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MOF-Based Biosensors for Early Detection of Alzheimer’s Disease Associated with Emerging Contaminants
Summary
This review explores how metal-organic framework-based biosensors could enable early detection of Alzheimer's disease biomarkers linked to exposure to emerging contaminants including microplastics and phthalates. The study suggests that chronic exposure to these environmental pollutants may contribute to neuroinflammation and protein aggregation associated with neurodegeneration, and that advanced biosensor technology could help identify at-risk individuals.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by synaptic dysfunction, neuronal loss, and cognitive decline. Cumulative epidemiological and mechanistic evidence implicates chronic exposure to emerging contaminants, including phthalates, particulate matter, microplastics, and per- and polyfluoroalkyl substances, as modulators of AD pathogenesis. These pollutants can penetrate the blood–brain barrier and aggravate oxidative stress, neuroinflammation, and aberrant protein aggregation, thereby accelerating neurodegenerative cascades. The interplay between environmental risk factors and molecular hallmarks of AD underscores the urgent requirement for highly sensitive biosensing strategies capable of detecting early-stage biomarkers with diagnostic precision. Metal–organic frameworks (MOF), with their exceptional surface area, tunable porosity, and chemical modularity, provide a versatile platform for biomolecular recognition and signal amplification. Advanced MOF-based constructs, including AuNP-integrated MOF, MOF@MXene heterostructures, core–shell composites, and MOF–carbon quantum dot hybrids, have demonstrated enhanced electron transfer kinetics, superior biocompatibility, and robust signal transduction for detecting amyloid- β(A β) species, tau isoforms, and neurofilament light chain. This review systematically evaluates MOF-based biosensors’ structural-functional attributes for AD biomarker detection, delineates their current limitations, and proposes mechanistically informed design strategies to accelerate translation toward clinically viable, point-of-care diagnostic platforms in populations that are environmentally predisposed to AD.
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