Abstract
Widely used in millions of atherosclerosis treatments, conventional metal stents, although pervasive, only provide mechanical support to narrowed arteries. However, many patients experience in-stent restenosis after implantation. Here we developed smart magnetoelastic stents that preserve mechanical functionality while enabling self-powered hemodynamic monitoring for continuous and timely diagnosis of in-stent restenosis. Using a clinical catheter, the smart stent is deployed in the swine carotid artery for in vivo hemodynamic sensing, enabling effective detection of induced stenosis through artificial intelligence-assisted signal interpretation. In vivo and in vitro studies demonstrate the biosafety of the smart stent through immune profiling, human cytokine analysis and single-cell RNA sequencing. These results underscore the smart stent’s potential for seamless integration into biological systems as a reliable diagnostic tool. This platform technology could potentially revolutionize current stent technology and contribute to improved strategies for managing atherosclerosis.
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Data availability
Data supporting the results in this study are available within the article and its Supplementary Information. Single-cell RNA-seq datasets generated for this paper are located in the Gene Expression Omnibus repository under accession number GSE312793. Source data are provided with this paper.
Code availability
The machine learning code in this study is available via GitHub at https://github.com/JCLABShare/STENT.
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Acknowledgements
J. Chen acknowledges the Vernroy Makoto Watanabe Excellence in Research Award at the UCLA Samueli School of Engineering; National Institutes of Health grants (award IDs: R01 HL175135 and R01 CA287326); the American Heart Association Innovative Project Award (award ID: 23IPA1054908); the American Heart Association Transformational Project Award (award ID: 23TPA1141360); the American Heart Association Second Century Early Faculty Independence Award (award ID: 23SCEFIA1157587); the Office of Naval Research Young Investigator Award (award ID: N00014-24-1-2065); and a National Science Foundation grant (award number: 2425858). S.L. acknowledges support from the California Institute for Regenerative Medicine (grant number: DISC2COVID19-11838). G.C. acknowledges the Amazon Doctoral Student Fellowship from Amazon Web Services and the UCLA Science Hub for Humanity and Artificial Intelligence. G.C. also acknowledges a Predoctoral Fellowship from the American Heart Association and the VIVA Foundation (award ID: 24PRE1193744).
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Authors and Affiliations
Contributions
J. Chen conceived the idea and guided the entire project. G.C., G.P.C. and J. Chen designed the experiments, analyzed the data, drew the figures and composed the paper. G.P.C., W.J.K., G.C., J.T., X.Z., L.G.D., T.C., A.C.W. and O.A.A. contributed to the animal study. Y.Y., Y.-R.L., J.T., T.C., Z.L. and S.L. contributed to the biosafety studies. Y.Y. and Y.-R.L. performed the single-cell sequencing and immune profiling experiments. J.Z., G.C. and W.W. contributed to the machine learning and data analysis. G.C., G.P.C., X.Z., J. Carol and Y.Z. contributed to the device design, fabrication and characterization. J. Chen, G.P.C., S.L., P.S.W. and W.W. contributed to the funding acquisition. K.S., G.C., G.P.C., J. Chen, P.S.W., S.L. and O.A.A. revised the paper. J.X. revised the figures. All authors have read the paper, agreed to its content and approved the final submission.
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Competing interests
G.P.C. is a consultant for Stryker Neurovascular, Medtronic, MicroVention, Rapid Medical, Cerenovus and NuVascular. A patent application related to this work has been filed, with J. Chen, G.P.C., G.C., A.C.W. and P.S.W. listed as inventors. The other authors declare no competing interests.
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Nature Cardiovascular Research thanks Amir Lotfi, Geoffrey Tison and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Catheter delivery of the smart stent.
a-b, Illustration (a) and photo (b) showing clinical catheter access via the established femoral arterial sheath for smart stent delivery. Scale bar, 12 mm. c, Delivery catheter navigation over glide wire. Real-time fluoroscopy is used to visualize the progression and position of the glide wire and catheter. Once the glide wire is successfully navigated to the desired location, it serves as a track along which the clinical catheter loaded with the smart stent can be advanced. Scale bar, 4 mm. d-e, Diagnostic angiograms evaluating a side branch jailed by the smart stent. (d) Post-implantation and (e) pre-implantation angiograms demonstrate that the smart stent does not obstruct flow to the side branch. Preserving anterograde blood flow in jailed branches is important for the utilization of the smart stent in a variety of vascular locations. The smart stent’s mesh structure is specially designed to prevent the complete obstruction of flow across the smart stent to the side branches. Scale bars, 3 mm. f-g, Versatile deployment of the smart stent in multiple locations. Notably, the appearance of the smart stent in the image (g) is lighter (fainter) in color compared to image (f). This variation in visualization is attributed to differences in tissue thickness and the distinct anatomical areas being imaged. Scale bars, 6 mm. Panel a has been partially created in BioRender. Liu, Z. (2025) https://biorender.com/tbie25f.
Extended Data Fig. 2 Induced stenosis in the pig carotid artery in vivo.
a, Fluoroscopic image showing the normal patent state with the deflated balloon catheter positioned in the pig carotid artery in vivo. b, Precision placement of the balloon catheter at the upstream position to induce the proximal stenosis. c, Precision placement of the balloon catheter inside the smart stent to induce the in-stent stenosis. d, Precision placement of the balloon catheter at the downstream position to induce the distal stenosis. Scale bars, 4 mm (a-d). e-h, Images showing incremental inflation of the balloon to induce controlled stenosis within the smart stent, starting with mild narrowing (e), gradually occluding more of the lumen (f–g), and finally reaching a severe level of stenosis (h). Scale bars, 5.6 mm (e-h). i, Hemodynamic sensing signals recorded during gradual balloon inflation to simulate progressively increasing stenosis severity.
Extended Data Fig. 3 AI-assisted interpretation of smart stent sensing signals.
a, Representative smart stent sensing signals included in the dataset. b, Standardized time-series preprocessing pipeline, including resampling, windowing, class balancing, normalization, train–validation split, and optional continuous wavelet transform (CWT) processing. c-d, Multiple neural network architectures were subsequently trained, including 1D Convolutional Neural Network (CNN), 1D CNN + Long Short-Term Memory (LSTM), and 2D CNN (c), to classify the blood flow sensing signals (d). Abbreviations: Conv., convolutional layer; FC layer, fully connected layer.
Extended Data Fig. 4 Quantification of human inflammatory cytokines and cell type markers.
a, Concentration of human TNF. b, Concentration of human VEGF-A. Data are presented as mean ± s.d. in a and b; n = 4 experiments. Significance is determined by a two-tailed t-test. N.S., not significant. c, Cell-type gene markers for each cluster, with the percentage of expressing cells and the average expression level used to identify cell types.
Extended Data Fig. 5 Clustering analysis of T cell subpopulations.
a, Uniform Manifold Approximation and Projection (UMAP) analysis of T cells, including naive CD4⁺ T cells, naive CD8⁺ T cells, and effector memory CD4⁺ T cells. Naive CD4⁺ T cells were identified as CD3E+, CD4+, CCR7high, TCF7high, SELLhigh, CD69low, FAS−, IL7R+ T cells. Naive CD8⁺ T cells were identified as CD3E+, CD8A+, CCR7high, TCF7high, SELLhigh, CD69low, FAS−, IL7R+ T cells. Effector memory CD4⁺ T cells were identified as CD3E+, CD4+, CCR7low, TCF7low, SELLlow, CD69high, FAS+, IL7R+ T cells. b, Cell-type gene markers for each cluster, with the percentage of expressing cells and the average expression level used to identify cell types.
Extended Data Fig. 6 Evaluation of the attributes of the smart stent.
The smart stent represents a self-powered diagnostic platform for continuous ISR diagnosis, extending the functionality of conventional metal stents beyond mechanical support. An evaluation is presented to outline its attributes across (1) platform technology, (2) clinical translation, and (3) biosafety. Together, these attributes suggest the potential of the smart stent as a practical diagnostic technology that could complement existing stent technologies and support improved management of atherosclerosis. Partially created in BioRender. Liu, Z. (2025) https://biorender.com/tbie25f.
Supplementary information
Supplementary Information (download PDF )
Supplementary Figs. 1−29, Tables 1−3 and Notes 1−29.
Supplementary Video 1 (download MP4 )
A standard catheter approach for smart stent delivery and deployment.
Supplementary Video 2 (download MP4 )
A clear surgical view of the carotid artery with the implanted smart stent.
Supplementary Video 3 (download MP4 )
Real-time fluoroscopy images showing the induced stenosis.
Supplementary Tables (download XLSX )
Supplementary Tables 1−3.
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Source Data Extended Data Figs. 2 and 4 (download XLSX )
Statistical source data for Extended Data Figs. 2 and 4.
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Chen, G., Kim, W.J., Yang, Y. et al. Self-powered in-stent restenosis diagnosis via magnetoelastic stents. Nat Cardiovasc Res 5, 155–167 (2026). https://doi.org/10.1038/s44161-025-00773-4
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DOI: https://doi.org/10.1038/s44161-025-00773-4
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