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Minki Hwang,Hyun-Seung Lee,Hui-Nam Pak,Eun Bo Shim 대한생리학회-대한약리학회 2016 The Korean Journal of Physiology & Pharmacology Vol.20 No.1
Vagal nerve activity has been known to play a crucial role in the induction and maintenance of atrial fibrillation (AF). However, it is unclear how the distribution and concentration of local acetylcholine (ACh) promotes AF. In this study, we investigated the effect of the spatial distribution and concentration of ACh on fibrillation patterns in an <i>in silico</i> human atrial model. A human atrial action potential model with an ACh-dependent K<sup>+</sup> current (I<sub>KAch</sub>) was used to examine the effect of vagal activation. A simulation of cardiac wave dynamics was performed in a realistic 3D model of the atrium. A model of the ganglionated plexus (GP) and nerve was developed based on the “octopus hypothesis”. The pattern of cardiac wave dynamics was examined by applying vagal activation to the GP areas or randomly. AF inducibility in the octopus hypothesis-based GP and nerve model was tested. The effect of the ACh concentration level was also examined. In the single cell simulation, an increase in the ACh concentration shortened APD<sub>90</sub> and increased the maximal slope of the restitution curve. In the 3D simulation, a random distribution of vagal activation promoted wavebreaks while ACh secretion limited to the GP areas did not induce a noticeable change in wave dynamics. The octopus hypothesis-based model of the GP and nerve exhibited AF inducibility at higher ACh concentrations. In conclusion, a 3D <i>in silico</i> model of the GP and parasympathetic nerve based on the octopus model exhibited higher AF inducibility with higher ACh concentrations.
Toward a grey box approach for cardiovascular physiome
Minki Hwang,Chae Hun Leem,Eun Bo Shim 대한약리학회 2019 The Korean Journal of Physiology & Pharmacology Vol.23 No.5
The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.
Hwang, Minki,Lee, Hyun-Seung,Pak, Hui-Nam,Shim, Eun Bo The Korean Society of Pharmacology 2016 The Korean Journal of Physiology & Pharmacology Vol.20 No.1
Vagal nerve activity has been known to play a crucial role in the induction and maintenance of atrial fibrillation (AF). However, it is unclear how the distribution and concentration of local acetylcholine (ACh) promotes AF. In this study, we investigated the effect of the spatial distribution and concentration of ACh on fibrillation patterns in an in silico human atrial model. A human atrial action potential model with an ACh-dependent $K^+$ current ($I_{KAch}$) was used to examine the effect of vagal activation. A simulation of cardiac wave dynamics was performed in a realistic 3D model of the atrium. A model of the ganglionated plexus (GP) and nerve was developed based on the "octopus hypothesis". The pattern of cardiac wave dynamics was examined by applying vagal activation to the GP areas or randomly. AF inducibility in the octopus hypothesis-based GP and nerve model was tested. The effect of the ACh concentration level was also examined. In the single cell simulation, an increase in the ACh concentration shortened $APD_{90}$ and increased the maximal slope of the restitution curve. In the 3D simulation, a random distribution of vagal activation promoted wavebreaks while ACh secretion limited to the GP areas did not induce a noticeable change in wave dynamics. The octopus hypothesis-based model of the GP and nerve exhibited AF inducibility at higher ACh concentrations. In conclusion, a 3D in silico model of the GP and parasympathetic nerve based on the octopus model exhibited higher AF inducibility with higher ACh concentrations.
Hwang, Minki,Park, Junbeum,Lee, Young-Seon,Park, Jae Hyung,Choi, Sung Hwan,Shim, Eun Bo,Pak, Hui-Nam IEEE 2015 IEEE Transactions on Biomedical Engineering Vol.62 No.2
<P>The heart characteristic length, the inverse of conduction velocity (CV), and the inverse of the refractory period are known to determine vulnerability to cardiac fibrillation (fibrillation number, FibN) in in silico or ex vivo models. The purpose of this study was to validate the accuracy of FibN through in silico atrial modeling and to evaluate its clinical application in patients with atrial fibrillation (AF) who had undergone radiofrequency catheter ablation. We compared the maintenance duration of AF at various FibNAF values using in silico bidomain atrial modeling. Among 60 patients (72% male, 54 ± 13 years old, 82% with paroxysmal AF) who underwent circumferential pulmonary vein isolation (CPVI) for AF rhythm control, we examined the relationship between FibNAF and postprocedural AF inducibility or induction pacing cycle length (iPCL). Clinical FibNAF was calculated using left atrium (LA) dimension (echocardiogram), the inverse of CV, and the inverse of the atrial effective refractory periods measured at proximal and distal coronary sinus. In silico simulation found a positive correlation between AF maintenance duration and FibN<SUB>AF</SUB> (R = 0.90, p <; 0.001). After clinical CPVI, FibN<SUB>AF</SUB> (0.296 ± 0.038 versus 0.192 ± 0.028, p <; 0.001) was significantly higher in patients with postprocedural AF inducibility (n = 41) than in those without (n = 19). Among 41 patients with postprocedural AF inducibility, FibN<SUB>AF</SUB> (P = 0.935, p <; 0.001) had excellent correlations with induction pacing cycle length. FibN<SUB>AF</SUB>, based on LA mass and wavelength, correlates well with AF maintenance in computational modeling and clinical AF inducibility after CPVI.</P>
Toward a grey box approach for cardiovascular physiome
Hwang, Minki,Leem, Chae Hun,Shim, Eun Bo The Korean Society of Pharmacology 2019 The Korean Journal of Physiology & Pharmacology Vol.23 No.5
The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.