College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
College of Pharmacy, Shaanxi University of Chinese Medicine, Xi’an 710021, China
Institute of Basic and Translational Medicine, Xi’an Medical University, Xi’an 710021, China
School of Mechanical Engineering, Chengdu University, Chengdu 610000, China
§ Ouyang Yue, Xuechuan Wang, and Mengdi Hou contributed equally to this work.
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The article introduces a double-layer gelatin-based bio-adhesive (DLGel) that exhibits strong adhesive strength on wet and dynamic tissues, promotes comprehensive wound healing through electrical stimulation, and possesses reversible underwater light-/thermal-adhesion, hemostatic performance, antimicrobial properties, and self-repair capability. An innovative deep learning algorithm is utilized to convert the electrical signals collected by DLGel into visual graphics, enabling real-time evaluations of postoperative wound anomalies and morphology. This research promotes intelligent non-clinical comprehensive treatment.
Abstract
Wound abnormalities such as secondary wound laceration and inflammation are common postoperative health hazards during clinical procedures. The continuous treatment, healing induction, and real-time visualization of wound status and complications, including wound re-tearing, inflammation, and morphology, are key focal points for comprehensive healthcare. Herein, an on-demand quadruple energy dissipative strategy was proposed for the nanoengineering of a physically and chemically synergistic double-layer gelatin-based bio-adhesive (DLGel) by combining a multi-network adhesive layer and a versatile electroactive energy dissipative layer based on contrivable interlocking micro-pillar arrays and crosslinked polymer chains. The subtly multiple energy dissipation designs enable DLGel with robust adhesive strength to omnipotently wet and dynamic tissue, providing a basis for reliable wound closure. DLGel achieves comprehensive wound-healing induction through electrical stimulation and possesses reversible underwater light/thermal adhesion, excellent hemostatic performance, outstanding antimicrobial properties, and self-repair capability. Furthermore, a novel deep-learning strategy is creatively established to respond to mechanical deformation due to wound anomalies. This strategy translates biological information into visual graphics, providing real-time early warning and assessment of postoperative wound-abnormality/-morphology, such as laceration, inflammation, and necrosis. Therefore, DLGel and its associated signal collection and processing protocol enable the integration of reliable wound closure, wound healing, and real-time postoperative wound-status warning and assessment within the unobservable and undetectable “black box” regions in a context of non-clinical comprehensive therapy.
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