AIMC Topic: Staphylococcus aureus
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Ultrasensitive Detection via Machine Learning-Optimized Bacterial-Imprinted Photoelectrochemical Biosensor with Active/Passive Dual-Mode Validation.
Analytical chemistry
Jul 26, 2025
Currently, the existing detection platforms face persistent challenges in achieving reliable bacterial identification within complex matrices, particularly in food and environmental specimens, where matrix interference effects substantially compromis...
Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for via metagenomic sequencing.
Microbiology spectrum
Jul 11, 2025
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...
Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells.
Current microbiology
Jul 11, 2025
Resistance to antimicrobial agents has become a global threat, estimated to cause 10-million deaths annually by 2050. Antimicrobial peptides are emerging as an alternative and offer advantages over traditional antibiotics. Antimicrobial peptides gene...
Experimentally profiling dielectric properties of Escherichia coli and Staphylococcus aureus by movement velocity and force.
Scientific reports
Jul 1, 2025
The gradual research in integrating artificial intelligence in the Dielectrophoresis system is rapid since the evolution of AI in every aspect of technology since the early 2020s. The benefits of AI integration into DEP systems include improving posi...
Integrated plasma and vegetation proteomic characterization of infective endocarditis for early diagnosis and treatment.
Nature communications
May 30, 2025
Infective endocarditis, a life-threatening condition, poses challenges for early diagnosis and personalized treatment due to insufficient biomarkers and limited understanding of its pathophysiology. Here, we performed proteomic profiling of plasma an...
Comparative study on antibacterial activities and removal of iron ions from water using novel modified sand with silver through the hydrothermal technique.
Scientific reports
May 8, 2025
The hydrothermal-calcination technique was used to modify raw sand with silver (Ag) at different weight percentages: 2%, 5%, and 10% using silver nitrate. The raw and sand-coated Ag nanoparticle samples were analyzed using various techniques, includi...
Performance and hypothetical clinical impact of an mNGS-based machine learning model for antimicrobial susceptibility prediction of five ESKAPEE bacteria.
Microbiology spectrum
Apr 17, 2025
UNLABELLED: Antimicrobial resistance is an escalating global health crisis, underscoring the urgent need for timely and targeted therapies to ensure effective clinical treatment. We developed a machine learning model based on metagenomic next-generat...
Pre-trained molecular representations enable antimicrobial discovery.
Nature communications
Apr 10, 2025
The rise in antimicrobial resistance poses a worldwide threat, reducing the efficacy of common antibiotics. Determining the antimicrobial activity of new chemical compounds through experimental methods remains time-consuming and costly. While compoun...
Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.
Nature communications
Mar 25, 2025
New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-po...
BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for and .
Journal of chemical information and modeling
Mar 14, 2025
Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately...
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