Antibiotic Sensitivity, a pivotal component of antimicrobial stewardship, plays a pivotal role in guiding healthcare practitioners towards effective and precise antibiotic therapy. By assessing the susceptibility of bacteria to various antibiotics, clinicians can make informed decisions on the most suitable treatment for bacterial infections. This process involves utilizing laboratory techniques to evaluate the response of microorganisms to different antimicrobial agents, helping identify the most potent and targeted drug. The significance of antibiotic sensitivity lies in its contribution to combating antibiotic resistance. Tailoring treatment based on the specific susceptibility patterns of bacteria minimizes the misuse and overuse of antibiotics, addressing the growing concern of drug-resistant infections. This approach not only ensures optimal patient care but also supports global efforts to preserve the efficacy of antibiotics for future generations.
Title : Diagnostic approaches, predictive and prognostic assessments, monitoring, treatment & management of infectious diseases and disease prevention
Sergey Suchkov, N.D. Zelinskii Institute for Organic Chemistry of the Russian Academy of Sciences & InMedStar, Russian Federation
Title : The accelerated timeline: Human ecology, climate change, and the next global outbreak
Claudia Ferreira, Sorbonne University, France
Title : Climate change and increasing death due to resistant infection
Yazdan Mirzanejad, University of British Columbia, Canada
Title : Recurrent klebsiella pneumoniae pyogenic liver abscess: Developing a literature-informed 0–2 scoring framework from a solved index case
Martha Grace McLean, Anne Burnett Marion School of Medicine at Texas Christian University, United States
Title : Post-hysterectomy pelvic abscess mimic: An AI-assisted diagnostic stewardship workflow
Setu Shiroya, Anne Burnett Marion School of Medicine at Texas Christian University, United States
Title : Building a clinical reasoning tool from post-transplant MRSA sepsis: A mentored ai workflow
Michaela Mitchell, Anne Burnett Marion School of Medicine at Texas Christian University, United States