Weize Xu

Background: Congenital heart disease (CHD) and autism spectrum disorder (ASD) co-occur more frequently than expected, suggesting shared developmental susceptibility. We aimed to identify cross-tissue molecular candidates linking cardiac and neurodevelopmental abnormalities. Methods: CHD- and ASD-associated genes were curated from MalaCards and SFARI Gene, and their intersection was defined as the CHD- and ASD-associated gene set (CAGES). Shared gene enrichment was assessed against the human protein-coding genome. CHD and ASD genome-wide association data were integrated with heart and brain cis-expression quantitative trait loci using Bayesian colocalization and summary data-based Mendelian randomization with HEIDI testing. Candidates were further evaluated using bulk transcriptomics, six machine learning feature selection methods, independent cohorts, and scTenifoldKnk virtual knockout analyses in developing human heart and cerebral cortex. Results: Seventy-three genes were shared between CHD and ASD. CHD7 showed suggestive colocalization for CHD and ASD, while lower genetically predicted CHD7 expression was associated with higher odds of both disorders, with nonsignificant HEIDI tests. MEF2C was downregulated in tetralogy of Fallot myocardium and selected by all six ASD machine learning methods, with consistently lower expression in ASD. CAGES-derived signatures discriminated independent CHD, ASD cord blood, and cortical cohorts. CHD7 virtual knockout analysis identified MEF2C as a computationally predicted regulatory candidate in the prenatal brain, particularly in late-fetal excitatory neurons and intermediate progenitor cells, with cell-type-specific predicted effects also observed in the developing heart. Conclusions: CHD and ASD share molecular susceptibility involving pleiotropic developmental mechanisms. CHD7 and MEF2C emerge as connected cross-tissue candidates across complementary genetically informed, transcriptomic, and developmental regulatory analyses, highlighting prenatal neuronal populations and developmental windows for investigating the heart–brain axis.

Myocardial ischemia-reperfusion injury (MIRI) remains a major unresolved problem in cardiovascular medicine. It commonly occurs in coronary heart disease (CHD) patients following reperfusion therapy or cardiopulmonary bypass (CPB)–assisted cardiac surgery. Despite the clinical necessity of restoring blood flow, reperfusion itself paradoxically exacerbates myocardial injury. The resulting therapeutic dilemma underscores the need for innovative cardioprotective strategies. Recent studies suggest that protein lactylation (Kla), a newly identified post-translational modification (PTM) derived from lactate metabolism, plays a crucial role in cardiomyocytes by regulating metabolic adaptation, injury repair, stress responses, cell death, and cardiac remodeling. However, there is still no comprehensive synthesis of the role of Kla in MIRI. In this review, we critically examine the emerging evidence linking Kla to the pathogenesis of MIRI and further discuss potential therapeutic strategies targeting this epigenetic mechanism to alleviate reperfusion-induced myocardial injury.

Zinc homeostasis plays a critical role in cellular function, yet its dysregulation may lead to cytotoxicity. Based on the unexpected finding that excessive zinc downregulates the transcription of cardiac-related factors in mouse cardiomyocytes, this study reveals a mechanistic pathway wherein cytoplasmic zinc overload reduces histone acetyltransferase activity, subsequently lowering histone acetylation and ultimately decreasing the transcriptional levels of target genes. The universality of this mechanism was further confirmed across multiple cell types. By investigating the phenomenon of zinc-induced autophagy regulated by acetylation, we explored the potential therapeutic implications of zinc as a drug. Through comparative analysis of cells with varying sensitivity to zinc, we identified aberrant expression of zinc transporters under physiological conditions as a primary factor contributing to zinc-induced toxicity. This finding suggests that zinc transporters may serve as potential therapeutic targets. This study is the first to elucidate the molecular link between zinc homeostasis and histone acetylation, providing a novel perspective for understanding zinc metabolism-related diseases and zinc-targeted therapies.

ObjectiveTo evaluate the feasibility and effectiveness of replacing manual auscultation with artificial intelligence (AI)-assisted cardiac auscultation within the dual-indicator screening strategy for early detection of congenital heart disease (CHD) in neonates in a real-world clinical setting.MethodsUsing data from the provincial CHD treatment network led by the Children’s Hospital of Zhejiang University School of Medicine, we retrospectively enrolled 41 320 neonates born between July 2020 and March 2023. All neonates underwent pulse oximetry (POX), AI-assisted auscultation, and manual auscultation. The traditional screening strategy was defined as “POX+manual auscultation,” and the intelligent strategy as “POX+AI-assisted auscultation.” A positive screening result was defined as a positive finding in either POX or the corresponding auscultation method (manual or AI). Echocardiography served as the gold standard for CHD diagnosis. True positive, false positive, true negative, and false negative results were determined, and the missed-diagnosis rate, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated. To assess the consistency of screening performance across institutions, analyses were also conducted separately in the four hospitals with the highest screening volumes.ResultsA total of 354 neonates were diagnosed with CHD. Compared with the traditional strategy, the intelligent strategy significantly reduced the missed-diagnosis rate (67.23% vs. 34.75%, P<0.01) and increased sensitivity (32.77% vs. 65.25%, P<0.01) and NPV (99.41% vs. 99.67%, P<0.01). However, specificity, PPV, and diagnostic accuracy were lower (all P<0.01). Across the four high-volume institutions, the intelligent strategy consistently showed a significant reduction in missed-diagnosis rate and increased sensitivity, along with decreased specificity and diagnostic accuracy, indicating robust performance across diverse clinical settings.ConclusionIn multicenter real-world practice, the intelligent screening strategy significantly reduces the missed-diagnosis rate of CHD and demonstrates stable screening performance across different institutions. Although diagnostic accuracy is moderately lower, AI-assisted auscultation is a feasible and clinically valuable alternative to manual auscultation in neonatal CHD screening, particularly when minimizing missed diagnoses is a priority.

Wearable ECG monitoring devices have become indispensable in personalized healthcare. However, dynamic signal acquisition during daily activities often introduces transient noise, which complicates signal classification and denoising, and may compromise diagnostic reliability. To address this challenge, this study proposes an ECG preprocessing framework based on multi-task learning, in which a fine-grained noise localization task is introduced to guide and assist both ECG signal quality assessment and denoising. Built upon a Transformer backbone and optimized with three task-specific loss functions, the proposed model leveraged weak supervision and pathological ECG data to learn robust noise-invariant representations. This design incorporates intra-class awareness, enabling the model to overcome various noise within the same quality category and to perform adaptive denoising beyond conventional inter-class-based approaches. Extensive experiments demonstrated state-of-the-art performance in both denoising and quality assessment, with weighted average F1-scores ranging from 95.72% to 98.49% and classification accuracy exceeding 95.68%. Moreover, under extremely severe noise conditions, the signal-to-noise ratio (SNR) is improved from −1.95 ± 3.83 dB to 12.20 ± 2.51 dB while preserving waveform fidelity. After pruning and quantization, the model could be effectively compressed, thereby enhancing its suitability for real-time deployment in edge computing scenarios. Overall, the proposed method not only preserved diagnostically important ECG waveforms and provided interpretable noise localization but also offers an efficient and clinically relevant solution for large-scale, real-time ECG monitoring.

Pulmonary atresia with an intact ventricular septum (PA/IVS) is a rare congenital heart disease. Pulmonary regurgitation is a major late postsurgical complication and pulmonary valve replacement is the mainstream treatment. We report the case of a 15-year-old boy with PA/IVS who developed severe pulmonary regurgitation after prior surgical repair. Intraoperative angiography revealed the absence of bilateral femoral venous trunks, making transcatheter pulmonary valve replacement (TPVR) not feasible. Transthoracic pulmonary valve replacement using a self-expanding Venus P-Valve was successfully performed. Postoperative recovery was uneventful, and follow-up showed good valve function and improved right ventricular performance. Transthoracic implantation of a self-expanding pulmonary valve may serve as a safe and feasible alternative for patients with severe pulmonary regurgitation who are unsuitable for TPVR due to unfavorable vascular anatomy.

Background: Gestational age at birth is shaped by complex maternal and paternal factors prior to conception, yet few studies have integrated multidimensional clinical indicators from both parents to estimate their quantitative effects. Methods: Using a large retrospective cohort with routinely collected preconception health data from both mothers and fathers, we applied a double machine learning (DML) framework combining Lasso, Random Forest and XGBoost regressors to estimate associations between parental biomarkers and gestational age at birth. Model interpretability was enhanced through Shapley Additive Explanations (SHAP) analysis, stratified interaction testing and estimation of individualized treatment effects (ITE). Results: Elevated maternal fasting glucose, alanine aminotransferase, platelet count and anti-hepatitis B core seropositivity were consistently associated with shortened gestational age at birth across all DML models. For instance, a 10 mmol/L increase in fasting glucose corresponded to 2.8–4.9 days shorter gestation. Paternal Treponema pallidum seropositivity and increased monocyte proportion, defined as the fraction of monocytes within total circulating white blood cells, also demonstrated significant associations, with the former linked to gestational shortening and the latter to modest extension. Stratified and interaction analyses revealed that paternal immune markers modified the associations of maternal metabolic indicators with gestational age at birth. SHAP-based interpretation confirmed model consistency and ITE analysis indicated marked heterogeneity, particularly for fasting glucose. Conclusions: This study demonstrates the utility of interpretable DML methods for quantifying the effects of multidimensional preconception health indicators from both parents on gestational age at birth. Our findings support integrating maternal and paternal screening into preconception risk assessments to enable early, targeted prevention of preterm birth. Given the established links between preterm birth and neonatal surgical conditions, early risk identification via parental screening may help inform perinatal care strategies and optimize resource allocation in pediatric surgery.

Background: A good prognosis of congenital heart disease (CHD) depends on early diagnosis and intervention. Under the current screening conditions, a significant proportion still go undetected. Metabolomics, as a phenotype‐correlated research methodology, remains underused in the study of CHD, which could provide the possibility to screen neonatal CHD efficiently. Methods: Data for the analysis are from >22 000 neonates captured in the Network Platform for CHD from April 2020 to November 2021 in 11 cities in China. After data matching and quality control, a total of 22 674 neonates were finally included and divided into the CHD group (n=1823), nonsignificant CHD group (n=17 968), and normal group (n=2748). Demographic and clinical characteristics and tandem mass spectrometry‐based metabolic data for genetic and metabolic disease screening were gathered and compared for all groups. Machine learning models based on metabolic biomarkers were constructed to screen CHD in neonates. Results: After quality control, 22 539 neonates were ultimately included. Among them, 1823 were diagnosed with CHD, 17 968 were nonsignificant CHD, and 2748 were normal. A total of 46 distinguishing metabolic biomarkers were identified, and we found that the CHD group had significantly lower levels of 17‐hydroxyprogesterone (CHD versus nonsignificant CHD, P 2 fold change=−0.16; CHD versus normal, P 2 fold change=−0.15). We constructed CHD and ventricular septal disease screening models based on metabolic biomarkers. The best fitting model achieved an area under the receiver operating characteristic curve of 0.745 (95% CI, 0.696–0.791). Conclusions: This study reveals the unique metabolic profile of neonates with CHD. The screening model demonstrates considerable potential in early neonatal CHD screening and reflects significant value from a health economics perspective.