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Review outlines molecular virulence and multidrug resistance in urinary tract infections, highlighting the need for microbiome-targeted therapies and antimicrobial stewardship.
Urinary tract infections (UTIs) represent a major global health burden, traditionally defined as microbial invasion of a sterile urinary tract, but now increasingly understood as a state of microbial dysbiosis involving disruption of the urinary microbiome. This review aims to synthesize current knowledge on the molecular mechanisms, etiological agents, clinical classification, and emerging therapeutic strategies in UTIs. The analysis integrates recent advances in microbiome research, molecular pathogenesis, and clinical guidelines. Uropathogenic Escherichia coli (UPEC) remains the predominant pathogen, utilizing virulence factors such as adhesins and intracellular bacterial community formation to establish persistent infection, while other organisms including Klebsiella pneumoniae, Proteus mirabilis, and Enterococcus faecalis contribute to disease complexity. The emergence of multidrug-resistant organisms, particularly among ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species), poses significant therapeutic challenges through mechanisms such as β-lactamase production, efflux pumps, and biofilm formation. Clinically, evolving classification systems emphasize infection localization rather than host factors, improving diagnostic and therapeutic precision. Diagnostic strategies rely on clinical assessment, urinalysis, and urine culture, while treatment increasingly incorporates antimicrobial stewardship principles. Emerging approaches, including immunoprophylaxis, bacteriophage therapy, and microbiome-targeted interventions, demonstrate promising results. In conclusion, UTIs are complex, multifactorial diseases requiring integrated molecular, clinical, and therapeutic approaches, with future advancements likely driven by precision medicine and artificial intelligence.
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Ene et al. (2026) studied this question.
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