Peptide-responsive photonic hydrogels integrated with deep learning assistance for early MMP-9 detection

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Strong concordance reported (specific metrics not provided)

• Primary Outcome: MMP-9 detection concordance with ELISA reference standard

• Nnt Or Sensitivity: Analytical sensitivity: 10.60 nm mL/ng; Detection limit: 0.62 ng/mL

• Confidence Interval: Not reported

Clinical Application

High feasibility for point-of-care testing with 10-minute results and smartphone integration Potential for remote and rural healthcare delivery; would require TGA approval as diagnostic device; could complement existing pathology services under Medicare Patients requiring MMP-9 monitoring for inflammatory diseases and cancer surveillance

Abstract

Matrix metalloproteinase-9 (MMP-9) is crucial for extracellular matrix remodeling, and its dysregulation is associated with inflammatory diseases and different forms of cancer. Conventional MMP-9 detection methods such as enzyme-linked immunosorbent assay (ELISA) are limited by complexity, expensive equipment, and lengthy antibody incubation times. These limitations prevent their use in point-of-care testing. An MMP-9 responsive photonic crystal (PC/PEG-M9SP) hydrogel has been developed to address these challenges. The hydrogel is synthesized from 4-arm polyethylene glycol-acrylate and an MMP-9 sensitive peptide via Michael-type addition reaction. Upon MMP-9-specific enzymatic cleavage, the hydrogel undergoes a structural reconfiguration, resulting in a distinct color shift. Integrated with a deep learning-based smartphone app, this platform enables both visual and quantitative detection within 10 min, achieving high sensitivity (10.60 nm mL/ng) and a detection limit of 0.62 ng/mL. Validation in complex biological fluids demonstrated strong concordance with ELISA, confirming the analytical reliability of the hydrogel. This system provides a rapid, portable, and cost-effective solution for accurate MMP-9 detection, with strong potential for clinical and point-of-care applications.

References

1. 1.Wang, Y., Liu, M., Liu, X., Hao, D., Wang, Y., Cai, Y., Li, J., Miao, X., Zhang, Y., Yang, X., Bai, Y., Wang, C., Tay, F. R., Aparicio, C., Zhao, Y., & Niu, L. (2026). Peptide-responsive photonic hydrogels integrated with deep learning assistance for early MMP-9 detection. Biosensors & Bioelectronics, 118444. https://doi.org/10.1016/j.bios.2026.118444