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Updated: May 7, 2026

The Synthesis of RGD-functionalized Hydrogels as a Tool for Therapeutic Applications
Published on: October 7, 2016
Engineered Hydrogels as Functional Components in Controllable Neuromodulation for Translational Therapeutics
Yanming Zhao1,2, Rujie Sun3, Zitian Wang1,2
1Institute of Biomedical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China.
Abstract:
Controllable neuromodulation leveraging multimodal triggers synergized with hydrogels represents a transformative therapeutic strategy for pro-regenerative neural repair. Strategic incorporation of programmable neuromodulatory interventions and engineered hydrogels within localized neural niches is critical for clinical translation, characterized by lower invasiveness and greater therapeutic efficacy. This review elucidates the physiochemical features of hydrogels, systematically classifying hydrogel-based neuromodulation into five distinct modes (electrical, ionic, biomechanical, optical, and biochemical) and highlighting the intrinsic multidimensional structural and chemical engineering employed to enhance neuromodulatory performance. Key principles of hydrogel design and fabrication are provided from the perspective of tissue-implant interactions, such as mechanical compatibility, electrointegration, adhesion, and wireless activation. Hydrogels embedded with low-impedance organic and inorganic components, such as conductive polymers and noble metals, are investigated to provide high-level evidence to enable precise cellular stimulation for intrinsic nerve repair, neural prosthesis, and brain-machine interface. Crucially, this review highlights the synergistic integration of these principles into multimodal, closed-loop systems, which combine functions like electrophysiological sensing with on-demand biochemical release for intelligent, autonomous therapies. Finally, this review confronts the critical challenges for clinical translation and discusses future directions, including the potential of artificial intelligence-driven materials design to accelerate the development of next-generation neural interfaces.
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