Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning.

Beat-by-beat arrhythmia detection in ambulatory electrocardiogram (ECG) monitoring is critical for the evaluation and prognosis of cardiac arrhythmias, however, it is a highly professional demanding and time-consuming task. Current methods for automatic beat-by-beat arrhythmia detection suffer from poor generalization ability due to the lack of large-sample and finely-annotated (labels are given to each beat) ECG data for model training. In this work, we propose a weakly supervised deep learning framework for arrhythmia detection (WSDL-AD), which permits training a fine-grained (beat-by-beat) arrhythmia detector with the use of large amounts of coarsely annotated ECG data (labels are given to each recording) to improve the generalization ability. In this framework, heartbeat classification and recording classification are integrated into a deep neural network for end-to-end training with only recording labels. Several techniques, including knowledge-based features, masked aggregation, and supervised pre-training, are proposed to improve the accuracy and stability of the heartbeat classification under weak supervision. The developed WSDL-AD model is trained for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) on five large-sample and coarsely-annotated datasets and the model performance is evaluated on three independent benchmarks according to the recommendations from the Association for the Advancement of Medical Instrumentation (AAMI). The experimental results show that our method improves the F 1 score of supraventricular ectopic beats detection by 8%-290% and the F1 of ventricular ectopic beats detection by 4%-11% on the benchmarks compared with the state-of-the-art methods of supervised learning. It demonstrates that the WSDL-AD framework can leverage the abundant coarsely-labeled data to achieve a better generalization ability than previous methods while retaining fine detection granularity. Therefore, this framework has a great potential to be used in clinical and telehealth applications. The source code is available at https://github.com/sdnjly/WSDL-AD.

Mechanisms of ventricular arrhythmias elicited by coexistence of multiple electrophysiological remodeling in ischemia: A simulation study.

Myocardial ischemia, injury and infarction (MI) are the three stages of acute coronary syndrome (ACS). In the past two decades, a great number of studies focused on myocardial ischemia and MI individually, and showed that the occurrence of reentrant arrhythmias is often associated with myocardial ischemia or MI. However, arrhythmogenic mechanisms in the tissue with various degrees of remodeling in the ischemic heart have not been fully understood. In this study, biophysical detailed single-cell models of ischemia 1a, 1b, and MI were developed to mimic the electrophysiological remodeling at different stages of ACS. 2D tissue models with different distributions of ischemia and MI areas were constructed to investigate the mechanisms of the initiation of reentrant waves during the progression of ischemia. Simulation results in 2D tissues showed that the vulnerable windows (VWs) in simultaneous presence of multiple ischemic conditions were associated with the dynamics of wave propagation in the tissues with each single pathological condition. In the tissue with multiple pathological conditions, reentrant waves were mainly induced by two different mechanisms: one is the heterogeneity along the excitation wavefront, especially the abrupt variation in conduction velocity (CV) across the border of ischemia 1b and MI, and the other is the decreased safe factor (SF) for conduction at the edge of the tissue in MI region which is attributed to the increased excitation threshold of MI region. Finally, the reentrant wave was observed in a 3D model with a scar reconstructed from MRI images of a MI patient. These comprehensive findings provide novel insights for understanding the arrhythmic risk during the progression of myocardial ischemia and highlight the importance of the multiple pathological stages in designing medical therapies for arrhythmias in ischemia.

Inter-subject registration-based one-shot segmentation with alternating union network for cardiac MRI images.

Medical image segmentation based on deep-learning networks makes great progress in assisting disease diagnosis. However, currently, the training of most networks still requires a large amount of data with labels. In reality, labeling a considerable number of medical images is challenging and time-consuming. In order to tackle this challenge, a new one-shot segmentation framework for cardiac MRI images based on an inter-subject registration model called Alternating Union Network (AUN) is proposed in this study. The label of the source image is warped with deformation fields discovered from AUN to segment target images directly. Initially, the volumes are pre-processed by aligning affinely and adjusting the global intensity to simplify subsequent deformation registration. AUN consists of two kinds of subnetworks trained alternately to optimize segmentation gradually. The first kind of subnetwork takes a pair of volumes as inputs and registers them using global intensity similarity. The second kind of subnetwork, which takes the predicted labels generated from the previous subnetwork and the labels refined using the information of intrinsic anatomical structures of interest as inputs, is intensity-independent and focuses attention on registering structures of interest. Specifically, the input of AUN is a pair of a labeled image with the texture in regions of interest removed and a target image. Additionally, a new similarity measurement more appropriate for registering such image pair is defined as Local Squared Error (LSE). The proposed registration-based one-shot segmentation pays attention to the problem of the lack of labeled medical images. In AUN, only one labeled volume is required and a large number of unlabeled ones can be leveraged to improve segmentation performance, which has great advantages in clinical application. In addition, the intensity-independent subnetwork and LSE proposed in this study empower the framework to segment medical images with complicated intensity distribution.

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model-based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the characteristic of category imbalance in designing a multi-label ECG classifier. The learning framework combines a residual convolutional network with a class-wise attention mechanism. We evaluate our method with a cost-sensitive metric on multiple realistic datasets. The results show that CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in a 5-fold cross-validation, outperforming other commonly used thresholding methods, including rank-based thresholding, proportion-based thresholding, and fixed thresholding. This demonstrates that, by taking into account the category imbalance and predefined cost information, our approach is effective in improving the performance and practicability of multi-label ECG classification models.

Effects of long-term fasting and confinement on the cardiovascular activity.

Fasting has been demonstrated to improve health and slow aging in human and other species; however, its impact on the human body in the confined environment is still unclear. This work studies the effects of long-term fasting and confined environment on the cardiovascular activities of human via a 10-day fasting experiment with two groups of subjects being in confined (6 subjects) and unconfined (7 subjects) environments respectively and undergoing the same four-stage fasting/feeding process. It is found that the confinement has significant influences on the autonomic regulation to the heart rate during the fasting process by altering the activity of the parasympathetic nervous system, which is manifested by the significant higher pNN50, rMSSD, and Ln-HF of heart rate variability (HRV) (p < 0.05) and slower heart rate (p < 0.01) in the confined group than that in the unconfined group. Furthermore, the long-term fasting induces a series of changes in both groups, including reduced level of serum sodium (p < 0.01), increased the serum calcium (p < 0.05), prolonged QTc intervals (p < 0.05), and reduced systolic blood pressures (p < 0.05). These effects are potentially negative to human health and therefore need to be treated with caution. Study of the effects of fasting and confinement on the cardiovascular activities.

Reciprocal interaction between IK1 and If in biological pacemakers: A simulation study.

Pacemaking dysfunction (PD) may result in heart rhythm disorders, syncope or even death. Current treatment of PD using implanted electronic pacemakers has some limitations, such as finite battery life and the risk of repeated surgery. As such, the biological pacemaker has been proposed as a potential alternative to the electronic pacemaker for PD treatment. Experimentally and computationally, it has been shown that bio-engineered pacemaker cells can be generated from non-rhythmic ventricular myocytes (VMs) by knocking out genes related to the inward rectifier potassium channel current (IK1) or by overexpressing hyperpolarization-activated cyclic nucleotide gated channel genes responsible for the "funny" current (If). However, it is unclear if a bio-engineered pacemaker based on the modification of IK1- and If-related channels simultaneously would enhance the ability and stability of bio-engineered pacemaking action potentials. In this study, the possible mechanism(s) responsible for VMs to generate spontaneous pacemaking activity by regulating IK1 and If density were investigated by a computational approach. Our results showed that there was a reciprocal interaction between IK1 and If in ventricular pacemaker model. The effect of IK1 depression on generating ventricular pacemaker was mono-phasic while that of If augmentation was bi-phasic. A moderate increase of If promoted pacemaking activity but excessive increase of If resulted in a slowdown in the pacemaking rate and even an unstable pacemaking state. The dedicated interplay between IK1 and If in generating stable pacemaking and dysrhythmias was evaluated. Finally, a theoretical analysis in the IK1/If parameter space for generating pacemaking action potentials in different states was provided. In conclusion, to the best of our knowledge, this study provides a wide theoretical insight into understandings for generating stable and robust pacemaker cells from non-pacemaking VMs by the interplay of IK1 and If, which may be helpful in designing engineered biological pacemakers for application purposes.

Automatic Detection of QRS Complexes Using Dual Channels Based on U-Net and Bidirectional Long Short-Term Memory.

OBJECTIVE: Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. The objective of this paper is to address automatic detection of QRS complexes. METHODS: In this paper, we proposed a new algorithm for automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the automatic detection of QRS complexes. RESULTS: The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database and CPSC2019 dataset, which achieved 99.06% and 95.13% for sensitivity, 99.22% and 82.03% for positive predictivity, and 98.29% and 78.73% accuracy on the two datasets respectively. CONCLUSION: Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task. SIGNIFICANCE: The proposed method not only has application potential for QRS complex detecting for large ECG data, but also can be extended to other medical signal research fields.

Three-Dimensional Liver Image Segmentation Using Generative Adversarial Networks Based on Feature Restoration.

Medical imaging provides a powerful tool for medical diagnosis. In the process of computer-aided diagnosis and treatment of liver cancer based on medical imaging, accurate segmentation of liver region from abdominal CT images is an important step. However, due to defects of liver tissue and limitations of CT imaging procession, the gray level of liver region in CT image is heterogeneous, and the boundary between the liver and those of adjacent tissues and organs is blurred, which makes the liver segmentation an extremely difficult task. In this study, aiming at solving the problem of low segmentation accuracy of the original 3D U-Net network, an improved network based on the three-dimensional (3D) U-Net, is proposed. Moreover, in order to solve the problem of insufficient training data caused by the difficulty of acquiring labeled 3D data, an improved 3D U-Net network is embedded into the framework of generative adversarial networks (GAN), which establishes a semi-supervised 3D liver segmentation optimization algorithm. Finally, considering the problem of poor quality of 3D abdominal fake images generated by utilizing random noise as input, deep convolutional neural networks (DCNN) based on feature restoration method is designed to generate more realistic fake images. By testing the proposed algorithm on the LiTS-2017 and KiTS19 dataset, experimental results show that the proposed semi-supervised 3D liver segmentation method can greatly improve the segmentation performance of liver, with a Dice score of 0.9424 outperforming other methods.

TRPV4 blockade suppresses atrial fibrillation in sterile pericarditis rats.

Atrial fibrillation (AF) commonly occurs after surgery and is associated with atrial remodeling. TRPV4 is functionally expressed in the heart, and its activation affects cardiac structure and functions. We hypothesized that TRPV4 blockade alleviates atrial remodeling and reduces AF induction in sterile pericarditis (SP) rats. TRPV4 antagonist GSK2193874 or vehicle was orally administered 1 day before pericardiotomy. AF susceptibility and atrial function were assessed using in vivo electrophysiology, ex vivo optical mapping, patch clamp, and molecular biology on day 3 after surgery. TRPV4 expression increased in the atria of SP rats and patients with AF. GSK2193874 significantly reduced AF vulnerability in vivo and the frequency of atrial ectopy and AF with a reentrant pattern ex vivo. Mechanistically, GSK2193874 reversed the abnormal action potential duration (APD) prolongation in atrial myocytes through the regulation of voltage-gated K+ currents (IK); reduced the activation of atrial fibroblasts by inhibiting P38, AKT, and STAT3 pathways; and alleviated the infiltration of immune cells. Our results reveal that TRPV4 blockade prevented abnormal changes in atrial myocyte electrophysiology and ameliorated atrial fibrosis and inflammation in SP rats; therefore, it might be a promising strategy to treat AF, particularly postoperative AF.

Role of Oxidation-Dependent CaMKII Activation in the Genesis of Abnormal Action Potentials in Atrial Cardiomyocytes: A Simulation Study.

Atrial fibrillation is a common cardiac arrhythmia with an increasing incidence rate. Particularly for the aging population, understanding the underlying mechanisms of atrial arrhythmia is important in designing clinical treatment. Recently, experiments have shown that atrial arrhythmia is associated with oxidative stress. In this study, an atrial cell model including oxidative-dependent Ca2+/calmodulin- (CaM-) dependent protein kinase II (CaMKII) activation was developed to explore the intrinsic mechanisms of atrial arrhythmia induced by oxidative stress. The simulation results showed that oxidative stress caused early afterdepolarizations (EADs) of action potentials by altering the dynamics of transmembrane currents and intracellular calcium cycling. Oxidative stress gradually elevated the concentration of calcium ions in the cytoplasm by enhancing the L-type Ca2+ current and sarcoplasmic reticulum (SR) calcium release. Owing to increased intracellular calcium concentration, the inward Na+/Ca2+ exchange current was elevated which slowed down the repolarization of the action potential. Thus, the action potential was prolonged and the L-type Ca2+ current was reactivated, resulting in the genesis of EAD. Furthermore, based on the atrial single-cell model, a two-dimensional (2D) ideal tissue model was developed to explore the effect of oxidative stress on the electrical excitation wave conduction in 2D tissue. Simulation results demonstrated that, under oxidative stress conditions, EAD hindered the conduction of electrical excitation and caused an unstable spiral wave, which could disrupt normal cardiac rhythm and cause atrial arrhythmia. This study showed the effects of excess reactive oxygen species on calcium cycling and action potential in atrial myocytes and provided insights regarding atrial arrhythmia induced by oxidative stress.

Heart failure-induced atrial remodelling promotes electrical and conduction alternans.

Heart failure (HF) is associated with an increased propensity for atrial fibrillation (AF), causing higher mortality than AF or HF alone. It is hypothesized that HF-induced remodelling of atrial cellular and tissue properties promotes the genesis of atrial action potential (AP) alternans and conduction alternans that perpetuate AF. However, the mechanism underlying the increased susceptibility to atrial alternans in HF remains incompletely elucidated. In this study, we investigated the effects of how HF-induced atrial cellular electrophysiological (with prolonged AP duration) and tissue structural (reduced cell-to-cell coupling caused by atrial fibrosis) remodelling can have an effect on the generation of atrial AP alternans and their conduction at the cellular and one-dimensional (1D) tissue levels. Simulation results showed that HF-induced atrial electrical remodelling prolonged AP duration, which was accompanied by an increased sarcoplasmic reticulum (SR) Ca2+ content and Ca2+ transient amplitude. Further analysis demonstrated that HF-induced atrial electrical remodelling increased susceptibility to atrial alternans mainly due to the increased sarcoplasmic reticulum Ca2+-ATPase (SERCA) Ca2+ reuptake, modulated by increased phospholamban (PLB) phosphorylation, and the decreased transient outward K+ current (Ito). The underlying mechanism has been suggested that the increased SR Ca2+ content and prolonged AP did not fully recover to their previous levels at the end of diastole, resulting in a smaller SR Ca2+ release and AP in the next beat. These produced Ca2+ transient alternans and AP alternans, and further caused AP alternans and Ca2+ transient alternans through Ca2+→AP coupling and AP→Ca2+ coupling, respectively. Simulation of a 1D tissue model showed that the combined action of HF-induced ion channel remodelling and a decrease in cell-to-cell coupling due to fibrosis increased the heart tissue's susceptibility to the formation of spatially discordant alternans, resulting in an increased functional AP propagation dispersion, which is pro-arrhythmic. These findings provide insights into how HF promotes atrial arrhythmia in association with atrial alternans.

Biological pacemaker: from biological experiments to computational simulation.

Pacemaking dysfunction has become a significant disease that may contribute to heart rhythm disorders, syncope, and even death. Up to now, the best way to treat it is to implant electronic pacemakers. However, these have many disadvantages such as limited battery life, infection, and fixed pacing rate. There is an urgent need for a biological pacemaker (bio-pacemaker). This is expected to replace electronic devices because of its low risk of complications and the ability to respond to emotion. Here we survey the contemporary development of the bio-pacemaker by both experimental and computational approaches. The former mainly includes gene therapy and cell therapy, whilst the latter involves the use of multi-scale computer models of the heart, ranging from the single cell to the tissue slice. Up to now, a bio-pacemaker has been successfully applied in big mammals, but it still has a long way from clinical uses for the treatment of human heart diseases. It is hoped that the use of the computational model of a bio-pacemaker may accelerate this process. Finally, we propose potential research directions for generating a bio-pacemaker based on cardiac computational modeling.

Influence of the distribution of fibrosis within an area of myocardial infarction on wave propagation in ventricular tissue.

The presence of fibrosis in heart tissue is strongly correlated with an incidence of arrhythmia, which is a leading cause of sudden cardiac death (SCD). However, it remains incompletely understood how different distributions, sizes and positions of fibrotic tissues contribute to arrhythmogenesis. In this study, we designed 4 different ventricular models mimicking wave propagation in cardiac tissues under normal, myocardial infarction (MI), MI with random fibrosis and MI with gradient fibrosis conditions. Simulation results of ideal square tissues indicate that vulnerable windows (VWs) of random and gradient fibrosis distributions are similar with low levels of fibrosis. However, with a high level of fibrosis, the VWs significantly increase in random fibrosis tissue but not in gradient fibrosis tissue. In addition, we systematically analyzed the effects of the size and position of fibrosis tissues on VWs. Simulation results show that it is more likely for a reentry wave to appear when the length of the infarcted area is greater than 25% of the perimeter of the ventricle, when the width is approximately half that of the ventricular wall, or when the infarcted area is attached to the inside or outside of the ventricular wall.

The role of the size of infarcted area on two kinds of vulnerable window in two dimension ventricular tissue.

AIMS: Sudden cardiac death (SCD) is mainly induced by ventricular arrhythmia, especially among patients with myocardial infarction (MI). Previous studies have shown that ventricular tachycardia and fibrillation are thought to be caused by re-entrant waves of excitation. Although in heterogeneous tissue, the traditional vulnerable window of unidirectional block and the vulnerable window when bidirectional propagation was initiated could coexist, little studies are based on effects of infarcted areas on both vulnerable windows. METHODS AND RESULTS: The electrophysiology remodeling was based on TP06 model in the present study. In simulation of two-dimension (2D) ideal models, excitation wave conduction in ventricular tissue was simulated under three different types of stimulus. 2D ideal models of ventricular tissue were constructed as loop areas in the center of a square tissue with the resolution of 600×600 grid points and cell size of 0.35mm. Simulation results showed that the traditional vulnerable window when unidirectional propagation was initiated was consistent no matter where the position of stimulus is and how long and how wide the size of the infarcted area is. However, the vulnerable window when bidirectional propagation was initiated varies in different conditions. CONCLUSION: The traditional vulnerable window could not reflect the role of the infarcted area on arrhythmogenesis. The vulnerable window when bidirectional propagation was initiated increases with the increasement of the width and the length of the infarcted area with the appropriate position of the stimulus, which could help us to conclude the arrhythmogenesis according to the size of infarcted areas.

Role of If Density on Electrical Action Potential of Bio-engineered Cardiac Pacemaker: A Simulation Study.

Due to the inevitable drawbacks of the implantable electrical pacemaker, the biological pacemaker was believed to be an alternative therapy for heart failure. Previous experimental studies have shown that biological pacemaker could be produced by genetically manipulating non-pacemaking cardiac cells by suppressing the inward rectifier potassium current (IK1) and expressing the hyperpolarization- activated current (If). However, the role of If in such bio-engineered pacemaker is not clear. In this study, we simulated the action potential of biological pacemaker cells by manipulating If-IK1 parameters (i.e., inhibiting IK1 as well as incorporating If) to analyze possible mechanisms by which different If densities control pacemaking action potentials. Our simulation results showed different pacing mechanism between the bioengineered pacemaking cells with and without If. In addition, it was shown that a greater If density might result in a slower pacing frequency, and excessive of it might produce an early-afterdepolarizations-like action potential due to a sudden release of calcium from sarcoplasmic reticulum into the cytoplasm. This study indicated that when IK1 was significantly suppressed, incorporating If may not enhance the pacing ability of biological pacemaker, but lead to abnormal dynamics of intracellular ionic concentration, increasing risks of dysrhythmia in the heart.

A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography.

Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convolutional networks (FCN) and deformable model. This method implemented a coarse-to-fine framework. Firstly, a new deep fusion network based on feature fusion and transfer learning, combining the residual modules, was proposed to achieve coarse segmentation of LV on 3DE. Secondly, we proposed a method of geometrical model initialization for a deformable model based on the results of coarse segmentation. Thirdly, the deformable model was implemented to further optimize the segmentation results with a regularization item to avoid the leakage between left atria and left ventricle to achieve the goal of fine segmentation of LV. Numerical experiments have demonstrated that the proposed method outperforms the state-of-the-art methods on the challenging CETUS benchmark in the segmentation accuracy and has a potential for practical applications.

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks.

Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.

Mechanism underlying impaired cardiac pacemaking rhythm during ischemia: A simulation study.

Ischemia in the heart impairs function of the cardiac pacemaker, the sinoatrial node (SAN). However, the ionic mechanisms underlying the ischemia-induced dysfunction of the SAN remain elusive. In order to investigate the ionic mechanisms by which ischemia causes SAN dysfunction, action potential models of rabbit SAN and atrial cells were modified to incorporate extant experimental data of ischemia-induced changes to membrane ion channels and intracellular ion homeostasis. The cell models were incorporated into an anatomically detailed 2D model of the intact SAN-atrium. Using the multi-scale models, the functional impact of ischemia-induced electrical alterations on cardiac pacemaking action potentials (APs) and their conduction was investigated. The effects of vagal tone activity on the regulation of cardiac pacemaker activity in control and ischemic conditions were also investigated. The simulation results showed that at the cellular level ischemia slowed the SAN pacemaking rate, which was mainly attributable to the altered Na+-Ca2+ exchange current and the ATP-sensitive potassium current. In the 2D SAN-atrium tissue model, ischemia slowed down both the pacemaking rate and the conduction velocity of APs into the surrounding atrial tissue. Simulated vagal nerve activity, including the actions of acetylcholine in the model, amplified the effects of ischemia, leading to possible SAN arrest and/or conduction exit block, which are major features of the sick sinus syndrome. In conclusion, this study provides novel insights into understanding the mechanisms by which ischemia alters SAN function, identifying specific conductances as contributors to bradycardia and conduction block.

Electrophysiological Properties and Viability of Neonatal Rat Ventricular Myocyte Cultures with Inducible ChR2 Expression.

Channelrhodopsin-2 (ChR2)-based optogenetic technique has been increasingly applied to cardiovascular research. However, the potential effects of ChR2 protein overexpression on cardiomyocytes are not completely understood. The present work aimed to examine how the doxycycline-inducible lentiviral-mediated ChR2 expression may affect cell viability and electrophysiological property of neonatal rat ventricular myocyte (NRVM) cultures. Primary NVRMs were infected with lentivirus containing ChR2 or YFP gene and subjected to cytotoxicity analysis. ChR2-expressing cultures were then paced electrically or optically with a blue light-emitting diode, with activation spread recorded simultaneously using optical mapping. Results showed that ChR2 could be readily transduced to NRVMs by the doxycycline-inducible lentiviral system; however, high-level ChR2 (but not YFP) expression was associated with substantial cytotoxicity, which hindered optical pacing. Application of bromodeoxyuridine significantly reduced cell damage, allowing stimulation with light. Simultaneous optical Vm mapping showed that conduction velocity, action potential duration, and dVm/dtmax were similar in ChR2-expressing and control cultures. Finally, the ChR2-expressing cultures could be optically paced at multiple sites, with significantly reduced overall activation time. In summary, we demonstrated that inducible lentiviral-mediated ChR2 overexpression might cause cytotoxicity in NRVM cultures, which could be alleviated without impairing electrophysiological function, allowing simultaneous optical pacing and Vm mapping.

A novel KCND3 mutation associated with early-onset lone atrial fibrillation.

Atrial fibrillation (AF) is the most common arrhythmia in the clinic. While previous studies have identified AF-associated mutations in several genes, the genetic basis for AF remains unclear. Here, we identified a novel T361S missense mutation in potassium voltage-gated channel, shal-related subfamily, member 3 (KCND3) from a Chinese Han family ancestor with lone AF. The wild-type (WT) or mutant T361S of Kv4.3 protein (encoded by KCND3) were co-expressed with the auxiliary subunit K+ channel-Interacting Protein (KChIP2) in HEK293 cells, and transient outward potassium current (Ito) were recorded using patch-clamp methods, and the surface or total protein levels of Kv4.3 were analyzed by western blot. Ito density, measured at 60 mV, for T361S was significantly higher than that for WT. Both the steady-state activation and inactivation curves showed a remarkable hyperpolarizing shift in T361S. Moreover, recovery from inactivation after a 500-ms depolarizing pulse was significantly delayed for T361S compared with that for WT. Mechanistically, the gain of function of Ito elicited by T361S was associated with the increased expression of cell surface and total cell protein of Kv4.3. The computer stimulation revealed that the T361S mutation shortened the action potential duration through an increased Itoin Human Atrial Model. In conclusion, we identified a novel T361S mutation in KCND3 associated with AF in the Chinese Han family. The T361S mutant result in the changes in channel kinetics as well as the up-regulation of Kv4.3 protein, which may be a critical driver for lone AF as observed in the patient.