Heart Rate Activity Research Articles

Acute GLP-1 Agonism Induces Arrhythmogenic Electrical Activity in Aged Mice Heart Through Impaired Cellular Na+ and Ca2+ Handlings: TheRole of CK2 Hyperphosphorylation.

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are known for their benefits in conditions like cardiovascular diseases in type 2 diabetes and obesity. They also show promise for aging-related conditions with minimal side effects. However, their impact on cardiovascular risk is still debated. Notably, some long-acting GLP-1RAs cause a sustained increase in heart rate on the first day of use without a clear mechanism. To understand their short-term effects, we examined acute GLP-1R agonism on the electrical activity of elderly hearts. In this study, we utilized in vivo electrocardiography, in vitro cellular electrophysiology experiments, and biochemical measurements on heart preparations from 6-month-old (Adult) and 24-month-old (aged) BALB/c mice. A single liraglutide injection (0.3 mg/kg) induced repetitive, self-sustained arrhythmogenic electrocardiograms in aged mice (24 months old) but had no effect on adults (6 months old) within the first 10 minutes. Acute application of liraglutide to isolated ventricular cardiomyocytes from aged mice significantly prolonged the late phase of action potential repolarization (APR90). This was due to suppressed K+ currents and increased persistent Na+currents (Late-INa), primarily through delayed recovery from inactivation of Na+ currents. Additionally, liraglutide increased Ca2+ spark frequency and wave formation by enhancing Ca2+ release from the sarcoplasmic reticulum, affecting both Na+ and Ca2+ regulation in aging cells. Liraglutide also induced casein kinase 2 (CK2) hyperphosphorylation in aged cardiomyocytes, which a CK2 inhibitor could reverse, normalizing APR90 by reducing Late-INa and enhancing K+ currents. These findings reveal that acute GLP-1R agonism can disrupt electrical signaling and induce arrhythmia in aged mice through CK2 hyperphosphorylation, providing new insights into the cardiovascular effects of GLP-1RAs in the elderly.

Cerebral Death: Unraveling the Mystery of the Silent Mind

The permanent loss of all brain stem functions, such as breathing, awareness, and cranial nerve reflexes, is known as brain stem death (BSD), and it presents a significant medical and ethical issue. While other bodily functions, such as heart activity and circulation, can be maintained with medical intervention, BSD is defined by the cessation of brain stem activity. The key characteristic that distinguishes BSD from other conditions, such as coma or a persistent vegetative state, is the complete and irreversible loss of brain stem function—a critical control centre for basic physiological processes. Diagnosing BSD requires strict clinical criteria and diagnostic testing to confirm the full and permanent nature of the condition. This typically involves a comprehensive neurological examination, assessment of cranial nerve reflexes, and confirmation through additional tests, such as cerebral blood flow studies or electroencephalography (EEG). A diagnosis of BSD carries significant ethical and legal implications, particularly in relation to organ donation, as it often plays a role in identifying potential donors. The ethical considerations surrounding BSD include its impact on families, the distinction between death and end-of-life care, and the challenges posed by varying legal and cultural perspectives. It highlights the importance of a clear diagnosis, obtaining informed consent, and adhering to established medical protocols to uphold human dignity and ethical standards. As medical technology advances and societal views evolve, the discussion around brain stem death continues to be a critical issue in both bioethics and modern medical practice.

Obstructive sleep apnea detection based on electrocardiogram signal using one-dimensional convolutional neural network

Obstructive sleep apnea (OSA) is a respiratory obstruction that occurs during sleep and is often known as snoring. OSA is often ignored even though it can cause cardiovascular problems. Early diagnosis is needed for prevention towards worse complications. OSA clinical diagnosis can use polysomnography (PSG) while the patient is sleeping. The PSG examination includes calculating total apnea plus hypopnea every hour during sleep. However, PSG examination tends to be high cost, takes a long time, and is impractical. Since OSA is related to breathing and heart activity, the electrocardiogram (ECG) examination is an alternative tool in OSA analysis. Therefore, this study proposes OSA detection on single lead ECG using one dimensional (1D)-convolutional neural network (CNN). The proposed CNN architecture consists of 4 convolutional layers, 4 pooling layers, 1 dropout layer, 1 flatten layers, 2 dropout layers, 1 dense layer with rectified linear unit (ReLU) activation function, and 1 dense layer with SoftMax activation function. The proposed method was then tested on the ECG sleep apnea dataset from PhysioNet. The proposed model produces an accuracy of 92.69% with the average pooling scenario. The proposed method is expected to help clinicians in diagnosing OSA based on ECG signals.

Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank

Remote photoplethysmography (rPPG) is a non-contact technology that monitors heart activity by detecting subtle color changes within the facial blood vessels. It provides an unconstrained and unconscious approach that can be widely applied to health monitoring systems. In recent years, research has been actively conducted to improve rPPG signals and to extract significant information from facial videos. However, rPPG can be vulnerable to degradation due to changes in the illumination and motion of a subject, and overcoming these challenges remains difficult. In this study, we propose a machine learning-based filter bank (MLFB) noise reduction algorithm to improve the quality of rPPG signals. The MLFB algorithm determines the optimal spectral band for extracting information on cardiovascular activity and reconstructing an rPPG signal using a support vector machine. The proposed approach was validated with an open dataset, achieving a 35.5% (i.e., resulting in a mean absolute error of 2.5 beats per minute) higher accuracy than those of conventional methods. The proposed algorithm can be integrated into various rPPG algorithms for the pre-processing of RGB signals. Moreover, its computational efficiency is expected to enable straightforward implementation in system development, making it broadly applicable across the healthcare field.

Advancements in wearable heart sounds devices for the monitoring of cardiovascular diseases

AbstractCardiovascular diseases remain a leading global cause of mortality, underscoring the urgent need for intelligent diagnostic tools to enhance early detection, prediction, diagnosis, prevention, treatment, and recovery. This demand has spurred the advancement of wearable and flexible technologies, revolutionizing continuous, noninvasive, and remote heart sound (HS) monitoring—a vital avenue for assessing heart activity. The conventional stethoscope, used to listen to HSs, has limitations in terms of its physical structure, as it is inflexible and bulky, which restricts its prospective applications. Recently, mechanoacoustic sensors have made remarkable advancements, evolving from primitive forms to soft, flexible, and wearable designs. This article provides an in‐depth review of the latest scientific and technological advancements by addressing various topics, including different types of sensors, sensing materials, design principles, denoising techniques, and clinical applications of flexible and wearable HS sensors. This transformative potential lies in the capacity for ongoing, remote, and personalized monitoring, promising enhanced patient outcomes, amplified remote monitoring capabilities, and timely diagnoses. Last, the article highlights current challenges and prospects for the future, suggesting techniques to advance HS sensing technologies for exciting real‐time applications.

Overcoming Lung Challenges in TA-NRP Assisted Heart Recovery in Donation After the Circulatory Determination of Death.

Thoraco-abdominal normothermic regional perfusion (TA-NRP), utilizing Extra Corporeal Membrane Oxygenation (ECMO) devices, has emerged as an effective strategy for heart recovery in donors declared dead by circulatory criteria (DCDD). After death declaration, TA-NRP restores heart activity by reperfusing the arrested heart with oxygenated blood at normothermia. Mechanical ventilation resumption in the donor enables weaning from ECMO and restores systemic circulation and oxygenation using the donor's heart and lungs. However, if pre-existing conditions prevent the donor's lungs from oxygenating blood post-cardiac activity restoration, weaning from veno-arterial ECMO may lead to systemic hypoxia, jeopardizing the restored cardiac function. Anticipating this scenario may guide planning a split ECMO circuit to facilitate earlier and more effective recovery of donor heart function post-ECMO weaning. This manuscript describes three cases of DCDD donors with hypoxic respiratory failure undergoing TA-NRP for heart recovery. By establishing a bridge in the arterial portion of the circuit, clamped out after weaning from veno-arterial ECMO, donor heart function was assessed exclusively with veno-venous ECMO support, leading to successful heart transplantation.

Trimetazidine restores autophagy via lncRNA H19/AMPK in diabetic heart: Implications for its therapeutic value against diabetic cardiomyopathy.

Previous studies have shown that trimetazidine (TMZ) alleviates diabetes-induced cardiac dysfunction. However, the underlying mechanism for its protective effects on cardiac function remains incompletely understood. Diminished autophagy was found in diabetic hearts, and restoration of autophagy generates cardioprotective effect. This study aims to investigate whether and how TMZ produces protective effect through increasing autophagic activity in the diabetic heart. A high-fat diet and low-dose streptozotocin were applied to induce type 2 diabetes mellitus (T2DM) in male C57BL/6 mice, followed by treatment with TMZ for 14 weeks before cardiac function was evaluated. To mimic the diabetic condition, the neonatal rat cardiomyocytes (NRCMs) were exposed to high glucose/palmitic acid (HP) in the presence or absence of TMZ. We found that TMZ treatment promotes autophagic flux in cardiomyocytes, which is impaired in diabetes. We further found that the AMPK and lncRNA H19 played critical roles in mediating TMZ-induced enhancement of autophagy in cardiomyocyte. We showed that TMZ treatment restored the level of H19 and phosphorylated AMPK (p-AMPK T172) in diabetic heart and NRCMs exposed to HP. Of note, the effect of TMZ on autophagy and p-AMPK was abolished by knockdown of H19. These findings indicated that TMZ is able to recover the cardiac autophagic activity which is impaired by T2DM, and the underlying mechanism accounted for this ability is mostly likely attributed to the restored expression of H19 and AMPK activity.

The Effects of Phenanthrene on Electrical Activity in Ventricular Cardiomyocytes of Atlantic Cod (<i>Gadus morhua</i>)

The production of oil on the Arctic shelf and its transport along the Northern Sea Route increase risks of pollution of the ecosystems in the Arctic seas with oil and oil products. Today, polyaromatic hydrocarbons are known as the most toxic oil components, and phenanthrene is predominant in terms of its concentration in oil and physiological effects. Phenanthrene affects the electrical activity of fish heart, but its effects are species-specific. At the same time, the effects of phenanthrene on cardiac function in Arctic fishes, including economically important commercial species, are studied not enough. This study examines the effects of phenanthrene on electrical activity and ionic currents in ventricular myocardium of Atlantic cod (Gadus morhua). The major ionic currents in cod myocardium were IKr, IK1, INa and ICa. Phenanthrene (1 μM) did not affect the duration of action potentials (APs) recorded in isolated cod ventricular cardiomyocytes using patch clamp method. Meanwhile, phenanthrene suppressed rapid delayed rectifier current IKr by 61.33 ± 3.94%, decreasing the repolarization reserve of the myocardium. Phenanthrene did not affect nor the level of resting membrane potential, not background inward rectifier current IK1. Also, application of phenanthrene decreased AP upstroke velocity in cod myocytes, which was due to the suppression of fast sodium current INa. Finally, phenanthrene slightly reduced the amplitude of calcium current ICa and accelerated its inactivation, which overall led to the decrease in ICa charge transfer. Thus, the effects of phenanthrene on cod myocardium at cellular level can be described as potentially proarrhythmic, which makes the populations of cod in Arctic seas vulnerable to pollution of the aquatic environment by oil components after oil spills due to technological disasters.

Role of RyR2 and SERCA2a in the Cardioprotective Effects of Vincanine and Pyrazoline Alkaloids

In the conducted studies, the effect of vincanine and pyrazoline indole alkaloids on cardiac muscle cell sarcoplasmic reticulum (SR) Ca2+-transport systems (RyR2 and SERCA2a) was investigated under normal and hypoxic conditions. The effect of vincanine and pyrazoline on ryanodine receptor (RyR2) was evaluated in the presence of ruthenium red and it was found that the role of RyR2 in the positive inotropic effect of these alkaloids is small. Also, in the presence of cyclopiazonic acid (IC50 = 5.6 µM), an inhibitor of sarcoplasmic reticulum Ca2+-ATPase (SERCA2a), the positive inotropic effect of vincanine and pyrazoline alkaloids was reduced. In general, the positive inotropic effects and cardioprotective properties of vincanine and pyrazoline alkaloids are mediated by modulation of SERCA2a function. HIGHLIGHTS Vincanine and pyrazoline indole alkaloids have a positive inotropic effect on the force of papillary muscle contraction of the rat heart. Vincanine and pyrazoline indole alkaloids have a cardioprotective effect and effectively eliminate the disturbances in the papillary muscle contractile activity of the rat heart caused by hypoxia. Vincanine and pyrazoline alkaloids effectively reverse hypoxia-induced SERCA2a dysfunction and normalize changes in rat cardiac papillary muscle contractile activity. GRAPHICAL ABSTRACT

Abstract 4138514: From Treatment to Trigger: A Case of Atomoxetine-Induced Brugada Pattern

Introduction: Brugada syndrome is an inherited cardiac disorder characterized by specific ECG patterns, notably the coved-type ST-segment elevation in the right precordial leads (V1-V3), leading to an increased risk of sudden cardiac death. This condition often stems from mutations in the SCN5A gene, which impair cardiac sodium channel function. The manifestation of Brugada Pattern, showing typical ECG findings without clinical criteria like sudden cardiac arrest or syncope, is significant. We highlight a case where atomoxetine, a selective norepinephrine reuptake inhibitor used for ADHD, unveiled a type 1 Brugada pattern. Case Presentation: A 26-year-old male with a history of ADHD on atomoxetine only presented to the emergency department with sudden substernal chest pain radiating to his left arm. His initial ECG showed right bundle branch block and type 1 Brugada pattern (Figure.1). Despite normal troponin levels and a negative coronary CT angiography, the patient's ECG abnormalities raised concerns. With no personal or familial history of arrhythmia or SCD and no other medications, atomoxetine was suspected as the trigger. Upon discontinuation, the patient's ECG type 1 Brugada pattern resolved (Figure. 2), and he remained asymptomatic with a normal Holter monitor at a 1-month follow-up. Discussion: Brugada syndrome is marked by dynamic ECG abnormalities, which can surface due to triggers like fever, certain medications, and electrolyte imbalances. Atomoxetine, an ADHD treatment and selective norepinephrine reuptake inhibitor, affects the human cardiac sodium channel (hNav1.5), encoded by the SCN5A gene. This interaction can disrupt heart action potentials, leading to conditions like long QT syndrome and Brugada pattern, by slowing cardiomyocyte depolarization and conduction. Its effect on hNav1.5 resembles that of antidepressants like Fluoxetine, increasing the risk of Brugada pattern. In our case, a patient's type 1 Brugada pattern, with no underlying heart disease or family history, pointed to atomoxetine as the cause. Discontinuing the drug resolved the ECG abnormalities. Conclusion: This case emphasizes the importance of assessing cardiovascular risk before prescribing atomoxetine, especially in individuals at risk for cardiac sodium channel dysfunctions. Immediate intervention and monitoring are critical to prevent severe arrhythmias.

Heart disease detection using an acceleration-deceleration curve-based neural network with consumer-grade smartwatch data

Cardiovascular disease (CVD) is the most important cause of morbidity and mortality worldwide. Early detection, prevention or even prediction is of pivotal importance to reduce the burden of cardiovascular disease and its associated costs. Low cost, consumer-grade smartwatches have the potential to revolutionize cardiovascular medicine by enabling continuous monitoring of heart rate and activity. When combined with machine learning(ML), the resulting large amounts of time series data hold the potential of detection, or exclusion of CVD. However, analyzing such large datasets is challenging due to the sparse presence of informative segments. Efficient selection of these segments is essential for developing predictive models for clinical deployment. The objective of this paper was to investigate the potential of an acceleration-deceleration curvebased ML model as a novel clinical indicator for the detection of cardiovascular diseases. We used data from the ME-TIME study; 42 participants from which 21 have a cardiovascular disease and 21 are health controls. Data from each subject was normalized to decrease inter-subject variability. A neural network model aggregated predictions per week. We showed that per-subject normalization by the peak value of curves during inactivity, aggregation of model predictions over a week, and using a contrastive loss, resulted in a predictive model with 99 % ± 3 % specificity and 40 % ± 49 % sensitivity on the development set, and 100 % specificity with 67 % ± 47 % sensitivity on the test set. Acceleration-deceleration curves are effective patterns for ruling out the presence of cardiovascular disease, but caution must be taken to properly pre-process the curves and carefully choosing a model that reduces the variability in the extracted curves.

Biogenesis and Regulation of the Freeze–Thaw Responsive microRNA Fingerprint in Hepatic Tissue of Rana sylvatica

Background: Freeze-tolerant animals undergo significant physiological and biochemical changes to overcome challenges associated with prolonged whole-body freezing. In wood frog Rana sylvatica (now Lithobates sylvaticus), up to 65% of total body water freezes in extracellular ice masses and, during this state of suspended animation, it is completely immobile and displays no detectable brain, heart, or respirometry activity. To survive such extensive freezing, frogs integrate various regulatory mechanisms to ensure quick and smooth transitions into or out of this hypometabolic state. One such rapid and reversible regulatory molecule capable of coordinating many aspects of biological functions is microRNA. Herein, we present a large-scale analysis of the biogenesis and regulation of microRNAs in wood frog liver over the course of a freeze–thaw cycle (control, 24 h frozen, and 8 h thawed). Methods/Results: Immunoblotting of key microRNA biogenesis factors showed an upregulation and enhancement of microRNA processing capacity during freezing and thawing. This was followed with RT-qPCR analysis of 109 microRNA species, of which 20 were significantly differentially expressed during freezing and thawing, with the majority being upregulated. Downstream bioinformatics analysis of miRNA/mRNA targeting coupled with in silico protein–protein interactions and functional clustering of biological processes suggested that these microRNAs were suppressing pro-growth functions, including DNA replication, mRNA processing and splicing, protein translation and turnover, and carbohydrate metabolism. Conclusions: Our findings suggest that this enhanced miRNA maturation capacity might be one key factor in the vital hepatic miRNA-mediated suppression of energy-expensive processes needed for long-term survival in a frozen state.

Galectin-3 zebrafish model: identification and pharmacological modulation of arrhythmogenic cardiomyopathy-related phenotypes

Abstract Background Arrhythmogenic Cardiomyopathy (ACM) is rare hereditary cardiac condition characterized by the substitution of ventricular myocardium with fibro-fatty tissue leading to high risk of life-threatening arrhythmias and sudden cardiac death, particularly among young individuals and athletes. Based on genotype-phenotype correlation, the spectrum of the ACM phenotype was shown to be broader than previously believed. Approximately 40% of ACM patients carry one or more putative pathogenic variants in genes encoding desmosomal proteins. Recently LGALS3 gene involvement in ACM early pathogenesis has been advocated. Aim To achieve a comprehensive understanding of LGALS3 role in the pathogenesis of AC. Methods This stable mutant line was generated by CRISPR/Cas9 strategy and then deeply characterized. Phenotyping included heart activity characterization using a recently published software specific for zebrafish, the pyHeart4Fish. Gene expression analysis was carried out by using RNAseq, Real Time PCR and signaling pathway reporter zebrafish lines. Immunofluorescence analysis were performed using and antibody against L-plastin, a leukocyte marker for the detection of inflammatory cells and Acridine Orange/Ethidium Bromide staining to check the possible presence of apoptotic and necrotic events. Results Morphological investigation revealed notable structural abnormalities such as pericardial effusion and/or hemopericardium. The heart activity analysis detected a diminished ventricle contractility and ejection fraction, with bradycardia and occurrence of arrhythmia events. Rna analysis highlighted the dysregulation of Wnt/β-catenin signaling pathway. Moreover, the L-plastin inflammatory marker revealed a significant infiltration of inflammatory cells in the heart as well as an elevated number of apoptotic and necrotic cells (figure 1). The pharmacological treatment, partially rescued a subset of AC-related cardiac phenotypes observed, including heart contractility, bradycardia and arrhythmia. Conclusion Our findings strongly validate our lgals3a zebrafish mutant as an effective model to elucidate its role in ACM related phenotype. This model accurately replicates the cardiac abnormalities and dysregulation in cell signaling that are characteristic of ACM. Further, observed cell death and inflammation and the partial recovery of cardiac phenotypes through pharmacological intervention, underscores the potential of targeting the Wnt/β-catenin signaling pathway as future perspective for ACM.Figure 1

Study protocol for an observational cohort study of heat stress impacts in pregnancy in The Gambia, West Africa

Climate change has resulted in an increase in heat exposure globally. There is strong evidence that this increased heat stress is associated with poor maternal and fetal outcomes, especially in vulnerable populations. However, there remains poor understanding of the biological pathways and mechanisms involved in the impact of heat in pregnancy. This observational cohort study of 764 pregnant participants based in sub-Saharan Africa, a geographical region at risk of extreme heat events, aims to evaluate the physiological and biochemical changes that occur in pregnancy due to heat stress. The key objectives of the study are to 1) map exposure to heat stress in the cohort and understand what environmental, social and community factors increase the risk of extreme heat exposure; 2) assess the impact of heat stress on maternal health, e.g. heat strain, subjective psychological well-being, sleep and activity level; 3) evaluate how heat stress impacts placenta structure and function; 4) determine how chronic heat exposure impacts birth outcomes; and 5) explore the epigenetic changes in the placenta and infant by heat stress exposure per trimester. Pregnant women will be recruited from two distinct regions in The Gambia to exploit the naturally occurring heat gradient across the country. Microclimate mapping of the area of recruitment will give detailed exposure measurements. Participants will be asked to wear a watch-style device at 28- and 35-weeks gestational age to evaluate maternal heart rate, activity and sleep. At the end of the week, an ultrasound scan will be performed to evaluate fetal size and placental blood flow. At delivery, birth outcomes will be recorded and maternal, placental and cord samples taken for epigenetic, biochemical and histological evaluation. Evaluation of neuro-behaviour and final infant samples will be taken at 1 month following birth.

A multi-adaptive neuro-fuzzy inference system with variable thresholds for heartbeat classification

Various heart disorders are non-invasively diagnosed using electrocardiograms (ECGs). An ECG records a variety of waveforms, including P, QRS, and T waves, which represent the electrical activity of the human heart. Cardiovascular diseases are diagnosed by examining the length, form, and spacing of these waveforms. This research develops a multi-adaptive, neuro-fuzzy inference system (MANFIS), enhanced by a variable threshold approach, to enhance heartbeat classification accuracy. The MIT-BIH arrhythmia database was utilized, and seven features were extracted from each record. A subtractive clustering method was employed to prepare the inputs for the MANFIS, enabling heartbeat classification. By applying a variable threshold to the MANFIS outputs, classification accuracy was further enhanced. The proposed method, termed variable-threshold MANFIS, can separately detect normal sinus rhythm, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature condition, and paced beat. This is achieved using six different ANFIS classifiers, each with its own threshold. The system was evaluated, achieving an accuracy of 98.33%, a sensitivity of 93.12%, a specificity of 99.66%, a precision of 98.33, and an F1-score of 95.44. A distinct feature of this machine-learning-based model is its controllable threshold, which delivers promising results across all training, testing, and validation datasets. The proposed diagnostic system is applicable in new automated medical instrumentation and serves as a valuable tool in cardiology.

Behavioral and emotional co-modulation during dog–owner interaction measured by heart rate variability and activity

Behavioral and physiological synchrony facilitate emotional closeness in attachment relationships. The aim of this pseudorandomized cross-over study was to investigate the emotional and physiological link, designated as co-modulation, between dogs and their owners. We measured the heart rate variability (HRV) and physical activity of dogs belonging to co-operative breeds (n = 29) and their owners during resting baselines and positive interaction tasks (Stroking, Training, Sniffing, Playing) and collected survey data on owner temperament and dog–owner relationship. Although overall HRV and activity correlated between dogs and their owners across tasks, task-specific analyses showed that HRV of dogs and owners correlated during free behaving (Pre- and Post-Baseline), whereas the activity of dogs and owners correlated during predefined interaction tasks (Stroking and Playing). Dog overall HRV was the only predictive factor for owner overall HRV, while dog height, ownership duration, owner negative affectivity, and dog–owner interaction scale predicted dog overall HRV. Thus, the characteristics of dog, owner, and the relationship modified the HRV responses in dog–owner dyads. The physiology and behavior of dogs belonging to co-operative breeds and their owners were therefore co-modulated, demonstrating physiological and emotional connection comparable to those found in attachment relationships between humans.

Modeling the contribution of cardiac fibroblasts in dilated cardiomyopathy using induced pluripotent stem cells (iPSCs).

Fibrosis is implicated in nearly all forms of cardiomyopathy and significantly influences disease severity and outcomes. The primary cell responsible for fibrosis is the cardiac fibroblast, which remains understudied relative to cardiomyocytes in the context of cardiomyopathy. The development of induced pluripotent stem cell-derived cardiac fibroblasts (iPSC-CFs) allows for the modeling of patient-specific disease characteristics and provides a scalable source of fibroblasts. iPSC-CFs are invaluable for understanding molecular pathways that affect disease progression and outcomes. This review explores various aspects of cardiomyopathy, with a focus on dilated cardiomyopathy (DCM), that can be modeled using iPSC-CFs and their application in drug discovery, given the current lack of approved therapies for cardiac fibrosis. We examine how iPSC-CFs can be utilized to study heart development, fibroblast heterogeneity, and activation, with the ultimate goal of developing better therapies for patients with cardiomyopathies. Significance Statement Here, we explore how iPSC-CFs can be used to study the fibrotic component of DCM. Most research has focused on cardiomyocytes, despite the significant contribution of fibroblasts to disease outcomes. iPSC-CFs serve as a valuable tool to elucidate molecular pathways leading to fibrosis, and paracrine interactions with cardiomyocytes, which are not fully understood. Gaining insights into these events could aid in the development of new therapies and enable the use of patient-derived iPSC-CFs for precision medicine, ultimately improving patient outcomes.

Predicting Mental Health Outcomes Using Wearable Device Data and Machine Learning

This paper proposes a machine learning- based system designed to predict mental health outcomes using wearable device data. The system is conceptualized to process physiological and behavioral data such as heart rate, sleep patterns, and activity levels collected from wearable technology. Key stages of the system include data preprocessing, feature extraction, and model training using multiple machine-learning algorithms, including Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. These models are combined using a voting-based ensemble classifier to improve prediction accuracy. While the system has not yet been implemented, expected results suggest that this approach will enhance prediction reliability and offer real-time insights into mental health conditions. The proposed system is envisioned to facilitate early detection of mental health disorders, thereby aiding in timely interventions and personalized care.

Linking brain-heart interactions to emotional arousal in immersive virtual reality.

The subjective experience of emotions is linked to the contextualized perception and appraisal of changes in bodily (e.g., heart) activity. Increased emotional arousal has been related to attenuated high-frequency heart rate variability (HF-HRV), lower EEG parieto-occipital alpha power, and higher heartbeat-evoked potential (HEP) amplitudes. We studied emotional arousal-related brain-heart interactions using immersive virtual reality (VR) for naturalistic yet controlled emotion induction. Twenty-nine healthy adults (13 women, age: 26 ± 3) completed a VR experience that included rollercoasters while EEG and ECG were recorded. Continuous emotional arousal ratings were collected during a video replay immediately after. We analyzed emotional arousal-related changes in HF-HRV as well as in BHIs using HEPs. Additionally, we used the oscillatory information in the ECG and the EEG to model the directional information flows between the brain and heart activity. We found that higher emotional arousal was associated with lower HEP amplitudes in a left fronto-central electrode cluster. While parasympathetic modulation of the heart (HF-HRV) and parieto-occipital EEG alpha power were reduced during higher emotional arousal, there was no evidence for the hypothesized emotional arousal-related changes in bidirectional information flow between them. Whole-brain exploratory analyses in additional EEG (delta, theta, alpha, beta and gamma) and HRV (low-frequency, LF, and HF) frequency bands revealed a temporo-occipital cluster, in which higher emotional arousal was linked to decreased brain-to-heart (i.e., gamma→HF-HRV) and increased heart-to-brain (i.e., LF-HRV → gamma) information flow. Our results confirm previous findings from less naturalistic experiments and suggest a link between emotional arousal and brain-heart interactions in temporo-occipital gamma power.

Heartbeat classification using various machine learning models: A comparative study

Cardiac arrhythmias, known as irregular heartbeats, pose a notable health threat that necessitates prompt diagnosis, as untreated arrhythmias can lead to severe heart complications. Among the various methods for arrhythmia detection, electrocardiography is the most prevalent due to its non-invasive monitoring of heart activity. However, manual electrocardiogram (ECG) analysis is inefficient and prone to errors, prompting the exploration of machine learning (ML) models for ECG feature recognition. Integrating ML models with ECG analysis can revolutionize cardiac diagnostics by improving healthcare efficiency and outcomes by enhancing the accuracy and consistency of existing approaches as well as their processing speed for large datasets. Unfortunately, current ML methods encounter two key limitations: prolonged training times and the need for manual feature selection. To address these issues, we propose using ML models enhanced with innovative techniques such as the Fourier transform (FT) and Gaussian noise injection for improved cardiac health assessment. To validate this approach, we utilized statistical tools, including Pearson correlation and p-values, to uncover relationships within the data. In addition, we employed the FT technique to extract and analyze frequency-domain features. Our comparative study of different ML models relied on metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic area under the receiver operating characteristic curve, demonstrating XGBoost’s impressive average recall of 0.956 with 99.96% overall accuracy. An average precision of 0.956 further underscored the accuracy of XGBoost’s predictions, indicating its high level of reliability in distinguishing various cardiac conditions. These results highlight the considerable potential of ML techniques for precise ECG-based clinical diagnoses, helping healthcare professionals make more accurate and timely decisions in patient care.