V241F KCNQ1 Mutation Shortens Electrical Wavelength and Reduces Ventricular Pumping Capabilities: A Simulation Study With an Electro-Mechanical Model

Heikhmakhtiar, Aulia Khamas; Rasyidin, Fakhmi Adi; Lim, Ki Moo

doi:10.3389/fphy.2018.00147

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…Even though we increased g Ks up to 100-fold to simulate extreme situations, the APD under the 100-fold-increased g Ks condition was 90 ms in the sinus rhythm and 74 ms during reentry (Supplementary Figure S1). It is similar to the APDs when the KCNJ2 E299V (72-90 ms) and KCNQ1 V241F mutations (76 ms) are expressed (Cerrone et al, 2013;Heikhmakhtiar et al, 2018). In their experiment, Banville et al successfully observed the APD variation during ventricular tachycardia.…”

Section: Discussionmentioning

confidence: 69%

Relationship Between Electrical Instability and Pumping Performance During Ventricular Tachyarrhythmia: Computational Study

Jeong

Lim

2020

Front. Physiol.

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Section: Discussionmentioning

confidence: 69%

Relationship Between Electrical Instability and Pumping Performance During Ventricular Tachyarrhythmia: Computational Study

Jeong

Lim

2020

Front. Physiol.

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“…Second, we only provide the electrophysiological analysis of the two mutations without considering the mechanical behavior, which previously observed for D172N, G229D, S140G, and V241F mutations. 25 31 32 33 Third, we were unable to compare our simulation results with experimental data due to the limited availability of patients with N588K or L532P mutations. Nonetheless, this computational study demonstrated the relationship between the L532P and N588K mutations with atrial arrhythmia and AF.…”

Section: Discussionmentioning

confidence: 99%

Proarrhythmogenic Effect of the L532P and N588K KCNH2 Mutations in the Human Heart Using a 3D Electrophysiological Model

et al. 2020

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Background Atrial arrhythmia is a cardiac disorder caused by abnormal electrical signaling and transmission, which can result in atrial fibrillation and eventual death. Genetic defects in ion channels can cause myocardial repolarization disorders. Arrhythmia-associated gene mutations, including KCNH2 gene mutations, which are one of the most common genetic disorders, have been reported. This mutation causes abnormal QT intervals by a gain of function in the rapid delayed rectifier potassium channel ( I Kr ). In this study, we demonstrated that mutations in the KCNH2 gene cause atrial arrhythmia. Methods The N588K and L532P mutations were induced in the Courtemanche-Ramirez-Nattel (CRN) cell model, which was subjected to two-dimensional and three-dimensional simulations to compare the electrical conduction patterns of the wild-type and mutant-type genes. Results In contrast to the early self-termination of the wild-type conduction waveforms, the conduction waveform of the mutant-type retained the reentrant wave (N588K) and caused a spiral break-up, resulting in irregular wave generation (L532P). Conclusion The present study confirmed that the KCNH2 gene mutation increases the vulnerability of the atrial tissue for arrhythmia.

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“…Since the ventricular mechanics were coupled with the lumped parameter model of the circulatory systems, we obtained LV and systemic artery (SA) pressures. To see a more detailed explanation for this electromechanical coupling technique, please refer to the study [27,28].…”

Section: Simulation Protocolmentioning

confidence: 99%

Comparison of Electromechanical Delay during Ventricular Tachycardia and Fibrillation under Different Conductivity Conditions Using Computational Modeling

Heikhmakhtiar

Lim

2020

Computational and Mathematical Methods in Medicine

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Electromechanical delay (EMD) is the time interval between local myocyte depolarization and the onset of myofiber shortening. Previously, researchers measured EMD during sinus rhythm and ectopic pacing in normal and heart failure conditions. However, to our knowledge, there are no reports regarding EMD during another type of rhythms or arrhythmia. The goal of this study was to quantify EMD during sinus rhythm, tachycardia, and ventricular fibrillation conditions. We hypothesized that EMD under sinus rhythm is longer due to isovolumetric contraction which is imprecise during arrhythmia. We used a realistic model of 3D electromechanical ventricles. During sinus rhythm, EMD was measured in the last cycle of cardiac systole under steady conditions. EMD under tachycardia and fibrillation conditions was measured during the entire simulation, resulting in multiple EMD values. We assessed EMD for the following 3 conduction velocities (CVs): 31 cm/s, 51 cm/s, and 69 cm/s. The average EMD during fibrillation condition was the shortest corresponding to 53.45 ms, 55.07 ms, and 50.77 ms, for the CVs of 31 cm/s, 51 cm/s, and 69 cm/s, respectively. The average EMD during tachycardia was 58.61 ms, 58.33 ms, and 52.50 ms for the three CVs. Under sinus rhythm with action potential duration restitution (APDR) slope 0.7, the average EMD was 66.35 ms, 66.41 ms, and 66.60 ms in line with the three CVs. This result supports our hypothesis that EMD under sinus rhythm is longer than that under tachyarrhythmia conditions. In conclusion, this study observed and quantified EMD under tachycardia and ventricular fibrillation conditions. This simulation study has widened our understanding of EMD in 3D ventricles under chaotic conditions.

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V241F KCNQ1 Mutation Shortens Electrical Wavelength and Reduces Ventricular Pumping Capabilities: A Simulation Study With an Electro-Mechanical Model

Cited by 6 publications

References 27 publications

Relationship Between Electrical Instability and Pumping Performance During Ventricular Tachyarrhythmia: Computational Study

Relationship Between Electrical Instability and Pumping Performance During Ventricular Tachyarrhythmia: Computational Study

Proarrhythmogenic Effect of the L532P and N588K KCNH2 Mutations in the Human Heart Using a 3D Electrophysiological Model

Comparison of Electromechanical Delay during Ventricular Tachycardia and Fibrillation under Different Conductivity Conditions Using Computational Modeling

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