Search Results (937)

Emery–Dreifuss muscular dystrophy (EDMD) is a rare inherited neuromuscular disorder within the spectrum of nuclear envelope diseases, classically characterized by early musculo-tendinous contractures, slowly progressive myopathy, and cardiac involvement dominated by conduction disease and arrhythmias, with variable evolution toward cardiomyopathy and heart failure. This narrative review provides a comprehensive and clinically actionable synthesis of cardiovascular manifestations across EDMD genotypes and phenotypes, outlining pragmatic diagnostic and therapeutic pathways for real-world care. A targeted literature search was performed in PubMed, Embase, and Web of Science, focusing on studies addressing cardiovascular involvement in EDMD. Relevant original studies, case series, registries, guideline documents, and high-quality reviews were selected and synthesized narratively, with particular emphasis on diagnostic strategies, risk stratification, and management approaches. Cardiac involvement in EDMD encompasses a broad and heterogeneous spectrum, including atrial disease and conduction disturbances, ventricular arrhythmias, dilated cardiomyopathy, thromboembolic complications, and sudden cardiac death. Phenotypic expression varies according to the underlying genetic substrate, with distinct atrial- and ventricular-dominant trajectories. Early recognition and structured cardiovascular surveillance are essential to guide timely intervention, including anticoagulation, device therapy, and heart failure management. Despite growing awareness, significant gaps remain in risk prediction and standardized management strategies. EDMD represents a paradigmatic model of cardiomyopathy characterized by prominent electrical instability and systemic involvement. A structured, genotype- and phenotype-informed approach centered on early surveillance, proactive arrhythmia and thromboembolic risk management and timely device therapy may improve clinical decision-making in real-world settings. Future perspectives include the integration of precision medicine and the development of gene- and pathway-targeted therapies, with the potential to shift from symptomatic management toward disease-modifying strategies. Full article

Blood pressure is a central marker of cardiovascular risk, but continuous monitoring remains difficult because cuff-based measurements are intermittent and uncomfortable. Photoplethysmography (PPG) is already ubiquitous in wearables and can, in principle, enable cuffless blood pressure estimation from a single optical signal. However, many deep learning approaches that perform well in floating-point are impractical for microcontroller-class devices, where memory budgets, latency, and integer-only arithmetic constrain what can be deployed. A key open question is which neural architectures retain accuracy after full-integer quantization, rather than only under desktop inference. Here, we show an end-to-end, microcontroller-oriented evaluation framework that benchmarks multiple 1D convolutional models for cuffless systolic and diastolic pressure estimation from single-channel PPG, jointly optimizing estimation error, model footprint, and quantization robustness. We find that floating-point accuracy alone is a poor predictor of deployability: some lightweight CNNs exhibit substantial performance drift after INT8 conversion, whereas a compact residual 1D CNN preserves its predictions with near-identical error statistics after integer quantization. We then deploy the selected integer-only model on an STM32N6 microcontroller using an industrial toolchain and confirm that on-device inference maintains low bias and limited error dispersion while meeting real-time constraints for continuous operation. These results highlight architecture-dependent quantization stability as a critical design dimension for sensor-edge intelligence and support the feasibility of fully on-device cuffless blood pressure monitoring without multimodal sensing or cloud processing. Full article

Background: Kounis syndrome is an allergic acute coronary syndrome precipitated by coronary vasospasm, plaque destabilization, stent thrombosis, or bypass occlusion. Cardiac surgery represents a uniquely high-risk setting due to cardiopulmonary bypass–associated inflammation and exposure to multiple pharmaceutical agents. Importantly, Kounis syndrome remains underrecognized in this context, as classical signs of anaphylaxis may be masked under general anesthesia and cardiopulmonary bypass, while ischemic manifestations may be misattributed to other perioperative conditions. Methods: A narrative review of PubMed-indexed literature was conducted to synthesize current evidence on the pathophysiology, perioperative triggers, clinical presentation, diagnostic strategies, and management of Kounis syndrome in cardiac surgery, with emphasis on intraoperative recognition and surgical decision-making. Published cases were retrieved involving perioperative cardiac surgery patients with a definite diagnosis of Kounis syndrome. Additionally, cases presenting with severe perioperative anaphylaxis and life-threatening cardiovascular involvement (grade III with cardiovascular collapse and grade IV with cardiac arrest) were included as possible Kounis syndrome, reflecting real-world diagnostic uncertainty in the intraoperative setting. Results: The literature review identified five cases of definite Kounis syndrome and ten cases of possible Kounis syndrome, including three cases with cardiovascular collapse and seven cases with cardiac arrest. Recurrent episodes were reported in several patients, particularly due to re-exposure to the triggering agent. In the context of cardiac surgery, Kounis syndrome is most frequently triggered by chlorhexidine, protamine, antibiotic prophylaxis, and anesthetic agents. The clinical presentation is often subtle during cardiopulmonary bypass. Vasoplegia, pulmonary hypertension, ventricular dysfunction, new regional wall-motion abnormalities, and hyperdynamic ventricles on transesophageal echocardiography commonly precede overt electrocardiographic changes. Diagnosis is primarily clinical and relies on intraoperative ultrasound, hemodynamic monitoring, serum tryptase, serum troponin, and, when indicated, coronary angiography. A dual-pathway approach addressing both anaphylaxis and myocardial ischemia is essential; however, one component may predominate, particularly in perioperative patients with limited clinical information, potentially leading to misdiagnosis. A multidisciplinary approach is therefore required for rapid diagnosis and individualized management. In refractory cases, cardiopulmonary bypass or ventricular assist devices may provide lifesaving support. Conclusions: Kounis syndrome remains underrecognized in cardiac surgery but carries significant morbidity. Increased clinical awareness, multidisciplinary collaboration, structured diagnostic approaches, and preventive strategies are essential to improve outcomes and reduce the risk of recurrence during future procedures. Full article

Background/Objectives: Cardiovascular diseases are the leading cause of death worldwide. Therefore, early diagnosis and treatment of these diseases are of critical importance. Stethoscopes are the easiest and fastest medical devices for the initial diagnosis of cardiovascular diseases. However, interpreting heart sounds requires considerable expertise. The use of artificial intelligence in healthcare for decision support has increased and become popular recently. Methods: The popular 2016 PhysioNet/CinC Challenge dataset, consisting of phonocardiogram (PCG) signals, was used to implement the proposed approach. Spectrogram and continuous wavelet transform (CWT) images of the PCG signals were first generated. This increased the distinguishability of the data in terms of both time and frequency components. These two-input images were tested on the developed Dual Time–Frequency Swin Transformer–ConvNeXt Attention Network (DTF-STCANet) model. To further improve classification accuracy, the Weighted KNN algorithm was preferred during the classification phase. Results: With the proposed approach, a 99.29% classification accuracy was achieved. Performance was compared with other state-of-the-art models. Conclusions: The proposed approach, through the integration of PCG signals with artificial intelligence, further strengthens the concept of early diagnosis of heart disease. Full article

Background/Objectives: Whole-blood viscosity (WBV) is increasingly used in cardiovascular risk assessment; however, inter-device comparability may depend on shear-rate definition. We performed a paired comparison of two scanning capillary viscometers to evaluate shear-dependent analytical agreement and its impact on clinical classification. Methods: In 300 identical blood samples, WBV was measured using Rheovis 2000A and Hemovister. Systolic WBV was defined at 300 s⁻¹ for both devices (shear-matched), whereas clinically defined diastolic WBV corresponded to 1 s⁻¹ for Rheovis 2000A and 5 s⁻¹ for Hemovister. Agreement was assessed using linear regression and Bland–Altman analysis. Hematocrit tertiles were examined as effect modifiers. Clinical agreement was evaluated using quadratic weighted Cohen’s κ. Results: Across matched shear rates (1000 to 1 s⁻¹), Hemovister yielded consistently higher WBV values than Rheovis 2000A, with statistically significant inter-device differences at all shear levels except 1000 s⁻¹. The magnitude of bias increased progressively as shear rate decreased, reaching −8.34 mPa·s at 1 s⁻¹. Under shear-matched systolic conditions (300 s⁻¹), the mean difference was −0.25 mPa·s (limits of agreement −1.72 to 1.22). In contrast, under clinically defined diastolic conditions (1 vs. 5 s⁻¹), the mean difference was 14.54 mPa·s (3.93 to 25.15), increasing across hematocrit tertiles. Clinical agreement was fair for systolic (κ = 0.31; 95% CI 0.24 to 0.39) and moderate for diastolic WBV (κ = 0.44; 95% CI 0.37 to 0.51). Notably, among samples classified as high by Hemovister, 72.8% (systolic) and 54.0% (diastolic) were reclassified as normal by Rheovis 2000A. Conclusions: Inter-device agreement in WBV measurement is strongly shear-dependent. Although numerical divergence increases at low shear, categorical concordance may remain moderate when device-specific reference thresholds are applied. Harmonization of shear definitions and reference frameworks may therefore be essential for consistent cross-platform interpretation. Full article

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, anatomical sites, calibration methods, and signal-processing approaches limit direct comparison of reported performance. In addition, challenges such as subject-specific calibration, motion artifacts, and limited clinical validation remain. Overall, this review highlights current progress and outlines key challenges for future development and translation of TENG-based cardiovascular monitoring systems. Full article

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. Experimental results demonstrate that, when using three physiological parameters as input, the ANN achieved a classification accuracy of 97.78%, substantially outperforming traditional approaches. Even under two-parameter input conditions, the model maintained strong predictive performance. These findings confirm the effectiveness and practical potential of the proposed framework for real-time, non-invasive CI assessment. Moreover, this research has received rigorous assessment and approval from the Institutional Review Board (IRB) under application number 202501987B0. Full article

Patients with heavily calcified coronary arteries represent a challenge in percutaneous coronary intervention (PCI), as severe calcification impairs device delivery and limits optimal stent expansion, leading to higher risks of stent thrombosis, restenosis, and adverse clinical outcomes. Approximately 20% of patients undergoing PCI exhibit severe coronary calcification, which independently predicts incomplete revascularization, increased mortality, and higher rates of major adverse cardiovascular events over mid-term follow-up. Recent advances have focused on improving the assessment and management of calcified lesions. Intracoronary imaging modalities, including intravascular ultrasound and optical coherence tomography, allow precise detection and characterization of calcium burden, overcoming the limitations of angiography. These tools play a pivotal role in guiding procedural strategy, enabling tailored selection of calcium-modifying techniques based on lesion morphology, and optimizing stent deployment. Technological innovations have significantly expanded therapeutic options. While non-compliant balloon angioplasty alone is often insufficient, adjunctive devices such as cutting and scoring balloons improve plaque modification in focal disease. Atherectomy techniques, including rotational and orbital systems, are effective for more complex lesions but require technical expertise and carry procedural risks. Intravascular lithotripsy has emerged as a promising, less aggressive modality capable of fracturing deep calcium, while excimer laser atherectomy offers an alternative for resistant lesions. Despite these advances, current evidence supporting calcium-modifying strategies is largely based on procedural outcomes rather than definitive improvements in long-term clinical endpoints. Meta-analyses and randomized trials have not demonstrated clear superiority of any single technique, and most studies remain underpowered. Intriguingly, recent data suggest that outcomes in treated calcified lesions may approximate those of non-calcified disease, raising the hypothesis that these technologies could mitigate the adverse impact of calcification. However, this remains unproven, highlighting the urgent need for adequately powered randomized trials to determine their true clinical benefit. Full article

Background and Objectives: Drug-coated balloons (DCBs) represent a novel, attractive strategy for coronary revascularization; however, data supporting their use in complex real-world populations remain limited. We aimed to evaluate the safety and efficacy of a DCB-first strategy in a predominantly acute coronary syndrome (ACS) and multivessel disease (MVD) population. Materials and Methods: We conducted a prospective two-center observational registry including 115 consecutive patients treated with a DCB-first strategy (DCB-only in 44 patients and a hybrid DCB–drug-eluting stent in 71 patients) for both de novo and in-stent coronary lesions. Bailout stenting was performed when required according to predefined criteria. Results: The study population was characterized by high clinical complexity, with 78.3% MVD and 67.8% presenting with ACS, including 10.5% ST-segment elevation myocardial infarctions. Bailout stenting was required in 12.2% of lesions. At 18 months, the target lesion revascularization (TLR) rate was 2.83%, while the device-oriented composite endpoint (DOCE; cardiac death, target vessel myocardial infarction or TLR) occurred in 4.7% of patients. The cumulative major adverse cardiovascular event (MACE) rate at 18 months was 14.8%, largely driven by the high-risk clinical profile of the cohort. Patients treated with a DCB-only strategy had a shorter duration of dual antiplatelet therapy compared with those treated with a hybrid strategy. Conclusions: In this two-center real-world registry including predominantly ACS and MVD patients, a DCB-first strategy was associated with low lesion-level event rates and acceptable mid-term clinical outcomes. These findings support the feasibility of a leave-nothing-behind approach in complex coronary disease when meticulous lesion preparation and provisional bailout stenting are applied. Full article

Percutaneous left atrial appendage occlusion (LAAO) has become an established alternative to long-term oral anticoagulation for stroke prevention in patients with atrial fibrillation, with expanding indications beyond those with absolute contraindications to anticoagulation. Alongside its broader adoption, device-related thrombus (DRT) has emerged as a clinically relevant complication that directly compromises the protective intent of LAAO. This comprehensive narrative review synthesizes contemporary evidence on the incidence, mechanisms, predictors, clinical impact, and management of DRT. DRT is a multifactorial phenomenon that carries an annual incidence ranging from 1.75% to almost 5%, resulting from the interplay between post-implant flow dynamics, device engineering, endothelialization processes, procedural factors, and patient-specific prothrombotic features. Accumulating data from observational registries links DRT to increased risks of ischemic stroke, systemic embolism, major adverse cardiovascular events (MACE), and mortality. Although evidence is growing, optimal management regimens for both the prevention and treatment of DRT remain undefined. Moreover, a lack of standardization also affects diagnosis and imaging surveillance, mainly performed by transesophageal echocardiography or cardiac computed tomography. By integrating mechanistic insights, clinical predictors, device-specific considerations, and therapeutic evidence, this review highlights current knowledge gaps and proposes practical considerations to inform individualized risk stratification, surveillance, and management of DRT in contemporary LAAO practice. Full article

Introduction: Childhood and adolescent obesity is a growing global health challenge associated with early metabolic and cardiovascular complications. This study aims to compare questionnaire-assessed physical activity patterns and lifestyle characteristics among children and adolescents with obesity and normal-weight peers and to explore their associations with clinical measurements and cardiometabolic risk. Assessing resting metabolic rate (RMR) by indirect calorimetry may provide additional insight into metabolic status beyond conventional anthropometric indicators. Methods: This prospective cross-sectional study included 58 children and adolescents aged 5–18 years with obesity and 30 normal-weight controls evaluated in Sfânta Maria Emergency Children’s Hospital Iași, Romania. Clinical data included anthropometric measurements and available biochemical parameters. RMR was assessed through indirect calorimetry (Fitmate Pro, Cosmed, Rome, Italy). Parents completed a structured lifestyle questionnaire adapted from validated international instruments, collecting information on physical activity, sedentary behavior, and wearable device use. Data analysis was conducted using SPSS 22.0, applying descriptive statistics and Pearson correlation analysis. Results: Children with obesity reported higher body mass index (BMI) (30.48 ± 5.31 kg/m²), higher RMR values, lower physical activity levels and greater sedentary time than controls. RMR correlated positively with BMI, central adiposity, blood pressure, waist-to-height, hepatic steatosis and exercise tolerance. Although electronic devices for monitoring physical activity were more frequently used in the obesity group, this was not associated with higher activity levels. Conclusions: Children and adolescents with obesity exhibited a clustered cardiometabolic risk profile and reduced physical activity. RMR measured by indirect calorimetry may contribute to a more comprehensive metabolic assessment in pediatric obesity. Full article

Endovascular therapies have transformed cardiovascular medicine, yet restenosis, thrombosis, and device failure remain common and poorly predictable complications. Increasing evidence suggests that immunothrombotic processes critically shape vascular recovery after device implantation. This includes neutrophil extracellular trap (NET) formation, innate immune polarization, and endothelial damage responses. Concurrently, advances in artificial intelligence (AI) are increasingly enabling continuous multimodal monitoring and adaptive clinical decision-making throughout the medical device life cycle. Here, we propose the concept of immunologically adaptive endovascular devices: a closed-loop paradigm in which patient immune status informs device selection, device–tissue interactions are interpreted via mechanistic biomarkers, and real-world monitoring dynamically updates risk and management. The study introduces (i) an immune–device interaction phenotype taxonomy linking device design features to measurable thrombo-inflammatory trajectories, (ii) a mechanistic framework defining interface signaling processes that enhance or resolve NET-driven responses, (iii) a minimum evidence model encompassing preclinical testing, clinical validation, and post-market surveillance, and (iv) a reference AI architecture for risk prediction, drift detection, and safety monitoring. This study also outlined testable predictions and a translational roadmap toward precision endovascular intervention and next-generation adaptive cardiovascular devices. Full article

Photoplethysmography (PPG) has emerged as an attractive modality for non-invasive cardiovascular monitoring due to its low cost, unobtrusive nature, and ubiquity in consumer wearable devices. Despite its potential, existing PPG-based arrhythmia detection systems remain limited in scope: (i) most target only atrial fibrillation, (ii) temporal localization of abnormal segments is rarely provided, and (iii) deep learning models lack explainability, hindering adoption in clinical workflows. We present a comprehensive and fully integrated framework for multi-class arrhythmia detection, segmentation, and explainability based on PPG waveforms, Heart Rate Variability (HRV), and structured clinical metadata. The proposed system introduces a CLIP-style contrastive learning module aligning PPG waveforms with clinical variables and rhythm-state textual descriptions using BioBERT; a multitask U-Net architecture performing 4-class classification and 1D segmentation; a Retrieval-Augmented Generation (RAG) pipeline leveraging Gemini Flash large language models to produce guideline-grounded diagnostic reports; and a real-time Streamlit-based web platform supporting inference, visualization, and database storage. The system significantly improves classification accuracy (from 86.27% to 91.19%) and segmentation Dice (from 0.5815 to 0.7167). These results demonstrate the feasibility of a robust, multimodal, and explainable PPG-based arrhythmia monitoring system for real-world applications. Full article

Competitive eSports impose substantial cognitive workload, yet performance evaluation still emphasizes post-match statistics without considering players’ cognitive states. We reviewed 30 papers that recorded physiological signals using sensors and utilized machine learning (ML) for predicting cognitive states and/or game performance. Findings showed that cardiovascular monitoring (heart rate variability/HRV) was the most prevalent modality (20/30 studies), followed by oculometry (10), electrodermal activity/EDA (9), and electroencephalogram/EEG (5); however, no standardized protocols (device/pre-processing/feature subset) were observed across HRV studies despite it being the most common measure. The best outcomes per construct (measure, accuracy) were: mental workload (pupillometry, ~82%), stress/arousal (EDA, p < 0.001), cognitive fatigue (pupil diameter/EEG, ~88%), expertise (EEG, ~92%), and tilt (EDA/HRV/eye-tracking, ~82–87%). Notably, current studies used small samples and were gender-imbalanced, while ML studies often lacked cross-validation. Only 2 of 30 studies examined flow state—a mental state of optimal performance characterized by total immersion and effortless execution—and interestingly, HRV showed decreases during stress/workload but increases during flow, suggesting context-dependent autonomic regulation. To address this gap, a new framework for flow detection is presented. This review will be of interest to game developers, eSports players, and coaches, and the reported findings may help towards improving player experience and game performance. Full article

Background: Giant cell myocarditis (GCM) is a rare condition with an incompletely understood immune pathogenesis, characterized by inflammatory damage to the myocardium and the presence of multinucleated giant cells on histopathological examination. The frequently fulminant and severe course requires rapid intervention for a correct diagnosis and the initiation of immunosuppressive therapy, which is often life-saving. Materials and methods: This article contains information from observational studies and case reports, systematically collected from prestigious publications such as JACC, NEJN, ESC, JCC, Heliyon, and Cureus found in the PubMed and ClinicalTrials.gov databases. Thus, 25 patients diagnosed with giant cell myocarditis between March 2019 and May 2025 were analyzed, with a focus not only on the initial clinical evolution, mortality incidence, and the need for heart transplantation but also on the incidence of major complications such as cardiogenic shock and malignant rhythm and conduction disorders refractory to drug treatment. These parameters were studied according to certain intrinsic factors that cannot be influenced, such as age at onset, gender, and associated pathology of the patient, as well as extrinsic factors that can be influenced, such as the time of diagnosis and the start of immunosuppressive therapy. The results obtained were compared with those in the literature from previous years, considering the limitations of the current study. Results: The selected patients were 13 women (52%) and 12 men (48%), mostly from the US and Japan, aged between 22 and 76 years, with an average age of 44.92 years. An associated autoimmune pathology was found in 40% of patients in this group, and previous cardiovascular pathology in 28%. Only 8% had a history of GCM. The clinical onset of new-onset heart failure, refractory to usual therapy, with progressive dyspnea as the cardinal symptom was found in 12 patients, representing 48% of cases; palpitations as an expression of rhythm or conduction disorders were found in five patients, representing 20%; precordial discomfort to precordial pain accompanied or not by ST-T segment changes was present in four patients, representing 16%; and general signs and symptoms or those of other organs were present in three (12%) cases. The diagnosis was made by histological examination of the biopsy fragment obtained by endomyocardial biopsy or from the myocardial fragment obtained during the implantation of mechanical cardiovascular support devices and, less frequently, on the explanted heart and at autopsy. In terms of progression, of the 25 patients, four (16%) died, four (16%) required heart transplantation, and 16 (64%) had a severe progression with cardiogenic shock, which required mechanical circulatory support in 11 (44%) cases. The outcome was mainly influenced by the early diagnosis and administration of immunosuppressive medication, but also by the age of the patients and associated chronic diseases. Conclusions: Giant cell myocarditis is a serious condition that, in the absence of rapid diagnosis and appropriate immunosuppressive therapy, has a fulminant, often fatal course. Clinical suspicion of giant cell myocarditis remains important in the initial diagnosis. Raising this suspicion, together with modern and improved paraclinical investigations compared to previous years, has led to faster diagnosis and administration of immunosuppressive therapy in this pathology. Histological examination remains the gold standard for final diagnosis, but it should be noted that it may be non-diagnostic. In the face of a strong suspicion of giant cell myocarditis, the best approach is to start immunosuppressive therapy and monitor the patient’s progress. Immunosuppressive treatment remains decisive in influencing the evolution of this condition, both through prompt administration and through the adaptation of therapeutic regimens to the evolution of patients. A more detailed understanding of the immune-mediated pathogenesis of GCM and the identification of clinical risk factors for unfavorable short- and long-term outcomes may enable earlier risk stratification and the development of more targeted, individualized therapeutic strategies. Full article