JACC: Clinical Electrophysiology

BackgroundSympathetic modulation via the stellate ganglia is increasingly recognized as a contributor to ventricular arrhythmogenesis after myocardial infarction. However, the mechanisms by which autonomic remodeling interacts with chronic infarct substrates to shape arrhythmic vulnerability remain incompletely understood. ObjectivesTo test the hypothesis that left- and right-sided stellate ganglion-mediated SNS modulation differentially reshapes ventricular arrhythmic vulnerability in chronic post-infarcted substrates, and that the RVI detects changes in vulnerability beyond conventional stimulation-based inducibility. MethodsFourteen patient-specific ventricular models with chronic post-infarcted remodeling were reconstructed from imaging data. A total of 336 simulations were performed under different combinations of stellate ganglion modulation, border zone remodeling, and fibroblast density. Arrhythmic vulnerability was quantified using 3D RVI mapping during paced rhythms and compared with conventional stimulation-based inducibility outcomes. ResultsStellate ganglion modulation induced marked, regionally heterogeneous changes in repolarization timing, resulting in lower and more negative RVI values in vulnerable regions. More negative RVI values reflect increased propensity for wavefront-waveback interaction and reentry initiation. Across the cohort, stellate modulation consistently decreased RVImin, even when inducibility outcomes remained unchanged. These findings indicate that SNS modulation can create a substrate more permissive to reentry independently of whether ventricular arrhythmia is triggered during programmed stimulation. ConclusionsStellate ganglion-mediated sympathetic modulation dynamically reshapes ventricular arrhythmic vulnerability in chronic post-infarcted substrates. RVI provides a spatially resolved, vulnerability-based metric that complements inducibility testing by revealing autonomic-substrate interactions underlying arrhythmogenesis Condensed AbstractSympathetic modulation via the stellate ganglia can alter ventricular repolarization and promote arrhythmogenesis after myocardial infarction, yet clinical responses remain heterogeneous. Using 14 patient-specific post-infarction ventricular models, we simulated left- and right-sided stellate modulation across combinations of border zone remodeling and fibrosis (336 simulations). Stellate modulation induced regionally heterogeneous repolarization shortening and reduced RVI values, even when programmed stimulation inducibility remained unchanged. These findings suggest that RVI captures substrate-level vulnerability beyond binary induction testing and may improve mechanistic assessment of autonomic-substrate interactions in chronic infarct substrates.

Cardiac arrhythmias are abnormal heart rhythms characterized by disordered electrical dynamics that impair cardiac function and pose a major global burden of morbidity and mortality. Early and accurate prediction of arrhythmic anomalies from physiological time series is crucial for effective intervention, yet remains challenging due to the nonlinear, nonstationary, and individualized nature of cardiac dynamics. Despite significant advances in machine learning-based arrhythmia detection, most existing methods operate as static classifiers on electrocardiographic signals and lack online prediction, patient-specific adaptation, and mechanistic interpretability. From a dynamical-systems perspective, arrhythmias represent qualitative regime transitions, often preceded by subtle, temporally extended deviations that are difficult to detect in real time. Here we introduce CASCADE (Chaotic Attractor Sensitivity for Cardiac Anomaly Detection), an online and personalized anomaly forecasting framework built on a special type of reservoir computing called Dynamical Systems Machine Learning (DynML). DynML employs ensembles of continuous-time nonlinear dynamical systems as chaotic reservoirs to reconstruct and forecast short-term cardiac dynamics on a beat-to-beat basis, training only a linear readout. This design enables efficient online adaptation without retraining the underlying dynamical model. Rather than relying on static beat-level classification, CASCADE identifies arrhythmic events as failures of short-term predictability, manifested as statistically significant deviations between predicted and observed dynamics relative to subject-specific baselines. Detection performance is governed by the intrinsic dynamical complexity of the reservoir, quantified by topological entropy. Reservoirs operating near critical entropy regimes optimally amplify subtle, temporally extended irregularities in heartbeat dynamics, rendering incipient arrhythmic signatures linearly separable at the readout level. Topological entropy thus serves both as a predictor of model performance and a principled control parameter for reservoir design. When evaluated on the MIT-BIH Arrhythmia dataset, CASCADE achieved consistently high F1 scores, precision, recall, and overall accuracy across diverse patient populations, demonstrating strong generalizability across clinical and real-world settings. By integrating chaotic reservoir computing, entropy-guided tuning, and online personalized forecasting, CASCADE reframes arrhythmia detection as a problem of dynamical regime transition rather than static classification. This perspective provides a scalable, interpretable, and computationally efficient framework for real-time cardiac monitoring and early-warning clinical decision support.

BackgroundPathogenic desmin (DES) variants have been implicated in early-onset atrial disease, yet the mechanisms by which desmin dysfunction alters atrial structure and function remain unclear. Desmin anchors the cytoskeleton to the nuclear envelope (NE) through the linker of nucleoskeleton and cytoskeleton (LINC) complex, suggesting that defects in this network may drive atrial cardiomyopathy. MethodsHuman desmin wild-type (WT) and the pathogenic variants p.S13F, p.N342D, and p.R454W were stably expressed in HL-1 atrial cardiomyocytes. Desmin organization, nuclear morphology, LINC-complex integrity (nesprin-3, lamin A/C), and DNA leakage, assessed by cyclic GMP-AMP synthase (cGAS), were analyzed by confocal microscopy. Action potential duration (APD) and calcium transients (CaT) were measured optically. Human myocardium samples from DES variant carriers were analyzed for validation. Data-independent acquisition (DIA) mass spectrometry profiled atrial proteomes from desmin-network (DN) and titin variant carriers and controls. The heat-shock proteins (HSPs) inducer geranylgeranylacetone (GGA) was evaluated for rescue effects. Resultsp.N342D caused severe filament-assembly defects with prominent perinuclear aggregates, whereas p.S13F showed mixed phenotypes with frequent perinuclear aggregates, and p.R454W largely preserved filamentous networks. p.N342D and p.S13F induced nuclear deformation with disrupted nesprin-3 and lamin A/C distribution. In p.N342D and p.S13F, desmin aggregates drove focal lamin A/C accumulation, nuclear envelope (NE) rupture, DNA leakage, and increased cGAS activation. DES variants significantly shortened APD20/90 and reduced CaT amplitude, indicating pro-arrhythmic electrical remodeling. Atrial proteomics revealed a DN-specific signature enriched for cytoskeletal, NE, intermediate filament, and chaperone pathways, consistent with the structural injury observed in vitro. GGA prevented desmin aggregation and nuclear morphology changes, and mitigated APD shortening in p.N342D-expressing cardiomyocytes. Human myocardium from DES variant carriers showed concordant desmin aggregation and polarized lamin A/C distribution. ConclusionsDES variants induce a desmin-dependent atrial cardiomyopathy characterized by cytoskeletal disorganization, disruption of LINC-complex, NE rupture with DNA leakage, and pro-arrhythmic electrophysiological remodeling. These findings provide mechanistic insight into how DN variants promote atrial disease. HSPs induction by GGA partially restores structural and functional integrity, identifying a potential therapeutic approach for desmin-related atrial cardiomyopathy. Clinical perspectiveWhat is new? O_LIPathogenic DES variants induce a previously unrecognized atrial cardiomyopathy characterized by desmin aggregation, and desmin-network (DN) collapse, disruption of the linker of nucleoskeleton and cytoskeleton (LINC) complex, and nuclear envelope rupture with DNA leakage. C_LIO_LIVariants that lead to desmin aggregation (e.g., p.N342D) cause focal lamin A/C polarization, cyclic GMP-AMP synthase (cGAS) activation, and structural injury at the nuclear envelope. C_LIO_LIDES variants produce pro-arrhythmic electrical remodeling, including action potential duration shortening and impaired Ca{superscript 2} handling in HL-1 atrial cardiomyocytes. C_LIO_LIAtrial proteomics from DN variant carriers reveals enrichment of pathways related to cytoskeletal, nuclear envelope, intermediate filament, and chaperone, supporting a desmin-dependent remodeling program. C_LIO_LIThe heat-shock protein inducer geranylgeranylacetone (GGA) prevents desmin aggregation, restores nuclear morphology, and mitigates electrical and Ca{superscript 2} handling remodeling. C_LI What are the clinical implications? O_LIThese findings establish DN dysfunction as a distinct cause of atrial cardiomyopathy, providing a mechanistic basis for the association between pathogenic DES variants and atrial arrhythmias, including atrial fibrillation. C_LIO_LINuclear envelope rupture and cytosolic DNA leakage represent new mechanistic evidence which links cytoskeletal injury and atrial arrhythmogenesis. C_LIO_LIIdentifying structural vulnerability in DES variant carriers fosters awareness of genetic counseling for atrial disease, enabling early detection and risk stratification. C_LIO_LIThe protective effects of GGA suggest that restoring proteostasis may be a therapeutic strategy for desmin-related atrial cardiomyopathy and potentially other genetic atrial diseases. C_LI Novelty and significance statementO_ST_ABSNoveltyC_ST_ABSThis study identifies a desmin-dependent atrial cardiomyopathy driven by cytoskeletal aggregation, LINC-complex disruption, and nuclear envelope rupture with DNA leakage. We show that pathogenic DES variants are associated with pro-arrhythmic molecular remodeling and that human atrial proteomics confirm nuclear envelope and cytoskeletal injury as core features. Importantly, the heat-shock protein-inducer GGA rescues structural, molecular, and electrophysiological defects, revealing a modifiable pathway in desmin-mediated atrial disease. SignificanceThese findings provide the first integrated mechanistic explanation linking DN variants to atrial cardiomyopathy. By uncovering nuclear envelope rupture and cGAS activation as key drivers of atrial cardiomyopathy, this work expands the molecular framework for inherited atrial disease and highlights proteostasis enhancement as a potential therapeutic strategy for patients carrying DES and related cytoskeletal variants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=166 HEIGHT=200 SRC="FIGDIR/small/26348559v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1fb0bfborg.highwire.dtl.DTLVardef@cfc00borg.highwire.dtl.DTLVardef@1493578org.highwire.dtl.DTLVardef@1556b61_HPS_FORMAT_FIGEXP M_FIG C_FIG

Class imbalance in clinical electrocardiogram (ECG) datasets limits the diagnostic sensitivity of automated arrhythmia classifiers, particularly for rare but clinically significant beat types. We propose a three-stage hybrid generative pipeline that combines a spectral-guided conditional Variational Autoencoder (cVAE), a class-conditional latent Denoising Diffusion Probabilistic Model (DDPM), and a Quantum Latent Refinement (QLR) module built on parameterized quantum circuits to augment minority arrhythmia classes in the MIT-BIH Arrhythmia Database. The QLR module applies a bounded residual correction guided by Maximum Mean Discrepancy minimization to align synthetic latent distributions with real class-specific latent banks. A lightweight 1D MobileNetV2 classifier evaluated over five independent random seeds and four augmentation ratios serves as the downstream benchmark. Our findings establish latent diffusion augmentation as an effective strategy for imbalanced ECG classification and motivate further investigation of quantum-classical hybrid methods in cardiac diagnostics.

Aims: Responses to remote pulmonary artery pressure data vary across programs. We evaluated SMART-HF, a structured pulmonary artery diastolic pressure (PAD)-guided workflow, in a community heart failure cohort. Methods: We retrospectively analysed adults with heart failure and an implanted pulmonary artery pressure sensor managed with SMART-HF. Pulmonary artery diastolic pressure (PAD) was calculated from prespecified 14-day windows at baseline, 90 days, and 6 months. Two hemodynamic management performance indices (HMPI) were prespecified: the 6-Month Delta HMPI (PAD reduction >2 mmHg from baseline) and the 90-Day Target HMPI (PAD [&le;]20 mmHg at 90 days). Exploratory analyses evaluated patients with baseline PAD >20 mmHg. Results: Of 37 patients, 36 had paired 90-day and 29 had paired 6-month windows. Mean PAD decreased from 18.3 +/- 7.0 to 16.1 +/- 6.3 mmHg at 90 days and from 18.8 +/- 6.8 to 15.5 +/- 5.8 mmHg at 6 months (both P < 0.001). The 90-Day Target HMPI was achieved in 26/36 (72.2%) and the 6-Month Delta HMPI in 19/29 (65.5%) [95% CI 45.7-82.1]. In the exploratory subgroup (baseline PAD >20 mmHg), mean PAD changes were -2.9 +/- 3.6 mmHg at 90 days (n = 19; P = 0.002) and -4.9 +/- 4.9 mmHg at 6 months (n = 15; P = 0.002). Conclusions: SMART-HF was associated with improved ambulatory pulmonary artery diastolic pressure control at 90 days and 6 months. Exploratory subgroup findings support further evaluation in patients with elevated baseline pulmonary artery diastolic pressure.

Background: Late gadolinium enhancement (LGE) quantification by cardiovascular magnetic resonance is central to risk stratification in hypertrophic cardiomyopathy (HCM), yet conventional techniques require contour tracing and region-of-interest (ROI) placement, which may reduce reproducibility and increase analysis time. We developed a novel visual standardized approach, the Visual Standardized Quantification of LGE (VISTAQ), that does not require myocardial contouring, arbitrary ROI positioning, or dedicated post-processing software. Methods: In this multicenter, multivendor retrospective study, LGE images from 400 patients (100 prior myocardial infarction, 250 HCM, 50 other non-ischemic heart diseases) were analyzed. VISTAQ subdivides each myocardial segment into transmural mini-segments and classifies LGE visually using predefined criteria, expressing global LGE burden as the percentage of positive mini-segments. Reproducibility was assessed in 250 patients across different observer expertise levels using intraclass correlation coefficients (ICC) and Bland?Altman analysis. In 100 HCM patients, VISTAQ was compared with conventional methods (mean+2SD, +5SD, +6SD, FWHM, visual thresholding). Prognostic performance was evaluated in 250 HCM patients over a median 5-year follow-up. Results: VISTAQ demonstrated excellent intra- and inter-observer reproducibility (ICC up to 0.98 and 0.97, respectively), consistent across disease subtypes. Compared with conventional techniques, VISTAQ showed similar ICC to FWHM but significantly lower net and absolute inter-observer differences (median absolute difference 1.3%). Mean+2SD markedly overestimated LGE, whereas mean+6SD slightly underestimated LGE compared with VISTAQ, mean+5SD, FWHM, and visual thresholding. Analysis time was substantially shorter with VISTAQ (median 105 vs. 375 seconds, p<0.0001). During follow-up, 21 hard cardiac events occurred in HCM population. An LGE threshold >10% predicted events with higher accuracy using VISTAQ (AUC 0.90; sensitivity 85%; specificity 94%) compared with mean+6SD (AUC 0.75; sensitivity 57%; specificity 93%). Conclusions: VISTAQ provides highly reproducible, time-efficient LGE quantification without dedicated software and demonstrates non-inferior prognostic discrimination in HCM compared with conventional threshold-based techniques.

Mitral valve prolapse (MVP) is the most common cause of primary mitral regurgitation and is associated with the development of malignant arrhythmias, often in the context of myocardial fibrosis. The genetic architecture of MVP, and whether there are genetic factors explaining why only some individuals with MVP have adverse outcomes, remains poorly understood. We performed a meta-analysis of genome-wide association studies (GWAS) for MVP encompassing 21,517 cases among a total sample size of over 2.2 million individuals. We discovered 89 genomic risk loci for MVP, of which 72 were novel findings. Prioritization of causal genes and pathways using epigenetic and transcriptomic data from mitral valve and extra-valvular tissues replicated known gene associations to MVP including those involved in TGF-{beta} signaling and extracellular matrix biology, but additionally emphasized a role in MVP for biological pathways relevant to cardiomyocyte biology. Accordingly, we identified several MVP risk loci with pleiotropy to cardiomyopathies, especially hypertrophic cardiomyopathy, and demonstrated a significant genetic correlation between MVP and hypertrophic cardiomyopathy. Finally, we interrogated snRNA-seq data in human papillary muscle tissue from two individuals with severe MVP, characterizing genes associated with both risk of papillary muscle fibrosis and MVP.

Background and Aims: Acute myocarditis (AM) is a T cell-mediated myocardial disease with clinical manifestations ranging from mild chest pain to cardiogenic shock. Reliable biomarkers to stratify patients and guide therapy are currently lacking. In particular, the extent of the dysregulation of inflammatory pathways, and the impact on myocardial dysfunction, remain elusive. Methods: Serum analyses were performed in prospectively recruited AM patients (n = 103) from two independent cohorts. Multimodal data integration combining profiling of cytokine and chemokine dysregulation with clinical biomarkers was used to define clinical phenotypes with distinct inflammatory signatures. Machine-learning and regression models were applied to determine biomarkers that indicate clinical severity. Results: Immuno-proteomic profiling revealed conserved inflammatory patterns across AM cohorts, dominated by T cell-related cytokines and chemokines. In addition, AM patients showed dysregulation of fibroblast-derived cytokines, including hepatocyte growth factor (HGF), bone morphogenic protein 4 (BMP4) and the BMP4 inhibitors Gremlin-1 (GREM1) and Gremlin-2 (GREM2). Data integration and unsupervised clustering revealed two immuno-clinical phenotypes, linking T cell activation and fibroblast dysregulation to disease severity. Machine learning-based analysis identified CXCL10, GREM2 and LVEF as critical parameters for stratifying disease severity. Conclusions: These findings highlight a systemic T cell activation signature as diagnostic hallmark of AM. In addition, dysregulation of fibroblast-derived tissue cytokines serves as an indicator for distinct immuno-clinical phenotypes in myocardial inflammatory disease. Thus, the clinically relevant link between T cell-driven immune activation, myocardial inflammation and fibroblast-driven remodelling provides a versatile set of parameters to identify severe manifestations of AM.

Purpose: Spatial distribution of coronary artery calcium (CAC) may provide additional prognostic value in patients undergoing SPECT and PET myocardial perfusion imaging (MPI). We aimed to automatically identify CAC in proximal segments from attenuation correction CT (CTAC) scans using artificial intelligence (AI) and to evaluate prognostic significance in two large international multicenter registries. Methods: From hybrid MPI/CT imaging (N=43,099) across 15 sites, we included 4,552 most relevant patients with 1) no prior coronary artery disease; 2) AI-derived mild CAC scores (1-99); and 3) normal perfusion (stress total perfusion deficit <5%). The independent associations between AI-identified proximal CAC and major adverse cardiovascular events (MACE) and all-cause mortality (ACM) were evaluated using multivariable Cox regression, likelihood ratio test (LRT), and continuous net reclassification index (NRI). Results: Among the patients with mild CAC and normal perfusion (mean age 65{+/-}12 years, 51% male), 1,730 (38%) had proximal CAC. Over 3.6 (inter-quartile interval 2.1, 5.2) years follow up, 599 (13%) and 444 (10%) patients had MACE or ACM, respectively. Proximal CAC was associated with an increased risk of MACE (adjusted hazard ratio [HR] 1.24, 95% CI 1.03-1.48, P=0.02) and ACM (adjusted HR 1.25, 95% CI 1.01-1.53, P=0.04) after the adjustment of CAC score and density, clinical risk factors, and perfusion deficit. Proximal CAC improved the risk stratification of MACE (LRT P=0.02; NRI 12%) and ACM (LRT P=0.04; NRI 12%). Conclusion: In patients with mild CAC and normal perfusion, AI detection of proximal CAC identified a higher-risk group for adverse outcomes, highlighting its prognostic utility.

ObjectiveTo explore the interpretation of unexpected results from a randomized controlled trial (RCT). Study Design and SettingAdjunctive frequentist (power and type{square}M error) and Bayesian analyses were performed on a recently published RCT reporting a statistically significant relative risk reduction (p <0.01) for caffeinated coffee drinkers compared with abstinence on atrial fibrillation (AF) recurrence. Individual patient data for the Bayesian survival models were reconstructed from the RCT published material and priors informed by the RCT power calculations. ResultsThe original RCT design had limited power for realistic effect sizes, increasing susceptibility to type{square}M (magnitude) error. Bayesian analyses also tempered the benefit for caffeinated coffee implied by standard statistical analysis resulting in only modest probabilities of clinically meaningful risk reductions (e.g., hazard ratio < 0.9 of 88% or a risk difference > 2% of 82%). ConclusionsSupplemental frequentist and Bayesian approaches can provide robustness checks for unexpected RCT findings, providing contextualization, clarifying distinctions between statistical and clinical significance, and guiding replication needs. HighlightsO_LIRandomized controlled trial (RCT) results may be unexpected and challenge prior beliefs C_LIO_LISupplemental frequentist and Bayesian analyses can clarify interpretation of surprising findings C_LIO_LIPower and type{square}M error assessments help evaluate design adequacy for realistic effects C_LIO_LIBayesian posterior probabilities provide additional nuanced insights into contextulaization and clinical significance C_LI

Modelling of hemodynamics in the circle of Willis (CoW) depends on vascular segmentation, which may vary based on imaging modality. Computed tomography angiography (CTA) is commonly used in clinic but involves radiation and injection of contrast agents, whereas magnetic resonance angiography (MRA) offers a non-invasive alternative. This study aims to compare CoW morphology and modelled cerebral perfusion pressure of CTA and MRA segmentations, validating if MRA can replace CTA in modelling workflows. CTA and time-of-flight MRA (TOF-MRA) of the CoW was performed in 19 patients undergoing elective aortic arch surgery (67{+/-}7 years, 8 women). The CoW was semi-automatically segmented based on signal intensity thresholding. A TOF-MRA threshold was optimized against the CTA segmentation, using the CTA as reference standard. Computational fluid dynamics (CFD) modelling with boundary conditions based on subject-specific flow rates from 4D flow MRI simulated cerebral perfusion pressure in the segmented geometries. A baseline simulation and a unilateral brain inflow simulation, i.e., occlusion of a carotid, were carried out. Linear mixed models indicated there was no effect of choice of modality on either average arterial lumen area (CTA - TOF-MRA: -0.2{+/-}1.3 mm2; p=0.762) or baseline pressure drops (0.2{+/-}1.9 mmHg; p=0.257). In the unilateral inflow simulation, we found no difference in pressure laterality (-6.6{+/-}18.4 mmHg; p=0.185) or collateral flow rate (10{+/-}46 ml/min; p=0.421). TOF-MRA geometries can with signal intensity thresholding be matched to produce similar morphology and modelled cerebral perfusion pressure to CTA geometries. The modelled pressure drops over the collateral arteries were sensitive to the segmentation regardless of modality.

BACKGROUND: Guidelines recommend aortic valve replacement (AVR) in patients with severe aortic regurgitation (AR) based on progressive changes in left ventricular (LV) function or size. We aimed to reassess the clinical relevance of current guideline recommendations pertaining to traditional echocardiographic measurements in routine practice. METHODS: Retrospective analysis of patients with severe AR who underwent serial echocardiographic follow-up over at least 18 months. The composite outcome was symptom-driven AVR, acute heart failure hospitalization, or death. We used a joint modelling approach to handle within-subject correlation and censoring. RESULTS: The cohort consisted of 140 patients, with a median follow?up of 93 months (interquartile range 58?130). LV end-systolic (LVESD) and fractional shortening (FS) showed a small but statistically significant longitudinal trend, while LVEDD did not. Changes in all three parameters in parallel joint models adjusted for age and gender were consistently associated with increased risk of the composite event. Each 1?mm increase in LVESD and LVEDD was associated with a 6% and 5% increase in risk, respectively; each 1% decrease in FS corresponded to a 12% increase in risk. Only 8 (5.7%) of patients were predicted to exceed the guideline-recommended LVEDD threshold of 65 mm over 10 years. Age at onset was also a significant risk factor, with each decade increasing risk by 65% for each of the three parallel joint models. CONCLUSIONS: LV parameters show modest changes over time, despite holding strong prognostic value in patients with severe AR. LVEDD, while associated with overall risk, does not predictably or significantly dilate over time in most patients. AVR decisions should be based on comprehensive clinical and volumetric assessment rather than waiting for simple linear progression to guideline cutoffs.

Background | Angina with nonobstructive coronary arteries (ANOCA) is a heterogeneous condition encompassing distinct endotypes representing different underlying pathophysiological mechanisms. Endothelial dysfunction is considered a central hallmark of ANOCA. However, studying patient-derived endothelial cells (ECs) remains challenging due to the limited availability of disease-specific endothelial samples. We therefore aimed to assess the feasibility of isolating and culturing ECs from catheterization material obtained during routine coronary function testing in ANOCA patients. Methods | Catheterization material was collected from 79 ANOCA patients (84% female, age 58{+/-}10 years) undergoing coronary function testing. ECs were isolated, expanded and characterized using immunostaining, flow cytometry, gene expression profiling and functional assays. Results | EC isolation was successful in 43% of cases and resulted in 34 primary EC cultures that were expanded up to passage 10. Isolation success was independent of clinical or procedural characteristics. Isolated cells exhibited typical EC morphology and expressed EC markers confirmed by immunostaining, flow cytometry and gene expression analyses. EC marker gene expression remained largely stable over passages. However, stress- and defense-related gene expression programs increased over time, while proliferation-related processes decreased. Functional assays demonstrated that the coronary catheterization-derived ECs showed typical properties of wound healing, angiogenesis, activation responses upon stimuli and monocyte adhesion. Conclusions | This study demonstrates the feasibility of isolating and expanding ECs directly from catheterization material collected during routine coronary function testing in ANOCA patients. These patient-derived ECs retain characteristic endothelial features and functionality. This approach offers primary EC cultures to study the mechanisms underlying endothelial dysfunction in ANOCA.

Abstract Background: ST-elevation myocardial infarction (STEMI) is reported to be a leading cause of mortality worldwide. While cardiac troponins are the gold standard for myocardial injury detection but creatine kinase-MB (CK-MB) and total creatine phosphokinase (CPK) retain prognostic use in resource-limited settings. Objective: To evaluate the prognostic significance of admission CK-MB and CPK levels in STEMI patients and to assess their association with hematological parameters for integrated risk stratification. Methods: This cross-sectional study enrolled 15 consecutive STEMI patients from the Punjab Institute of Cardiology, Lahore, during January 2024. Comprehensive laboratory analysis including cardiac biomarkers (CK-MB, CPK, troponin-I, LDH), complete blood count, renal function, serum electrolytes, and metabolic parameters, was performed on admission. Pearson correlation and comparative statistical analyses were also conducted to assess the relationships between cardiac biomarkers and hematological indices. Results: The cohort includes 15 patients (mean age 50.1 +/- 12.2 years; 73.3% male). Cardiac biomarker elevation was prevalent: CK-MB was elevated in 12/15 (80%), CPK was elevated in 12/15 (80%), with concordant elevation in 11/15 (73.3%), which indicates extensive myocardial necrosis. Troponin-I showed the highest elevation rate at 13/15 (86.7%). Hematological abnormalities included anemia (60%), WBC elevation (53.3%), and RBC reduction (40%). Random glucose averaged 150.80 +/- 63.55 mg/dL, with 66.7% highlighted the hyperglycemia. Remarkably, electrolyte balance was preserved in all of the patients (0% sodium, potassium, and bicarbonate abnormalities), indicating maintained homeostasis. Pearson correlation analysis revealed a significant correlation between CK-MB and CPK (r = 0.615, p = 0.0126), while correlations between cardiac biomarkers and hematological parameters were weak (p > 0.05). Risk stratification identified 53.3% of patients as high-risk who required intensive management. Conclusions: CK-MB and CPK demonstrate significant concordance and retain prognostic value in STEMI patients, particularly in resource-limited settings where troponin access may be constrained. While troponin-I remains the most sensitive biomarker, combined assessment of conventional cardiac enzymes supports reliable evaluation of myocardial injury. Hematological parameters reflect systemic response but show limited correlation with cardiac biomarkers.

Background: Deep brain stimulation (DBS) is a treatment option for Parkinson disease (PD). However, the effect of DBS on the arterial pressure (AP) remains unexplored. We aimed to develop an artificial baroreflex system for treating orthostatic hypotension (OH) due to central baroreflex failure in patients with PD. To achieve this, we developed an appropriate algorithm after estimating the dynamic responses of the AP to DBS using a white noise system identification method. Methods: We randomly performed DBS while measuring the AP tonometrically in 3 trials involving 3 patients with PD treated with DBS. We calculated the frequency response of the AP to the DBS using a fast Fourier transform algorithm. Finally, the feedback correction factors were determined via numerical simulation. Results: The frequency responses of the systolic AP to random DBS were identifiable in all 3 trials, and the steady state gain was 8.24 mmHg/STM. Based on these results, the proportional correction factor was set to 0.12, and the integral correction factor was set to 0.018. The computer simulation revealed that the system could quickly and effectively attenuate a sudden AP drop induced by external disturbances such as head-up tilting. Conclusion: An artificial baroreflex system with DBS may be a novel therapeutic approach for OH caused by central baroreflex failure.

BackgroundTimely cancer diagnosis in children and adolescents is critical to improving outcomes, yet substantial variation in diagnostic intervals persists across cancer types and care settings. We aimed to quantify time to diagnosis and assess variations by patient, demographic, and system-level factors. MethodsWe conducted a retrospective population-based study of children and adolescents aged 0-19 years diagnosed with one of 12 common cancers between 2010 and 2022 in Quebec, Canada. The diagnostic interval was defined as the time from first cancer-related healthcare encounter to diagnosis. We calculated medians and interquartile ranges (IQR) overall and by cancer type and used multivariable quantile regression to identify factors associated with time to diagnosis at the 25th, 50th, and 75th percentiles. ResultsAmong 2,927 individuals with cancer, diagnostic intervals varied by cancer type and age. Median intervals were longest for carcinomas (100 days; IQR 33-192) and shortest for leukemias (8 days; IQR 3-44). Compared with children living in Montreal, living in regional areas and other large urban centres was associated with longer 50th and 75th percentiles of time to diagnosis for hepatic and central nervous system (CNS) tumours. Diagnostic intervals were shorter in the post-pandemic period (2020-2022) across several cancer sites, with CNS tumours showing reductions across all quantiles. InterpretationDiagnostic timeliness differed by cancer type, age, and rurality, but not by sex, material, or social deprivation. The shorter diagnostic intervals observed in the post-pandemic period suggest that pandemic-related changes in care pathways may have expedited diagnosis for some cancers.

ObjectivesWe determined the frequency of sexually transmitted infection (STI) testing among people accessing sexual health services (SHS) in England. MethodsWe assessed STI testing frequency in face-to-face and online SHSs in England using data from the GUMCAD STI surveillance system. We quantified different combinations of tests (e.g. single chlamydia test or full STI screen), number of tests completed in 2024 and test positivity by sociodemographic and behavioural characteristics, as well as clinical setting and outcomes. ResultsOverall, there were 2,222,028 attendances at SHS in England in 2024 that involved tests for chlamydia, gonorrhoea, syphilis and/or HIV. Most of these attendances involved tests for all four of these STIs. Most people accessing SHS in England tested once (80.1%), and a small minority (1.9%) tested at least quarterly (4+ times). Some groups had a comparably larger proportion of quarterly testers; these included gay, bisexual, and other men who have sex with men (GBMSM) (6.7%), London residents (3.6%), online testers (2.5%), people using HIV-PrEP (13%), and people with 5+ partners in the previous 3 months (10.6%). Only 10.5% of GBMSM reporting higher-risk sexual behaviours tested quarterly despite recommendations for quarterly testing in this group. ConclusionsThe majority of those who tested for STIs in England in 2024 only tested once. The minority who tested at least quarterly had a higher proportion of GBMSM, people using HIV-PrEP, London residents and people reporting higher risk behaviours. Quarterly testing often appears to be aligned with current testing recommendations in England; however, we also observed that only a low proportion of behaviourally high-risk GBMSM and HIV-PrEP users are meeting these recommendations. It is important to acknowledge groups with lower or higher testing frequency when developing interventions and updating guidelines related to STI testing. WHAT IS ALREADY KNOWN ON THIS TOPICThe effectiveness of asymptomatic testing for chlamydia and gonorrhoea in gay, bisexual and other men who have sex with men (GBMSM), and the potential impact of the consequent increased antibiotic use on rising antimicrobial resistance and individual harm has recently been questioned. Testing and treatment remains a key pillar of STI prevention and management; despite this, there is limited evidence of STI testing frequency within sexual services (SHS) on a national level. WHAT THIS STUDY ADDSThis analysis shows that the majority of people attending SHSs in England in 2024 tested once, and only a small proportion of behaviourally high-risk people tested frequently. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYAwareness of groups that are behaviourally high risk but testing infrequently is important to guide interventions and messaging regarding STI testing. The low levels of frequent testing, even among those who would be recommended quarterly testing under UK guidelines, provides important context for wider discussion around asymptomatic STI screening.

Wearable-derived physiological features have been associated with disease risk, but most current studies focus on single conditions, limiting understanding of cross-disease patterns. This study adopts a trans-diagnostic approach to examine whether wearable data capture shared and condition-specific physiological signatures across multiple chronic conditions spanning physical and mental health, and then evaluates the utility of these features for disease classification. A total of 9,301 patients with at least 21 days of consecutive FitBit data from the All of Us Controlled Tier Dataset version 8 were analyzed. Disease subcohorts included cardiovascular disease (CVD), diabetes, obstructive sleep apnea (OSA), major depressive disorder (MDD), anxiety, bipolar disorder, and attention-deficit/ hyperactivity disorder (ADHD), chosen based on prevalence and relevance. Logistic regression and XGBoost models were fitted for each disease subcohort versus the control cohort. We found that compared to using just baseline demographic and lifestyle features, incorporating wearable-derived features enabled improved classification performance in all subcohorts for both models, except for ADHD where improvement was mainly observed for ROC-AUC in logistic regression model likely due to the smaller sample size in ADHD subcohort. The largest performance gains were observed in MDD (increase in ROC-AUC of 0.077 for Logistic regression, 0.071 for XGBoost; p < 0.001) and anxiety (increase in ROC-AUC of 0.077 for logistic regression, 0.108 for XGBoost; p < 0.001). This study provides one of the first comprehensive transdiagnostic evaluations of wearable-derived features for disease classification, highlighting their potential to enhance risk stratification in the real-world setting as a practical complement to clinical assessments and providing a foundation to explore more fine-grained wearable data. Author summaryWearable devices such as fitness trackers and smartwatches are becoming increasingly popular and affordable, providing continuous measurements of heart rate, physical activity, and sleep. Alongside the growing digitization of health records, this creates new opportunities for large-scale, real-world health studies. In this study, we analyzed wearable-derived physiological patterns across a range of chronic conditions spanning both physical and mental health to better understand how these signals relate to disease risk. We found that incorporating wearable-derived heart rate, activity and sleep features improved disease risk classification across several conditions, with particularly strong gains for major depressive disorder and anxiety. By examining how individual features contributed to model predictions, we also identified meaningful associations between physiological signals and disease risk. For example, both duration and day-to-day variation of deep and rapid eye movement (REM) sleep were associated with increased risk in certain conditions. Our study supports the development of real-time, automated tools to assess disease risk alongside clinical care.

Childhood socioeconomic position (SEP) can have lifelong effects on health. Many studies have used adult height as a surrogate marker for early-life conditions. In this study, we derived the non-genetic component of height, calculated as the residual from sex-specific standardized height regressed on genetically predicted height, as a surrogate for childhood SEP, using data from the Hispanic Community Healthy Study/Study of Latinos (2008-2011). A positive residual would indicate favorable early-life conditions promoting growth, while a negative residual indicates early-life adversity that may stunt the development. The height residual was associated with early-life variables such as parental education, year of birth, US nativity and age at first migration to the US (50 states/DC), supporting the validity of height residual as a surrogate for early-life conditions. Furthermore, a height residual was positively associated with better cardiovascular health (CVH) and cognitive function among middle-aged and older adults. Interestingly, among <35 years old, the height residual was negatively associated with the "Lifes Essential 8" clinical CVH scores. These results suggest the non-genetic component of height as a surrogate for childhood environment, with predictive value for CVH and cognitive function.

BackgroundAccelerometer-derived behavioral phenotype captures multidimensional aspects of human behavior extending well beyond physical activity, encompassing light exposure, step counts, physical activity patterns, sleep, and circadian rhythms. Whether these five domains constitute a unified behavioral architecture underlying cancer risk and whether circadian organization and light exposure confer incremental predictive value beyond movement volume alone remains to be comprehensively established. MethodsWe conducted an accelerometer-wide association study (AWAS) encompassing the complete accelerometer-derived behavioral exposome across five behavioral domains in UK Biobank participants with valid wrist accelerometry data. Incident solid cancers were designated as the primary endpoint, with prespecified site-specific solid cancers and hematological malignancy as secondary outcomes. Cox proportional hazards models with age as the timescale were used. The minimal covariate set served as the primary reporting tier, followed by sensitivity analyses additionally adjusting for adiposity/metabolic factors, independent activity patterns, shift work history, and accelerometry measurement quality. Nominal statistical significance was defined as two-sided P < 0.05 ResultsAmong 89,080 participants, 6,598 incident solid cancer events were observed over a median follow-up of 8.39 years. In the minimally adjusted model, the pan-solid-tumor association atlas was dominated by signals from activity volume, inactivity fragmentation, and circadian rhythm. Higher overall acceleration (HR per SD: 0.91, 95% CI: 0.89-0.94) and higher daily step counts (HR: 0.93, 95% CI: 0.90-0.95) were independently associated with reduced solid cancer risk, while inactivity fragmentation metrics were consistently linked to higher risk. Notably, circadian rhythms, most prominently cosinor mesor (Midline Estimating Statistic of Rhythm under cosinor model), emerged as leading inverse risk signals, underscoring the independent contribution of circadian behavioral architecture. Site-specific analyses revealed pronounced heterogeneity across tumor sites. Lung cancer exhibited a robust inverse activity-risk gradient, while breast cancer showed reproducible associations with MVPA. Most strikingly, nocturnal light exposure demonstrated a tumor-site-specific association confined to pancreatic cancer, a signal absent across all other sites examined. Associations for uterine cancer were predominantly inactivity-related and substantially attenuated following adjustment for adiposity and metabolic factors. ConclusionsAcross five accelerometer-derived behavioral domains, solid cancers as a whole were most consistently associated with a high-movement, low-fragmentation, and circadian-coherent behavioral profile. While site-specific heterogeneity exists, the broad cancer risk landscape is dominated by movement volume, inactivity fragmentation, and circadian rhythmicity. Light exposure, although more localized in its contribution, demonstrates a potentially novel and specific association with pancreatic cancer risk. These findings support a five-domain behavioral exposome framework for cancer epidemiology and, importantly, position circadian rhythm integrity and nocturnal light exposure as critically understudied dimensions warranting dedicated mechanistic investigation.