AIMC Topic: Staphylococcus aureus

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Detection of bone infections using Vis-NIR and SWIR hyperspectral imaging coupled with machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Bone infections, such as fracture-related and periprosthetic joint infections, present significant diagnostic and therapeutic challenges in orthopaedic surgery. Current diagnosic standards rely primarily on tissue cultures of intraoperatively obtaine...

Application of MALDI-TOF MS-based peptidome profiling for the identification of Bacillus cereus, Staphylococcus aureus, and Escherichia coli in single and mixed inoculum.

Food chemistry
The detection of mixed-species bacterial samples plays a vital role in ensuring food safety, yet research in this area remains notably limited. This study investigates the integration of MALDI-TOF MS-derived peptidome profiles with artificial intelli...

Experimentally profiling dielectric properties of Escherichia coli and Staphylococcus aureus by movement velocity and force.

Scientific reports
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
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
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
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
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
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
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...

Exploration of Novel Antimicrobial Agents against Foodborne Pathogens via a Deep Learning Approach.

Journal of agricultural and food chemistry
The emergence of antibiotic-resistant bacteria poses a severe threat to food safety and human health, necessitating an urgent search for novel antimicrobial agents that can be applied in the food industry. This study utilizes a deep learning approach...