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Updated: Aug 13, 2026

Multiplexed Single-molecule Force Proteolysis Measurements Using Magnetic Tweezers
Published on: July 25, 2012
A Novel Approach for Optimizing Molecularly Imprinted Polymer Composition in Electrochemical Detection of Collagen
Naphatsawan Vongmanee1, Jindapa Nampeng1, Katesirin Rattanapithan1
1Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Abstract:
Collagen peptides are key structural proteins that play an important role in maintaining the integrity and proper function of multiple tissues in the human body. Their breakdown is recognized as an important biomarker for various degenerative conditions, including the loss of muscle mass, joint and bone disorders, and compromised skin health. Current analytical approaches for collagen detection, such as ultraviolet spectrometry, enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and histochemical staining, are widely used but often expensive, time-consuming, and reliant on specific laboratory instrumentation, limiting their practicality for routine or rapid diagnostics. This study reports a novel biosensor for collagen peptide detection based on molecularly imprinted polymers (MIPs) integrated with screen-printed electrodes (SPEs). Electrochemical measurements revealed a clear correlation between collagen concentration and current response, confirming effective molecular binding within the imprinted matrix. The optimized MIP-modified electrode exhibited a detection range of 0.1-1000 µg/mL with a limit of detection (LOD) of 1.0106 µg/mL, limit of quantification (LOQ) of 4.46 µg/mL, sensitivity of 8.3816, and correlation coefficient (R2 = 0.9436). These results highlight strong selectivity and sensitivity toward collagen peptides. The proposed MIP-based biosensor provides a rapid, low-cost platform for detecting collagen degradation products and holds potential for early diagnosis and future clinical applications in degenerative disease monitoring.
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