AIMC Topic: Microbial Sensitivity Tests
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Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.
Nature microbiology
Jan 17, 2025
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptide...
Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in Mannheimia haemolytica.
Veterinary microbiology
Jan 8, 2025
Mannheimia haemolytica is one of the most common causative agents of bovine respiratory disease (BRD); however, antibiotic resistance in this species is increasing, making treatment more difficult. Integrative-conjugative elements (ICE), a subset of ...
Optimizing the production and efficacy of antimicrobial bioactive compounds from in combating multi-drug-resistant pathogens.
Frontiers in cellular and infection microbiology
Jan 6, 2025
BACKGROUND: The rise of antibiotic-resistant pathogens has intensified the search for novel antimicrobial agents. This study aimed to isolate from local soil samples and evaluate its antimicrobial properties, along with optimizing the production of ...
Soil Microbiology
Staphylococcus aureus
Drug Resistance, Multiple, Bacterial
RNA, Ribosomal, 16S
Bacillus subtilis
DNA, Bacterial
Anti-Bacterial Agents
Candida albicans
Spectroscopy, Fourier Transform Infrared
Escherichia coli
Anti-Infective Agents
Culture Media
Streptomyces
Microbial Sensitivity Tests
Antibacterial effects of thyme oil loaded solid lipid and chitosan nano-carriers against Salmonella Typhimurium and Escherichia coli as food preservatives.
PloS one
Dec 31, 2024
OBJECTIVES: Escherichia coli and Salmonella Typhimurium are frequent causes of foodborne illness affecting many people annually. In order to develop natural antimicrobial agents against these microorganisms, thyme oil (TO) was considered as active an...
Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.
The Journal of infection
Dec 30, 2024
BACKGROUND: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.
External validation of predictive models for antibiotic susceptibility of urine culture.
BJU international
Dec 22, 2024
OBJECTIVE: To develop, externally validate, and test a series of computer algorithms to accurately predict antibiotic susceptibility test (AST) results at the time of clinical diagnosis, up to 3 days before standard urine culture results become avail...
Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery.
PloS one
Dec 20, 2024
Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that are even better at eliminating infections. The fundamental transformation in a variety of scientific discip...
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...
Antimicrobial activity of L. Pers against periodontal pathogen: .
PeerJ
Dec 18, 2024
BACKGROUND: is widely recognised as a periodontal pathogen. In recent years, there has been growing interest in the use of medicinal plant extracts as alternative treatments for periodontitis to combat the emergence of antibiotic-resistant bacteria....
Machine Learning-Assisted High-Throughput Screening for Anti-MRSA Compounds.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
BACKGROUND: Antimicrobial resistance is a major public health threat, and new agents are needed. Computational approaches have been proposed to reduce the cost and time needed for compound screening.
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