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Miniaturized Implantable Fluorescence Probes Integrated with Metal-Organic Frameworks for Deep Brain Dopamine Sensing
Wei Ling1,2, Xue Shang3, Chaonan Yu4
1Research Center for Augmented Intelligence, Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou 311121, China.
ACS Nano
|April 1, 2024
Summary
This study introduces a tiny, implantable probe using fluorescent metal-organic frameworks (MOFs) for precise deep brain dopamine monitoring. This technology aids in understanding neurological diseases and brain function.
Area of Science:
- Neuroscience
- Materials Science
- Biomedical Engineering
Background:
- Continuous neurotransmitter monitoring is crucial for understanding neural mechanisms and neurological diseases.
- Existing methods may lack the sensitivity or miniaturization required for deep brain applications.
Purpose of the Study:
- To develop a miniaturized, implantable fluorescence probe for sensitive deep brain dopamine sensing.
- To demonstrate the probe's utility in real-time dopamine monitoring during in vivo experiments.
Main Methods:
- Integration of thinned light-emitting diodes (LEDs) and phototransistors into a 120 μm thick probe.
- Utilizing a fluorescent metal-organic framework (MOF) with specific dopamine binding capabilities.
- Development of a compact, wireless circuit for data acquisition and transmission.
- In vivo testing in rats for continuous dopamine level monitoring during deep brain stimulation.
Main Results:
- Achieved a highly sensitive dopamine detection limit of 79.9 nM.
- Successfully monitored real-time dopamine dynamics in rat brains during deep brain stimulation.
- Demonstrated favorable biocompatibility through cytotoxicity and immunofluorescence analyses.
Conclusions:
- The developed probe offers a novel platform for sensitive, real-time dopamine monitoring in deep brain regions.
- The integration of MOFs and flexible electronics paves the way for advanced brain-computer interfaces.
- This technology holds potential for neuroscience research, disease tracing, and smart diagnostic applications.

