High-Resolution Measurement of Local Activation Time Differences From Bipolar Electrogram Amplitude

Up-regulated novel-miR-17 promotes hypothermic reperfusion arrhythmias by negatively targeting Gja1 and mediating activation of the PKC/c-Jun signaling pathway

BACKGROUND

Hypothermic ischemia-reperfusion arrhythmia is a common complication of cardiothoracic surgery under cardiopulmonary bypass, but few studies have focused on this type of arrhythmia. Our prior study discovered reduced myocardial Cx43 protein levels may be linked to hypothermic reperfusion arrhythmias. However, more detailed molecular mechanism research is required.

METHOD

The microRNA and mRNA expression levels in myocardial tissues were detected by real-time quantitative PCR (RT-qPCR). Besides, the occurrence of hypothermic reperfusion arrhythmias and changes in myocardial electrical conduction were assessed by electrocardiography and ventricular epicardial activation mapping. Furthermore, bioinformatics analysis, applying antagonists of miRNA, western blotting, immunohistochemistry, a dual luciferase assay, and pearson correlation analysis were performed to investigate the underlying molecular mechanisms.

RESULTS

The expression level of novel-miR-17 was up-regulated in hypothermic ischemia-reperfusion myocardial tissues. Inhibition of novel-miR-17 upregulation ameliorated cardiomyocyte edema, reduced apoptosis, increased myocardial electrical conduction velocity, and shortened the duration of reperfusion arrhythmias. Mechanistic studies showed that novel-miR-17 reduced the expression of Cx43 by directly targeting Gja1 while mediating the activation of the PKC/c-Jun signaling pathway.

CONCLUSION

Up-regulated novel-miR-17 is a newly discovered pro-arrhythmic microRNA that may serve as a potential therapeutic target and biomarker for hypothermic reperfusion arrhythmias.

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.

Impact of electrode orientation, myocardial wall thickness, and myofiber direction on intracardiac electrograms: numerical modeling and analytical solutions

Intracardiac electrograms (iEGMs) are time traces of the electrical potential recorded close to the heart muscle. We calculate unipolar and bipolar iEGMs analytically for a myocardial slab with parallel myofibers and validate them against numerical bidomain simulations. The analytical solution obtained via the method of mirrors is an infinite series of arctangents. It goes beyond the solid angle theory and is in good agreement with the simulations, even though bath loading effects were not accounted for in the analytical calculation. At a large distance from the myocardium, iEGMs decay as 1/R (unipolar), 1/R ² (bipolar and parallel), and 1/R ³ (bipolar and perpendicular to the endocardium). At the endocardial surface, there is a mathematical branch cut. Here, we show how a thicker myocardium generates iEGMs with larger amplitudes and how anisotropy affects the iEGM width and amplitude. If only the leading-order term of our expansion is retained, it can be determined how the conductivities of the bath, torso, myocardium, and myofiber direction together determine the iEGM amplitude. Our results will be useful in the quantitative interpretation of iEGMs, the selection of thresholds to characterize viable tissues, and for future inferences of tissue parameters.

An Efficient Hybrid Methodology for Local Activation Waves Detection under Complex Fractionated Atrial Electrograms of Atrial Fibrillation

Local activation waves (LAWs) detection in complex fractionated atrial electrograms (CFAEs) during catheter ablation (CA) of atrial fibrillation (AF), the commonest cardiac arrhythmia, is a complicated task due to their extreme variability and heterogeneity in amplitude and morphology. There are few published works on reliable LAWs detectors, which are efficient for regular or low fractionated bipolar electrograms (EGMs) but lack satisfactory results when CFAEs are analyzed. The aim of the present work is the development of a novel optimized method for LAWs detection in CFAEs in order to assist cardiac mapping and catheter ablation (CA) guidance. The database consists of 119 bipolar EGMs classified by AF types according to Wells' classification. The proposed method introduces an alternative Botteron's preprocessing technique targeting the slow and small-ampitude activations. The lower band-pass filter cut-off frequency is modified to 20 Hz, and a hyperbolic tangent function is applied over CFAEs. Detection is firstly performed through an amplitude-based threshold and an escalating cycle-length (CL) analysis. Activation time is calculated at each LAW's barycenter. Analysis is applied in five-second overlapping segments. LAWs were manually annotated by two experts and compared with algorithm-annotated LAWs. AF types I and II showed 100% accuracy and sensitivity. AF type III showed 92.77% accuracy and 95.30% sensitivity. The results of this study highlight the efficiency of the developed method in precisely detecting LAWs in CFAEs. Hence, it could be implemented on real-time mapping devices and used during CA, providing robust detection results regardless of the fractionation degree of the analyzed recordings.