Cardiac mitochondria exhibit dynamic functional clustering

Multi-oscillatory behavior of mitochondrial inner membrane potential ΔΨm in self-organized cardiac mitochondrial networks can be triggered by metabolic or oxidative stress. Spatio-temporal analyses of cardiac mitochondrial networks have shown that mitochondria are heterogeneously organized in synchronously oscillating clusters in which the mean cluster frequency and size are inversely correlated, thus suggesting a modulation of cluster frequency through local inter-mitochondrial coupling. In this study, we propose a method to examine the mitochondrial network's topology through quantification of its dynamic local clustering coefficients. Individual mitochondrial ΔΨm oscillation signals were identified for each cardiac myocyte and cross-correlated with all network mitochondria using previously described methods (Kurz et al., 2010a). Time-varying inter-mitochondrial connectivity, defined for mitochondria in the whole network whose signals are at least 90% correlated at any given time point, allowed considering functional local clustering coefficients. It is shown that mitochondrial clustering in isolated cardiac myocytes changes dynamically and is significantly higher than for random mitochondrial networks that are constructed using the Erdös–Rényi model based on the same sets of vertices. The network's time-averaged clustering coefficient for cardiac myocytes was found to be 0.500 ± 0.051 (N = 9) vs. 0.061 ± 0.020 for random networks, respectively. Our results demonstrate that cardiac mitochondria constitute a network with dynamically connected constituents whose topological organization is prone to clustering. Cluster partitioning in networks of coupled oscillators has been observed in scale-free and chaotic systems and is therefore in good agreement with previous models of cardiac mitochondrial networks

A novel technique for guiding ablative therapy of cardiac arrhythmias

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1999.Includes bibliographical references (leaves 173-180).by Antonis A. Armoundas.Ph.D

Method and apparatus for guiding ablative therapy of abnormal biological electrical excitation

This invention involves method and apparatus for guiding ablative therapy of abnormal biological electrical excitation. In particular, it is designed for treatment of cardiac arrhythmias. In the method of this invention electrical signals are acquired from passive electrodes, and an inverse dipole method is used to identify the site of origin of an arrhytmia. The location of the tip of the ablation catheter is similarly localized from signals acquired from the passive electrodes while electrical energy is delivered to the tip of the catheter. The catheter tip is then guided to the site of origin of the arrhythmia, and ablative radio frequency energy is delivered to its tip to ablate the site

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice

Design Implementation and Evaluation of a Mobile Continuous Blood Oxygen Saturation Monitoring System

Objective: In this study, we built a mobile continuous Blood Oxygen Saturation (SpO2) monitor, and for the first time, explored key design principles towards daily applications. Methods: We firstly built a customized wearable computer that can sense two-channel photoplethysmogram (PPG) signals, and transmit the signals wirelessly to smartphone. Afterwards, we explored many SpO2 model building principles, focusing on linear/nonlinear models, different PPG parameter calculation methods, and different finger types. Moreover, we further compared PPG sensor placement principles by comparing different hand configurations and different finger configurations. Finally, a dataset collected from eleven human subjects was used to evaluate the mobile health monitor and explore all of the above design principles. Results: The experimental results show that the root mean square error of the SpO2 estimation is only 1.8, indicating the effectiveness of the system. Conclusion: These results indicate the effectiveness of the customized mobile SpO2 monitor and the selected design principles. Significance: This research is expected to facilitate the continuous SpO2 monitoring of patients with clinical indications

Mitochondrial redox and pH signaling occurs in axonal and synaptic organelle clusters

Redox switches are important mediators in neoplastic, cardiovascular and neurological disorders. We recently identified spontaneous redox signals in neurons at the single mitochondrion level where transients of glutathione oxidation go along with shortening and re-elongation of the organelle. We now have developed advanced image and signal-processing methods to re-assess and extend previously obtained data. Here we analyze redox and pH signals of entire mitochondrial populations. In total, we quantified the effects of 628 redox and pH events in 1797 mitochondria from intercostal axons and neuromuscular synapses using optical sensors (mito-Grx1-roGFP2; mito-SypHer). We show that neuronal mitochondria can undergo multiple redox cycles exhibiting markedly different signal characteristics compared to single redox events. Redox and pH events occur more often in mitochondrial clusters (medium cluster size: 34.1 ± 4.8 μm2). Local clusters possess higher mitochondrial densities than the rest of the axon, suggesting morphological and functional inter-mitochondrial coupling. We find that cluster formation is redox sensitive and can be blocked by the antioxidant MitoQ. In a nerve crush paradigm, mitochondrial clusters form sequentially adjacent to the lesion site and oxidation spreads between mitochondria. Our methodology combines optical bioenergetics and advanced signal processing and allows quantitative assessment of entire mitochondrial populations

Neuronal Na+ Channels Are Integral Components of Pro-Arrhythmic Na+/Ca2+ Signaling Nanodomain That Promotes Cardiac Arrhythmias During β-Adrenergic Stimulation

SummaryAlthough triggered arrhythmias including catecholaminergic polymorphic ventricular tachycardia (CPVT) are often caused by increased levels of circulating catecholamines, the mechanistic link between β-adrenergic receptor (AR) stimulation and the subcellular/molecular arrhythmogenic trigger(s) is unclear. Here, we systematically investigated the subcellular and molecular consequences of β-AR stimulation in the promotion of catecholamine-induced cardiac arrhythmias. Using mouse models of cardiac calsequestrin-associated CPVT, we demonstrate that a subpopulation of Na+ channels, mainly the neuronal Na+ channels (nNav), colocalize with ryanodine receptor 2 (RyR2) and Na+/Ca2+ exchanger (NCX) and are a part of the β-AR-mediated arrhythmogenic process. Specifically, augmented Na+ entry via nNav in the settings of genetic defects within the RyR2 complex and enhanced sarcoplasmic reticulum (SR) Ca2+-ATPase (SERCA)-mediated SR Ca2+ refill is both an essential and a necessary factor for arrhythmogenesis. Furthermore, we show that augmentation of Na+ entry involves β-AR–mediated activation of CAMKII, subsequently leading to nNav augmentation. Importantly, selective pharmacological inhibition as well as silencing of Nav1.6 inhibit myocyte arrhythmic potential and prevent arrhythmias in vivo. Taken together, these data suggest that the arrhythmogenic alteration in Na+/Ca2+ handling evidenced ruing β-AR stimulation results, at least in part, from enhanced Na+ influx through nNav. Therefore, selective inhibition of these channels and of Nav1.6 in particular can serve as a potential antiarrhythmic therapy