scholarly journals In silico investigation of the mechanisms underlying atrial fibrillation due to impaired Pitx2

2020 ◽  
Vol 16 (2) ◽  
pp. e1007678 ◽  
Author(s):  
Jieyun Bai ◽  
Andy Lo ◽  
Patrick A. Gladding ◽  
Martin K. Stiles ◽  
Vadim V. Fedorov ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
In Silico

scholarly journals Populations of in silico myocytes and tissues reveal synergy of multiatrial‐predominant K + ‐current block in atrial fibrillation

2020 ◽  
Author(s):  
Haibo Ni ◽  
Alex Fogli Iseppe ◽  
Wayne R. Giles ◽  
Sanjiv M. Narayan ◽  
Henggui Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
In Silico ◽  
Current Block ◽  
K Current

scholarly journals Inherited and Acquired Rhythm Disturbances in Sick Sinus Syndrome, Brugada Syndrome, and Atrial Fibrillation: Lessons from Preclinical Modeling

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3175
Author(s):  
Laura Iop ◽  
Sabino Iliceto ◽  
Giovanni Civieri ◽  
Francesco Tona
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular Diseases ◽  
Brugada Syndrome ◽  
In Silico ◽  
Cardiac Fibrosis ◽  
Sick Sinus Syndrome ◽  
In Vitro Models ◽  
Global Functioning

Rhythm disturbances are life-threatening cardiovascular diseases, accounting for many deaths annually worldwide. Abnormal electrical activity might arise in a structurally normal heart in response to specific triggers or as a consequence of cardiac tissue alterations, in both cases with catastrophic consequences on heart global functioning. Preclinical modeling by recapitulating human pathophysiology of rhythm disturbances is fundamental to increase the comprehension of these diseases and propose effective strategies for their prevention, diagnosis, and clinical management. In silico, in vivo, and in vitro models found variable application to dissect many congenital and acquired rhythm disturbances. In the copious list of rhythm disturbances, diseases of the conduction system, as sick sinus syndrome, Brugada syndrome, and atrial fibrillation, have found extensive preclinical modeling. In addition, the electrical remodeling as a result of other cardiovascular diseases has also been investigated in models of hypertrophic cardiomyopathy, cardiac fibrosis, as well as arrhythmias induced by other non-cardiac pathologies, stress, and drug cardiotoxicity. This review aims to offer a critical overview on the effective ability of in silico bioinformatic tools, in vivo animal studies, in vitro models to provide insights on human heart rhythm pathophysiology in case of sick sinus syndrome, Brugada syndrome, and atrial fibrillation and advance their safe and successful translation into the cardiology arena.

scholarly journals In Silico Optimization of Atrial Fibrillation-Selective Sodium Channel Blocker Pharmacodynamics

2012 ◽  
Vol 102 (5) ◽  
pp. 951-960 ◽  
Author(s):  
Martin Aguilar-Shardonofsky ◽  
Edward J. Vigmond ◽  
Stanley Nattel ◽  
Philippe Comtois
Keyword(s):  
Atrial Fibrillation ◽  
Sodium Channel ◽  
In Silico ◽  
Channel Blocker ◽  
Sodium Channel Blocker

scholarly journals 1008Effectiveness of atrial fibrillation rotor ablation is dependent on conduction velocity: an in-silico 3-dimensional modeling study

EP Europace ◽  
2018 ◽  
Vol 20 (suppl_1) ◽  
pp. i191-i192
Author(s):  
B Lim ◽  
M Hwang ◽  
J S Song ◽  
A J Ryu ◽  
B Joung ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Conduction Velocity ◽  
In Silico ◽  
3 Dimensional ◽  
Dimensional Modeling ◽  
Modeling Study

scholarly journals Alteration of cerebrovascular haemodynamic patterns due to atrial fibrillation: an in silico investigation

2017 ◽  
Vol 14 (129) ◽  
pp. 20170180 ◽  
Author(s):  
S. Scarsoglio ◽  
A. Saglietto ◽  
M. Anselmino ◽  
F. Gaita ◽  
L. Ridolfi
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular System ◽  
In Silico ◽  
Cerebral Circulation ◽  
Social Impact ◽  
Extreme Values ◽  
Normal Sinus Rhythm ◽  
Correlation Coefficients ◽  
Systemic Arterial Pressure ◽  
Linear Regression Models

There has recently been growing evidence that atrial fibrillation (AF), the most common cardiac arrhythmia, is independently associated with the risk of dementia. This represents a very recent frontier with high social impact for the number of individuals involved and for the expected increase in AF incidence in the next 40 years. Although a number of potential haemodynamic processes, such as microembolisms, altered cerebral blood flow, hypoperfusion and microbleeds, arise as connecting links between the two pathologies, the causal mechanisms are far from clear. An in silico approach is proposed that combines in sequence two lumped-parameter schemes, for the cardiovascular system and the cerebral circulation. The systemic arterial pressure is obtained from the cardiovascular system and used as the input for the cerebral circulation, with the aim of studying the role of AF on the cerebral haemodynamics with respect to normal sinus rhythm (NSR), over a 5000 beat recording. In particular, the alteration of the haemodynamic (pressure and flow rate) patterns in the microcirculation during AF is analysed by means of different statistical tools, from correlation coefficients to autocorrelation functions, crossing times, extreme values analysis and multivariate linear regression models. A remarkable signal alteration, such as a reduction in signal correlation (NSR, about 3 s; AF, less than 1 s) and increased probability (up to three to four times higher in AF than in NSR) of extreme value events, emerges for the peripheral brain circulation. The described scenario offers a number of plausible cause–effect mechanisms that might explain the occurrence of critical events and the haemodynamic links relating to AF and dementia.

scholarly journals Electrophysiological Rotor Ablation in In-Silico Modeling of Atrial Fibrillation: Comparisons with Dominant Frequency, Shannon Entropy, and Phase Singularity

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0149695 ◽  
Author(s):  
Minki Hwang ◽  
Jun-Seop Song ◽  
Young-Seon Lee ◽  
Changyong Li ◽  
Eun Bo Shim ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Shannon Entropy ◽  
In Silico ◽  
Dominant Frequency ◽  
Phase Singularity ◽  
In Silico Modeling
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