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Microbial Sensitivity Tests

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Design, synthesis and antimicrobial activity of 6-N-substituted chitosan derivatives.

Bioorganic & medicinal chemistry letters
Three novel 6-N-substituted chitosan derivatives were designed and synthesised and characterized by FTIR and NMR. The degree of substitution was calculated by elemental analysis results. The antimicrobial activities of the target compounds were evalu...

Application of the multifactor dimensionality reduction method in evaluation of the roles of multiple genes/enzymes in multidrug-resistant acquisition in Pseudomonas aeruginosa strains.

Epidemiology and infection
Multidrug-resistant Pseudomonas aeruginosa (MDRPA) infections are major threats to healthcare-associated infection control and the intrinsic molecular mechanisms of MDRPA are also unclear. We examined 348 isolates of P. aeruginosa, including 188 MDRP...

Machine Learning-Based Discovery of a Novel Noncovalent MurA Inhibitor as an Antibacterial Agent.

Chemical biology & drug design
The bacterial cell wall is crucial for maintaining the integrity of bacterial cells. UDP-N-acetylglucosamine 1-carboxyethylene transferase (MurA) is an important enzyme involved in bacterial cell wall synthesis. Therefore, it is an important target f...

Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.

Microbial biotechnology
Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models fo...

Integrating MALDI-TOF Mass Spectrometry with Machine Learning Techniques for Rapid Antimicrobial Resistance Screening of Foodborne Bacterial Pathogens.

Methods in molecular biology (Clifton, N.J.)
Although MALDI-TOF mass spectrometry (MS) is considered as the gold standard for rapid and cost-effective identification of microorganisms in routine laboratory practices, its capability for antimicrobial resistance (AMR) detection has received limit...

Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.

Briefings in bioinformatics
Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discove...

Prediction of Antibiotic Susceptibility in E. coli Isolates Using Machine Learning.

Studies in health technology and informatics
Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 million by 2050. This resistance, primarily due to the overuse of antibiotics, complicates the treatment o...

De novo synthetic antimicrobial peptide design with a recurrent neural network.

Protein science : a publication of the Protein Society
Antibiotic resistance is recognized as an imminent and growing global health threat. New antimicrobial drugs are urgently needed due to the decreasing effectiveness of conventional small-molecule antibiotics. Antimicrobial peptides (AMPs), a class of...

Leveraging Graph Neural Networks for MIC Prediction in Antimicrobial Resistance Studies.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Antimicrobial resistance (AMR) poses a significant challenge in healthcare and public health, with organisms such as nontyphoidal Salmonella leading the way due to their escalating resistance to antimicrobial agents. This situation severely complicat...