Ventricular arrhythmias are the leading cause of mortality in patients with ischemic heart diseases, such as myocardial infarction (MI). Computational simulation of cardiac electrophysiology provides insights into these arrhythmias and their treatment. However, only sparse information is available on crucial model parameters, for instance, the anisotropic intracellular electrical conductivities. Here, we introduced an approach to estimate these conductivities in normal and MI hearts. We processed and analyzed images from confocal microscopy of left ventricular tissue of a rabbit MI model to generate 3D reconstructions. We derived tissue features including the volume fraction of myocytes (V(myo)), gap junctions-containing voxels (V(gj)), and fibrosis (V(fibrosis)). We generated models of the intracellular space and intercellular coupling. Applying numerical methods for solving Poisson's equation for stationary electrical currents, we calculated normal (Ï(myo,n)), longitudinal (Ï(myo,l)), and transverse (Ï(myo,t)) intracellular conductivities. Using linear regression analysis, we assessed relationships of conductivities to tissue features. V(gj) and V(myo) were reduced in MI vs. control, but V(fibrosis) was increased. Both Ï(myo,l) and Ï(myo,n) were lower in MI than in control. Differences of Ï(myo,t) between control and MI were not significant. We found strong positive relationships of Ï(myo,l) with V(myo) and V(gj), and a strong negative relationship with V(fibrosis). The relationships of Ï(myo,n) with these tissue features were similar but less pronounced. Our study provides quantitative insights into the intracellular conductivities in the normal and MI heart. We suggest that our study establishes a framework for the estimation of intracellular electrical conductivities of myocardium with various pathologies.
Confocal microscopy-based estimation of intracellular conductivities in myocardium for modeling of the normal and infarcted heart.
阅读:5
作者:Greiner Joachim, Sankarankutty Aparna C, Seidel Thomas, Sachse Frank B
| 期刊: | Computers in Biology and Medicine | 影响因子: | 6.300 |
| 时间: | 2022 | 起止号: | 2022 Jul;146:105579 |
| doi: | 10.1016/j.compbiomed.2022.105579 | ||
特别声明
1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。
2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。
3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。
4、投稿及合作请联系:info@biocloudy.com。
