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Introduction: In recent years, high-density microelectrode arrays (HD-MEAs) have emerged as a valuable tool in preclinical research for characterizing the electrophysiology of human induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CMs). HD-MEAs enable the capturing of both extracellular and intracellular signals on a large scale, while minimizing potential damage to the cell. However, despite technological advancements of HD-MEAs, there is a lack of effective data-analysis platforms that are capable of processing and analyzing the data, particularly in the context of cardiac arrhythmias and drug testing. Methods: To address this need, we introduce CardioMEA, a comprehensive data-analysis platform designed specifically for HD-MEA data that have been obtained from iPSCCMs. CardioMEA features scalable data processing pipelines and an interactive web-based dashboard for advanced visualization and analysis. In addition to its core functionalities, CardioMEA incorporates modules designed to discern crucial electrophysiological features between diseased and healthy iPSC-CMs. Notably, CardioMEA has the unique capability to analyze both extracellular and intracellular signals, thereby facilitating customized analyses for specific research tasks. Results and discussion: We demonstrate the practical application of CardioMEA by analyzing electrophysiological signals from iPSC-CM cultures exposed to seven antiarrhythmic drugs. CardioMEA holds great potential as an intuitive, userfriendly platform for studying cardiac diseases and assessing drug effects. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmia is currently considered to be the direct cause of death in a majority of sudden unexplained death (SUD) cases, yet the genetic predisposition and corresponding endophenotypes contributing to SUD remain incompletely understood. In this study, we aimed to investigate the involvement of Coenzyme Q (CoQ) deficiency in SUD. First, we re-analyzed the exome sequencing data of 45 SUD and 151 sudden infant death syndrome (SIDS) cases from our previous studies, focusing on previously overlooked genetic variants in 44 human CoQ deficiency-related genes. A considerable proportion of the SUD (38%) and SIDS (37%) cases were found to harbor rare variants with likely functional effects. Subsequent burden testing, including all rare exonic and untranslated region variants identified in our case cohorts, further confirmed the existence of significant genetic burden. Based on the genetic findings, the influence of CoQ deficiency on electrophysiological and morphological properties was further examined in a mouse model. A significantly prolonged PR interval and an increased occurrence of atrioventricular block were observed in the 4-nitrobenzoate induced CoQ deficiency mouse group, suggesting that CoQ deficiency may predispose individuals to sudden death through an increased risk of cardiac arrhythmia. Overall, our findings suggest that CoQ deficiency-related genes should also be considered in the molecular autopsy of SUD. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmias are the leading cause of death and pose a huge health and economic burden globally. Electrocardiography (ECG) is an effective technique for the diagnosis of cardiovascular diseases because of its noninvasive and cost-effective advantages. However, traditional ECG analysis relies heavily on the clinical experience of physicians, which can be challenging and time-consuming to produce valid diagnostic results. This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi-head attention (CBGM model). Specifically, the model consists of seven convolutional layers with varying filter sizes (4, 16, 32, and 64) and three pooling layers, respectively, while the BiGRU module includes two layers with 64 units each followed by multi-head attention (8-heads). The combination of CNN and BiGRU effectively captures spatio-temporal features of ECG signals, with multi-head attention comprehensively extracted global correlations among multiple segments of ECG signals. The validation in the MIT-BIH arrhythmia database achieved an accuracy of 99.41%, a precision of 99.15%, a specificity of 99.68%, and an F1-Score of 99.21%, indicating its robust performance across different evaluation metrics. Additionally, the model's performance was evaluated on the PTB Diagnostic ECG Database, where it achieved an accuracy of 98.82%, demonstrating its generalization capability. Comparative analysis against previous methods revealed that our proposed CBGM model exhibits more higher performance in automatic classification of arrhythmia and can be helpful for assisting clinicians by enabling real-time detection of cardiac arrhythmias during routine ECG screenings. [ABSTRACT FROM AUTHOR]

Background: One of the most commonly experienced symptoms of premenstrual disorder (PMD) is anxiety, and there is a notable rise in sympathomimetic activity in this patient group. Studies have linked fluctuations in systemic autonomic tone to electrocardiography (ECG) changes. This study aims to investigate the relationship between anxiety, a common symptom of PMD, and alterations in QT dispersion (QTd) and P‐wave dispersion (Pd) in adolescent females. Methods: This cross‐sectional study included female adolescents aged 12–18 with regular menstruation for at least 3 months. Participants completed the premenstrual syndrome scale (PMSS) and were divided into two groups, PMD and control, according to the PMSS score. A standard 12‐lead body surface ECG was performed and QTd and Pd values were determined in each participant. Results: Of the 43 participants, 27 were categorized into the PMD group, with a mean age of 15.15 ± 1.43 years. Age at menarche and menstrual cycle patterns were comparable between the PMD and control groups. Statistical analysis revealed significantly higher Pmin (p = 0.010) and Pd values (p < 0.001) in the PMD group compared to controls. A positive correlation between PMSS scores and Pd (p = 0.049) was also observed. Conclusions: Changes in atrial conduction and ventricular repolarization due to the pathophysiology of PMD may increase the risk of developing atrial and ventricular tachyarrhythmias over time. Screening patients with PMD using an ECG may be useful in identifying potentially at‐risk adolescents. [ABSTRACT FROM AUTHOR]

OBJECTIVES Degenerative mitral regurgitation is associated with heart failure, arrhythmia and mortality. The impact of sex on timing of surgical referral and outcomes has not been reported comprehensively. We examined preoperative status and surgical outcomes of male versus female degenerative mitral valve regurgitation patients undergoing surgery. METHODS We reviewed our institutional database for all patients undergoing surgery for degenerative mitral regurgitation between 2013 and 2021. Preoperative clinical and echocardiographic variables, surgical characteristics and outcomes were compared, and left atrial strain in available images. RESULTS Of 963 patients, 314 (32.6%) were female. Women were older (67 vs 64 years, P  = 0.031) and more often had bileaflet prolapse (19.4% vs 13.8%, P  = 0.028), mitral annular calcification (12.1% vs 5.4%, P  < 0.001) and tricuspid regurgitation (TR; 31.8% vs 22.5%, P  = 0.001). Indexed left ventricular end-diastolic and end-systolic diameters were higher in women, with 29.4 vs 26.7 mm/m2 (P  < 0.001) and 18.2 vs 17 mm/m2 (P  < 0.001), respectively, and left atrial conduit strain lower (17.6% vs, 21.2%, P  = 0.001). Predicted risk of mortality was 0.73% vs 0.54% in men (P  = 0.023). Women required mechanical circulatory support more frequently (1.3% vs 0%, P  = 0.011), had longer intensive care unit stay (29 vs 26 h, P  < 0.001), mechanical ventilation (5.4 vs 5 h, P  = 0.036), and overall hospitalization (7 vs 6 days, P  < 0.001). There was no difference in long-term reoperation-free survival (P  = 0.35). CONCLUSIONS Women undergoing mitral valve repair are older and show indicators of more advanced disease with long-standing left ventricular impairment. Guidelines may need to be adjusted and address this disparity, to improve postoperative recovery times and outcomes. [ABSTRACT FROM AUTHOR]

Background: Catecholaminergic polymorphic ventricular tachycardia (CPVT), an inheritable arrhythmia, poses a grave risk of syncope or cardiac arrest. Case Presentation: A 23‐year‐old female with CPVT history and implantable cardioverter‐defibrillator (ICD) implantation sought tooth extraction of her third molars. She underwent the procedure under general anesthesia to mitigate VT risk. Defibrillation function was temporarily halted during anesthesia induction, managed with total intravenous anesthesia. The postoperative course remained uneventful, obviating defibrillation intervention. Conclusions: This case underscores the efficacy of general anesthesia in averting VT onset during oral surgery for CPVT patients, ensuring a safe and uneventful postoperative recovery. [ABSTRACT FROM AUTHOR]

In general, arrhythmias, also called cardiac arrhythmia, heart arrhythmia, or dysrhythmias, are abnormal heartbeats which include too fast or too slow. The Cardiovascular Disease (CVD) is a significant cause of death, and the death rate is increasing every year. The Electrocardiogram (ECG) majorly contributes to the CVD diagnosis, providing information about the heartbeat. An automatic detection and classification of arrhythmia performs a significant role in managing and curing cardiovascular diseases. Deep Learning (DL)-based algorithms have emerged as effective solutions in medical applications, particularly in cardiac arrhythmia diagnosis. In this research, a DL-based multi-modal approach is proposed for the classification of cardiac arrhythmia. The MIT-BIH dataset is utilized to evaluate the performance of the proposed method. The proposed method considers physiological signals along with the MIT-BIH dataset to improve accuracy. The Discrete Wavelet Transform (DWT) is used for pre-processing the MIT-BIH dataset. The DL methods of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) are utilized for classifying cardiac arrhythmia. The proposed method is evaluated using various performance metrics such as Accuracy, Specificity, Sensitivity, F1-score, and Cohens kappa. [ABSTRACT FROM AUTHOR]

Background: Left atrial catheterization is a common procedure in electrophysiology labs to treat arrhythmogenic substrates on the left side of the heart. Needle transseptal puncture is the standard approach, but it can lead to complications related to device design or operator technique. To reduce these complications, needle-free alternatives have been explored. Objective: This study aims to report the first multicenter application of the needle-free transseptal access (NeFTA) approach, assessing its safety and efficacy in patients undergoing electrophysiological procedures. Methods: This retrospective, observational multicenter study evaluated the safety and efficacy of the NeFTA approach in patients undergoing ablation of left arrhythmogenic substrates across 3 electrophysiology centers in Mexico. NeFTA uses only a guidewire, without a needle or sharp guidewire. The sheath, with a dilator, is guided into the fossa ovalis under fluoroscopic guidance, using anterior force and clockwise torque to allow the guidewire to puncture the septum with minimal risk. Results: The NeFTA technique was used in 366 patients. Most sheaths were deflectable, with nondeflectable sheaths in 43.4% of cases. Left atrial access via NeFTA was successful in 96.18% of cases, with pericardial effusion as the only complication (0.55% rate). Conclusion: This technique was reproducible and safe, achieving successful access on the first or second attempt in 96.18% of cases, regardless of the operator.

Abstract Background Cardiac arrhythmia has been reported as a significant complication of thoracic radiotherapy. Both bradyarrhythmias and tachyarrhythmias have been reported, highlighting the arrhythmia-modulating potential of radiation in certain oncologic therapies. This study aimed to analyse the arrhythmic burden in patients with cardiac implantable electrical devices (CIEDs) undergoing thoracic irradiation, examining both immediate effects of radiotherapy and long-term sequelae post-therapy. Methods and results A retrospective cohort study was conducted involving patients with CIEDs who received thoracic radiotherapy between January 2012 and December 2022. Two distinct analyses were performed involving (1) daily CIED follow-ups during radiotherapy and (2) long-term arrhythmic outcomes post-therapy. For long-term outcomes, Patients were matched in a 1:2 ratio with non-irradiated controls based on age, sex, cardiovascular risk factors, cardiac disease, and beta-blocker use. Statistical analyses included negative binomial regression and propensity score matching. A total of 186 patients underwent daily CIED monitoring during radiotherapy, with 79 receiving thoracic irradiation. Thoracic irradiation was negatively associated with atrial arrhythmia (OR 0.11 [0.02;0.70, 95% CI], adjusted p = 0.0498) and there was a tendency towards less ventricular events (OR 0.14 [0.02;1.41, 95% CI], adjusted p = 0.3572) during radiotherapy in a univariate regression analysis. This association was not significant in the multivariate (OR 0.44 [0.10;1.80, 95%-CI], p = 0.16) model including a history of atrial fibrillation, diabetes and beta-blocker use. Coronary artery disease was associated with an increase in atrial and ventricular arrhythmia. For the long-term analysis, 122 patients were followed up after thoracic (N = 33) and non-thoracic radiation (N = 89) and compared to 244 matched controls drawn from approximately 10.000 CIED-patients. There was no significant increase in arrhythmic events compared to controls over a median follow-up of 6.6 months. A previous history of ventricular or atrial arrhythmic events was the strongest predictor for events during the follow-up. Conclusion Thoracic radiotherapy can be safely administered in patients with CIEDs. However, patients with a history of arrhythmia are more prone to arrhythmic events during and after radiation. These findings highlight the need for personalized arrhythmia management strategies and further research to understand the mechanisms underlying the antiarrhythmic effects of thoracic radiation. Graphical Abstract

Abstract Cardiac arrhythmias are the leading cause of death and pose a huge health and economic burden globally. Electrocardiography (ECG) is an effective technique for the diagnosis of cardiovascular diseases because of its noninvasive and cost-effective advantages. However, traditional ECG analysis relies heavily on the clinical experience of physicians, which can be challenging and time-consuming to produce valid diagnostic results. This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi-head attention (CBGM model). Specifically, the model consists of seven convolutional layers with varying filter sizes (4, 16, 32, and 64) and three pooling layers, respectively, while the BiGRU module includes two layers with 64 units each followed by multi-head attention (8-heads). The combination of CNN and BiGRU effectively captures spatio-temporal features of ECG signals, with multi-head attention comprehensively extracted global correlations among multiple segments of ECG signals. The validation in the MIT-BIH arrhythmia database achieved an accuracy of 99.41%, a precision of 99.15%, a specificity of 99.68%, and an F1-Score of 99.21%, indicating its robust performance across different evaluation metrics. Additionally, the model’s performance was evaluated on the PTB Diagnostic ECG Database, where it achieved an accuracy of 98.82%, demonstrating its generalization capability. Comparative analysis against previous methods revealed that our proposed CBGM model exhibits more higher performance in automatic classification of arrhythmia and can be helpful for assisting clinicians by enabling real-time detection of cardiac arrhythmias during routine ECG screenings.

Progress in the treatment of arrhythmias is associated with the development and implementation in clinical medicine of a number of electrophysiological methods that make it possible to determine and localize the formation and conduction of electrical excitation with a high degree of reliability. At the same time, an increase in the number of invasive procedures can lead to an increase in complications associated with the interventions, which in turn requires radiology specialists to understand the main stages of the intervention technique, and, accordingly, the mechanisms of development of possible complications and their localization. The purpose of the article is to present modern research data on the use of computed and magnetic resonance imaging in determining complications during interventional and surgical treatment of cardiac arrhythmias.

Background and aim: Cardiac arrhythmia diagnostic yield improves with increased duration of monitoring. We investigated patient comfort, diagnostic quality of ECG, and arrhythmia diagnostic yield using a single lead longer term external cardiac monitor (ECM). Methods: The observational ECM feasibility study enrolled patients with increased risk of cardiac arrhythmia. The ECM investigational prototype was designed using a chest strap with dry electrodes connected to module capable of triggered loop recording of ECG, and automatic detection of arrhythmia. In group-A of study (24-h inpatient), patients wore ECM and Holter that recorded ECG from the ECM and adhesive electrodes. In group-B of study (12-weeks ambulatory), at monthly follow-ups patients filled out a comfort survey and device stored arrhythmia episodes were reviewed. Results: The study enrolled 34 patients (38 % females, average age 57.5 years, 65 % had palpitations, 12 % had syncope). Diagnostic quality ECG was recorded on 76.5 % of the monitoring duration in 12 of 20 patients with reviewable data in group-A, with motion artifacts causing loss in ECG signal for 18.7 % of the time. In 14 patients in group-B, 94.9 % of the survey responses indicated that ECM was comfortable to wear. Cardiac arrhythmia was observed in 4 of 17 patients (24 %) in group-A and 9 of 14 patients (64 %) in group-B in device recorded episodes. All ECM detected pause and tachycardia were inappropriate detections due to motion artifacts and temporary device removal. Conclusion: The chest strap-based ECM device was mostly comfortable to wear and recorded diagnostic quality ECG in three-fourth of monitoring period. Cardiac arrhythmia was observed in 64 % of patients over 3-month monitoring along with large number of motion artifact induced inappropriate detections.

Abstract Cardiac arrhythmia, a disorder of abnormal electrical activity of the heart that disturbs the rhythm of the heart, thereby affecting its normal function, is one of the leading causes of death from heart disease worldwide and causes millions of deaths each year. Currently, treatments for arrhythmia include drug therapy, radiofrequency ablation, cardiovascular implantable electronic devices (CIEDs), including pacemakers, defibrillators, and cardiac resynchronization therapy (CRT). However, these traditional treatments have several limitations, such as the side effects of medication, the risks of device implantation, and the complications of invasive surgery. Nanotechnology and nanomaterials provide safer, effective and crucial treatments to improve the quality of life of patients with cardiac arrhythmia. The large specific surface area, controlled physical and chemical properties, and good biocompatibility of nanobiomaterials make them promising for a wide range of applications, such as cardiovascular drug delivery, tissue engineering, and the diagnosis and therapeutic treatment of diseases. However, issues related to the genotoxicity, cytotoxicity and immunogenicity of nanomaterials remain and require careful consideration. In this review, we first provide a brief overview of cardiac electrophysiology, arrhythmia and current treatments for arrhythmia and discuss the potential applications of nanobiomaterials before focusing on the promising applications of nanobiomaterials in drug delivery and cardiac tissue repair. An in-depth study of the application of nanobiomaterials is expected to provide safer and more effective therapeutic options for patients with cardiac arrhythmia, thereby improving their quality of life. Graphical Abstract

Introduction: Pregnant women with pre-excitation syndrome are more likely to develop supraventricular tachycardia (SVT) during pregnancy and delivery, leading to an increased risk of adverse events. Method: This was a retrospective study of 309 pregnancies in 280 women (29 women had two pregnancies in this series) with pre-excitation syndrome who delivered at West China Second University Hospital from June 2011 to October 2021. All the 309 pregnant women with pre-excitation syndrome were divided into SVT and non-SVT groups to analyze the cardiac and obstetric complications. Results: Among the included pregnant women in the past 10 years, the prevalence of pre-excitation syndrome was 0.24% (309/127725). There were 309 cases with pre-excitation syndrome in all hospitalized pregnant women. Among them, 62 (20.1%, 62/309) had a history of SVT. In the 62 cases with SVT during pregnancy, 22 (35.5%) cases had a history of SVT. Gestational diabetes mellitus was associated with SVT during pregnancy. The cesarean section rate was 88.7% in the SVT group, which was significantly higher than that in the non-SVT group (64.8%) (P < 0.001). Cases with SVT during pregnancy had more cardiac and obstetric complications. Four fetal deaths were recorded in the SVT group. Additionally, 29 women experienced two pregnancies during the study period, among whom, five received radiofrequency ablation after the first delivery and obtained better outcomes in the second pregnancy. Conclusion: The adverse outcomes such as cardiac complications, maternal and fetal complications (PROM, prematurity, SGA, fetal distress, etc.) in pregnant women with pre-excitation syndrome were closely related to SVT, with possible risk factors including history of SVT before pregnancy, cardiac function, heart organic abnormalities, and gestational diabetes mellitus. [ABSTRACT FROM AUTHOR]

Aim: Cardiac arrhythmias are among the most important pathologies that cause sudden death. The exploration of new therapeutic options against arrhythmias with low undesirable effects is of paramount importance. Methods: However, the convenient and typical animal model for screening the potential lead compound becomes a very critical modality, particularly in anti-arrhythmia. In this study, mice were intraperitoneally (i.p.) injected with BaCl2, CaCl2, and adrenaline to induce arrhythmia, and simultaneously compared with BaCl2-induced rats. Results: Electrocardiogram (ECG) showed that the majority of mice repeatedly developed ventricular bigeminy, ventricular tachycardia (VT), and ventricular fibrillation (VF) after BaCl2-injection as seen in rats. The ECG of mice developed ventricular bigeminy and VT after CaCl2 and AT after adrenaline i.p. injection. Additionally, acute cardiac arrhythmia after BaCl2 i.p. injection could be reverted by drugs (lidocaine and amiodarone) administration. Additionally, the different routes of administration for various chemical-induced arrhythmia in both mice and rats were also retrieved from PubMed and summarized. Comparing this approach with previous studies after the literature review reveals that arrhythmia of BaCl2-induced i.p. mice is compatible with the induction of other routes. Conclusions: This study brings an alternative experimental model to investigate antiarrhythmic theories and provides a promising approach to discovering new interventions for acute arrhythmias. [ABSTRACT FROM AUTHOR]

The effects of Gastrodin (GD) on cerebral ischemia stimulated researchers to investigate its possible role in the progression of arrhythmia associated with cardiac ischemia-reperfusion (IR) damage in rats. 40 Sprague-Dawley rats were divided into four groups: Sham, Model, GD 50 mg/kg, and GD 100 mg/kg. Myocardial ischemia (MI) was caused by the procedure of ligating the left coronary artery, followed by reperfusion. Heart rate (HR), mean arterial pressure (MAP), and rate pressure product (RPP) in rats were assessed before and after ischemia and reperfusion, as well as cardiac arrhythmia in experimental rats. The I/R damage was evaluated by measuring levels of Na +-K+ATPase and Ca2+-Mg2+ATPase, Creatine Kinase-MB (CK-MB), Cardiac Troponin I (cTnI), Gap Junction α-1 (GJα-1), Phospho-GJα-1/total-GJα-1, Kir2.1, Bax, Bcl-2, and oxidative indicators. MGL’s Autodock and Vina programs were used for in silico docking studies to identify possible interactions between GJα-1 and Gastrodin. The animals in the model group expressed a substantial decrease in HR, MAP, and RPP compared to the Sham group. GD-treated rats revealed slightly higher values compared to the model group. Expression of CK-MB and cTnI was reduced, and Na+-K+ATPase and Ca2+-Mg2+ATPase expression was increased on GD pre-conditioning. Phospho-Cx43/total-Cx43 ratio and Bax expression were increased, whereas GD reduced Bcl-2 expression. In silico molecular docking studies suggested the potential binding of GD with the GJα-1 protein, thus confirming the in vivo results. GD corrected the arrhythmia in rats subjected to I/R injury by increasing Na+-K+ATPase and Ca2+-Mg2+ATPase expression, targeting GJα-1, and modulating the expression of Kir2.1. [ABSTRACT FROM AUTHOR]

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Introduction: Fibrotic scar in the heart is known to act as a substrate for arrhythmias. Regions of fibrotic scar are associated with slowed or blocked conduction of the action potential, but the detailed mechanisms of arrhythmia formation are not well characterised and this can limit the effective diagnosis and treatment of scar in patients. The aim of this computational study was to evaluate different representations of fibrotic scar in models of 2D 10 x 10 cm ventricular tissue, where the region of scar was defined by sampling a Gaussian random field with an adjustable length scale of between 1.25 and 10.0 mm. Methods: Cellular electrophysiology was represented by the Ten Tusscher 2006 model for human ventricular cells. Fibrotic scar was represented as a spatially varying diffusion, with different models of the boundary between normal and fibrotic tissue. Dispersion of activation time and action potential duration (APD) dispersion was assessed in each sample by pacing at an S1 cycle length of 400 ms followed by a premature S2 beat with a coupling interval of 323 ms. Vulnerability to reentry was assessed with an aggressive pacing protocol. In all models, simulated fibrosis acted to delay activation, to increase the dispersion of APD, and to generate re-entry. Results: A higher incidence of re-entry was observed in models with simulated fibrotic scar at shorter length scale, but the type of model used to represent fibrotic scar had a much bigger influence on the incidence of reentry. Discussion: This study shows that in computational models of fibrotic scar the effects that lead to either block or propagation of the action potential are strongly influenced by the way that fibrotic scar is represented in the model, and so the results of computational studies involving fibrotic scar should be interpreted carefully. [ABSTRACT FROM AUTHOR]

Background: A growing proportion of people experience incomplete recovery months after contracting coronavirus disease 2019 (COVID-19). These COVID-19 survivors develop a condition known as post-COVID syndrome (PCS), where COVID-19 symptoms persist for > 12 weeks after acute infection. Limited studies have investigated PCS risk factors that notably include pre-existing cardiovascular diseases (CVD), which should be examined considering the most recent PCS data. This review aims to identify CVD as a risk factor for PCS development in COVID-19 survivors. Methods: Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) checklist, systematic literature searches were performed in the PubMed, Scopus, and Web of Science databases from the earliest date available to June 2023. Data from observational studies in English that described the association between CVD and PCS in adults (≥ 18 years old) were included. A minimum of two authors independently performed the screening, study selection, data extraction, data synthesis, and quality assessment (Newcastle-Ottawa Scale). The protocol of this review was registered under PROSPERO (ID: CRD42023440834). Results: In total, 594 studies were screened after duplicates and non-original articles had been removed. Of the 11 included studies, CVD including hypertension (six studies), heart failure (three studies), and others (two studies) were significantly associated with PCS development with different factors considered. The included studies were of moderate to high methodological quality. Conclusion: Our review highlighted that COVID-19 survivors with pre-existing CVD have a significantly greater risk of developing PCS symptomology than survivors without pre-existing CVD. As heart failure, hypertension and other CVD are associated with a higher risk of developing PCS, comprehensive screening and thorough examinations are essential to minimise the impact of PCS and improve patients' disease progression. [ABSTRACT FROM AUTHOR]

Mutations within the SCN5A gene, which encodes the a-subunit 5 (NaV1.5) of the voltage-gated Na+ channel, have been linked to three distinct cardiac arrhythmia disorders: long QT syndrome type 3, Brugada syndrome (BrS), and cardiac conduction disorder. In this study, we have identified novel missense mutations (p.A385T/R504T) within SCN5A in a patient exhibiting overlap arrhythmia phenotypes. This study aims to elucidate the functional consequences of SCN5A mutants (p.A385T/R504T) to understand the clinical phenotypes. Whole-cell patch-clamp technique was used to analyze the NaV1.5 current (INa) in HEK293 cells transfected with the wild-type and mutant SCN5A with or without SCN1B co-expression. The amplitude of INa was not altered in mutant SCN5A (p.A385T/R504T) alone. Furthermore, a rightward shift of the voltage-dependent inactivation and faster recovery from inactivation was observed, suggesting a gain-of-function state. Intriguingly, the coexpression of SCN1B with p.A385T/R504T revealed significant reduction of INa and slower recovery from inactivation, consistent with the loss-of-function in Na+ channels. The SCN1B dependent reduction of INa was also observed in a single mutation p.R504T, but p.A385T co-expressed with SCN1B showed no reduction. In contrast, the slower recovery from inactivation with SCN1B was observed in A385T while not in R504T. The expression of SCN1B is indispensable for the electrophysiological phenotype of BrS with the novel double mutations; p.A385T and p.R504T contributed to the slower recovery from inactivation and reduced current density of NaV1.5, respectively. [ABSTRACT FROM AUTHOR]

Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-propagation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtained by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier uses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artificial feed-forward neural networks provides a rich framework for studying the Back Propagation Algorithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance characteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, specificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperforms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the most effective classifier for identifying cardiac arrhythmias. [ABSTRACT FROM AUTHOR]

Studies indicate cardiac arrhythmia is one of the leading causes of death in the world. The risk of a stroke may be reduced when an irregular and fast heart rate is diagnosed. Since it is non-invasive, electrocardiograms are often used to detect arrhythmias. Human data input may be error-prone and time-consuming because of these limitations. For early detection of heart rhythm problems, it is best to use deep learning models. In this paper, a hybrid bio-inspired algorithm has been proposed by combining whale optimization (WOA) with adaptive particle swarm optimization (APSO). The WOA is a recently developed meta-heuristic algorithm. APSO is used to increase convergence speed. When compared to conventional optimization methods, the two techniques work better together. MIT-BIH dataset has been utilized for training, testing and validating this model. The recall, accuracy, and specificity are used to measure efficiency of the proposed method. The efficiency of the proposed method is compared with state-of-art methods and produced 98.25 % of accuracy. [ABSTRACT FROM AUTHOR]

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction. [ABSTRACT FROM AUTHOR]

It has been proposed that procedures which upregulate mitochondrial biogenesis and autophagy by replacing damaged mitochondria with healthy ones may prevent the development of several heart diseases. A member of serine and threonine kinases, adenosine monophosphate-activated protein kinase (AMPK), could play essential roles in the autophagy and/or mitophagy. AMPK is widely distributed in various cells, which might play diverse regulatory roles in different tissues and/or organs. In fact, changes in the kinase function of AMPK due to alteration of activity have been linked with diverse pathologies including cardiac disorders. AMPK can regulate mitochondrial biogenesis via peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) signaling and also improve oxidative mitochondrial metabolism through inhibition of mechanistic/mammalian target of rapamycin (mTOR) pathway, which may also modulate the autophagy/mitophagy through autophagy activating kinase 1 (ULK1) and/or transforming growth factor beta (TGF-β) signaling. Therefore, the modulation of AMPK in autophagy/mitophagy pathway might probably be thought as a therapeutic tactic for several cardiac disorders. As kinases are amongst the most controllable proteins, in general, the design of small molecules targeting kinases might be an eye-catching avenue to modulate cardiac function. Some analyses of the molecular biology underlying mitophagy suggest that nutraceuticals and/or drugs including specific AMPK modulator as well as physical exercise and/or dietary restriction that could modulate AMPK may be useful against several heart diseases. These observations may virtually be limited to preclinical studies. Come to think of these, however, it is speculated that some nutraceutical regimens might have positive potential for managing some of cardiac disorders. [ABSTRACT FROM AUTHOR]

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Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Precision medicine, which among other aspects includes an individual's genomic data in diagnosis and management, has become the standard‐of‐care for Mendelian cardiovascular disease (CVD). However, early identification and management of asymptomatic patients with potentially lethal and manageable Mendelian CVD through screening, which is the promise of precision health, remains an unsolved challenge. The reduced costs of genomic sequencing have enabled the creation of biobanks containing in‐depth genetic and health information, which have facilitated the understanding of genetic variation, penetrance, and expressivity, moving us closer to the genotype‐first screening of asymptomatic individuals for Mendelian CVD. This approach could transform health care by diagnostic refinement and facilitating prevention or therapeutic interventions. Yet, potential benefits must be weighed against the potential risks, which include evolving variant pathogenicity assertion or identification of variants with low disease penetrance; costly, stressful, and inappropriate diagnostic evaluations; negative psychological impact; disqualification for employment or of competitive sports; and denial of insurance. Furthermore, the natural history of Mendelian CVD is often unpredictable, making identification of those who will benefit from preventive measures a priority. Currently, there is insufficient evidence that population‐based genetic screening for Mendelian CVD can reduce adverse outcomes at a reasonable cost to an extent that outweighs the harms of true‐positive and false‐positive results. Besides technical, clinical, and financial burdens, ethical and legal aspects pose unprecedented challenges. This review highlights key developments in the field of genotype‐first approaches to Mendelian CVD and summarizes challenges with potential solutions that can pave the way for implementing this approach for clinical care.

IntroductionIn recent years, high-density microelectrode arrays (HD-MEAs) have emerged as a valuable tool in preclinical research for characterizing the electrophysiology of human induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CMs). HD-MEAs enable the capturing of both extracellular and intracellular signals on a large scale, while minimizing potential damage to the cell. However, despite technological advancements of HD-MEAs, there is a lack of effective data-analysis platforms that are capable of processing and analyzing the data, particularly in the context of cardiac arrhythmias and drug testing.MethodsTo address this need, we introduce CardioMEA, a comprehensive data-analysis platform designed specifically for HD-MEA data that have been obtained from iPSCCMs. CardioMEA features scalable data processing pipelines and an interactive web-based dashboard for advanced visualization and analysis. In addition to its core functionalities, CardioMEA incorporates modules designed to discern crucial electrophysiological features between diseased and healthy iPSC-CMs. Notably, CardioMEA has the unique capability to analyze both extracellular and intracellular signals, thereby facilitating customized analyses for specific research tasks.Results and discussionWe demonstrate the practical application of CardioMEA by analyzing electrophysiological signals from iPSC-CM cultures exposed to seven antiarrhythmic drugs. CardioMEA holds great potential as an intuitive, userfriendly platform for studying cardiac diseases and assessing drug effects.

Acute Urinary Retention (UR) is a common perioperative complication with incidences between 5-70%. Orthopaedic patients tend to have this complication at a comparatively higher incidence rate (8-55%) than any other surgeries. Acute UR is multifactorial and can occur irrespective of any pre-existing urological condition. Acute distension due to UR is sometimes associated with comparatively rare manifestations of cardiovascular morbidity in the form of rhythm disturbances and massive haemodynamic alterations. In this case, a 62-year-old, 50 kg female with no co-morbidities was scheduled for open reduction of a fractured right forearm under General Anaesthesia (GA) in the supine position. Towards the end of the surgery, there was an abrupt change in the patient’s haemodynamics, starting from hypertension with tachycardia to hypotension with tachycardia followed by extreme bradycardia. A quick inspection revealed bladder overdistension due to the blockade of the urinary catheter. After saline flushing and decompression of the bladder, the haemodynamics returned to normal within five minutes. Acute bladder overdistension is a relatively benign complication that may prove fatal if not addressed promptly.

Abstract Pathogenic BAG5 variants recently linked to dilated cardiomyopathy (DCM) prompt further investigation into phenotypic, mutational, and pathomechanistic aspects. We explored the clinical and molecular characteristics of DCM associated with BAG5 variants, uncovering the consistently severe manifestations of the disease and its impact on the endoplasmic reticulum (ER) stress response. The analysis involved three siblings affected by DCM and arrhythmia, along with their four unaffected siblings, their unaffected father, and their mother who exhibited arrhythmia. The parents were consanguineous. Exome and Sanger sequencing identified a novel BAG5 variant, c.444_445delGA (p.Lys149AsnfsTer6), homozygous in affected siblings and heterozygous in parents and unaffected siblings. We generated heterozygous and homozygous Bag5 point mutant knock-in (KI) mice and evaluated cardiac pathophysiology under stress conditions, including tunicamycin (TN) administration. Bag5−/− mice displayed no abnormalities up to 12 months old and showed no anomalies during an exercise stress test. However, following TN injection, Bag5−/− mice exhibited significantly reduced left ventricular fractional shortening (LVFS) and ejection fraction (LVEF). Their cardiac tissues exhibited a notable increase in apoptotic cells, despite non-distinctive changes in CHOP and GRP78 levels. Interestingly, only Bag5 KI male mice demonstrated arrhythmia, which was more pronounced in Bag5−/− than in Bag5+/−males. Here, our study reveals a novel BAG5 mutation causing DCM by impairing the ER stress response, with observed sex-specific arrhythmia differences.

Pathogenic BAG5 variants recently linked to dilated cardiomyopathy (DCM) prompt further investigation into phenotypic, mutational, and pathomechanistic aspects. We explored the clinical and molecular characteristics of DCM associated with BAG5 variants, uncovering the consistently severe manifestations of the disease and its impact on the endoplasmic reticulum (ER) stress response. The analysis involved three siblings affected by DCM and arrhythmia, along with their four unaffected siblings, their unaffected father, and their mother who exhibited arrhythmia. The parents were consanguineous. Exome and Sanger sequencing identified a novel BAG5 variant, c.444_445delGA (p.Lys149AsnfsTer6), homozygous in affected siblings and heterozygous in parents and unaffected siblings. We generated heterozygous and homozygous Bag5 point mutant knock-in (KI) mice and evaluated cardiac pathophysiology under stress conditions, including tunicamycin (TN) administration. Bag5−/− mice displayed no abnormalities up to 12 months old and showed no anomalies during an exercise stress test. However, following TN injection, Bag5−/− mice exhibited significantly reduced left ventricular fractional shortening (LVFS) and ejection fraction (LVEF). Their cardiac tissues exhibited a notable increase in apoptotic cells, despite non-distinctive changes in CHOP and GRP78 levels. Interestingly, only Bag5 KI male mice demonstrated arrhythmia, which was more pronounced in Bag5−/− than in Bag5+/−males. Here, our study reveals a novel BAG5 mutation causing DCM by impairing the ER stress response, with observed sex-specific arrhythmia differences. [ABSTRACT FROM AUTHOR]

Desmin, the most abundant intermediate filament in cardiomyocytes, plays a key role in maintaining cardiomyocyte structure by interconnecting intracellular organelles, and facilitating cardiomyocyte interactions with the extracellular matrix and neighboring cardiomyocytes. As a consequence, mutations in the desmin gene (DES) can lead to desminopathies, a group of diseases characterized by variable and often severe cardiomyopathies along with skeletal muscle disorders. The basic desmin intermediate filament structure is composed of four segments separated by linkers that further assemble into dimers, tetramers and eventually unit‐length filaments that compact radially to give the final form of the filament. Each step in this process is critical for proper filament formation and allow specific interactions within the cell. Mutations within the desmin gene can disrupt filament formation, as seen by aggregate formation, and thus have severe cardiac and skeletal outcomes, depending on the locus of the mutation. The focus of this review is to outline the cardiac molecular consequences of mutations located in the C‐terminal part of segment 2B. This region is crucial for ensuring proper desmin filament formation and is a known hotspot for mutations that significantly impact cardiac function. [ABSTRACT FROM AUTHOR]

Background: Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a fatal arrhythmic disease. Herein, we report a case of third molar extraction performed under general anesthesia to avoid ventricular tachycardia. Case Presentation: A 28‐year‐old woman presented with constant pain and bilateral mandibular gingival swelling. The patient had a history of CPVT diagnosis at 5 years of age, which had been managed with beta‐blockers and sodium channel blockers. Due to the contraindication of using epinephrine and the risk of CPVT induction due to pain, the extraction was conducted under general anesthesia. Conclusion: Oral surgery in CPVT patients requires appropriate anesthetic agent selection. [ABSTRACT FROM AUTHOR]

Heart disease is one of the most serious health issues that people faced today. Almost 50 million people worldwide suffer from cardiovascular problems. Electrocardiogram (ECG) signals are more important for diagnosing and monitoring patients with various cardiovascular diseases. This article proposes a novel SANN (Siamese Adversarial Neural Network-based Cardiac Arrhythmia Detection) CAD model for detecting different arrhythmias. First, an optimized Savitzky-Golay (OS-G) digital filter model is used to enhance and smooth the ECG signal. OS-G filtering uses a Tunicate Swarm Optimization (TSO) algorithm to adjust the polynomial order and window size to preserve the characteristics of P, Q, R, S, T, and U waves. A hybrid feature extraction process called Pathfinding Least Absolute Shrinkage and Selection Vector Regression (PLASSVR), which integrates regression analysis and pathfinding optimization, is employed to extract the significant features. The extracted features are fed to a novel Siamese neural network based on the Generative Adversarial Network (SNN-GAN) to classify the signals for arrhythmia disease detection. Furthermore, the Gannet Optimization Algorithm (GOA) is applied in the SNN-GAN model to change the hyperparameters. The proposed SANN CAD model is implemented in the Python platform via the MIT-BIH Arrhythmia Database. The performance of the SANN-CAD model is evaluated against various evaluation criteria and compared to traditional classifiers, using optimization for matching and no optimization for matching. The maximum classification accuracy that the SANN CAD model achieves is 99.74% and 98.23% for with and without optimization, and is superior to conventional classifiers. [ABSTRACT FROM AUTHOR]

Duchenne muscular dystrophy (DMD) is an X‐linked recessive myopathy due to mutations in the dystrophin gene. Diaphragmatic weakness in DMD causes hypoventilation and elevated afterload on the right ventricle (RV). Thus, RV dysfunction in DMD develops early in disease progression. Herein, we deliver a 30‐min sustained RV preload/afterload challenge to isolated hearts of wild‐type (Wt) and dystrophic (Dmdmdx‐4Cv) mice at both young (2–6 month) and middle‐age (8–12 month) to test the hypothesis that the dystrophic RV is susceptible to dysfunction with elevated load. Young dystrophic hearts exhibited greater pressure development than wild type under baseline (Langendorff) conditions, but following RV challenge exhibited similar contractile function as wild type. Following the RV challenge, young dystrophic hearts had an increased incidence of premature ventricular contractions (PVCs) compared to wild type. Hearts of middle‐aged wild‐type and dystrophic mice had similar contractile function during baseline conditions. After RV challenge, hearts of middle‐aged dystrophic mice had severe RV dysfunction and arrhythmias, including ventricular tachycardia. Following the RV load challenge, dystrophic hearts had greater lactate dehydrogenase (LDH) release than wild‐type mice indicative of damage. Our data indicate age‐dependent changes in RV function with load in dystrophin deficiency, highlighting the need to avoid sustained RV load to forestall dysfunction and arrhythmia. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is introduced to diagnose the cardiac arrhythmias. Automatic diagnosis of cardiac arrhythmias is crucial for successful treatment of heart diseases, and nowadays, machine learning is used to facilitate this purpose. For accurate classification of arrhythmias, it is vital to extract some appropriate features which distinguish different classes. In this research, a deep convolutional neural network is used to extract the features. Since the heart rates of various patients are very different, arrhythmias of a specific class will be placed in different locations of feature space. To reduce the intra-class variations, each patient's heart rate is mapped with a dedicated function to increase its similarity to the heart rate of a training patient. The proposed patient-specific mapping decreases the intra-class variation and significantly increases the classification accuracy of cardiac arrhythmias. The calculated accuracy is about 91.08%, which shows the better performance compared to other similar works. [ABSTRACT FROM AUTHOR]

Background: Early detection of atrial fibrillation (AF) is essential to prevent stroke and other cardiac and embolic complications. We compared the diagnostic properties for AF detection of the percentage of successive RR interval differences greater than or equal to 30 ms or 3.25% of the previous RR interval (pRR30 and pRR3.25%, respectively), and asymmetric entropy descriptors of RR intervals. Previously, both pRR30 and pRR3.25% outperformed many other heart rate variability (HRV) parameters in distinguishing AF from sinus rhythm (SR) in 60 s electrocardiograms (ECGs). Methods: The 60 s segments with RR intervals were extracted from the publicly available Physionet Long-Term Atrial Fibrillation Database (84 recording, 24 h Holter ECG). There were 31,753 60 s segments of AF and 32,073 60 s segments of SR. The diagnostic properties of all parameters were analysed with receiver operator curve analysis, a confusion matrix and logistic regression. The best model with pRR30, pRR3.25% and total entropic features (H) had the largest area under the curve (AUC)—0.98 compared to 0.959 for pRR30—and 0.972 for pRR3.25%. However, the differences in AUC between pRR30 and pRR3.25% alone and the combined model were negligible from a practical point of view. Moreover, combining pRR30 and pRR3.25% with H significantly increased the number of false-negative cases by more than threefold. Conclusions: Asymmetric entropy has some potential in differentiating AF from SR in the 60 s RR interval time series, but the addition of these parameters does not seem to make a relevant difference compared to pRR30 and especially pRR3.25%. [ABSTRACT FROM AUTHOR]

There have been many connections between medical science and artificial intelligence in recent years. Many problems arise with the integrity of communication. Cardiac arrhythmia, carried out using artificial intelligence methods, is one of the most dangerous diseases in the field of prevention. Topics introduced in artificial intelligence are the automatic selection of balancing and classification algorithms. In this study, metrics for machine learning algorithm selection are presented. The first problem is the problem of choosing the best balancing algorithm to balance the data sets, introduced as triangle rate (TR). The second issue to be studied is selecting the best automatic classification algorithm. The third action was to use a scoring algorithm to predict sinus and non-sinus arrhythmias. The heptagonal reinforcement learning (HRL) achieved results competitive with standard algorithms by combining three types of algorithms. The data used in this study was a 12-lead electrocardiogram (ECG) database of arrhythmias. The number of patients examined in this dataset is 10,646. The HRL algorithm has improved the previous algorithms by 5%, achieving 86% cardiac arrhythmia prediction. [ABSTRACT FROM AUTHOR]

On-line detection of arrhythmia in 12-lead electrocardiogram signals by deep learning models is essential for clinical care. If an 8-byte floating point data type is used to define each sample in a 12-lead ECG signal, the volume of a Rosent-18 class is 800.4 MB (100.06 M * 8 B). This model is challenging to apply to devices with minimal hardware. Consequently, these models are inadequate for practical purposes, and their utilization is restricted when it comes to low-capacity devices within emerging fields like the Internet of Medical Things. This article introduces a technique that aims to categorize irregularities in 12-lead electrocardiogram signals on edge devices. The method utilizes a lightweight learning approach for the classification of arrhythmias. The evident originality of this work is the use of different evaluations to deploy the suggested model on a device with hardware limitations. After employing the Tensor Flow Lite platform, a compact model has been derived from it. This model has been deployed on an Android device as an edge device, carrying forward from the previous context. According to the assessment, the suggested classification model, designed to categorize 11 different irregularities within the electrocardiogram (ECG) dataset comprising 10,646 patients, achieves an accuracy level comparable to 83.45%. Ultimately, the performed comparisons reveal that the proposed model exhibits competitive performance when compared to alternative approaches that rely on standard deep learning models. [ABSTRACT FROM AUTHOR]