AIMC Topic: Anti-Bacterial Agents

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Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm.

Analytical and bioanalytical chemistry
The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and b...

Combining deep learning and droplet microfluidics for rapid and label-free antimicrobial susceptibility testing of colistin.

Biosensors & bioelectronics
Efficient tools for rapid antibiotic susceptibility testing (AST) are crucial for appropriate use of antibiotics, especially colistin, which is now often considered a last resort therapy with extremely drug resistant Gram-negative bacteria. Here, we ...

Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy.

Journal of hazardous materials
This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel reverse synthesis strategy for inorganic catalysts. We meticulously analyzed data from 96 studies, ensurin...

Ratiometric fluorescence sensor based on deep learning for rapid and user-friendly detection of tetracycline antibiotics.

Food chemistry
The detection of tetracycline antibiotics (TCs) in food holds great significance in minimizing their absorption within the human body. Hence, this study aims to develop a rapid, convenient, real-time, and accurate detection method for detecting antib...

Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods.

Epidemiology and health
OBJECTIVES: Over-the-counter (OTC) antibiotic use can cause antibiotic resistance, threatening global public health gains. To counter OTC use, this study used machine learning (ML) methods to identify predictors of OTC antibiotic use in rural Pune, I...

Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach.

Chemosphere
Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamet...

Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model.

Journal of advanced research
INTRODUCTION: Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contaminat...

Determination of minimum inhibitory concentrations using machine-learning-assisted agar dilution.

Microbiology spectrum
UNLABELLED: Effective policy to address the global threat of antimicrobial resistance requires robust antimicrobial susceptibility data. Traditional methods for measuring minimum inhibitory concentration (MIC) are resource intensive, subject to human...

Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges.

Clinical therapeutics
There is growing interest in exploiting the advances in artificial intelligence and machine learning (ML) for improving and monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concept...