Authors
Teng Wu, Kun Wang, Bai Sun, Zelin Cao, Junxiang Gu, Meirou Liang, Yifan Le, Fan Li, Jian Wang, Yifan Xiao, Mengna Wang, Longhui Fu, Kaikai Gao, Haoyuan Wang, Hui Ma, Xincheng Du, Jiajun Liu, Jianqiang Qu, Chang Liu, Guangdong Zhou, Jinyou Shao, Xianxia Yan
Published in
ACS applied materials & interfaces. Oct 01, 2025. Epub Oct 01, 2025.
Abstract
Craniotomy, a complex neurosurgical intervention, carries significant risks of postoperative complications including intracerebral hemorrhage (ICH) and cerebral edema, causing elevated intracranial pressure (ICP) and life-threatening cerebral herniation. However, the current ventricular catheter ICP monitoring technologies pose risks of infection and hemorrhage, and restrict patient mobility during medical procedures. There is thus an urgent need to develop ICP monitoring technologies that simultaneously achieve sensitivity, safety, and portability. Memristors, with their integrated memory, sensing, and neuromorphic computing capabilities, offer a promising solution to traditional monitoring bottlenecks. In this work, we innovatively developed an Ag/WO3/MnO2/FTO-structured memristor and validated its pressure signal encoding capability in vitro via integration with a pressure sensor. A collagenase-induced ICH animal model was established to simulate postcraniotomy intracranial hypertension. Following model induction, the sensor-memristor system was implanted for intracranial pressure signal acquisition and encoding. The encoded signals were prospectively processed through a memristor-based logic circuit for noise reduction, and analyzed and classified via a memristive neural network. This study demonstrates the potential of implantable memristor-sensor system for postcraniotomy ICP monitoring and underscores its role in enhancing neurosurgical care, which also provides innovative insights for designing efficient, real-time, and low-power consumption implantable pressure monitoring devices for medical health monitoring.
PMID:
41032885
Bibliographic data and abstract were imported from PubMed on 02 Oct 2025.
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