A dual-mode surface-enhanced Raman scattering and piezoresistive sensing platform for biomolecular and biomechanical analysis in Parkinson's disease.

Current non-invasive assessments for Parkinson's disease (PD) are predominantly single-modality, leaving an unmet need for an integrated platform combining biomolecular and biomechanical detection. Here, an integrated dual-mode sensing platform based on a flexible non-woven fabric (NWFs) device for PD is provided. Silver nano-contacts are electrolessly deposited onto cotton fibers to form a biomimetic spider-leg-like microarchitecture, which concurrently supports localized surface plasmon resonance (LSPR) for surface-enhanced Raman scattering (SERS) and strain-responsive piezoresistive behavior. The NWFs@Au@Ag substrate exhibits good cytocompatibility with 100% cell viability. Using the NWFs@Au@Ag SERS substrate for standard α-synuclein molecule testing, the 1670-1680 cm-1 band verifies the formation of β-sheet-rich amyloid aggregates. In saliva from PD and MSA patients, SERS detects spectral signatures associated with α-synuclein, most notably an altered amide I band at 1677 cm-1 and the difference in β-sheet is one of the important contributors to the spectral differences. Simultaneously, the piezoresistive mode quantitatively monitors orbicularis oculi strain during blink and eye-scanning movements. The strain signals show strong concordance with the Movement Disorders Society Unified Parkinson's Disease Rating Scale (Part III), and capture disease progression and acute levodopa response with temporal resolution superior to conventional electromyography (EMG). In 30 PD patients, artificial intelligence-assisted analysis of horizontal eye-scanning strain waveforms achieves 90.9% classification accuracy. By combining biomolecular fingerprinting and biomechanical function assessment, this dual-mode platform could overcome the single-modality limitation of existing PD non-invasive evaluation methods and offer an explorable path for point-of-care screening and longitudinal monitoring of disease progression.