Xianbao Liu

Background and Aims: Calcific aortic valve disease (CAVD) lacks pharmacologic therapy. Mitophagy impairment contributes to various degenerative diseases, but whether it occurs in calcified aortic valves and during human valvular interstitial cell (hVIC) osteogenic differentiation remains unknown. We therefore investigated the role of mitophagy in CAVD and whether its pharmacological treatment prevents disease progression. Methods: Single-cell RNA sequencing (scRNA-seq) was performed on human aortic valves and hVICs. TJ0113, a novel orally available mitophagy activator from our previous screening, was tested in two CAVD mouse models. Multi-omics analyses and functional rescue experiments were performed to elucidate the mechanism. Results: Mitophagy was impaired in calcified human aortic valves and during hVIC osteogenic differentiation. TJ0113 activated MCL1-mediated mitophagy and attenuated disease progression in vitro and in vivo. Mechanistically, the mitophagy receptor MCL1 anchors a fraction of PRMT3 on mitochondria. During osteogenic differentiation, a dysregulated mitochondrial-to-nuclear communication is initiated by the increased release of PRMT3 into the cytosol, where it interacts with BACH1 and impairs BACH1 nuclear translocation. This leads to a widespread loss of BACH1 genomic occupancy, relieving BACH1-mediated transcriptional repression of the pro-osteogenic factor PARM1 and promoting hVIC osteogenic differentiation. Mitophagy activation by TJ0113 restored MCL1-PRMT3 binding and reversed these alterations. Conclusions: Mitophagy impairment drives CAVD through dysregulated mitochondrial-to-nuclear communication mediated by the MCL1-PRMT3-BACH1-PARM1 axis. Pharmacological mitophagy activation by TJ0113 corrects this communication and attenuates disease progression, representing a promising therapeutic strategy for CAVD.

With population aging and the rising incidence of cardiac disease, interventional cardiology, as a cornerstone of treatment for conditions such as coronary and structural heart disease, faces persistent challenges, including stringent precision requirements, heavy reliance on operator experience, and variability in diagnostic and therapeutic efficiency. With robust capabilities in data mining, image recognition, real-time analysis, and predictive modeling, artificial intelligence (AI) offers a novel pathway to address the aforementioned challenges and support the intelligent transformation of interventional cardiology. This narrative review briefly outlines the background to the application of AI in interventional cardiology, focuses on core supporting technologies including deep learning, machine learning, and computer vision, and summarizes current research and clinical applications of AI across preprocedural risk assessment and planning, intraprocedural real-time navigation and early warning of complications, and postprocedural follow-up and prognosis prediction. It also analyzes the principal challenges currently facing AI in this field, such as data quality and security issues, insufficient algorithm interpretability, barriers to clinical translation, and constraints from ethical and regulatory frameworks. Finally, in light of technological advances and clinical needs, this review outlines future directions, including the integration of AI with interventional devices, multimodal data integration and modeling, and the development of personalized interventional strategies, thereby providing a reference for promoting the standardized and large-scale application of AI technology in interventional cardiology.

The global public health burden of aortic valve disease (AVD) is growing due to its high mortality rate and lack of suitable pharmaceutical therapies in clinical practice, a challenge that is largely attributable to our poor understanding of its complicated underlying mechanisms. A better understanding of the pathological processes underlying AVD is urgently needed to identify novel therapeutic options. Appropriate animal models are emerging as vital tools for basic and translational research, and the development of new models and the important information they have provided has significantly aided our research. However, different animal models have unique limitations in their ability to mimic human AVD pathology. In this review, we summarize the available AVD animal models and provide a detailed description of the major principles underlying each animal model, including methodology, pathological features, and potential use and application in AVD research. Both the advantages and disadvantages of specific models are also carefully highlighted; thus this comprehensive review provides a valuable animal model reference for both basic and preclinical researchers.

Background/Objectives: The atherogenic index of plasma (AIP) reflects atherogenic dyslipidemia, but the clinical relevance of its longitudinal patterns after transcatheter aortic valve replacement (TAVR) remains unclear. We aimed to characterize AIP trajectories and explore their associations with clinical outcomes and echocardiographic changes after TAVR. Methods: This retrospective analysis included 1548 patients with at least two AIP measurements from within 7 days before TAVR through follow-up. Group-based trajectory modeling was used to identify longitudinal AIP patterns. Cox proportional hazards models and linear mixed-effects models were applied to assess clinical outcomes and longitudinal echocardiographic changes, respectively. Because trajectory assignment incorporated post-TAVR measurements, the analyses were considered exploratory. Results: Three distinct AIP trajectories were identified: low-level (n = 431, 27.8%), intermediate-level (n = 807, 52.1%), and high-level (n = 310, 20.0%). Higher trajectories were characterized by less favorable cardiometabolic profiles. Compared with the low-level trajectory, neither the intermediate-level trajectory (adjusted HR 0.83, 95% CI 0.60–1.16; p = 0.277) nor the high-level trajectory (adjusted HR 1.09, 95% CI 0.74–1.61; p = 0.663) was significantly associated with major adverse cardiovascular and cerebrovascular events. No significant associations with all-cause mortality or longitudinal echocardiographic recovery were detected. Conclusions: Distinct longitudinal AIP trajectories characterized different cardiometabolic profiles after TAVR, but no statistically significant association with major clinical outcomes or echocardiographic recovery was detected. These findings should be interpreted cautiously given temporal overlap in trajectory ascertainment, selection related to repeated measurements, and limited event numbers.

Background/Objectives: Horizontal aorta, characterized by a high aortic angle, represents a challenging anatomical feature during transcatheter aortic valve replacement (TAVR), but its association with cerebral ischemic injury remains unclear. This study investigated the association between horizontal aorta and diffusion-weighted magnetic resonance imaging (DW-MRI)-detected cerebral infarctions after TAVR. Methods: We retrospectively enrolled 411 consecutive patients with severe aortic stenosis undergoing transfemoral self-expanding TAVR between March 2017 and May 2022. Aortic angle was defined as the angle between the aortic annular plane and the horizontal plane on preprocedural computed tomography, and horizontal aorta was defined as an aortic angle > 60°. Patients were categorized into horizontal aorta and non-horizontal aorta groups. The incidence, number, and volume of new DW-MRI lesions were compared between groups. Results: The median age was 74.0 years (interquartile range [IQR]: 69.0 to 79.0); 54.5% were male, and the median Society of Thoracic Surgeons score was 3.83% (IQR: 2.29 to 6.39). Horizontal aorta was present in 69 patients (16.8%). Procedural complications were infrequent and comparable between groups. Although the incidence of new cerebral infarctions was similar between groups (89.9% vs. 83.0%; p = 0.157), patients with horizontal aorta had more cerebral infarction lesions (median, 4.0 vs. 3.0; p = 0.031), larger total lesion volume (320.0 vs. 180.0 mm³; p = 0.017), and larger lesion volume per infarction (70.0 vs. 51.7 mm³; p = 0.036). Multivariable analysis identified horizontal aorta as an independent predictor of the number of post-TAVR cerebral infarctions. Conclusions: Horizontal aorta is relatively common among patients undergoing TAVR and is independently associated with a greater burden of DW-MRI-detected subclinical cerebral ischemic injury.

Constructing three-dimensional (3D) cell assemblies is a critical step in bioengineering, and biomimetic fidelity largely depends on cellular density and spatial distribution. However, directly increasing cell density in bioinks often causes substantial cell loss during bioprinting. Here, we introduce a Magneto-Archimedes effect based 3D cell economic bioassembly strategy (3D MACE) that achieves high cell density (up to 10⁸ cells/mL), low cell loss (<5%), high controllability, and excellent accessibility. Using a vertically aligned pair of magnet arrays with identical polarization direction, a 3D magnetic field pattern was generated in the intervening space, which remotely drive and assemble diamagnetic cells in a paramagnetic culture medium to form 3D configurations according to the magnetic field patterns. This setup is easy to assemble, extends the effective manipulation height, and enables complex 3D architectures beyond spheroid based assemblies. The 3D MACE method allows precise cell manipulation within confined and structurally complex environments (e.g., porous scaffolds and meshes), facilitates the formation of customized patterns such as the Bagua trigram, and automatically separates cells in distributions by mass density. Using this approach, we readily generated 3D cell migration and angiogenesis models in-vitro. This work presents a novel 3D bioassembly strategy that effectively resolves the trade off between achieving high density cell constructs and minimizing cell loss during biofabrication.