Results 251 to 260 of about 3,746,898 (399)
Resistance patterns and virulence factors of Pseudomonas aeruginosa in hospitalized patients: A Saudi Arabian study. [PDF]
Moursi SA +10 more
europepmc +1 more source
Artificial intelligence (AI) technology is revolutionizing antimicrobial drug development. In response to increasingly severe antimicrobial resistance challenges, AI can efficiently predict pathogen evolutionary trends, identify potential drug targets, and accelerate compound design and optimization, thereby significantly shortening the development ...
Kexin Li +6 more
wiley +1 more source
Microbial Primer: The R-pyocins of <i>Pseudomonas aeruginosa</i>. [PDF]
Estrada I +4 more
europepmc +1 more source
Death after transplantation. An analysis of sixty cases [PDF]
Dahrling, BE +3 more
core +1 more source
Antimicrobial resistance (AMR) disseminates throughout the soil–plant continuum via complex microbial interactions. Plants shape root‐ and leaf‐associated microbiomes that sustain plant health; however, soil‐borne legacies—enriched with antibiotic‐producing microbes and resistance genes—govern AMR dynamics across agroecosystems.
Zufei Xiao +11 more
wiley +1 more source
Phenotypic and Molecular Analysis of Fosfomycin Resistance Among <i>P. aeruginosa</i> Isolates from Cystic Fibrosis Patients. [PDF]
Poormehr P +4 more
europepmc +1 more source
TargetGen‐recurrent neural network (RNN), an advanced generative model, generated 28,708 unique and novel compounds, identifying SAK‐2970 as a potent antibiotic against drug‐resistant Staphylococcus aureus. Exhibiting strong biofilm inhibition, high therapeutic efficacy, and minimal systemic toxicity in vivo, SAK‐2970 highlights its safety and clinical
Shakeel Ahmad Khan +2 more
wiley +1 more source

