AIMC Topic: Bacteria
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Antimicrobial Activity of Tea and Agarwood Leaf Extracts Against Multidrug-Resistant Microbes.
BioMed research international
Dec 19, 2024
Emerging multidrug-resistant (MDR) strains are the main challenges to the progression of new drug discovery. To diminish infectious disease-causing pathogens, new antibiotics are required while the drying pipeline of potent antibiotics is adding to t...
Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water.
ACS applied materials & interfaces
Dec 12, 2024
The challenge of rapid identification of bacteria in food and water still persists as a major health problem. To tackle this matter, we have developed a single-probe liquid crystal (LC)-based optical sensing platform for the differentiation of five c...
Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River.
Microbiology spectrum
Dec 9, 2024
Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods often rely on physical evidence and circumstantial clues, but the emerging field of forensic microb...
A supervised machine learning tool to predict the bactericidal efficiency of nanostructured surface.
Journal of nanobiotechnology
Dec 3, 2024
The emergence and rapid spread of multidrug-resistant bacterial strains is a growing concern of public health. Inspired by the natural bactericidal surfaces of lotus leaves and shark skin, increasing attention has been focused on the use of mechano-b...
Intelligent identification of foodborne pathogenic bacteria by self-transfer deep learning and ensemble prediction based on single-cell Raman spectrum.
Talanta
Dec 2, 2024
Foodborne pathogenic infections pose a significant threat to human health. Accurate detection of foodborne diseases is essential in preventing disease transmission. This study proposed an AI model for precisely identifying foodborne pathogenic bacter...
High-content imaging and deep learning-driven detection of infectious bacteria in wounds.
Bioprocess and biosystems engineering
Dec 2, 2024
Fast and accurate detection of infectious bacteria in wounds is crucial for effective clinical treatment. However, traditional methods take over 24 h to yield results, which is inadequate for urgent clinical needs. Here, we introduce a deep learning-...
A Universal Method for Fingerprinting Multiplexed Bacteria: Evolving Pruned Sensor Arrays via Machine Learning-Driven Combinatorial Group-Specificity Strategy.
ACS nano
Dec 2, 2024
Array-based sensing technology holds immense potential for discerning the intricacies of biological systems. Nevertheless, developing a universal strategy for simultaneous identification of diverse types of multianalytes and meeting the diagnostic ne...
Feasibility study of machine learning to explore relationships between antimicrobial resistance and microbial community structure in global wastewater treatment plant sludges.
Bioresource technology
Nov 26, 2024
Wastewater sludges (WSs) are major reservoirs and emission sources of antibiotic resistance genes (ARGs) in cities. Identifying antimicrobial resistance (AMR) host bacteria in WSs is crucial for understanding AMR formation and mitigating biological a...
Unsupervised Learning-Assisted Acoustic-Driven Nano-Lens Holography for the Ultrasensitive and Amplification-Free Detection of Viable Bacteria.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Nov 22, 2024
Bacterial infection is a crucial factor resulting in public health issues worldwide, often triggering epidemics and even fatalities. The accurate, rapid, and convenient detection of viable bacteria is an effective method for reducing infections and i...
Interpretation of Bacterial Smears and Cultures Using Artificial Intelligence.
Clinics in laboratory medicine
Nov 18, 2024
The use of artificial intelligence (AI) to aid in the diagnosis of infectious diseases is a growing area of interest. AI-based applications in the clinical microbiology laboratory have shown great prospect for the automated interpretation of smears a...
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