Search Results (181)

Brain-derived neurotrophic factor (BDNF) impacts parasympathetic nervous system function by increasing the excitability of cardioinhibitory parasympathetic neurons in the brainstem, ultimately lowering heart rate (HR) and heightening resting parasympathetic tone. Yet, whether BDNF and its high-affinity receptor—tropomyosin receptor kinase B (TrkB)—also act more distally, i.e., at the level of cholinergic-sensitive intrinsic cardiac ganglia (ICGs), remains unclear. Hence, we conducted morphological and functional studies in neural crest-specific BDNF knockout mice (ncBDNF KO), a model that selectively ablates BDNF signaling in neural crest-derived autonomic structures, including the intrinsic cardiac nervous system. ncBDNF mice exhibited a significant rise in resting heart rate with unchanged baseline contractile performance, thus supporting the role of endogenous BDNF in maintaining physiological parasympathetic restraint. When examining the ICGs, immunofluorescence analysis revealed a highly compartmentalized organization, with BDNF being predominantly confined to cholinergic neuronal somata and TrkB mainly clustered instead in S100-positive satellite glial cells, thus unveiling a previously unrecognized neuron–glia BDNF/TrkB ICG pattern. Next, we directly infused BDNF in Langendorff-perfused isolated WT mouse hearts and observed a rapid and reproducible bradycardic response that was abrogated by atropine but potentiated by neostigmine, hence attesting to the cholinergic nature of such bradycardia. Of note, BDNF maintained its positive inotropic effects under muscarinic blockade, as witnessed by the enhanced left ventricular developed pressure, maximal dP/dt, and rate-pressure product, congruent with direct BDNF-evoked myocardial TrkB agonism. Thus, ICGs are additional relevant relay stations interposed between BDNF/TrkB signaling and parasympathetic modulation of heart function. Although through different molecular paths, BDNF-mediated modulation of ICG firing can coordinate with the previously reported BDNF positive inotropy/lusitropy to adapt cardiac performance to increased workload and/or stress conditions. Full article

Introduction: Kawasaki disease (KD) is an acute systemic vasculitis and the leading cause of acquired pediatric heart disease in high-income countries. Coronary artery aneurysms (CAA) represent the most severe complication and drive long-term cardiovascular risk. Despite improved outcomes with early intravenous immunoglobulin therapy, follow-up strategies remain heterogeneous, particularly for patients showing CAA regression. Dynamic risk stratification based on coronary Z-scores has been proposed, but long-term real-world data are still needed to optimize surveillance. Methods: We conducted a single-center, retrospective study including pediatric patients (age 1 month–18 years) with KD complicated by CAA, followed at Bambino Gesù Children’s Hospital (Rome) between 1999 and 2024. Coronary involvement was assessed using Boston Z-scores of the right coronary artery, left main coronary artery, and left anterior descending artery. CAA severity over time was analyzed using a composite MAX SCORE (highest Z-score among coronary branches) along with the 1-YEAR MAX SCORE (highest MAX SCORE reached within the 1 year of disease). The distribution and timing of cardiac computed tomography angiography (CCTA) and exercise stress testing (EST) during follow-up were analyzed in relation to coronary severity. Results: Among 502 KD patients, 122 (24.3%) developed CAA; 113 were included in the analysis. Mean age at diagnosis was 24.6 months (M/F 3.5:1). Multivessel involvement was observed in 72%, most frequently affecting the left anterior descending artery. Long-term follow-up ≥10 years was available for 31.9% of patients. Most changes in coronary severity occurred within the first year after disease onset, with complete CAA regression in 76.1% of patients. Conversely, 53% of patients affected by giant aneurysms at 12 months showed persistent severe disease at last follow-up. EST (164 tests in 40 patients) was almost universally negative for inducible ischemia (163/164), whereas CCTA (47 exams in 35 patients) was preferentially performed early and in higher-risk patients. Test prescription correlated more closely with 1-YEAR MAX SCORE than with contemporaneous severity. Echocardiography showed systematic differences compared with CCTA for right coronary and left anterior descending artery Z-scores. Discussion: In our experience, early coronary status was closely associated with the intensity of long-term surveillance strategies in KD. The 1-YEAR MAX SCORE was associated with subsequent patterns of coronary evolution, and the continuous 1-year Maximum Z-score showed good discriminatory ability for persistent CAA on ROC analysis, pending external validation. While the low rate of positive findings on EST raises questions about its diagnostic yield in real-world practice, CCTA provided detailed anatomical characterization. Overall, these findings suggest that early coronary severity may help inform individualized, severity-driven follow-up strategies, warranting confirmation in prospective multicenter studies. Full article

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, driving the need for reliable tools for early risk prediction. Artificial intelligence (AI) applied to electrocardiograms (ECGs) has shown strong predictive performance for future cardiac events, yet its clinical adoption remains limited by the lack of transparency and trust associated with black-box models. This systematic review examines recent advances in AI-based cardiovascular prediction, focusing on the combined challenges of multi-modal data fusion and clinically actionable explainability. Following PRISMA 2020 guidelines, we analyzed 65 peer-reviewed studies published between 2018 and 2025, identified through a systematic search of PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and Google Scholar. The reviewed literature reveals that while most AI-ECG models achieve high predictive accuracy, typically AUC 0.85–0.95, the majority rely on post hoc explainability techniques that offer limited clinical insight, and 61.5% of included studies implement no explainability method at all. External validation remains critically underutilized, performed by only 12.3% of studies, and multi-modal approaches integrating ECG data with electronic health records, biomarkers, or genomics represent only 27.7% of the reviewed literature. While these multi-modal models demonstrate improved contextualization and predictive performance, they remain insufficiently validated and inconsistently interpretable. Among studies employing XAI techniques, attention mechanisms were the most prevalent approach (28% of XAI studies), followed by saliency maps (20%), SHAP (16%), and LIME (8%). Only 9.2% of studies were prospective or clinical trials, underscoring the gap between algorithmic development and real-world clinical deployment. Applying a pre-specified four-level clinical actionability scoring framework (Level 0–3), we found that the majority of studies (61.5%) scored at Level 0 (no actionability), with only 9.2% reaching Level 3 (demonstrated clinical impact), confirming that the clinical translation gap extends beyond trial design to encompass the broader absence of clinically contextualised evaluation of AI-ECG systems. This review highlights a persistent and critical gap between predictive performance and clinical usability, and outlines four key directions for developing AI-ECG systems that can better support trustworthy clinical decision-making: (1) developing inherently interpretable architectures, (2) advancing unified multi-modal fusion and explanation frameworks, (3) establishing standardized benchmarks for explainability evaluation, and (4) conducting robust prospective validation measuring real-world patient outcomes. Full article

The adoption of pulse oximetry screening (POS) in low- and middle-income countries remains limited. We describe the development, implementation, and evaluation of a POS program at two of the leading tertiary centers in Accra, Ghana, based on a modified American Academy of Pediatrics protocol. Screening was conducted by trained research assistants. Operational experiences of research assistants were evaluated using a mixed-methods approach, including post-implementation surveys (n = 14) and focus group discussions (n = 10). Although participants felt comfortable with the level of instruction, they recommended incorporating simulation exercises to strengthen management of failed screens. Key challenges included difficulty locating newborns due to fragmented wards and paper-based records, equipment durability issues, and complex coordination for echocardiography following failed screens. Early discharge practices and high neonatal intensive care unit admissions reduced screening coverage among high-risk infants. Despite limited referral systems and surgical capacity, the POS pilot increased provider awareness, improved diagnostic clarity and demonstrated feasibility. Sustainable expansion will require more efficient referral networks as well as investment in pediatric cardiology training and cardiac surgical infrastructure. Full article

Background and Objectives: Despite substantial advances in surgical techniques, anesthesia, and perioperative care, postoperative complications remain a major source of morbidity, mortality, prolonged hospitalization, and healthcare utilization following adult cardiac surgery (CS). Increasing evidence suggests that many of these complications are influenced by modifiable perioperative factors that can be addressed through multidisciplinary care. Materials and Methods: A narrative review was conducted to synthesize current evidence regarding perioperative practices associated with the prevention of postoperative complications in adult CS. A comprehensive literature search of PubMed/MEDLINE, Scopus, and Web of Science identified studies published between January 2015 and April 2026, supplemented by landmark studies and relevant clinical guidelines. Results: The identified evidence was organized into four major domains: infection prevention practices, physiological optimization strategies, protocol adherence and patient safety measures, and organizational and human factors. The strongest evidence supports timely antimicrobial prophylaxis, standardized infection prevention bundles, perioperative glycemic control, maintenance of normothermia, and patient blood management as key interventions associated with improved postoperative outcomes. Surgical safety checklists, standardized perioperative pathways, and adherence to evidence-based protocols further contributed to improved patient safety and consistency of care. Emerging evidence also highlighted the importance of communication, teamwork, safety culture, workload management, and healthcare professionals’ knowledge in facilitating successful implementation of perioperative interventions. Conclusions: Prevention of postoperative complications following CS requires a multidisciplinary, systems-based approach integrating evidence-based clinical interventions with standardized perioperative protocols and effective organizational practices that facilitate consistent implementation of evidence-based perioperative care. Future research should focus on prospective evaluation of integrated perioperative strategies, development of practical risk-stratification models, and further investigation of organizational determinants influencing implementation and postoperative outcomes. Full article

Background/Objectives: Acute coronary syndrome (ACS) remains one of the leading causes of cardiovascular mortality worldwide. Rapid diagnosis and timely initiation of reperfusion therapy are crucial for improving patient outcomes. The aim of this narrative review was to summarize current evidence on prehospital 12-lead ECG teletransmission in the diagnosis and management of ACS and to evaluate its impact on systems of care and clinical outcomes. Methods: A narrative literature review was conducted using the PubMed database. The literature search was completed on 15 May 2026 and included publications from 1998 onwards. The review included studies evaluating prehospital ECG teletransmission, ST elevation myocardial infarction (STEMI) networks, interpretation models, treatment delays, and organizational and logistics aspects of ECG teletransmission systems. Current guidelines from the European Society of Cardiology and the 2025 ACC/AHA/ACEP/NAEMSP/SCAI guidelines were also included. Results: Available evidence indicates that prehospital ECG teletransmission enables earlier STEMI diagnosis, direct referral to primary coronary intervention (PCI)-capable centers, and earlier activation of catheterization laboratories. Multiple observational studies and meta-analyses have demonstrated reductions in first medical contact-to-device and door-to-device times associated with teletransmission systems. Limitations include interpretation variability, false-positive catheterization laboratory activations, and infrastructure-related barriers. Conclusions: ECG teletransmission is an essential component of contemporary regional STEMI care systems and contributes to improved organization of care and shorter reperfusion delays. Further development and prospective validation of artificial intelligence-assisted ECG interpretation and integrated telemedicine systems may enhance diagnostic accuracy and the effectiveness of prehospital cardiac care. Full article

Background/Objectives: The non-neuronal cardiac cholinergic system (NNCCS) is known to synthesize ACh independently of the parasympathetic nervous system, thereby regulating cardiac homeostasis, which includes sustainability of energy metabolism, anti-inflammatory and anti-ischemic properties, electrical stability, and mitochondrial calcium handling. Given these beneficial functions of NNCCS, we were prompted to search for an inducer. One such inducer is SNPiP, a novel low-molecular-weight chemical compound developed by us. SNPiP accelerates ACh synthesis in the heart via cGMP elevation and, intriguingly, enhances diastolic function, increasing cardiac output and end-systolic pressure without elevating heart rate. However, the pharmacokinetics of SNPiP remain unknown, which led us to conduct the present study. Methods and Results: We found that the half-life of SNPiP in the blood was extremely short, similar to that of a nitric oxide (NO) donor, S-nitroso-N-acetyl-DL-penicillamine. This short half-life is caused by the rapid distribution of SNPiP into organs, including the heart, kidney, and liver. In addition, once transferred into blood cells, SNPiP itself became stable and remained intact for up to 1 h. Moreover, the short half-life was partly explained by the rapid degradation of SNPiP and concomitant loss of the nitroso group in the blood. Notably, when rats were treated with SNPiP, NO levels in the heart elevated bimodally: immediately after administration and again about 12 h later, coinciding with the previous report of NNCCS upregulation and accelerated ACh synthesis with NO production. Importantly, our previous transcriptome analysis of SNPiP-treated hearts supports these findings, as it revealed upregulation of diastolic function-related genes and proteins. Conclusions: Collectively, these results clarify the pharmacokinetics of SNPiP and demonstrate that, despite a shorter half-life, SNPiP is efficiently distributed to the heart, where it confers beneficial effects through induction of NNCCS. Full article

Background: Infective endocarditis is a serious disease affecting cardiac tissues and may arise following episodes of transient bacteremia. Oral prosthetic pressure lesions, characterized by mucosal ulcerations, may facilitate the entry of pathogenic microorganisms into the systemic circulation. In predisposed individuals, the presence of such lesions may therefore contribute to the development of infective endocarditis. The objective of this study was to assess dental professionals’ knowledge, awareness, and self-reported clinical practices related to the possible relationship between oral prosthetic pressure lesions and infective endocarditis. Materials and Methods: A cross-sectional observational study was conducted using an anonymous online questionnaire specifically developed for this study and subjected to preliminary content validation and pilot assessment before administration. The survey was distributed online to oral health professionals working in the Italian public and private sectors; for the present analysis, only complete responses provided by licensed dental professionals practicing in Italy were included. Results: Among the 268 dental professionals analyzed, an overall limited awareness of the possible relationship between oral prosthetic pressure lesions and infective endocarditis emerged. Only 16.8% of participants reported being familiar with the most recent guidelines for the prevention of infective endocarditis. Conclusions: The findings suggest the need to strengthen continuing education, improve familiarity with current infective endocarditis prevention guidelines, and promote more structured clinical protocols for the assessment and management of prosthetic pressure lesions in patients at risk. Full article

Background: Inflammatory bowel disease (IBD) is a chronic inflammatory disorder that confers an increased risk of venous thromboembolism (VTE) and subsequent pulmonary embolism (PE). The risk stems from chronic systemic inflammation promoting endothelial dysfunction and hypercoagulability. Data on specific inpatient outcomes and procedural needs in patients with IBD with acute PE remains limited. This study explores these outcomes at a national level. Methods: We conducted a Nationwide Inpatient Sample (NIS) database analysis (2016–2020). Adult hospitalizations for acute PE were identified using ICD-10-CM codes and stratified based on IBD status. Multivariable regression analysis was performed to determine independent associations between IBD status and in-hospital mortality, length of stay (LOS), cardiac complications, and ICU-level interventions (intubation, central venous catheterization (CVC), arterial line placement, requirement of vasopressors), and blood transfusion. Results: Among 377,143 acute PE hospitalizations, 4123 (1.1%) had IBD. Patients with IBD were younger (58.72 vs. 62.78 years, p < 0.001) and had lower prevalence of diabetes mellitus, hypertension, end-stage renal disease (ESRD), dyslipidemia, overweight/obesity, coronary artery disease and smoking status (p < 0.05). Despite a favorable baseline profile, patients with IBD had a longer length of stay (LOS) (8.82 vs. 7.30 days, p < 0.001) but no significant association with in-hospital mortality (aOR = 0.93, p = 0.281). Multivariable analysis showed patients with IBD had higher odds of requiring CVC placement (OR = 1.42, p < 0.001), vasopressors (OR = 1.22, p = 0.05), and blood transfusions (OR = 1.78, p < 0.001). Conversely, they had lower odds of cardiac arrest (OR = 0.64, p < 0.001) and cor pulmonale (OR = 0.32, p = 0.012). Conclusions: patients with IBD with acute PE represent a complex population with high resource utilization. Future research is needed the development of IBD-specific PE risk stratification, targeted management, prophylactic and therapeutic anticoagulation guidelines. Full article

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality in the elderly worldwide. Over the past two decades, there has been a wealth of evidence of a close relationship between autonomic nervous system activity and cardiovascular mortality, including sudden cardiac death. Heart rate variability (HRV), derived from photoplethysmographic (PPG) signals, is increasingly recognized as a promising non-invasive digital marker for evaluating autonomic nervous system function and stratifying CVD risk. The application of machine learning algorithms to PPG-derived HRV analysis offers a promising approach for improving CVD risk stratification and facilitating the development of personalized medicine strategies. Background/Objectives: To evaluate the potential of heart rate variability indicators in predicting the risk of developing CVD in individuals aged 65 years and older. Methods: The study involved individuals aged 65 years and older, divided into two groups: those with a risk of developing CVD (n = 54) and those without risk (n = 46). The first stage included a questionnaire as well as anthropometric and hemodynamic measurements. At the second stage, a PPG was performed using the Eldar computer photoplethysmograph and Eldar-Vario software, followed by an analysis of time-domain and spectral HRV parameters. Statistical data analysis was conducted using the SPSS Statistics 22.0 software package, focusing on the evaluation of associations between HRV indicators and the presence of CVD. Interpretable machine learning models were developed using logistic regression and a random forest algorithm within a nested cross-validation framework. In addition to the discriminatory characteristics, Brier score, LogLoss, calibration analysis, error matrices, permutation importance, and SHAP interpretation were analyzed in the study. Results: In patients with cardiovascular diseases, a statistically significant decrease in heart rate variability was revealed: SDNN by 2 times (26 [Q1–Q3: 15, 35] ms), pNN50 by 3.5 times (4 [3, 5]%), TINN by 5 times (31 [20, 51] ms), and HRV by 2.5 times (6 [4, 8.7]). In addition, a decrease was seen in the spectral components of VLF by one-fold (2450 [Q1–Q3: 2450, 4500] ms²), LF by four-fold (750 [750, 1500] ms²) and HF by five-fold (450 [450, 750] ms²) (p < 0.05). At the same time, there was a significant increase in the VLF/HF and LF/HF ratios, which indicates a predominance of sympathetic activity. According to the results of the correlation analysis, statistically significant associations of HRV indicators with age, physical activity level, body mass index and systolic blood pressure were revealed. The results of machine learning also revealed the association of HRV with arterial hypertension, physical activity and BMI. The best final results were demonstrated by a random forest model with a combined set of clinical and HRV signs of HF and RMSSD (ROC-AUC was 0.9988). The signs of heart rate variability obtained by photoplethysmography demonstrated additional prognostic value in relation to clinical signs. PPG-derived HRV features demonstrated additional discriminatory value for cardiovascular risk stratification. Conclusions: The obtained data demonstrate a close association between the risk of developing cardiovascular disease and autonomic nervous system dysfunction. The decrease in heart rate variability is most pronounced in elderly individuals with existing cardiovascular disease and can be considered a potential tool for developing diagnostic, prognostic, and risk stratification strategies. The use of machine learning demonstrated that heart rate variability features obtained using photoplethysmography improve diagnostic prognostication and classification of cardiovascular diseases compared to models based solely on clinical data. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Obstructive sleep apnea (OSA) constitutes a chronic disorder characterized by recurrent upper airway collapse during sleep. This condition is prevalent among patients with cardiac rhythm disturbances and represents a potent independent risk factor for arrhythmia. Although most studies have concentrated on the association between OSA and atrial fibrillation (AF), numerous investigations have established connections with ventricular and supraventricular arrhythmias. Arrhythmogenesis in OSA represents a complex multifactorial phenomenon. Acute mechanisms involve induction of negative intrathoracic pressure during the effort to breathe, which triggers recurrent episodes of hypoxia, hypercapnia, alterations in carbon dioxide and acid–base equilibrium, as well as surges in sympathetic nervous system activity. Chronic intermittent hypoxia (CIH) and negative thoracic pressure (NTP) induce atrial stretch, chronic structural remodeling, and elevated vagal tone, thereby heightening susceptibility to bradycardic and conduction arrhythmias. Intermediate pathways through which OSA may precipitate arrhythmia encompass heightened systemic inflammation, oxidative stress, a prothrombotic state, and vascular dysfunction. Long-term OSA is linked with atrial enlargement and fibrosis, ventricular hypertrophy, hypertension, and coronary artery disease. These factors predispose to cardiac arrhythmias through the following mechanisms: shortening of the atrial effective refractory period, abnormal automaticity, promotion of slowed and heterogeneous conduction, enhancement of reentrant arrhythmia persistence, and prolongation of the QT interval. In this paper, we aim to present the pathophysiological mechanisms underpinning the association between obstructive sleep apnea and cardiac arrhythmias. Understanding the precise pathophysiological pathways by which obstructive sleep apnea contributes to arrhythmogenesis will enable targeted preventive stratification of patients at risk for cardiovascular events and promote the development of innovative therapies to attenuate OSA-induced arrhythmogenicity. Full article

Healthcare professionals must acquire and maintain both declarative knowledge and fine psychomotor skills across a wide range of clinical procedures. Human working memory is physiologically limited, and the high cognitive demands of clinical environments frequently contribute to medical errors and adverse events. Intra-individual performance variability—driven by fatigue, stress, and motivation—represents a further challenge that conventional medical safety education has not adequately addressed. According to the World Health Organization, patient harm ranks fourteenth in the global burden of disease, with approximately 10% of hospitalised patients in high-income countries experiencing harm within healthcare facilities. This study reports the design, theoretical rationale, and preliminary outcomes of an augmented reality (AR) glasses system for hands-free, self-directed medical procedural training, developed from a human factors and ergonomics (HFE) perspective. The system integrates a see-through head-mounted display (HMD; Epson Moverio BT-40S), bone-conduction earphones (Shokz OpenComm), and an industrial-grade voice recognition application (NEC Solution Innovators), achieving fully hands-free operation compatible with aseptic technique. Content design is grounded in cognitive load theory (CLT) and the cognitive theory of multimedia learning (CTML), extended by neuroscientific evidence on multisensory integration and memory consolidation. More than 40 procedure-specific modules have been developed in-house at Tokyo University of Technology, spanning airway management, vascular access, respiratory therapy, dialysis, and cardiac support. In a four-year longitudinal survey (virtual reality (VR) simulator; n = 286), major satisfaction items consistently exceeded the scale midpoint. In an AR endotracheal suctioning cohort (n = 38/22), procedural flow understanding was rated 3.95/5.0. A peer-reviewed randomised controlled trial (Clinical Simulation in Nursing, n = 36) demonstrated significantly superior skill improvement (p < 0.001) and learning motivation (p = 0.001) in the AR group versus textbook self-practice. Principal ergonomic limitations of current HMD hardware—excessive weight, narrow field of view, and absence of medical-grade certification—are documented, and AI-based real-time procedural assessment is identified as a priority for the next research phase. Full article

Diseases linked to abnormal cardiac electrical activity, such as arrhythmias and heart failure, are closely associated with impaired electrical signal conduction between cardiomyocytes. Existing research on nano-communication has primarily used simplified one-dimensional cellular models, which inadequately represent the complex structure and signal transmission dynamics of three-dimensional cardiomyocytes. This limitation hampers a comprehensive understanding of the mechanisms underlying these diseases at the microscale level. To overcome this, the present study developed a subthreshold cardiac communication system based on a three-dimensional cardiomyocyte model. By simulating signal propagation through coupled cell chains and utilizing on–off keying modulation for information encoding, the transmission performance of this system was systematically evaluated in a noisy environment. Under optimized conditions, the system achieved high-fidelity information transfer, and modulated noise levels can enhance signal quality through a random facilitation effect. This study offers a novel analytical framework for exploring the communication mechanisms of weak signals between cardiomyocytes and the pathophysiology of diseases associated with abnormal cardiac electrical activity. Full article

Conductive intracardiac communication (CIC) is one of the most innovative and promising communication technologies in multi-point cardiac pacing schemes that utilize the heart as the transmission channel in recent years. Current research predominantly focuses on static channel characteristics. Although some studies have explored dynamic responses, they are largely confined to basic amplitude–frequency and amplitude–time behaviors, lacking in-depth analysis of underlying dynamic mechanisms such as path loss, shadowing, multipath, and Doppler effects. Designing CIC systems solely on the basis of static properties can result in inaccurate channel estimation, distorted channel state information (CSI), and elevated bit error rate (BER). To solve the problems of dynamic channel measurement and modeling, this paper for the first time proposes a dynamic channel modeling method for CIC based on sinusoidal response and impulse response. Firstly, we develop a physical simulation and miniaturized measurement setup to measure the dynamic cardiac channel, and analyze the amplitude–frequency characteristics and amplitude–time characteristics. The influence of factors such as instrument differences, heart rate, flow rate, and motion artifacts is also discussed. Secondly, we systematically analyze the path loss, shadowing effect, multipath effect, and Doppler effect of the CIC channel. Combined with the dynamic channel characteristics and parameters, we propose a composite fading dynamic channel model and analyze the BER performance of baseband signal transmission and On–Off Keying (OOK) modulation systems. We conclude that (1) the CIC channel exhibits capacitive characteristics. Fixed electrodes can effectively suppress motion artifacts. (2) The dynamic channel gain of CIC varies periodically with the heartbeat, and the fluctuation range of the signal is less than 1–2 dB. (3) The dynamic CIC channel presents extremely weak shadow fading, no significant multipath, and no measurable Doppler characteristics, belonging to an extremely slow-fading channel. This work provides effective dynamic channel measurement approaches and a parameter basis for the transceiver design of CIC and a reliable model for the simulation of CIC systems. Full article