Deep Learning Assisted Heteroporous Covalent Organic Framework Hydrogels Enable Dual-Mode Tracking and Analysis to...

Area of Science:

• Biomaterials Science
• Neuroscience
• Wearable Technology

Background:

• Parkinson's disease (PD) pathophysiology involves resting tremor and dopamine dysregulation.
• Early detection of subtle PD manifestations is challenging.
• Current monitoring methods lack real-time, integrated capabilities.

Purpose of the Study:

• To develop a wearable hydrogel (Gel@COF) for simultaneous dopamine level and resting tremor monitoring.
• To enable early detection and intervention for Parkinson's disease.
• To provide convenient, real-time rehabilitation monitoring for PD patients.

Main Methods:

• Fabrication of Gel@COF by encapsulating functionalized COF-101 within a self-crosslinked hydrogel.
• Utilizing COF-101's fractal and heteroporous structure for dopamine recognition and enhanced proton conductivity.
• Integration with a convolutional neural network (CNN) algorithm for data analysis and prediction.

Main Results:

• Gel@COF demonstrated high sensitivity at 1% strain and a fast response/recovery time (16.0 ms).
• The material exhibited visual fluorescence changes due to its unique topological structure.
• The integrated CNN algorithm achieved over 95% evaluation and prediction accuracy for PD.

Conclusions:

• Gel@COF offers a dual-functional wearable solution for concurrent dopamine and tremor monitoring.
• The system shows significant potential for early Parkinson's disease detection and intervention.
• This technology provides real-time, convenient rehabilitation monitoring with high diagnostic accuracy.