Tiny sensors, big hope: ML-optimized nanodiagnostics for TBI in Sanfilippo syndrome

Sanfilippo syndrome (mucopolysaccharidosis type III) is a rare autosomal recessive disorder marked by neurodegeneration due to defective heparan sulfate metabolism. Diagnostic and therapeutic limitations persist, particularly for detecting comorbid conditions like traumatic brain injury (TBI), which requires sensitive, rapid tools for timely intervention. Emerging ML-optimized nanodiagnostic platforms offer a breakthrough, allowing early-stage, high-fidelity detection of TBI biomarkers in biofluids. Recent studies employing surface-enhanced Raman spectroscopy (SERS) and multiplex electrochemiluminescence (ECL) sensors demonstrate the capacity for real-time, low-volume, point-of-care TBI diagnosis. However, limitations remain in adapting these models for rare pediatric populations due to data scarcity, blood-brain barrier constraints, and biomarker kinetics. We call for expanded validation, interdisciplinary collaboration, and the development of wearable diagnostics to personalize care and improve outcomes in Sanfilippo syndrome and beyond.