
معرفی
Jeffrey R. Aeschlimann is a faculty member at the University of Connecticut's School of Pharmacy, Department of Pharmacy Practice, specializing in infectious diseases pharmacotherapy and antibiotic resistance research. His work bridges clinical practice and laboratory investigations, focusing on optimizing antimicrobial treatments and understanding resistance mechanisms.
- Educational Background: Doctor of Pharmacy (University of Utah, 1994-1996), Post-doctoral Fellowship (Wayne State University, 1996-1998), Bachelor of Science in Pharmacy (University of Connecticut, 1993-1998).
His research interests include the impact of multidrug efflux pumps on antibiotic activity, pharmacodynamics of Gram-negative and Gram-positive bacteria, development of in vitro infection models, and active learning methodologies in pharmacy education. He investigates how bacterial biofilms contribute to resistance and evaluates novel dosing strategies to mitigate resistance emergence.
His 2003 publications and conference presentations highlight studies on vancomycin degradation products, efflux pump dynamics, and fluoroquinolone combination therapies, emphasizing in vitro modeling and resistance prevention. These works align with his focus on pharmacodynamics and clinical microbiology.
- Scientific Awards:
- ACCP Infectious Diseases Practice & Research Network Minisabbatical Award
- Society of Infectious Diseases Pharmacists Research Award
- ACCP Aventis Infectious Diseases Research Award
- University of Connecticut Faculty Large Grant Competition Award
Jeffrey Aeschlimann has received multiple research awards and grants from institutions like ACCP, Society of Infectious Diseases Pharmacists, and Bayer Pharmaceuticals. His work has been funded by entities such as Ortho-McNeil Pharmaceuticals and the University of Connecticut Faculty Large Grant Competition.
He is affiliated with the UConn Health Center and works in School of Pharmacy, Room 346, Storrs Campus, with expertise in modeling human infections and teaching Pharm.D. students using problem-based learning approaches.
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