Searching for Arrhythmia Solutions by Quantifying and Unraveling Atrial Transient Chaos

We commonly assume our heart maintains a steady rhythm, a crucial aspect of our well-being, often predictable until it suddenly isn't. Cardiac arrhythmias, marked by abnormal heart rhythms resulting from electrical impulse irregularities, affect a significant number of individuals. The most prevalent arrhythmia is atrial fibrillation (AF), a progressive condition afflicting over 7.6 million people aged 65 and older in the EU. Symptoms such as chest pain, fatigue, and weakness not only diminish the quality of life but also lead to additional complications and possibly death. Interestingly, most initial AF episodes spontaneously resolve, a phenomenon that remains incompletely understood despite substantial advancements in knowledge over the years. Particularly enigmatic is the triggering mechanism for such spontaneous termination.To tackle this challenge, we need innovative and advanced biomedical models capable of exploring the intricate mechanisms behind spontaneous termination. One such mechanism, known in the mathematics community as transient chaos, plays a significant role in this endeavor. In recent times, digital twins, i.e. computational models simulating biological cells or tissues, have rapidly gained prominence, aiding the exploration of new pharmacological and clinical therapies. The field of cardiology is no exception, with digital twins effectively replicating the behavior of cardiomyocytes (normal rhythm, spontaneous termination, and arrhythmia) within digital hearts. We intend to employ these digital twins to delve into the origins and precursors of spontaneous termination at an ionic level with ""Searching for Arrhythmia Solutions by Quantifying and Unraveling Atrial Transient CHaos"" (SASQUATCH), opening the door for the development of novel AF treatment methods.The synergy between fellow and host is ideal for this biophysical (and fundamentally interdisciplinary) project, as it combines their respective expertise in biophysical modelling.""