AIMC Topic: Microbial Sensitivity Tests

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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 ...

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...

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...

Time-Lapse Deep Learning for Single-Cell Subcellular Structural Phenotypic Antimicrobial Susceptibility Testing.

Analytical chemistry
Antimicrobial resistance (AMR) is a global health concern that complicates the effective treatment of infections, resulting in an increased severity of illness and elevated healthcare costs. Traditional phenotypic antimicrobial susceptibility testing...

CarbaDetector: a machine learning model for detecting carbapenemase-producing Enterobacterales from disk diffusion tests.

Nature communications
Carbapenemase-producing Enterobacterales (CPE) are considered among the highest threats to global health by WHO. Their detection is difficult and time-consuming. We developed a random-forest machine learning (ML) model, CarbaDetector, to predict carb...

Label-free phenotypic antimicrobial susceptibility testing on microfluidic platforms: a review of advances and translation.

Mikrochimica acta
The escalating global threat of antimicrobial resistance (AMR) necessitates a paradigm shift towards rapid and scalable diagnostic technologies. Conventional antimicrobial susceptibility testing (AST) methods, while reliable, are hindered by prolonge...

Identifying Structure-Activity Relationships for Cyanine-Derived Antibiotics Using Machine Learning and Commercial Large Language Models.

Journal of chemical information and modeling
Understanding the structure-activity relationship (SAR) of antibiotic scaffolds is crucial for the development of antibiotics to counter the growing crisis of antimicrobial resistant bacteria. However, an overwhelming space of structural features imp...

The Use of DeepQSAR Models for the Discovery of Peptides with Enhanced Antimicrobial and Antibiofilm Potential.

Journal of chemical information and modeling
Increasing concerns regarding prolonged antibiotic usage have spurred the search for alternative treatments. Antimicrobial peptides (AMPs), first discovered in the 1980s, have exhibited significant potential against a broad range of bacteria. Short-s...

Design, synthesis, deep learning-guided prediction, and biological evaluation of novel pyridine-thiophene-based imine-benzalacetophenone hybrids as promising antimicrobial agent.

Journal of computer-aided molecular design
Antimicrobial resistance (AMR) remains a global health crisis, necessitating the development of novel therapeutics against multidrug-resistant pathogens. In this study, ten (10) hybrid imine-benzalacetophenone derivatives (7a-7j), incorporating pyrid...