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用于体内双向信号传递的耐用且抗疲劳的软周边神经假肢。

Durable and Fatigue-Resistant Soft Peripheral Neuroprosthetics for In Vivo Bidirectional Signaling.

机构信息

School of Medicine, Sungkyunkwan University, Suwon, 16419, Republic of Korea.

Center for Biomaterials, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.

出版信息

Adv Mater. 2021 May;33(20):e2007346. doi: 10.1002/adma.202007346. Epub 2021 Mar 19.

Abstract

Soft neuroprosthetics that monitor signals from sensory neurons and deliver motor information can potentially replace damaged nerves. However, achieving long-term stability of devices interfacing peripheral nerves is challenging, since dynamic mechanical deformations in peripheral nerves cause material degradation in devices. Here, a durable and fatigue-resistant soft neuroprosthetic device is reported for bidirectional signaling on peripheral nerves. The neuroprosthetic device is made of a nanocomposite of gold nanoshell (AuNS)-coated silver (Ag) flakes dispersed in a tough, stretchable, and self-healing polymer (SHP). The dynamic self-healing property of the nanocomposite allows the percolation network of AuNS-coated flakes to rebuild after degradation. Therefore, its degraded electrical and mechanical performance by repetitive, irregular, and intense deformations at the device-nerve interface can be spontaneously self-recovered. When the device is implanted on a rat sciatic nerve, stable bidirectional signaling is obtained for over 5 weeks. Neural signals collected from a live walking rat using these neuroprosthetics are analyzed by a deep neural network to predict the joint position precisely. This result demonstrates that durable soft neuroprosthetics can facilitate collection and analysis of large-sized in vivo data for solving challenges in neurological disorders.

摘要

软神经假肢可以监测感觉神经元的信号并传递运动信息,从而有潜力替代受损的神经。然而,实现与外周神经接口的设备的长期稳定性具有挑战性,因为外周神经中的动态机械变形会导致设备中的材料降解。在这里,我们报道了一种用于外周神经双向信号传递的耐用且抗疲劳的软神经假肢设备。该神经假肢设备由金纳米壳(AuNS)涂覆的银(Ag)薄片分散在坚韧、可拉伸和自修复聚合物(SHP)中的纳米复合材料制成。纳米复合材料的动态自修复特性允许 AuNS 涂覆的薄片的渗流网络在降解后重建。因此,它在设备-神经界面处的重复、不规则和剧烈变形下的降解电性能和机械性能可以自动自我恢复。当该设备被植入大鼠坐骨神经时,可获得超过 5 周的稳定双向信号传递。使用这些神经假肢从活体行走大鼠收集的神经信号由深度神经网络进行分析,以精确预测关节位置。该结果表明耐用的软神经假肢可以促进用于解决神经障碍挑战的大尺寸体内数据的收集和分析。

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