AIMC Topic: Bacteria

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Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

Biochemistry
The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target...

Label-free rapid antimicrobial susceptibility testing with machine-learning based dynamic holographic laser speckle imaging.

Biosensors & bioelectronics
Antimicrobial resistance (AMR) presents a significant global challenge, creating an urgent need for rapid and sensitive antimicrobial susceptibility testing (AST) methods to guide timely treatment decisions. Traditional AST techniques, such as broth ...

Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.

Journal of chemical information and modeling
While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine le...

Freeze-Thaw Imaging for Microorganism Classification Assisted with Artificial Intelligence.

ACS nano
Fast and cost-effective microbial classification is crucial for clinical diagnosis, environmental monitoring, and food safety. However, traditional methods encounter challenges including intricate procedures, skilled personnel needs, and sophisticate...

Profiling the gut microbiota to assess infection risk in -colonized patients.

Gut microbes
Vornhagen et al. introduced a model combining gut microbiota structure and genotype to assess infection risk in -colonized patients. Building on their findings, we investigated the gut microbiota composition and genotype in 16 colonized patients, f...

Efficient detection of foodborne pathogens via SERS and deep learning: An ADMIN-optimized NAS-Unet approach.

Journal of hazardous materials
Amid the increasing global challenge of foodborne diseases, there is an urgent need for rapid and precise pathogen detection methods. This study innovatively integrates surface-enhanced Raman Spectroscopy (SERS) with deep learning technology to devel...

Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning.

Environmental research
Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. In this study, we identified 33 different MPs features including their abundance, shape, color, size...

Predicting biomass conversion and COD removal in wastewater treatment by phototrophic bacteria with interpretable machine learning.

Journal of environmental management
Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to complex operational and environmental interactions. Neural Ordinary Differential Equations, Elastic Net, ...

Automatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning.

Journal of microscopy
The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image opt...

Robotic versus manual disinfection of global priority pathogens at COVID-19-dedicated hospitals.

American journal of infection control
BACKGROUND: Twelve bacterial families identified as global priority pathogens (GPPs) pose the greatest threat to human health due to declining antibiotic efficacy. Robotics, a swift and contactless tool for disinfecting hospital surfaces, was sought ...