Background and purpose: The real-world safety and performance of the Micra VR leadless pacemaker have been well-established by previous clinical trials. However, there is little clinical data from these studies specific to patients in China. The purpose of this study was to characterize the safety and performance of the Micra VR leadless pacemaker in a Chinese cohort.Methods: The primary endpoints of the Micra Acute Performance Greater China (MAP China) study were acute (<= 30 days post-implant) and 12-month major complication rate following implantation of the Micra VR leadless pacemaker. Electrical performance was characterized from the time of implant through 12-month follow-up. Patient baseline and procedural characteristics and major complications were compared to the China pre-market and global Post-Approval Registry studies.Results: A total of 100 patients were enrolled and successfully implanted with the Micra VR system between June 2021 and October 2022 across 11 sites in mainland China. Average follow-up was 11.1 +/- 4.1 months. One major complication was reported in 1 patient within 30 days from the time of implant for an acute and 12-month complication rate of 1.0% (95% confidence interval: 0.025%-5.446%). Risk of major complications was in line with the China pre-market and global Post-Approval Registry studies.Conclusion: The Micra VR leadless pacemaker had high implant success and low major complication rates in patients from China, in agreement with the China pre-market and global Post-Approval Registry studies.

Importance Left bundle-branch pacing (LBBP) has been proposed as an alternative to biventricular pacing (BiVP) for patients with heart failure with left bundle-branch block (LBBB). However, robust clinical evidence from randomized clinical trials is lacking. Objective To evaluate the long-term clinical outcomes of LBBP and BiVP. Design, Setting, and Participants This multicenter, prospective, randomized clinical trial enrolled 200 patients at 6 centers in China with a left ventricular ejection fraction (LVEF) of 35% or less and LBBB from October 2020 to March 2022. This study was took place from October 2020 to September 2024. These data were analyzed September 2024 to December 2024. Interventions Patients were randomly assigned in a 1:1 ratio to receive either LBBP or BiVP. Main Outcomes and Measures The primary end point was the time to death from any cause or heart failure hospitalization (HFH). The secondary end points included all-cause death, HFH, echocardiographic response (absolute increase in LVEF ≥5%), and super response (absolute increase in LVEF ≥15% or improvement of LVEF to ≥50%) rates. Results Of the 200 included patients, 136 were male and 64 were female. The success rate was 98% in the LBBP group and 94% in the BiVP group ( P = .28). The median follow-up duration was 36 (range, 33-39) months. The primary end point of time to death or HFH was significantly lower in the LBBP group compared with BiVP (8% vs 28%; hazard ratio [HR], 0.26; 95% CI, 0.12-0.57; P < .001). There was no significant difference in all-cause mortality between the groups (2.0% vs 5.0%; HR, 0.40; 95% CI, 0.08-2.04; P = .25). However, LBBP significantly reduced the risk of HFH (7.0% vs 28.0%; HR, 0.23; 95% CI, 0.10-0.52; P < .001). The echocardiographic response rates were similar in both groups (86.0% vs 81.0%; P = .34) but the super-response rate was higher in the LBBP group (55.0% vs 36.0%; P < .007). Conclusions and Relevance In this study, LBBP was superior to BiVP in reducing the risk of death or HFH in patients with LBBB and severely reduced LVEF. Further trials are warranted in this patient population. Trial Registration Chinese Clinical Trial Registry identifier: ChiCTR2000036554

Background: Acute complete occlusive myocardial infarction (ACOMI) represents the most severe high-risk subtype of acute myocardial infarction (AMI) that requires urgent revascularization. However, conventional STEMI-based electrocardiogram (ECG) diagnosis exhibits low sensitivity for the identification of ACOMI, leading to frequent missed diagnosis. Current AI-ECG models are designed to detect AMI or STEMI rather than ACOMI, limiting their clinical applicability. Against this background, we aimed to develop an interpretable dual-modal AI-ECG model to accurately identify ACOMI and validate its performance in real-world populations. Methods: We conducted a dual-center retrospective cohort study in China between Dec 28 2018 and Dec 31 2025. All enrolled participants had a definitive diagnosis of AMI and paired pre-procedural 12-lead ECG and coronary angiography datasets. The development cohort from Zhongshan Hospital, Fudan University including 6762 cases was randomly split into training, internal validation and internal test subsets. We developed a dual-modal deep learning framework integrating ECG signal backbone and image backbone. Model performance was comprehensively evaluated from discrimination, calibration and net clinical benefit, and further compared with STEMI criteria and physicians. After confirming the optimal fused dual-modal model, we further validated its discrimination performance using an independent external retrospective cohort of 889 patients from Shanghai Tenth People’s Hospital, Tongji University. Findings: The development cohort (Zhongshan Hospital) comprised 6762 paired ECG–angiography records from 5332 patients, among which 2075 were confirmed ACOMI. The external test cohort (Tenth People’s Hospital) included 889 ECG records from 877 patients, and 251 records were confirmed ACOMI. In the internal test, our AI-ECG model demonstrated superior diagnostic performance compared with conventional STEMI criteria and ECG experts for ACOMI, achieving an AUROC of 0.878 (95% CI 0.858–0.897) and an AUPRC of 0.825 (95% CI 0.793–0.855). At the optimal threshold, the model yielded a sensitivity of 0.779 and specificity of 0.833, far exceeding the sensitivity of standard STEMI criteria (0.312, P<0.001) while maintaining comparable specificity (0.845, P=0.529).In the external test cohort, the model retained robust generalisability with an AUROC of 0.841 (95% CI 0.811–0.870) and AUPRC of 0.701 (95% CI 0.642–0.759), with a markedly higher sensitivity (0.741 vs 0.203 for STEMI criteria, P<0.001). Calibration curves and low stable Brier scores (0.132 internal; 0.137 external) confirmed strong alignment between predicted probabilities and observed ACOMI events. Decision curve analysis (DCA) demonstrated sustained positive net clinical benefit of the AI-ECG model across all clinically relevant risk thresholds in both cohorts. Kaplan–Meier cumulative incidence curves showed nearly in-hospital major adverse cardiovascular events (MACEs) rates between CAG-confirmed ACOMI patients and AI-predicted ACOMI patients (5.5% vs 5.4%). Interpretation: The dual-modal AI-ECG model demonstrated superior diagnostic performance. It addresses the low-sensitivity limitation of conventional STEMI criteria and supports timely revascularisation decision-making for patients with suspected acute coronary syndrome.

Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often restricted to common arrhythmias and may generalize poorly across populations or clinically subtle diseases. We developed ECG Contrastive Language-Image Pre-training (ECGCLIP), a signal-language contrastive learning framework that aligns ECG waveforms with expert diagnostic reports. ECGCLIP was pre-trained on 2,837,962 ECG studies from 1,324,856 patients and evaluated on a held-out internal test set plus nine independent external cohorts comprising about 1.5 million ECGs. Evaluation covered 89 downstream tasks, including 45 ECG diagnoses, 39 echocardiographic targets, and 5 rare cardiac diseases, using PRAUC as the primary metric. ECGCLIP consistently improved performance over random initialization and Merl-R18 baselines. On the internal test set, ECGCLIP-R34 achieved strong performance for atrial fibrillation (PRAUC 0.900) and ST-segment elevation myocardial infarction (PRAUC 0.383), with robust generalization across all external cohorts. It also improved low-prevalence and diagnostically elusive diseases, including Ebstein anomaly, constrictive pericarditis, dextrocardia, and cardiac amyloidosis, with internal PRAUC values of 0.253, 0.175, 0.121, and 0.201, respectively. ECGCLIP was data efficient, matching or exceeding full-dataset baseline performance with only 10

BACKGROUND:Right ventricular pacing (RVP) is associated with an increased risk of pacing-induced cardiomyopathy (PICM) in patients with a high pacing burden. Left bundle branch pacing (LBBP), a more physiological pacing modality, may better preserve cardiac function. OBJECTIVES:This randomized trial aimed to evaluate the clinical outcomes of LBBP vs RVP in patients with a high pacing burden with high risk of cardiac dysfunction. METHODS:In this prospective, multicenter, randomized controlled trial, 160 patients with a high pacing burden with high risk of cardiac dysfunction were randomly assigned in a 1:1 ratio to either LBBP or RVP. The primary endpoint was a composite of all-cause mortality, heart failure hospitalization, or PICM. Secondary endpoints were the individual components of the primary endpoints, echocardiographic parameters, and NYHA functional class. RESULTS:During a median follow-up duration of 36 months, the primary endpoint occurred in 9 patients in the LBBP group and in 25 patients in the RVP group (11.6% vs 33.9%; HR: 0.310; 95% CI: 0.145-0.664; P = 0.001), mainly driven by PICM (6.5% vs 18.2%; subdistribution HR: 0.324; 95% CI: 0.119-0.883; P = 0.028). No significant differences were observed in all-cause mortality (P = 0.391) and heart failure hospitalization (P = 0.100) between 2 groups. LBBP showed superior improvements over RVP in left ventricular ejection fraction (mean difference: 5.34; 95% CI: 3.18-7.50; P < 0.001), left ventricular end-diastolic diameter (mean difference: -3.06; 95% CI: -4.38 to -1.73; P < 0.001), and left ventricular end-systolic diameter (mean difference: -3.74; 95% CI: -5.07 to -2.41; P < 0.001) from baseline to 36 months. Patients in the LBBP group also showed favored NYHA functional class compared with those in the RVP group at the 36-month follow-up (1.66 ± 0.60 vs 1.90 ± 0.56, P = 0.014). CONCLUSIONS:In patients with a high pacing burden with high risk of cardiac dysfunction, LBBP significantly reduced the risk of the composite outcome, driven primarily by a decreased risk of PICM, and is associated with better echocardiographic improvements and clinical function. (A multicenter, prospective, randomized, controlled trial of left bundle branch pacing and right ventricular pacing in preventing deterioration of cardiac function in patients with ventricular pacing dependence [LBBP-FAVOUR]; ChiCTR2000036553).

Background For patients with a high risk of sudden cardiac death, despite the benefits of an implantable cardioverter defibrillator (ICD), some patients are still at high risk of death.Aim The purpose of this study was to develop and validate a nomogram predicting all-cause mortality for patients with an ICD.Methods We retrospectively analysed the data of multicentre ICD registration study from 2010 to 2014 in China. A total of 617 ICD patients formed a development cohort. The physical activity monitored by ICD and clinical data was collected. Univariate and multivariate Cox regression analyses were used to screen mortality predictors and construct the nomogram. The performance of the nomogram was evaluated by the consistency index (C-index) and the calibration curve. Additionally, extensive subgroup and sensitivity analyses were conducted to evaluate the model’s robustness. A total of 196 ICD patients formed a validation cohort.Results In the development cohort, physical activity, diabetes and left ventricular end-diastolic diameter were selected as independent prognostic factors. The nomogram was constructed by these three factors. The C-index of the nomogram was 0.80 (95% CI 0.75 to 0.84). The calibration curve showed that the predicted survival probability of the nomogram was in good agreement with the actual survival probability. In the validation cohort, the C-index of the nomogram was 0.74 (95% CI 0.64 to 0.84), and the calibration curve still maintained good consistency. Crucially, the nomogram maintained stable and excellent discriminative capacity across primary and secondary prevention subgroups, as well as for predicting specific cardiac and sudden cardiac death.Conclusions Our study develops and validates a nomogram predicting all-cause mortality for patients with an ICD by integrating the physical activity monitored by ICD and clinical data. The nomogram performs well and can provide personalised death risk assessment for ICD patients.Trial registration number ChiCTR-ONRC-13003695.

Importance:Left bundle-branch pacing (LBBP) has been proposed as an alternative to biventricular pacing (BiVP) for patients with heart failure with left bundle-branch block (LBBB). However, robust clinical evidence from randomized clinical trials is lacking. Objective:To evaluate the long-term clinical outcomes of LBBP and BiVP. Design, Setting, and Participants:This multicenter, prospective, randomized clinical trial enrolled 200 patients at 6 centers in China with a left ventricular ejection fraction (LVEF) of 35% or less and LBBB from October 2020 to March 2022. This study was took place from October 2020 to September 2024. These data were analyzed September 2024 to December 2024. Interventions:Patients were randomly assigned in a 1:1 ratio to receive either LBBP or BiVP. Main Outcomes and Measures:The primary end point was the time to death from any cause or heart failure hospitalization (HFH). The secondary end points included all-cause death, HFH, echocardiographic response (absolute increase in LVEF ≥5%), and super response (absolute increase in LVEF ≥15% or improvement of LVEF to ≥50%) rates. Results:Of the 200 included patients, 136 were male and 64 were female. The success rate was 98% in the LBBP group and 94% in the BiVP group (P = .28). The median follow-up duration was 36 (range, 33-39) months. The primary end point of time to death or HFH was significantly lower in the LBBP group compared with BiVP (8% vs 28%; hazard ratio [HR], 0.26; 95% CI, 0.12-0.57; P < .001). There was no significant difference in all-cause mortality between the groups (2.0% vs 5.0%; HR, 0.40; 95% CI, 0.08-2.04; P = .25). However, LBBP significantly reduced the risk of HFH (7.0% vs 28.0%; HR, 0.23; 95% CI, 0.10-0.52; P < .001). The echocardiographic response rates were similar in both groups (86.0% vs 81.0%; P = .34) but the super-response rate was higher in the LBBP group (55.0% vs 36.0%; P < .007). Conclusions and Relevance:In this study, LBBP was superior to BiVP in reducing the risk of death or HFH in patients with LBBB and severely reduced LVEF. Further trials are warranted in this patient population. Trial Registration:Chinese Clinical Trial Registry identifier: ChiCTR2000036554.

BACKGROUND:Unexplained syncope and palpitations are common chief complaints in the outpatient department of cardiology. Their sporadic and unpredictable poses significant diagnostic challenges. Implantable cardiac monitors (ICMs) can monitor arrhythmia events efficiently, and can overcome the shortcomings of traditional electrocardiogram (ECG) detection tools such as Holter. However, the high cost of imported ICM devices limits accessibility for patients in China. The MA01-100 (Singular Medical, Suzhou, China), the first domestically developed ICM, may offer a cost-effective alternative. This study aimed to evaluate the safety and efficacy of the MA01-100 compared with standard surface electrocardiography (ECG) for arrhythmia monitoring in Chinese patients. METHODS:A prospective, paired design trial was conducted across six hospitals in China. Sixty-four participants underwent implantation of the MA01-100 ICM device. As the self-control group, patients were also assessed using a surface ECG at implantation, and 30- and 90-day follow-ups. The primary efficacy endpoints were the sensitivity and positive predictive value (PPV) of R wave detection at 30 days post-implantation. Secondary endpoints included R wave amplitude stability, QRS complex morphology consistency, arrhythmia event detection rate, arrhythmia event identification accuracy, clinical performance evaluation of the programmer, and remote transmission function assessment. Safety was evaluated by the incidence rates of major adverse events (MAEs), serious adverse events (SAEs), and device defects within 3 months of follow-up. RESULTS:All 64 patients who met the inclusion criteria were enrolled in the trial. They underwent successful cardiac monitor implantation with acceptable sensitivity and PPV of R wave detection. The mean sensitivity was 99.98 ± 0.10%, with a minimum of 99.34%. The two-sided 95% confidence interval (CI) for the compliance rate was (94.40%-100.00%). The mean PPV was 99.94 ± 0.35%, with a minimum of 97.35%. The two-sided 95% CI for the compliance rate was (94.40%-100.00%). Two patients (3.1%) experienced mild implantation site infections; no device defects or SAEs were observed. All pre-defined efficacy and safety benchmarks were met. CONCLUSION:This study provides the first prospective, multicenter clinical evaluation of the safety and efficacy of an intracardiac monitor (ICM) independently developed in China. It systematically validated the device's clinical performance, thereby filling the gap in China's ICM field across the entire chain of "independent development-clinical validation-translational application." The results demonstrate that the ICM meets the requirements for clinical use, providing new support for improving the accessibility of arrhythmia diagnostic services in China and offering a reference for formulating globally cost-effective cardiac monitoring strategies.

Background: The triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-c) has been linked to cardiovascular risk. However, its association with device-detected atrial high-rate episodes (AHRE) remains unclear. This study aimed to explore the relationships of TG/HDL-c with incident AHRE and mortality. Methods: This retrospective cohort study included patients implanted with pacemakers equipped with home-monitoring capability and without previous atrial fibrillation, atrial flutter, or atrial tachycardia. AHRE were defined as episodes with a burden exceeding 15 minutes during follow-up. The primary endpoint was AHRE, and the secondary endpoints were all-cause mortality and cardiovascular mortality. Findings: During a mean follow-up of 75.8±16.8 months, AHRE occurred in 303 of 1,463 patients (21.1%). Restricted cubic spline analysis revealed a significant U-shaped nonlinear association of TG/HDL-c ratio with AHRE, all-cause mortality and cardiovascular mortality (all P<0.001 for overall, all P<0.05 for nonlinearity). Multivariable Cox analysis showed that TG/HDL-c was independently and inversely associated with AHRE (hazard ratio [HR] 0.878, 95% confidence interval [CI] 0.771–0.999, P=0.049). As a category variable, compared with the low TG/HDL-c group, the moderate (HR 0.654, 95% CI 0.486–0.881, P=0.005) and high (HR 0.726, 95% CI 0.542–0.973, P=0.032) groups had significantly lower AHRE risk. Notably, the moderate group exhibited the lowest risk for all-cause and cardiovascular mortality. Interpretation: In pacemaker patients undergoing continuous home monitoring, lower TG/HDL-c was independently associated with a higher long-term risk of incident AHRE, and the association was non-linear. TG/HDL-c may serve as an accessible metabolic marker for AHRE risk stratification in this population. Further prospective studies are required to validate these findings and clarify the observed associations with mortality.

Background:Sudden cardiac death (SCD) is associated with severe electrocardiogram (ECG) abnormalities. Current prediction relies heavily on static ECG parameters, limiting accuracy. This study aimed to explore dynamic ECG parameters, particularly the S-wave area and its circadian variations, as novel markers for SCD risk prediction. Methods:All participants were divided into three different SCD risk groups based on their disease status at the time of enrollment. Dynamic single-lead ECG data was collected continuously for 24 hours and segmented into 1,440 one-minute intervals with time information tags from 0:00 to 24:00. Forty-two ECG parameters, including the S-wave area, were analyzed. Randomly selected 70% of the samples from Sun Yat-sen Memorial Hospital to construct training set and remaining samples to construct independent test set. Student's t-test was used to compare the expression differences of ECG parameters in different SCD risks patients at different time points within a day. Repeatedly attempted to establish multivariate logistics regression models combining different time points and ECG parameters and performed five-fold cross validation sequentially. Selected time point-ECG parameter combined model with the highest AUC to conduct further univariate logistic regression and calculate odds ratio (OR) of each time point-ECG parameter combination. Results:From September 2017 to December 2020, 289 participants were enrolled: 43 at high risk of SCD (SCDHR), 138 with heart failure (HF), and 108 healthy controls (HC). Significant circadian variations in ECG parameters were observed. In the SCDHR group, key parameters significantly increased during 16:00-22:00, while the HF group showed distinct changes from 21:00-06:00. Logistic regression achieved robust performance in distinguishing groups: SCDHR vs. HC (AUC =0.887 training; AUC =0.747, accuracy =0.755, precision =0.800 test), SCDHR vs. HF (AUC =0.857 training; AUC =0.714, accuracy =0.681, precision =0.280 test) and HF vs. HC (AUC =0.965 training; AUC =0.842, accuracy =0.704, precision =0.867 test). Decision curve analysis and calibration curve showed good clinical performance of three logistics models for each comparison pair. Conclusions:Dynamic ECG parameters, especially time-dependent variations in the S-wave area, were strongly associated with the SCD risk. They may develop into promising markers enhancing predictive accuracy for SCD stratification after further large-scale and prospective validation.

Background: Micra-AV leadless pacemaker could achieve atrioventricular synchrony (AVS) by sensing atrial mechanics. However, Micra-AV showed low atrial tracking capability and AVS percentage in some patients. Objectives: Echocardiographic parameters were testified to predict AVS of Micra-AV, which could potentially facilitate to screen suitable patients for Micra-AV. Methods: Patients with atrioventricular block and indicated for Micra-AV were included. Ultrasonic speckle tracking imaging was used to assess atrial contraction/conduit/reservoir strain. Early diastolic (E) and atrial systolic (A) peak flow velocities, early diastolic (E') and atrial systolic (A') tissue Doppler velocities at mitral or tricuspid annulus were measured. AVS was defined as AV interval ≤ 300ms by Holter monitor. Results: 60 patients diagnosed with atrioventricular block were included. For AVS with 70% and above, tricuspid E/A ratio < 1.62 (OR = 3.237, 95% CI: 1.495 to 7.012, p = 0.003) and right atrial contraction strain < -3.3% (OR = 3.794, 95% CI: 1.718 to 7.765, p = 0.001) were verified as potent predictor. For AVS with 80% and above, tricuspid E/A ratio < 1.48 (OR = 6.571, 95% CI: 2.998 to 15.02, p < 0.001) and right atrial contraction strain < -4.8% (OR = 7.667, 95% CI: 3.441 to 16.81, p < 0.001) were verified as potent predictor. Nonetheless, left atrial parameters failed to attain statistical significance. Conclusions: Right atrial echocardiographic parameters are predominant over left ones in relevancy of AVS. Tricuspid E/A and right atrial contraction strain showed great potency to predict AVS independently in Micra-AV.

To investigate the relationship between abdominal obesity and long-term prognosis in patients with a pacemaker. In the SUMMIT Study, patients were categorized by baseline waist circumference into obesity, normal, and lean groups. WC was measured at the midpoint between the last rib and hip bone after exhalation. Regular follow-ups were conducted, with all-cause mortality as the primary endpoint and cardiac death as the secondary endpoint. In total, 492 patients were included in the analysis. The average baseline waist circumference was 84.2 ± 12.7 cm, and abdominal obesity was observed in 37.6

Inflammation-induced cardiac dysfunction, driven by an abnormal immune response, significantly contributes to sepsis-related mortality. Controlling excessive pro-inflammatory cytokine production by immune cells remains a significant challenge. This study investigated the role of N-acetyltransferase 10 (NAT10) in macrophage activation and its contribution to inflammation-induced cardiac dysfunction. Using bone marrow-derived macrophages and an endotoxemia mouse model, we found that NAT10 is significantly upregulated in response to lipopolysaccharide (LPS) due to the deubiquitinating enzyme USP39, which stabilizes the NAT10 protein. ac4C RNA sequencing identified ETS2 as a direct target of NAT10, where the ac4C modification enhanced ETS2 mRNA stability and translation, promoting a pro-inflammatory phenotype in macrophages. NAT10 deficiency reduces LPS-induced macrophage activation and cytokine production, improving cardiac function in mice. Pharmacological inhibition of NAT10 using remodelin produced similar protective effects. Our findings reveal a novel post-transcriptional pathway and highlight the therapeutic potential of targeting NAT10 to mitigate inflammation-induced cardiac injury in endotoxemia.

BACKGROUND:Accurately predicting the clinical trajectory of patients with implantable cardioverter-defibrillators (ICDs) is critical for guiding their care and management. Machine learning (ML) methods surpass traditional statistical approaches by addressing complex data patterns and variability, providing more precise and personalized risk estimates. METHODS:This retrospective study included patients from four major hospitals in China. Data from three hospitals were used for training and internal tests, while data from the remaining hospital were used for external tests. Six ML models were developed and validated. Model discrimination was measured using the area under the receiver operating characteristic curve (AUROC). Kaplan-Meier survival curves were generated by stratifying patients into high-risk and low-risk groups based on the optimal model's predictions. Interpretation analysis was performed to rank the importance of predictive features. RESULTS:A total of 3175 patients were studied. The multilayer perceptron (MLP) model demonstrated superior predictive accuracy, with the AUROC of 0.70 and 0.72 in internal and external test sets, respectively, outperforming other models. Kaplan-Meier curves show distinct survival trends over time between high-risk and low-risk groups, with stratification determined by the MLP model using a Youden's index cut-off value of 0.3443 (p < 0.001). Among the seven key predictors identified, glomerular filtration rate (GFR) was the most influential factor. CONCLUSIONS:The MLP model effectively predicted 3-year survival for ICD or cardiac resynchronization therapy defibrillator (CRT-D) patients and accurately stratified them into distinct risk groups. The integration of MLP and SHapley Additive exPlanations (SHAP) provided explicit explanations for individualized risk predictions, facilitated clinical decision-making, and supported the optimization of treatment strategies. TRIAL REGISTRATION:ClinicalTrials.gov identifier: NCT05396313.

Background Heart failure (HF) is characterized by chronic inflammation and pathological remodeling, with macrophage-mediated oxidative stress and inflammasome activation playing key roles in disease progression. Mitophagy regulates mitochondrial quality control and restrains inflammatory activation. Methods We investigated whether Kirenol, a flavonoid with antioxidant and autophagy-enhancing properties, ameliorates pressure overload-induced HF via mitophagy regulation in macrophages. A murine transverse aortic constriction (TAC) model was employed. Cardiac function, remodeling, and inflammation were assessed by echocardiography, histology, qPCR, immunoblotting, and flow cytometry. In vitro studies were conducted in Ang II-stimulated bone marrow-derived macrophages. Results Kirenol significantly improved cardiac function, reduced hypertrophy and fibrosis, and suppressed inflammatory responses in TAC mice. Mechanistically, Kirenol enhanced macrophage mitophagy, reduced mitochondrial ROS production, and inhibited NLRP3 inflammasome activation and IL-1β release. These protective effects were abrogated by mitophagy inhibition using cyclosporin A. Conclusions Kirenol exerts cardioprotective effects in pressure overload-induced HF by promoting macrophage mitophagy and suppressing inflammasome-mediated inflammation. This identifies Kirenol as a potential therapeutic agent targeting immune-metabolic dysfunction in HF.

Mixed lineage kinase 3 (MLK3), a member of the MAP3K family, is known to participate in cellular stress and inflammatory responses, but its role in neutrophil-mediated myocardial ischemia–reperfusion (I/R) injury remains unclear. In this study, we investigated the function and downstream signaling of MLK3 in neutrophils using genetically modified mouse models with neutrophil-specific MLK3 knockout or overexpression. MLK3 deficiency in neutrophils reduced infarct size, improved cardiac function, and decreased neutrophil infiltration, NET formation, and pro-inflammatory cytokine release following I/R. Transcriptomic profiling revealed that MLK3 promotes the expression of the antimicrobial peptide CRAMP by stabilizing and activating the transcription factor C/EBPβ. Administration of exogenous CRAMP abolished the protective effects of MLK3 deletion, confirming its functional relevance. Furthermore, treatment with CEP-1347, a small-molecule MLK3 inhibitor, attenuated myocardial injury, reduced apoptosis, and limited adverse remodeling in vivo. In acute myocardial infarction (AMI) patients, elevated levels of phosphorylated MLK3 (pMLK3) in circulating neutrophils were associated with increased levels of MPO-DNA, cTnT, and CK-MB, as well as a trend toward higher rates of cardiovascular rehospitalization. These findings identify a neutrophil-intrinsic MLK3–C/EBPβ–CRAMP axis that amplifies myocardial inflammation and injury, and suggest MLK3 as a promising therapeutic target and potential biomarker for ischemic heart disease.

BACKGROUND AND AIMS:Valvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiography (AI-ECG) models to diagnose and predict future moderate or severe regurgitant VHDs (rVHDs), including mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR). METHODS:The AI-ECG models were developed in a data set of 988 618 ECG and transthoracic echocardiogram pairs from 400 882 patients from Zhongshan Hospital, Shanghai, China. The AI-ECG models used a residual convolutional neural network with a discrete-time survival loss function. External evaluation was performed in outpatients from a secondary care data set from Beth Israel Deaconess Medical Center, Boston, USA, consisting of 34 214 patients with linked echocardiography. RESULTS:In the internal test set, the AI-ECG models accurately predicted future significant MR [C-index 0.774, 95% confidence interval (CI) 0.753-0.792], AR (0.691, 95% CI 0.657-0.720), and TR (0.793, 95% CI 0.777-0.808). In age- and sex-adjusted Cox models, the highest risk quartile had a hazard ratio (HR) of 7.6 (95% CI 5.8-9.9, P < .0001) for risk of future significant MR, compared with the lowest risk quartile. For future AR and TR, the equivalent HRs were 3.8 (95% CI 2.7-5.5) and 9.9 (95% CI 7.5-13.0), respectively. These findings were confirmed in the transnational external test set. Imaging association analyses demonstrated AI-ECG predictions were associated with subclinical chamber remodelling. CONCLUSIONS:This study developed AI-ECG models to diagnose and predict rVHDs and validated the models in a transnational and ethnically distinct cohort. The AI-ECG models could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.

Background: Electrocardiography (ECG) is a cornerstone of cardiovascular disease (CVD) diagnosis, but it faces limitations in spatial resolution and susceptibility to artifacts. Recent advances in vision-language foundation models, such as CLIP, offer potential for enhancing ECG interpretation by aligning multimodal data. This study introduces ECGCLIP, a novel model enabling the diagnosis of a broad spectrum of CVD by integrating ECG waveforms with clinical annotations. Methods: ECGCLIP was trained on 5 million ECG-image/report pairs from multicenter datasets, annotated by experienced physicians. Using a self-supervised contrastive learning framework, the model aligned ECG signals with textual interpretations. Performance was evaluated on 45 ECG tasks (e.g., arrhythmias, conduction disorders) and 29 echocardiography tasks (e.g., valvular diseases, heart failure) across internal and external validation cohorts, by comparing precision-recall AUC (PRAUC) under varying data regimes. Results: ECGCLIP achieved PRAUC improvements up to 0.5873 (e.g., AAI pacing: 0.0511 → 0.6384) and 0.5253 for rare conditions (e.g., Wolff-Parkinson-White syndrome at 1% data). Critical conditions like ST-elevation myocardial infarction (STEMI) showed gains of 0.2170 (0.1156 → 0.3326), addressing traditional ECG limitations in ischemia detection. The model enhanced detection of valvular diseases (e.g., mitral stenosis: Δ+0.211) and heart failure (LVEF < 40%: Δ+0.139), with 79% generalizability retention for tricuspid regurgitation in external validation. With only 1% training data, ECGCLIP matched or exceeded full-data baselines (e.g., sinus rhythm PRAUC: 0.9747 vs. 0.9877). Gains in low-incidence diseases (e.g., hyperkalemia: Δ+0.0169) highlighted efficacy in sparse-data scenarios. External validation showed an overall PRAUC improvement of 0.1565, with consistent gains in ventricular pre-excitation (Δ+0.5253) but gaps in structural anomalies (e.g., mitral stenosis external Δ+0.100 vs. internal Δ+0.211), reflecting ECG's dependence on functional sequelae. Conclusion: ECGCLIP establishes a new paradigm for ECG interpretation by leveraging vision-language foundation models. It demonstrates exceptional data efficiency, enabling accurate diagnosis of diverse cardiac conditions—including rare and critical diseases—with minimal labeled data. The model's robustness across diseases supports its potential for deployment in resource-limited settings, enhancing accessibility and precision in CVD care.