Conduction system disturbances resulting in permanent pacemaker implantation (PPI) are common complications after transcatheter aortic valve implantation (TAVI). In some patients, there is delayed recovery of the conduction system post-procedure. This study aims to report the incidence and predictors of ventricular pacing (VP) rate≦1% at 1 year after TAVI.

This is a post-hoc sub-study of the LANDMARK multicentre trial, which randomized 768 patients in a 2:1:1 ratio to the Myval (n = 384) transcatheter heart valve (THV) series or contemporary THVs (Sapien [n = 192] and Evolut [n = 192] series) for the treatment of severe aortic stenosis. Overall, 122 (15.9%) patients underwent PPI within 30 days after TAVI, and 1-year pacemaker follow-up data were retrospectively collected in 99 patients. Pacemaker recovery (PMR) was defined as a VP rate ≦1% at follow-up.

PMR occurred in 18% (18/99) of patients. The PMR group was younger than the non-PMR group (78.6 ± 3.0 vs 81.1 ± 5.1 years, p = 0.045). Implantation depth under the non-coronary cusp did not differ between groups (5.7 ± 3.5 vs 5.8 ± 2.8 mm, p = 0.94). There were no significant differences in PMR rates based on THV type: Myval 25% (11/44), Sapien 19% (5/27), and Evolut 7% (2/28) (p = 0.16). In multivariable logistic regression, atrial fibrillation was associated with lower odds of PMR (odds ratio 0.09, 95% confidence interval 0.00-0.77, p = 0.02.

At 1 year, conduction system recovery (VP≦1%) was observed in 18% of patients who underwent PPI after TAVI, with no significant difference among the Myval, Sapien and Evolut series. Atrial fibrillation was associated with lower odds of recovery.

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, yet their underlying immune mechanisms are still incompletely understood. A central limitation of current frameworks is that they largely conceptualize immune responses at the level of individual cell types, without fully accounting for how spatial context shapes cellular behavior. Over the past decade, bulk and single-cell transcriptomic approaches have substantially expanded our understanding of immune heterogeneity in cardiovascular tissues, revealing diverse macrophage, lymphocyte, and stromal populations implicated in disease progression. However, these approaches inherently disrupt tissue architecture and therefore fail to capture how immune cells operate within structured microenvironments. Recent advances in spatial transcriptomics, multiplexed imaging, and single-cell multi-omics now enable the interrogation of gene expression within intact tissues, providing direct insight into the spatial organization of immune responses. These technologies reveal that cardiovascular inflammation and repair are not diffusely distributed processes but are instead organized into discrete, spatially constrained niches. Within these niches, cellular identity, anatomical positioning, and local signaling gradients converge to regulate key processes including inflammation, fibrosis, and vascular remodeling. Evidence from myocardial infarction, atherosclerosis, and vascular injury demonstrates that microenvironments such as inflammatory foci, fibrotic border zones, and lipid-rich plaque regions orchestrate dynamic interactions among macrophages, T cells, fibroblasts, and endothelial cells. Here, we synthesize recent advances in spatial immunomics to propose a conceptual shift from cell-centric to niche-centric models of cardiovascular disease, framing tissues as structured immune ecosystems. We discuss computational strategies that integrate single-cell sequencing, spatial transcriptomics, and imaging data to reconstruct cellular neighborhoods and infer intercellular communication networks, while highlighting current limitations in resolving causality from spatial correlations. Finally, we explore the translational potential of spatial immunomics, including the identification of spatially defined biomarkers, the development of niche-targeted therapeutic strategies, and the construction of predictive models linking tissue architecture to clinical outcomes.

Pediatric obesity is an emerging health issue worldwide resulting in an increased risk of cardiovascular diseases upon reaching adulthood. These risks are further exacerbated by socioeconomic and demographic factors. The systematic literature review seeks to comprehensively evaluate future cardiovascular complications arising from pediatric obesity, particularly focusing on hypertension, dyslipidemia, and metabolic disorders.

A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines of studies published between 2000 and 2024. Databases such as PubMed, Scopus, Web of Science, and Embase were accessed to retrieve relevant literature on childhood obesity and cardiovascular risk factors. A rigorous inclusion and exclusion protocol was employed with selected studies including children under 18 years with obesity and evaluating long-term cardiovascular risk in adulthood. Cross-sectional studies lacking follow-up, non-English studies, conference proceedings, and reviews were excluded.

Twenty-five longitudinal cohort studies were selected essentially revealing childhood obesity to be a highly influential factor of cardiovascular disease in adulthood. Pediatric obesity was highlighted to exert early-life cardiovascular changes such as arterial stiffness, endothelial dysfunction, and diabetes. These early life alterations underscore the necessity of early life interventions. Furthermore, socioeconomic status and demographics, especially lower-income populations where minorities and poor people are exposed to poor eating habits, lack of physical activity, and poor healthcare.

Pediatric obesity is strongly associated with future cardiovascular complications. Mitigating this risk requires early interventions and comprehensive prevention strategies. Future studies should focus on longitudinal studies to find sustainable solutions to lower childhood obesity-led cardiovascular complications.

Varicella is a common viral disease of childhood that is usually benign and self-limiting, but it can lead to rare complications, including streptococcal toxic shock syndrome (STSS) and Kawasaki disease (KD). The simultaneous or sequential occurrence of these 2 complications after varicella has been reported very rarely. A 2.5-year-old girl presented with fever, swelling, and neck erythema 5 days after varicella infection and a history of ibuprofen use. She was admitted with a diagnosis of cellulitis. Despite initial antibiotic therapy, the patient's clinical condition rapidly deteriorated, and she developed shock, thrombocytopenia, organ failure, and pleural effusion. The patient was transferred to the pediatric intensive care unit, intubated, and received Intravenous Immunoglobulin (IVIG). Blood culture revealed beta-hemolytic Streptococcus group A, confirming the diagnosis of STSS. After recovery and discharge from the hospital, the child presented again with fever and peeling fingertips and was admitted with suspicion of KD. Echocardiography revealed coronary artery ectasia, and she received IVIG again. The ectasia resolved on subsequent follow-up. This is one of the rarest cases of sequential occurrence of STSS and KD after varicella infection. Clinical attention to unusual complications of varicella, timely diagnosis, and appropriate treatment are essential in preventing severe and long-term outcomes in pediatric patients.

Norepinephrine (NE) in the peripheral nervous system plays crucial roles in regulating peripheral organs in health and disease. However, the spatiotemporal dynamics of sympathetic NE release and its underlying mechanisms remain poorly characterized due to technical challenges. Here, we developed and validated a Slice ElectroChemistry (SEC) method to record sympathetic NE release in heart slices with combined super-resolution and high sensitivity at 1 μm × 1 ms × 1 nM as in patch-clamp recordings. By using the SEC method, we revealed the increased NE release, impaired NE reuptake, increased releasable NE-vesicle pool, and impaired vesicle recycling of sympathetic nerves in the heart of transverse aortic constriction-induced heart failure (HF) mouse model, and defined the increased expression of Cav2.2 calcium channel as a central mechanism mediating the facilitation of NE release and thus the pathogenesis of HF, clarifying a longstanding puzzle about the kinetic changes of cardiac sympathetic NE release in HF. Beyond the heart, SEC enables NE release recording in other peripheral organs and human tissues, providing a robust toolset to investigate sympathetic NE dynamics across diverse pathophysiological conditions.

Cardiovascular diseases (CVDs) continue to be the world's leading cause of death, driven by intricate processes such as inflammation, endothelial dysfunction, oxidative stress, and apoptosis. MicroRNAs (miRNAs), small non-coding RNAs that influence mRNA translation, have become recognized as key regulators of these harmful mechanisms in conditions like heart attack, heart failure, and atherosclerosis. Because they remain stable in the bloodstream, miRNAs hold promise as convenient, minimally invasive biomarkers for early CVD detection, assessing risk, and forecasting outcomes. On the treatment side, targeting miRNAs offers a way to correct disrupted molecular pathways with precision, though moving this approach into clinical use presents hurdles such as refining delivery methods, avoiding unintended effects, and achieving specificity for certain tissues. Current studies emphasize progress in understanding how miRNAs contribute to CVD development and their emerging potential in personalized care. Yet, translating these findings into routine practice will require overcoming technical challenges and establishing consistent standards. This evolving area holds great potential to transform cardiovascular medicine through precise diagnostics and targeted therapies, potentially addressing current shortcomings in treatment effectiveness.

Stroke often results in upper limb impairment. Although many patients regain independent ambulation, upper limb function recovery remains incomplete, which limits activities of daily living (ADL) and reduces quality of life. Optimizing upper limb rehabilitation strategies continues to represent a major challenge in stroke care. This article presents a randomized controlled trial protocol investigating the effects of progressive WBVV training on upper limb function and corticospinal excitability in subacute stroke patients. Forty-eight subacute stroke patients are allocated to an experimental group (n = 24) and a control group (n = 24). The control group receives conventional rehabilitation training, while the experimental group receives additional progressive WBVV training. This intervention offers the advantages of easy implementation, low energy consumption, and favorable tolerability for subacute stroke patients. The WBVV protocol is gradually intensified over four weeks by adjusting frequency and amplitude. After four weeks of intervention, both groups showed significant improvements in upper limb function, ADL ability and neurophysiological indicators (p < 0.05). The experimental group achieved significantly better outcomes in Fugl-Meyer Assessment of Upper Extremity (FMA-UE), Wolf Motor Function Test (WMFT) and Modified Barthel Index (MBI) scores compared with the control group (p < 0.05). Both groups presented shortened motor evoked potential latency and increased peak-to-peak amplitude, but no significant between-group differences were found in neurophysiological measures (p > 0.05). These findings indicate that WBVV combined with conventional rehabilitation can effectively promote upper limb functional recovery and ADL performance in subacute stroke patients. Further research is required to verify its effects on corticospinal excitability.

This paper presents a computational bench-marking assessment of Ensemble Learning algorithms in the prediction of heart disease, combining different Machine Learning algorithms, such as hard voting, soft voting, and stacking, in a single framework. The evaluation was conducted using publicly available cardiovascular dataset obtained from the Kaggle repository (https://www.kaggle.com/datasets/sid321axn/heart-statlog-cleveland-hungary-final) comprising 1,190 instances and 11 clinical features. The process involves data preprocessing, which includes handling missing values, removing outliers, scaling variables and class balancing to ensure uniform input feature selection, based on Random Forest (RF), is used to eliminate unnecessary features. Among the evaluated models, the stacking ensemble classifier achieved the highest overall accuracy of 91.88% on the test dataset. Although additional metrics such as precision, recall and F1-score were computed for comparative analysis, the emphasis of this study remains on methodological benchmarking rather than clinical validation. Various base classifiers, including Decision Tree, Random Forest, AdaBoost, and XGBoost, are applied and tested independently. These models are then combined using ensemble techniques with hard voting, soft voting, and stacking. In stacking, Logistic Regression is used as the meta-model, which is trained on cross-validated predictions of the out-of-fold samples to avoid overfitting. Evaluations are carried out using accuracy as the primary criterion for comparison, so that individual classification systems and their combination strategies can be compared uniformly in the same preprocessing and validation environment. Though performance metrics are provided for comparative indications, the emphasis of the approach lies in the development and evaluation of strategies and not in their clinical assessment. This protocol makes it easy to compare ensemble machine learning algorithms on publicly available cardiovascular datasets and helps to make a systematic comparison of data preprocessing and ensemble configuration approaches.

Hepatic ischemia-reperfusion injury (IRI) is an important factor affecting the prognosis of liver transplantation patients. The role of Arrb2 in liver injury is unclear. Our study aimed to determine the role of Arrb2 in hepatic IRI and to identify its underlying mechanisms.

An analysis of clinical samples was conducted to assess the association between Arrb2 expression and the prognosis of liver transplantation. A 70% hepatic ischemia/reperfusion model in mice was established to verify the mechanism of Arrb2 in hepatocytes promoting M2 macrophage polarization in attenuating hepatic IRI by regulating 6-ketoLCA. A model of hypoxia/reoxygenation in vitro was established to investigate the molecular mechanism of 6-ketoLCA in promoting M2 macrophage polarization and pharmacological screening.

Arrb2 in hepatocytes has been shown to provide significant liver protection against hepatic IRI, primarily through promoting the polarization of liver macrophages to M2. Arrb2 remodels bile acids and upregulates 6-ketoLCA through Cyp7a1 in hepatocytes, promoting M2 polarization of macrophages, thereby alleviating hepatic IRI. Mechanistically, TGR5 plays a crucial role in promoting the induction of M2 polarization in macrophages by 6-ketoLCA. Pharmacological screening indicates that dutasteride enhances the activity of the Arrb2 promoter and upregulates Arrb2 expression in hepatocytes, thereby mitigating hepatic IRI.

Arrb2 in hepatocytes attenuates hepatic IRI by promoting macrophages toward the M2 phenotype through bile acid. Moreover, dutasteride, a selective agonist of Arrb2, emerges as a promising targeted therapeutic agent for the clinical management of liver injury.

Electrocardiogram (ECG) has been widely used in the diagnosis of cardiovascular disease (CVD). Current deep learning methods for CVD prediction using ECG often lack generalizability and interpretability, resulting in limited performance. Here, we have developed a self-supervised Electrocardiogram Large-scale Foundation Model (ECG-LFM) through pre-training over ten million 12-lead ECGs from multiple ECG datasets. To enhance ECG representation, ECG-LFM integrates contrastive learning with masked language modeling in a self-supervised manner, enabling the model to capture both global contextual information and fine-grained patterns within ECG signals. It was fine-tuned to predict eight types of CVDs and achieved an average area under the receiver operating characteristic curve (AUROC) of 0.930 from multiple datasets, which demonstrates improved performance compared to existing methods. The important ECG-LFM derived features (EDFs) are able to represent known CVD biomarkers, indicating the high interpretability of ECG-LFM. Applications of the EDFs in genome-wide association study identified 24 significant single nucleotide polymorphisms (SNPs) (P-value < 5×10^-8, LD r² < 0.01) associated with ECG, including 8 novel findings. The genetic causal effects of EDFs on the CVDs were evaluated by Mendelian randomization, indicating 2 CVDs and 4 EDFs having causal relationships. Overall, ECG-LFM provides accurate prediction for CVDs and novel genetic insights for ECG.