Biomimetic Analysis of Neurotransmitters for Disease Diagnosis through Light-Driven Nanozyme Sensor Array and Machine...

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Neuroscience

Background:

• Neurological diseases (e.g., Alzheimer's, Parkinson's) present a global health challenge.
• Disrupted neurotransmitter regulation is a hallmark of these disorders.
• Current diagnostic methods are slow and lack early-stage sensitivity.

Purpose of the Study:

• To develop a novel photoresponsive nanozyme sensor array for rapid, sensitive, and multiplexed neurotransmitter detection.
• To integrate metal-organic frameworks (MOFs) and machine learning for enhanced diagnostic capabilities.
• To enable early diagnosis and continuous monitoring of neurological conditions.

Main Methods:

• Fabrication of a photoresponsive nanozyme sensor array using Zn(II) meso-Tetra(4-carboxyphenyl)porphine (ZnTCPP)-based MOFs.
• Utilizing light-driven catalysis to accelerate nanozyme response speed.
• Employing machine learning algorithms for patterned neurotransmitter detection and analysis, mimicking the mammalian olfactory system.

Main Results:

• The sensor array demonstrated rapid and sensitive detection of neurotransmitters within minutes.
• High precision was maintained even in complex biological samples (serum, cerebrospinal fluid).
• The system successfully detected neurotransmitter signatures associated with neurological disorders, including Alzheimer's disease.

Conclusions:

• The novel photoresponsive nanozyme sensor array offers a significant advancement in neurological disease diagnostics.
• This biomimetic platform provides potential for early detection and ongoing monitoring of conditions like Alzheimer's disease.
• The integration of MOFs and machine learning facilitates accurate neurotransmitter analysis in complex biological matrices.