Trovato C, Passini E, Nagy N, et al., Human Purkinje in silico model enables mechanistic investigations into automaticity and pro-arrhythmic abnormalities, Journal of Molecular and Cellular Cardiology (2019), https://doi.org/10.1016/j.yjmcc.2020.04.001
Increasing computer power and novel algorithms could predict future heart health of patients.
In recent times, researchers have increasingly found that the power of computers and artificial intelligence is enabling more accurate diagnosis of a patient’s current heart health and can provide an accurate projection of future heart health, potential treatments and disease prevention.
Now in a paper published in European Heart Journal, researchers from Oxford University, working in collaboration with academic, industry, clinical and regulatorypartners, and several other international initiatives as the Personalised In-Silico Cardiology consortium, show how linking computer and statistical models can improve clinical decisions relating to the heart.
The team of researchers believe that the power of computational models in cardiovascular medicine could also provide us with more control over our daily heart health. Much like the popularity of wearable monitoring devices, a digital twin of our hearts could inform us about its current health and alert wearers to any risk factors. The research predicts we could see the technology in action within the next 5-10 years.
A fellowship position is available for a period of 3 years, tenable at Oxford University, on the project entitled “PersonaliseAF” to start before 1 July 2020. The successful applicant will be employed by the University on a Marie Sklodowska-Curie Innovative Training Network within the project “PersonalizeAF”, whose overall vision is to change the paradigm of classification and diagnosis of atrial fibrillation by delivering a precision medicine strategy based on the personalised characterisation of each atrial substrate and disease manifestation.
The closing date for applications is 12.00 noon on 24 February 2020.
For full details click here
Peter Marinov presented at the Cardiac Physiome 2019 (Maastricht, NL) on the work entitiled "MRI-based computer simulations of arrhythmogenic cardiomyophathy (ACM) explain disease remodelling responsible for different clinical ECG phenotypes".
The research of the Computational Cardiovascular Science group in Hypertrophic Cardiomyopathy, by combining electrocardiogram and imaging data with machine learning and computer simulations, has been highlighted by the Virtual Physiological Human Institute as their very first success story on how computational techniques can revolutionize the knowledge of cardiac diseases and improve patient care. You can read their summary following this link, or read the full paper here.
Other recent works include our clinical translational study on explaining mechanisms of drug action and efficacy in Hypertrophic Cardiomyopathy, that can be found in this link.
Professor Blanca Rodriguez gave a keynote talk on human silico trials in cardiac pharmacology at the IX Annual Meeting of the Italian Chapter of the European Society of Biomechanics (ESB-ITA) held in Bologna, Italy on the 30th of September 2019.
Hector Martinez-Navarro attended the Compbiomed Conference, 25-27 September 2019. He delivered the talk "HPC simulations for in-silico drug trials in humans: therapeutic strategies in acute myocardial ischemia". He also chaired the session "Organ Modelling and Simulation", focused on haemodynamics, and with great presentations by Mirko Bonfanti, Gaia Franzetti and Giulia Luraghi, among others.
4 members of our team (Elisa Passini, Cristian Trovato, Polina Mamoshina and Francesca Margara) attended the Safety Pharmacology Society Annual Meeting 2019 in Barcelona last week, presenting 4 posters and giving 2 oral presentations. We received a total of 4 awards: Student Travel Award to Francesca Margara; Junior Investigator Travel Award to Cristian Trovato; two 3rd places (tie) in the poster competition, to Cristian Trovato and Elisa Passini. Our research was well received and we came back with lots of new ideas on how to apply our computer models to safety pharmacology.
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