AIMC Topic: Microbial Sensitivity Tests

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Antimicrobial peptide developed with machine learning sequence optimization targets drug resistant Staphylococcus aureus in mice.

The Journal of clinical investigation
As antimicrobial resistance rises, new antibacterial candidates are urgently needed. Using sequence space information from over 14,743 functional antimicrobial peptides (AMPs), we improved the antimicrobial properties of citropin 1.1, an AMP with wea...

Accelerating antimicrobial stewardship: An AI-CDSS approach to combating multidrug-resistant pathogens in the era of increasing resistance.

Clinica chimica acta; international journal of clinical chemistry
OBJECTIVES: The World Health Organization has identified Klebsiella pneumoniae (KP) and Pseudomonas aeruginosa (PA) as significant public health threats owing to high antibiotic resistance. Traditional antibiotic susceptibility testing (AST) methods,...

Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry
Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...

Machine Learning Accelerated Discovery of Antimicrobial Inorganic Nanomaterials.

The journal of physical chemistry letters
The growing prevalence of infectious diseases and the increasing threat of bacterial resistance have drawn widespread attention to antimicrobial inorganic nanomaterials. However, the diversity, abundance, and complex mechanisms of these materials pre...

Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries.

Journal of infection in developing countries
INTRODUCTION: Antimicrobial resistance (AMR) is a global health challenge, and antimicrobial susceptibility testing (AST) is vital for guiding treatment. Although widely used, the Kirby-Bauer method depends on skilled interpretation, which can be tim...

Precise Detection of Carbapenem-Resistant and Hypervirulent Using MOF-Derived Bimetallic Nanocube Hybrid Nanosheet.

Analytical chemistry
Carbapenem resistance and hypervirulence represent two distinct evolutionary pathways in , posing significant challenges in clinical settings. Of particular concern are convergent strains that combine both traits, complicating timely diagnosis and tr...

Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides.

Biomacromolecules
Identifying host defense peptides (HDPs) that are effective against drug-resistant infections is challenging due to their vast sequence space. Artificial intelligence (AI)-guided design can accelerate HDP discovery, but it traditionally requires larg...

Machine Learning-Based Discovery of a Novel Noncovalent MurA Inhibitor as an Antibacterial Agent.

Chemical biology & drug design
The bacterial cell wall is crucial for maintaining the integrity of bacterial cells. UDP-N-acetylglucosamine 1-carboxyethylene transferase (MurA) is an important enzyme involved in bacterial cell wall synthesis. Therefore, it is an important target f...

Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.

Microbial biotechnology
Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models fo...

Integrating MALDI-TOF Mass Spectrometry with Machine Learning Techniques for Rapid Antimicrobial Resistance Screening of Foodborne Bacterial Pathogens.

Methods in molecular biology (Clifton, N.J.)
Although MALDI-TOF mass spectrometry (MS) is considered as the gold standard for rapid and cost-effective identification of microorganisms in routine laboratory practices, its capability for antimicrobial resistance (AMR) detection has received limit...