Parag Gajendragadkar

I was a British Heart Foundation funded Clinical Research Training Fellow investigating the role of Nitric Oxide Synthase 1 Adaptor Protein (NOS1AP) on cardiac repolarisation and arrhythmogenesis using a combination of in-vitro work and in-vivo physiological investigations. Training including skills in basic laboratory techniques (Western Blotting, q-PCR, NOS1 assays) as well as animal handling and in-vivo work (murine echocardiography, atrial fibrillation [AF] induction) to ex-vivo work (isolated heart VT stimulation, optical mapping). Also as part of my DPhil I diversified to investigate the causal relevance of ECG parameters on AF at a large population level, using genetic epidemiology techniques working in the Big Data Institute as part of the Nuffield Department of Population Health.

I won the European Society of Cardiology Young Investigator Award in Population Sciences for this work (Paris Congress, 2019) and successfully defended a DPhil entitled 'Understanding the role of NOS1AP on ECG parameters and arrhythmogenesis: insights from mice and men' supervised by Professor Barbara Casadei, Dr Jillian Simon and Professor Jemma C. Hopewell.

I completed my undergraduate and clinical training at Downing College, University of Cambridge and qualified in 2008. After doing Foundation Training in East Anglia, I was a Wellcome Trust funded Academic Clinical Fellow and completed an MPhil in Translational Medicine and Therapeutics from the University of Cambridge. I then became a Cardiology Registrar and after completing my core training have subspecialised in the field of Electrophysiology and Devices. I moved to the University of Oxford as a British Heart Foundation funded Clinical Research Training Fellow to pursue research as a DPhil student, before finishing my training and returning as a post-CCT Senior Clinical Fellow in Electrophysiology at the John Radcliffe alongside research commitments.

My research interests lie in translational electrophysiological research. In particular, I am interested in using insights from data from large populations to guide individualised therapies for arrhythmias.

ORCID

0000-0003-3312-0183