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Peer-reviewed veterinary case report

Microbial-Responsive Wound Dressings Based on Biopolymer Degradation Strategy for Detecting Bacterial Infections.

Year:
2026
Authors:
Sadati S et al.
Affiliation:
Warwick Medical School · United Kingdom

Abstract

Chronic wounds remain a major clinical challenge due to their strong association with antibiotic-resistant microbial biofilms. These nonhealing wounds demand advanced therapeutic strategies that go beyond passive protection to actively monitor and respond to changes in the wound environment. To address this, we propose an activity-based sensing strategy that detects bacterial proteolytic activity using composition-tunable biopolymer films that degrade in response to pathogen-secreted enzymes. Gelatin films cross-linked with (3-glycidyloxypropyl)trimethoxysilane (GPTMS) and blended with poly(ethylene oxide) (PEO) were engineered to undergo selective peptide-bond cleavage by proteolytic activity. The incorporation of PEO enhanced water uptake and accelerated enzymatic degradation, with the optimal composition (25% PEO) exhibiting 4-fold faster mass loss compared to cross-linked gelatin, reaching ∼80% degradation within 12-24 h in the presence of the bacterial pathogen <i>Pseudomonas aeruginosa</i> and ∼35% within 24-48 h with drug resistant <i>Staphylococcus aureus</i>. Real-time acoustic measurements revealed distinct degradation kinetics and viscoelastic signatures at nanoscale that correlated with <i>P. aeruginosa</i> protease activity, while Fourier-transform infrared spectroscopy and scanning electron microscopy confirmed structural and morphological changes following enzymatic exposure. Together, these findings establish a label-free, enzyme-responsive sensing platform that transduces bacterial activity, including biofilm-associated proteolysis, into quantitative physical signals. These findings establish composition-tunable enzyme-responsive biopolymer degradation as a viable broad-spectrum platform responding to total proteolytic activity. As no pathogen-specific recognition elements are required, this platform offers excellent potential to detect challenging polymicrobial infections.

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Original publication: https://europepmc.org/article/MED/41589846