A Closed‐Loop Nanosystem Based on Piezoelectric Sensor and Pd‐Nanoshell Photothermal Ablation for Renal Denervation to Treat Hypertension

Renal sympathetic nerves play a crucial role in the pathogenesis of hypertension, and renal denervation (RDN) is a new solution for patients with refractory hypertension. However, current RDN techniques show inconsistent results in clinical application probably owing to incomplete endovascular ablation of the sympathetic nerves and a lack of measures to localize and assess efficacy. In this study, a closed-loop RDN system consisting of a sensing unit with a piezoelectric thin-film sensor (PTFS) and a treatment unit with a hollow Pd nanoparticle shell (PdNPS) with a diameter of 202.0 nm for photothermal neural ablation is constructed. The PTFS can monitor and collect arterial pulsation and blood pressure (BP) and direct PdNPS to maximize RDN. PdNPS maintains a local temperature of 58-62 °C under near-infrared-II irradiation (1,064 nm) to achieve effective RDN within a range of 90-120 s treatment window. Photothermal ablation significantly inhibits the activities of renal sympathetic nerves post-procedure and after one month and reduces the elevation of BP by > 50%. The novel closed-loop system enables safe and efficient targeting, dynamic monitoring, and ablation of the renal sympathetic nerves. This closed-loop system provides a new strategy for RDN technology and even for treating sympathetic nerve-related chronic diseases.

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Publication Details

Journal
Advanced Materials
Published
2024-09-17
DOI
https://doi.org/10.1002/adma.202410383
Citations
7
Primary Topic
Heart Rate Variability and Autonomic Control
Type
article
Field-Weighted Citation Impact
2.81

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article

A Closed‐Loop Nanosystem Based on Piezoelectric Sensor and Pd‐Nanoshell Photothermal Ablation for Renal Denervation to Treat Hypertension

Chengzhe Liu, Lilei Yu, Chenlu Wang, Liping Zhou et al.
7 citations
Advanced Materials
Heart Rate Variability and Autonomic Control
2.81
article

A Closed‐Loop Nanosystem Based on Piezoelectric Sensor and Pd‐Nanoshell Photothermal Ablation for Renal Denervation to Treat Hypertension

Chengzhe Liu, Lilei Yu, Chenlu Wang, Liping Zhou, Mengqi Zeng, Shishang Guo, Jiaming Qiao, Xiaoya Zhou, Lei Fu, Hong Jiang, Xinrui Han, Zizhuo Zhang, Qinxuan Zu, Mingkun Huang, Jiale Wang
article en
7 citations

Abstract

Renal sympathetic nerves play a crucial role in the pathogenesis of hypertension, and renal denervation (RDN) is a new solution for patients with refractory hypertension. However, current RDN techniques show inconsistent results in clinical application probably owing to incomplete endovascular ablation of the sympathetic nerves and a lack of measures to localize and assess efficacy. In this study, a closed-loop RDN system consisting of a sensing unit with a piezoelectric thin-film sensor (PTFS) and a treatment unit with a hollow Pd nanoparticle shell (PdNPS) with a diameter of 202.0 nm for photothermal neural ablation is constructed. The PTFS can monitor and collect arterial pulsation and blood pressure (BP) and direct PdNPS to maximize RDN. PdNPS maintains a local temperature of 58-62 °C under near-infrared-II irradiation (1,064 nm) to achieve effective RDN within a range of 90-120 s treatment window. Photothermal ablation significantly inhibits the activities of renal sympathetic nerves post-procedure and after one month and reduces the elevation of BP by > 50%. The novel closed-loop system enables safe and efficient targeting, dynamic monitoring, and ablation of the renal sympathetic nerves. This closed-loop system provides a new strategy for RDN technology and even for treating sympathetic nerve-related chronic diseases.

Advanced MaterialsVol. 36(44)
Wuhan University (CN), Yangtze Optical Electronic (China) (CN), Renmin Hospital of Wuhan University (CN)
National Natural Science Foundation of China, Foundation for Innovative Research Groups of Hubei Province of China, National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 8%
Heart Rate Variability and Autonomic Control
2.81
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