光刺激新技术能加速大脑类器官成熟

Core Viewpoint - The GraMOS technology developed by the Sanford Consortium for Regenerative Medicine at UC San Diego accelerates the development and maturation of brain organoids, providing new insights into neurodegenerative diseases like Alzheimer's and enabling real-time control of robotic devices by organoids [1][2]. Group 1: Technology Overview - GraMOS utilizes the unique optoelectronic properties of graphene to convert light signals into gentle electrical stimulation, promoting connections and communication between neurons [2]. - This method mimics the natural input signals received by the brain, facilitating the development of neural networks without invasive techniques [2]. - Regular application of GraMOS leads to stronger neural connections, more organized neural networks, and improved communication efficiency, particularly in organoid models derived from Alzheimer's patients [2]. Group 2: Applications and Implications - The technology opens new pathways for research into neurological diseases, brain-machine interface development, and the integration of living neural tissue with technological systems [1][2]. - A proof-of-concept experiment demonstrated the integration of graphene-connected brain organoids into a robotic system, allowing the robot to respond to environmental stimuli in just 50 milliseconds [2]. - The research signifies a major breakthrough in the application of graphene in neuroscience, nanotechnology, and neuroengineering, potentially leading to a powerful platform for studying neurodegenerative diseases and developmental brain disorders [2][3]. Group 3: Future Prospects - GraMOS has two primary applications: accelerating the maturation of the nervous system for more physiologically relevant disease observation and enabling brain organoids to respond to external environments, showcasing significant potential in AI [3]. - The combination of graphene's multifunctionality with the biological characteristics of brain organoids is redefining the boundaries of neuroscience, potentially leading to transformative changes in basic research, AI, and medical engineering [3].