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The sinoatrial node (SAN), the primary pacemaker of the heart, is responsible for the initiation and robust regulation of sinus rhythm. 3D mapping studies of the ex-vivo human heart suggested that the robust regulation of sinus rhythm relies on specialized fibrotically-insulated pacemaker compartments (head, center and tail) with heterogeneous expressions of key ion channels and receptors. They also revealed up to five sinoatrial conduction pathways (SACPs), which electrically connect the SAN with neighboring right atrium (RA). To elucidate the role of these structural-molecular factors in the functional robustness of human SAN, we developed comprehensive biophysical computer models of the SAN based on 3D structural, functional and molecular mapping of ex-vivo human hearts. Our key finding is that the electrical insulation of the SAN except SACPs, the heterogeneous expression of If, INa currents and adenosine A1 receptors (A1R) across SAN pacemaker-conduction compartments are required to experimentally reproduce observed SAN activation patterns and important phenomena such as shifts of the leading pacemaker and preferential SACP. In particular, we found that the insulating border between the SAN and RA, is required for robust SAN function and protection from SAN arrest during adenosine challenge. The heterogeneity in the expression of A1R within the human SAN compartments underlies the direction of pacemaker shift and preferential SACPs in the presence of adenosine. Alterations of INa current and fibrotic remodelling in SACPs can significantly modulate SAN conduction and shift the preferential SACP/exit from SAN. Finally, we show that disease-induced fibrotic remodeling, INa suppression or increased adenosine make the human SAN vulnerable to pacing-induced exit blocks and reentrant arrhythmia. In summary, our computer model recapitulates the structural and functional features of the human SAN and can be a valuable tool for investigating mechanisms of SAN automaticity and conduction as well as SAN arrhythmia mechanisms under different pathophysiological conditions.

Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.

Atrial fibrillation (AF) is the most common cardiac dysrhythmia and percutaneous catheter ablation is widely used to treat it. Panoramic mapping with multi-electrode catheters has been used to identify ablation targets in persistent AF but is limited by poor contact and inadequate coverage of the left atrial cavity. In this paper, we investigate the accuracy with which atrial endocardial surface potentials can be reconstructed from electrograms recorded with non-contact catheters. An in-silico approach was employed in which “ground-truth” surface potentials from experimental contact mapping studies and computer models were compared with inverse potential maps constructed by sampling the corresponding intracardiac field using virtual basket catheters. We demonstrate that it is possible to 1) specify the mixed boundary conditions required for mesh-based formulations of the potential inverse problem fully, and 2) reconstruct accurate inverse potential maps from recordings made with appropriately designed catheters. Accuracy improved when catheter dimensions were increased but was relatively stable when the catheter occupied >30% of atrial cavity volume. Independent of this, the capacity of non-contact catheters to resolve the complex atrial potential fields seen in reentrant atrial arrhythmia depended on the spatial distribution of electrodes on the surface bounding the catheter. Finally, we have shown that reliable inverse potential mapping is possible in near real-time with meshless methods that use the Method of Fundamental Solutions.

Objective: This study was aimed at validating the accuracy of a proposed algorithm for fully automatic 3D left atrial segmentation and to compare its performance with existing deep learning algorithms. Methods: A two-stage method with a shared 3D U-Net was proposed to segment the 3D left atrium. In this architecture, the 3D U-Net was used to extract 3D features, a two-stage strategy was used to decrease segmentation error caused by the class imbalance problem, and the shared network was designed to decrease model complexity. Model performance was evaluated with the DICE score, Jaccard index and Hausdorff distance. Results: Algorithm development and evaluation were performed with a set of 100 late gadolinium-enhanced cardiovascular magnetic resonance images. Our method achieved a DICE score of 0.918, a Jaccard index of 0.848 and a Hausdorff distance of 1.211, thus, outperforming existing deep learning algorithms. The best performance of the proposed model (DICE: 0.851; Jaccard: 0.750; Hausdorff distance: 4.382) was also achieved on a publicly available 2013 image data set. Conclusion: The proposed two-stage method with a shared 3D U-Net is an efficient algorithm for fully automatic 3D left atrial segmentation. This study provides a solution for processing large datasets in resource-constrained applications. Significance Statement: Studying atrial structure directly is crucial for comprehending and managing atrial fibrillation (AF). Accurate reconstruction and measurement of atrial geometry for clinical purposes remains challenging, despite potential improvements in the visibility of AF-associated structures with late gadolinium-enhanced magnetic resonance imaging. This difficulty arises from the varying intensities caused by increased tissue enhancement and artifacts, as well as variability in image quality. Therefore, an efficient algorithm for fully automatic 3D left atrial segmentation is proposed in the present study.

Introduction: Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia and percutaneous catheter ablation is widely used to treat it. Panoramic mapping with multi-electrode catheters can identify ablation targets in persistent AF, but is limited by poor contact and inadequate coverage.Objective: To investigate the accuracy of inverse mapping of endocardial surface potentials from electrograms sampled with noncontact basket catheters.Methods: Our group has developed a computationally efficient inverse 3D mapping technique using a meshless method that employs the Method of Fundamental Solutions (MFS). An in-silico test bed was used to compare ground-truth surface potentials with corresponding inverse maps reconstructed from noncontact potentials sampled with virtual catheters. Ground-truth surface potentials were derived from high-density clinical contact mapping data and computer models.Results: Solutions of the intracardiac potential inverse problem with the MFS are robust, fast and accurate. Endocardial surface potentials can be faithfully reconstructed from noncontact recordings in real-time if the geometry of cardiac surface and the location of electrodes relative to it are known. Larger catheters with appropriate electrode density are needed to resolve complex reentrant atrial rhythms.Conclusion: Real-time panoramic potential mapping is feasible with noncontact intracardiac catheters using the MFS.Significance: Accurate endocardial potential maps can be reconstructed in AF with appropriately designed noncontact multi-electrode catheters.

Point clouds are a widely used format for storing information in a memory-efficient and easily manipulatable representation. However, research in the application of point cloud mapping and subsequent organ reconstruction with deep learning, is limited. In particular, current methods for left atrium (LA) visualization using point clouds recorded from clinical mapping during cardiac ablation are proprietary and remain difficult to validate. Many clinics rely on additional imaging such as MRIs/CTs to improve the accuracy of LA mapping. In this study, for the first time, we proposed a novel deep learning framework for the automatic 3D surface reconstruction of the LA directly from point clouds acquired via widely used clinical mapping systems. The backbone of our framework consists of a 30-layer 3D fully convolutional neural network (CNN). The architecture contains skip connections that perform multi-resolution processing to maximize information extraction from the point clouds and ensure a high-resolution prediction by combining features at different receptive levels. We used large kernels with increased receptive fields to address the sparsity of the point clouds. Residual blocks and activation normalization were further implemented to improve the feature learning on sparse inputs. By utilizing a light-weight design with low-depth layers, our CNN took approximately 10 s per patient. Independent testing on two cross-modality clinical datasets showed excellent dice scores of 93% and surface-to-surface distances below 1 pixel. Overall, our study may provide a more efficient, cost-effective 3D LA reconstruction approach during ablation procedures, and potentially lead to improved treatment of cardiac diseases.

Feld's friendship paradox is a widely accepted observation that states that the friends of any particular individual tend to have more friends on average than the individual has. Due to its implications for information transmission, the friendship paradox has become a generalized paradigm in many disciplines. However, how many of an individual's friends have more friends than the individual does is still unknown, yet interesting. In this paper, we revisited the Feld's friendship paradox and we found that only a limited number of a person's friends have more friends than the person herself has. This conclusion was reached from empirical studies using real-world networks and is true regardless of the number of one's neighbours, which contradicts the intuitive deduction from the friendship paradox. For one thing, if a person is unpopular, the number of her friends that have more friends than she does grows with the number of friends that she makes. This observation crystallizes the tenable margin of the friendship paradox. In another case, if a person is popular, as she acquires more friends, fewer of her friends have more friends than she does. This finding suggests an observation bias in the friendship paradox, which makes individuals feel less popular than their friends. Besides enriching our knowledge of the friendship paradox in psychological science, the findings reported here are also beneficial for technical areas such as large-scale triangle discovery. Although the friendship paradox was proposed from the perspective of the number of neighbours, the findings reported here can shed light on theories and applications from different disciplines like information, happiness, obesity, and extroversion, to name a few.

The role of of voltage-gated sodium channels (Nav) in pacemaking and conduction of the human sinoatrial node is unclear. Here, the authors investigate existence and function of neuronal and cardiac Nav in human sinoatrial nodes, and demonstrate their alterations in explanted human diseased hearts.

Micro-anatomical reentry has been identified as a potential driver of atrial fibrillation (AF). In this paper, we introduce a novel computational method which aims to identify which atrial regions are most susceptible to micro-reentry. The approach, which considers the structural basis for micro-reentry only, is based on the premise that the accumulation of electrically insulating interstitial fibrosis can be modelled by simulating percolation-like phenomena on spatial networks. Our results suggest that at high coupling, where micro-reentry is rare, the micro-reentrant substrate is highly clustered in areas where the atrial walls are thin and have convex wall morphology, likely facilitating localised treatment via ablation. However, as transverse connections between fibres are removed, mimicking the accumulation of interstitial fibrosis, the substrate becomes less spatially clustered, and the bias to forming in thin, convex regions of the atria is reduced, possibly restricting the efficacy of localised ablation. Comparing our algorithm on image-based models with and without atrial fibre structure, we find that strong longitudinal fibre coupling can suppress the micro-reentrant substrate, whereas regions with disordered fibre orientations have an enhanced risk of micro-reentry. With further development, these methods may be useful for modelling the temporal development of the fibrotic substrate on an individualised basis.

Pancreatic volume and fat fraction are critical prognoses for metabolic diseases like type 2 diabetes (T2D). Magnetic Resonance Imaging (MRI) is a required non-invasive quantification method for the pancreatic fat fraction. The dramatic development of deep learning has enabled the automatic measurement of MR images. Therefore, based on MRI, we intend to develop a deep convolutional neural network (DCNN) that can accurately segment and measure pancreatic volume and fat fraction. This retrospective study involved abdominal MR images from 148 diabetic patients and 246 healthy normoglycemic participants. We randomly separated them into training and testing sets according to the proportion of 80:20. There were 2364 recognizable pancreas images labeled and pre-treated by an upgraded superpixel algorithm for a discernible pancreatic boundary. We then applied them to the novel DCNN model, mimicking the most accurate and latest manual pancreatic segmentation process. Fat phantom and erosion algorithms were employed to increase the accuracy. The results were evaluated by dice similarity coefficient (DSC). External validation datasets included 240 MR images from 10 additional patients. We assessed the pancreas and pancreatic fat volume using the DCNN and compared them with those of specialists. This DCNN employed the cutting-edge idea of manual pancreas segmentation and achieved the highest DSC (91.2%) compared with any reported models. It is the first framework to measure intra-pancreatic fat volume and fat deposition. Performance validation reflected by regression R2 value between manual operation and trained DCNN segmentation on the pancreas and pancreatic fat volume were 0.9764 and 0.9675, respectively. The performance of the novel DCNN enables accurate pancreas segmentation, pancreatic fat volume, fraction measurement, and calculation. It achieves the same segmentation level of experts. With further training, it may well surpass any expert and provide accurate measurements, which may have significant clinical relevance.

Background: Atrial fibrillation (AF) is associated with calcium (Ca2+) handling remodeling and increased spontaneous calcium release events (SCaEs). Nevertheless, its exact mechanism remains unclear, resulting in suboptimal primary and secondary preventative strategies.Methods: We searched the PubMed database for studies that investigated the relationship between SCaEs and AF and/or its risk factors. Meta-analysis was used to examine the Ca2+ mechanisms involved in the primary and secondary AF preventative groups.Results: We included a total of 74 studies, out of the identified 446 publications from inception (1982) until March 31, 2020. Forty-five were primary and 29 were secondary prevention studies for AF. The main Ca2+ release events, calcium transient (standardized mean difference (SMD) = 0.49; I2 = 35%; confidence interval (CI) = 0.33–0.66; p < 0.0001), and spark amplitude (SMD = 0.48; I2 = 0%; CI = −0.98–1.93; p = 0.054) were enhanced in the primary diseased group, while calcium transient frequency was increased in the secondary group. Calcium spark frequency was elevated in both the primary diseased and secondary AF groups. One of the key cardiac currents, the L-type calcium current (ICaL) was significantly downregulated in primary diseased (SMD = −1.07; I2 = 88%; CI = −1.94 to −0.20; p < 0.0001) and secondary AF groups (SMD = −1.28; I2 = 91%; CI = −2.04 to −0.52; p < 0.0001). Furthermore, the sodium–calcium exchanger (INCX) and NCX1 protein expression were significantly enhanced in the primary diseased group, while only NCX1 protein expression was shown to increase in the secondary AF studies. The phosphorylation of the ryanodine receptor at S2808 (pRyR-S2808) was significantly elevated in both the primary and secondary groups. It was increased in the primary diseased and proarrhythmic subgroups (SMD = 0.95; I2 = 64%; CI = 0.12–1.79; p = 0.074) and secondary AF group (SMD = 0.66; I2 = 63%; CI = 0.01–1.31; p < 0.0001). Sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) expression was elevated in the primary diseased and proarrhythmic drug subgroups but substantially reduced in the secondary paroxysmal AF subgroup.Conclusions: Our study identified that ICaL is reduced in both the primary and secondary diseased groups. Furthermore, pRyR-S2808 and NCX1 protein expression are enhanced. The remodeling leads to elevated Ca2+ functional activities, such as increased frequencies or amplitude of Ca2+ spark and Ca2+ transient. The main difference identified between the primary and secondary diseased groups is SERCA expression, which is elevated in the primary diseased group and substantially reduced in the secondary paroxysmal AF subgroup. We believe our study will add new evidence to AF mechanisms and treatment targets.

Abstract Background Diabetes mellitus is an important risk factor for atrial fibrillation (AF) development. Sodium–glucose co-transporter-2 (SGLT-2) inhibitors are used for the treatment of type 2 diabetes mellitus (T2DM). Their cardioprotective effects have been reported but whether they prevent AF in T2DM patients are less well-explored. We tested the hypothesis that the SGLT-2 inhibitor, empagliflozin, can prevent atrial remodeling in a diabetic rat model. Methods High-fat diet and low-dose streptozotocin (STZ) treatment were used to induce T2DM. A total of 96 rats were randomized into the following four groups: (i) control (ii) T2DM, (iii) low-dose empagliflozin (10 mg/kg/day)/T2DM; and (iv) high-dose empagliflozin (30 mg/kg/day)/T2DM by the intragastric route for 8 weeks. Results Compared with the control group, left atrial diameter, interstitial fibrosis and the incidence of AF inducibility were significantly increased in the DM group. Moreover, atrial mitochondrial respiratory function, mitochondrial membrane potential, and mitochondrial biogenesis were impaired. Empagliflozin treatment significantly prevented the development of these abnormalities in DM rats, likely via the peroxisome proliferator-activated receptor-c coactivator 1α (PGC-1α)/nuclear respiratory factor-1 (NRF-1)/mitochondrial transcription factor A (Tfam) signaling pathway. Conclusions Empagliflozin can ameliorate atrial structural and electrical remodeling as well as improve mitochondrial function and mitochondrial biogenesis in T2DM, hence may be potentially used in the prevention of T2DM-related atrial fibrillation.

Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population worldwide and is associated with high levels of morbidity and mortality. Catheter ablation (CA) has become one of the first line treatments for AF, but its success rates are suboptimal, especially in the case of persistent AF. Computational approaches have shown promise in predicting the CA strategy using simulations of atrial models, as well as applying deep learning to atrial images. We propose a novel approach that combines image-based computational modelling of the atria with deep learning classifiers trained on patient-specific atrial models, which can be used to assist in CA therapy selection. Therefore, we trained a deep convolutional neural network (CNN) using a combination of (i) 122 atrial tissue images obtained by unfolding patient LGE-MRI datasets, (ii) 157 additional synthetic images derived from the patient data to enhance the training dataset, and (iii) the outcomes of 558 CA simulations to terminate several AF scenarios in the corresponding image-based atrial models. Four CNN classifiers were trained on this patient-specific dataset balanced using several techniques to predict three common CA strategies from the patient atrial images: pulmonary vein isolation (PVI), rotor-based ablation (Rotor) and fibrosis-based ablation (Fibro). The training accuracy for these classifiers ranged from 96.22 to 97.69%, while the validation accuracy was from 78.68 to 86.50%. After training, the classifiers were applied to predict CA strategies for an unseen holdout test set of atrial images, and the results were compared to outcomes of the respective image-based simulations. The highest success rate was observed in the correct prediction of the Rotor and Fibro strategies (100%), whereas the PVI class was predicted in 33.33% of the cases. In conclusion, this study provides a proof-of-concept that deep neural networks can learn from patient-specific MRI datasets and image-derived models of AF, providing a novel technology to assist in tailoring CA therapy to a patient.

Background Atrial fibrillation (AF) driver mechanisms are obscured to clinical multielectrode mapping approaches that provide partial, surface‐only visualization of unstable 3‐dimensional atrial conduction. We hypothesized that transient modulation of refractoriness by pharmacologic challenge during multielectrode mapping improves visualization of hidden paths of reentrant AF drivers for targeted ablation. Methods and Results Pharmacologic challenge with adenosine was tested in ex vivo human hearts with a history of AF and cardiac diseases by multielectrode and high‐resolution subsurface near‐infrared optical mapping, integrated with 3‐dimensional structural imaging and heart‐specific computational simulations. Adenosine challenge was also studied on acutely terminated AF drivers in 10 patients with persistent AF. Ex vivo, adenosine stabilized reentrant driver paths within arrhythmogenic fibrotic hubs and improved visualization of reentrant paths, previously seen as focal or unstable breakthrough activation pattern, for targeted AF ablation. Computational simulations suggested that shortening of atrial refractoriness by adenosine may (1) improve driver stability by annihilating spatially unstable functional blocks and tightening reentrant circuits around fibrotic substrates, thus unmasking the common reentrant path; and (2) destabilize already stable reentrant drivers along fibrotic substrates by accelerating competing fibrillatory wavelets or secondary drivers. In patients with persistent AF, adenosine challenge unmasked hidden common reentry paths (9/15 AF drivers, 41±26% to 68±25% visualization), but worsened visualization of previously visible reentry paths (6/15, 74±14% to 34±12%). AF driver ablation led to acute termination of AF. Conclusions Our ex vivo to in vivo human translational study suggests that transiently altering atrial refractoriness can stabilize reentrant paths and unmask arrhythmogenic hubs to guide targeted AF driver ablation treatment.

Segmentation and 3D reconstruction of the human atria is of crucial importance for precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia. However, the current manual segmentation of the atria from medical images is a time-consuming, labor-intensive, and error-prone process. The recent emergence of artificial intelligence, particularly deep learning, provides an alternative solution to the traditional methods that fail to accurately segment atrial structures from clinical images. This has been illustrated during the recent 2018 Atrial Segmentation Challenge for which most of the challengers developed deep learning approaches for atrial segmentation, reaching high accuracy (>90% Dice score). However, as significant discrepancies exist between the approaches developed, many important questions remain unanswered, such as which deep learning architectures and methods to ensure reliability while achieving the best performance. In this paper, we conduct an in-depth review of the current state-of-the-art of deep learning approaches for atrial segmentation from late gadolinium-enhanced MRIs, and provide critical insights for overcoming the main hindrances faced in this task.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is a major cause of stroke and morbidity. Recent genome-wide association studies have shown that paired-like homeodomain transcription factor 2 (Pitx2) to be strongly associated with AF. However, the mechanisms underlying Pitx2 modulated arrhythmogenesis and variable effectiveness of antiarrhythmic drugs (AADs) in patients in the presence or absence of impaired Pitx2 expression remain unclear. We have developed multi-scale computer models, ranging from a single cell to tissue level, to mimic control and Pitx2-knockout atria by incorporating recent experimental data on Pitx2-induced electrical and structural remodeling in humans, as well as the effects of AADs. The key findings of this study are twofold. We have demonstrated that shortened action potential duration, slow conduction and triggered activity occur due to electrical and structural remodelling under Pitx2 deficiency conditions. Notably, the elevated function of calcium transport ATPase increases sarcoplasmic reticulum Ca2+ concentration, thereby enhancing susceptibility to triggered activity. Furthermore, heterogeneity is further elevated due to Pitx2 deficiency: 1) Electrical heterogeneity between left and right atria increases; and 2) Increased fibrosis and decreased cell-cell coupling due to structural remodelling slow electrical propagation and provide obstacles to attract re-entry, facilitating the initiation of re-entrant circuits. Secondly, our study suggests that flecainide has antiarrhythmic effects on AF due to impaired Pitx2 by preventing spontaneous calcium release and increasing wavelength. Furthermore, our study suggests that Na+ channel effects alone are insufficient to explain the efficacy of flecainide. Our study may provide the mechanisms underlying Pitx2-induced AF and possible explanation behind the AAD effects of flecainide in patients with Pitx2 deficiency.

Abstract Cardiac arrhythmias and conduction disturbances are accompanied by structural remodelling of the specialised cardiomyocytes known collectively as the cardiac conduction system. Here, using contrast enhanced micro-computed tomography, we present, in attitudinally appropriate fashion, the first 3-dimensional representations of the cardiac conduction system within the intact human heart. We show that cardiomyocyte orientation can be extracted from these datasets at spatial resolutions approaching the single cell. These data show that commonly accepted anatomical representations are oversimplified. We have incorporated the high-resolution anatomical data into mathematical simulations of cardiac electrical depolarisation. The data presented should have multidisciplinary impact. Since the rate of depolarisation is dictated by cardiac microstructure, and the precise orientation of the cardiomyocytes, our data should improve the fidelity of mathematical models. By showing the precise 3-dimensional relationships between the cardiac conduction system and surrounding structures, we provide new insights relevant to valvar replacement surgery and ablation therapies. We also offer a practical method for investigation of remodelling in disease, and thus, virtual pathology and archiving. Such data presented as 3D images or 3D printed models, will inform discussions between medical teams and their patients, and aid the education of medical and surgical trainees.

Functional analysis has shown that the p.Met207Val mutation was linked to atrial fibrillation and caused an increase in transactivation activity of PITX2c, which caused changes in mRNA synthesis related to ionic channels and intercellular electrical coupling. We assumed that these changes were quantitatively translated to the functional level. This study aimed to investigate the potential impact of the PITX2c p.Met207Val mutation on atrial electrical activity through multiscale computational models. The well-known Courtemanche-Ramirez-Nattel (CRN) model of human atrial cell action potentials (APs) was modified to incorporate experimental data on the expected p.Met207Val mutation-induced changes in ionic channel currents (INaL, IKs, and IKr) and intercellular electrical coupling. The cell models for wild-type (WT), heterozygous (Mutant/Wild type, MT/WT), and homozygous (Mutant, MT) PITX2c cases were incorporated into homogeneous multicellular 1D and 2D tissue models. Effects of this mutation-induced remodeling were quantified as changes in AP profile, AP duration (APD) restitution, conduction velocity (CV) restitution and wavelength (WL). Temporal and spatial vulnerabilities of atrial tissue to the genesis of reentry were computed. Dynamic behaviors of re-entrant excitation waves (Life span, tip trajectory and dominant frequency) in a homogeneous 2D tissue model were characterized. Our results suggest that the PITX2c p.Met207Val mutation abbreviated atrial APD and flattened APD restitution curves. It reduced atrial CV and WL that facilitated the conduction of high rate atrial excitation waves. It increased the tissue's temporal vulnerability by increasing the vulnerable window for initiating reentry and increased the tissue spatial vulnerability by reducing the substrate size necessary to sustain reentry. In the 2D models, the mutation also stabilized and accelerated re-entrant excitation waves, leading to rapid and sustained reentry. In conclusion, electrical and structural remodeling arising from the PITX2c p.Met207Val mutation may increase atrial susceptibility to arrhythmia due to shortened APD, reduced CV and increased tissue vulnerability, which, in combination, facilitate initiation and maintenance of re-entrant excitation waves.

Background: Atrial fibrillation (AF) leads to a loss of transverse connections between myocyte strands that is associated with an increased complexity and stability of AF. We have explored the interaction between longitudinal and transverse coupling, and the relative contribution of the sodium (INa) and calcium (ICa) current to propagation, both in healthy tissue and under diseased conditions using computer simulations.Methods: Two parallel strands of atrial myocytes were modeled (Courtemanche et al. ionic model). As a control condition, every single cell was connected both transversely and longitudinally. To simulate a loss of transverse connectivity, this number was reduced to 1 in 4, 8, 12, or 16 transversely. To study the interaction with longitudinal coupling, anisotropy ratios of 3, 9, 16, and 25:1 were used. All simulations were repeated for varying degrees of INa and ICa block and the transverse activation delay (TAD) between the paced and non-paced strands was calculated for all cases.Results: The TAD was highly sensitive to the transverse connectivity, increasing from 1 ms at 1 in 1, to 25 ms at 1 in 4, and 100 ms at 1 in 12 connectivity. The TAD also increased when longitudinal coupling was increased. Both decreasing transverse connectivity and increasing longitudinal coupling enhanced the synchronicity of activation of the non-paced strand and increased the propensity for transverse conduction block. Even after long TADs, the action potential upstroke in the non-paced strand was still mainly dependent on the INa. Nevertheless, ICa in the paced strand was essential to provide depolarizing current to the non-paced strand. Loss of transverse connections increased the sensitivity to both INa and ICa block. However, when longitudinal coupling was relatively high, transverse propagation was more sensitive to ICa block than to INa block.Conclusions: Although transverse propagation depends on both INa and ICa, their relative contribution, and sensitivity to channel blockade, depends on the distribution of transverse connections and the axial conductivity. This simple two-strand model helps to explain the nature of atrial discontinuous conduction during structural remodeling and provides an opportunity for more effective drug development.

Due to advances in corrective surgery, congenital heart disease has an ever growing patient population. Atrial arrhythmias are frequently observed pre- and post-surgical correction. Pharmaceutical antiarrhythmic therapy is not always effective, therefore many symptomatic patients undergo catheter ablation therapy. In patients with atrioventricular septal defects (AVSD), ablation therapy itself has mixed success; arrhythmogenic recurrences are common, and because of the anatomical displacement of the atrioventricular node, 3-degree heart block post-ablation is a real concern. In order to develop optimal and safe ablation strategies, the field of congenital cardiac electrophysiology must combine knowledge from clinical electrophysiology with a thorough understanding of the anatomical substrates for arrhythmias. Using image-based analysis and multi-cellular mathematical modeling of electrical activation, we show how the anatomical alterations characteristic of an AVSD serve as arrhythmogenic substrates. Using ex-vivo contrast enhanced micro-computed tomography we imaged post-mortem the heart of a 5 month old male with AVSD at an isometric spatial resolution of 38 μm. Morphological analysis revealed the 3D disposition of the cardiac conduction system for the first time in an intact heart with this human congenital malformation. We observed displacement of the compact atrioventricular node inferiorly to the ostium of the coronary sinus. Myocyte orientation analysis revealed that the normal arrangement of the major atrial muscle bundles was preserved but was modified in the septal region. Models of electrical activation suggest the disposition of the myocytes within the atrial muscle bundles associated with the “fast pathway,” together with the displaced atrioventricular node, serve as potential substrates for re-entry and possibly atrial fibrillation. This study used archived human hearts, showing them to be a valuable resource for the mathematical modeling community, and opening new possibilities for the investigations of arrhythmogenesis and ablation strategies in the congenitally malformed heart.

Background: Meta-analysis is a widely used tool in which weighted information from multiple similar studies is aggregated to increase statistical power. However, the exponential growth of publications in key areas of medical science has rendered manual identification of relevant studies increasingly time-consuming. The aim of this work was to develop a machine learning technique capable of robust automatic study selection for meta-analysis. We have validated this approach with an up-to-date meta-analysis to investigate the association between diabetes mellitus (DM) and new-onset atrial fibrillation (AF).Methods: The PubMed online database was searched from 1960 to September 2017 where 4,177 publications that mentioned both DM and AF were identified. Relevant studies were selected as follows. First, publications were clustered based on common text features using an unsupervised K-means algorithm. Clusters that best matched the selected set of potentially relevant studies (a “training” set of 139 articles) were then identified by using maximum entropy classification. The 139 articles selected automatically on this basis were screened manually to identify potentially relevant studies. To determine the validity of the automated process, a parallel set of studies was also assembled by manually screening all initially searched publications. Finally, detailed manual selection was performed on the full texts of the studies in both sets using standard criteria. Quality assessment, meta-regression random-effects models, sensitivity analysis and publication bias assessment were then conducted.Results: Machine learning-assisted screening identified the same 29 studies for meta-analysis as those identified by using manual screening alone. Machine learning enabled more robust and efficient study selection, reducing the number of studies needed for manual screening from 4,177 to 556 articles. A pooled analysis using the most conservative estimates indicated that patients with DM had ~49% greater risk of developing AF compared with individuals without DM. After adjusting for three additional risk factors i.e., hypertension, obesity and heart disease, the relative risk was 23%. Using multivariate adjusted models, the risk for developing AF in patients with DM was similar for all DM subtypes. Women with DM were 24% more likely to develop AF than men with DM. The risk for new-onset AF in patients with DM has also increased over the years.Conclusions: We have developed a novel machine learning method to identify publications suitable for inclusion in meta-analysis.This approach has the capacity to provide for a more efficient and more objective study selection process for future such studies. We have used it to demonstrate that DM is a strong, independent risk factor for AF, particularly for women.

BackgroundStructural remodeling of human atria plays a key role in sustaining atrial fibrillation (AF), but insufficient quantitative analysis of human atrial structure impedes the treatment of AF. We aimed to develop a novel 3‐dimensional (3D) structural and computational simulation analysis tool that could reveal the structural contributors to human reentrant AF drivers. Methods and ResultsHigh‐resolution panoramic epicardial optical mapping of the coronary‐perfused explanted intact human atria (63‐year‐old woman, chronic hypertension, heart weight 608 g) was conducted during sinus rhythm and sustained AF maintained by spatially stable reentrant AF drivers in the left and right atrium. The whole atria (107×61×85 mm3) were then imaged with contrast‐enhancement MRI (9.4 T, 180×180×360‐μm3 resolution). The entire 3D human atria were analyzed for wall thickness (0.4–11.7 mm), myofiber orientations, and transmural fibrosis (36.9% subendocardium; 14.2% midwall; 3.4% subepicardium). The 3D computational analysis revealed that a specific combination of wall thickness and fibrosis ranges were primarily present in the optically defined AF driver regions versus nondriver tissue. Finally, a 3D human heart–specific atrial computer model was developed by integrating 3D structural and functional mapping data to test AF induction, maintenance, and ablation strategies. This 3D model reproduced the optically defined reentrant AF drivers, which were uninducible when fibrosis and myofiber anisotropy were removed from the model. ConclusionsOur novel 3D computational high‐resolution framework may be used to quantitatively analyze structural substrates, such as wall thickness, myofiber orientation, and fibrosis, underlying localized AF drivers, and aid the development of new patient‐specific treatments.

Metabolic syndrome (MetS) has been linked to a higher prevalence of sudden cardiac death (SCD), but the mechanisms are not well understood. One possible underlying mechanism may be an abnormal modulation of autonomic activity, which can be quantified by analyzing heart rate variability (HRV). Our aim was to investigate the modifications of short-term HRV in an experimental rabbit model during the time-course of MetS development. NZW rabbits were randomly assigned to a control (n = 10) or a MetS group (n = 13), fed 28 weeks with control or high-fat, high-sucrose diets. After anesthesia, a 15-min ECG recording was acquired before diet administration and at weeks 14 and 28. We analyzed short RR time series using time-domain, frequency-domain and nonlinear analyses. A mixed-model factorial ANOVA was used for statistical analysis. Time-domain analysis showed a 52.4% decrease in the standard deviation of heart rate in animals from the MetS group at week 28, but no changes in the rest of parameters. In the frequency domain, we found a 9.7% decrease in the very low frequency and a 380.0% increase of the low frequency bands in MetS animals at week 28, whereas high frequency remained unchanged. Nonlinear analyses showed increased complexity and irregularity of the RR time series in MetS animals.