Showing 1 to 10 of 710 articles

Whole-genome sequencing and comparative genomics reveal antimicrobial potential and adaptive traits of Bacillus velezensis AM12.

Functional & integrative genomics
The global rise of antimicrobial resistance has intensified the demand for novel antimicrobial agents with broad-spectrum efficacy and unique mechanisms of action. Herein, a marine-derived strain, Bacillus velezensis (B. velezensis) AM12, exhibiting ...

Construction and temporal external validation of interpretable machine-learning models for predicting tigecycline-associated hypofibrinogenemia.

European journal of clinical pharmacology
BACKGROUND AND PURPOSE: There is a paucity of available clinical tools with which to accurately predict the risk of tigecycline-associated hypofibrinogenemia, an adverse reaction with a high incidence and serious consequences. This study aimed to dev...

Magnetic Field-Driven Strategies for Biofilm Disruption: From Iron Oxide Nanoparticles to Adaptive Swarms of Magnetic Microrobots.

ACS nano
Biofilms, structured communities of microbial cells embedded in extracellular polymeric substances, are notorious for their resilience against conventional antimicrobial treatments. They contribute significantly to chronic infections and industrial b...

Design of Highly Potent Antibiofilm, Antimicrobial Peptides Using Explainable Artificial Intelligence.

Journal of chemical information and modeling
Antimicrobial peptides have emerged as a potential alternative to traditional small-molecule antimicrobials. They possess broad-spectrum efficacy and increasingly confront the challenges of bacterial resistance, especially the adaptive resistance of ...

Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.

PLoS biology
Antimicrobial resistance (AMR) poses a growing threat to human health. Increasingly, genome sequencing is being applied for the surveillance of bacterial pathogens, producing a wealth of data to train machine learning (ML) applications to predict AMR...

Antimicrobial use and resistance.

BMJ (Clinical research ed.)
Antimicrobial resistance affects the delivery of safe and effective healthcare. Antimicrobial resistance has attracted strong political focus, with the 2024 United Nations General Assembly high level meeting providing a clear commitment to reducing m...

Predicting prolonged dalbavancin exposure using machine learning: a validated strategy for individualized redosing.

Antimicrobial agents and chemotherapy
Dalbavancin is a long-acting lipoglycopeptide increasingly used off-label for complex Gram-positive infections requiring prolonged therapy. Its extended half-life enables simplified regimens, but interindividual pharmacokinetic variability and pathog...

Machine learning-integrated electrochemical sensing of ciprofloxacin for digital point-of-care therapeutic drug monitoring.

Mikrochimica acta
Timely and precise therapeutic drug monitoring (TDM) is critical for managing pharmacokinetic variability and optimizing individualized therapy, particularly during public health crises such as the COVID-19 pandemic. Herein, we optimized integrated m...

Integrating AI-assisted SERS Biosensing and Photoactivated Antibacterial Therapy in Au@CuSe for Combating Multidrug-Resistant Bacteria.

Analytical chemistry
The escalating global crisis of multidrug-resistant (MDR) bacteria demands innovative strategies that bypass conventional antibiotic limitations. This study introduced a multifunctional Au@CuSe core-shell nanoplatform integrating artificial intellige...

Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study.

Journal of clinical microbiology
UNLABELLED: Matrix-assisted laser desorption-ionization-time of flight (MALDI-TOF) mass spectra can be used to predict antimicrobial resistance (AMR) using machine learning (ML). This study aimed to validate the performance of ML models for AMR predi...