Dr Nenad Macesic
- AMR EVOLUTION IN PATIENTS, GENOMICS
- CLINICAL & MACHINE LEARNING APPROACHES TO IMPROVING DIAGNOSIS
AND TREATMENT OF AMR
Nenad’s research focuses on using clinical medicine, bacterial genomic data and artificial intelligence approaches to improve the prevention, diagnosis and treatment of AMR infections. He recently conducted a genomic survey of clinical polymyxin- resistant (PR) Klebsiella pneumoniae to determine the molecular mechanisms of PR and the role of polymyxin exposure versus transmission in PR emergence. This work identified that exposure with de novo resistance emergence was a significant factor for resistance in the majority of patients, rather than transmission. Nenad’s work has also examined colonization dynamics and infection in transplant recipients through innovative use of active surveillance and whole-genome sequencing (WGS). From this, is developing AI methods for predicting resistance evolution in patients.
As a physician, Nenad cares for patients with infectious complications following solid organ and hematopoietic stem cell transplants.
- Integrating genomic and artificial intelligence approaches to combat antimicrobial resistance.
- Using clinical medicine, bacterial genomic data and artificial intelligence approaches to improve the prevention, diagnosis and treatment of AMR infections. Genomic approaches for understanding resistance to polymyxins in carbapenem-resistant Enterobacteriaceae.
- Predicting resistance in Klebsiella pneumoniae through machine learning analysis of genomic data.
- Translation of novel developments in whole-genome sequencing, machine learning and healthcare analytics into better outcomes for patients.
Chair: Clinical unmet needs working group