Facile fabrication of functional hyaluronic acid-/konjac glucomannan-based injectable hydrogel as wound closure and anti-microbial material for the treatment of burn wound healing
Source abstract
Abstract
Hydrogel dressings with self-healing properties hold immense promise for enhancing the effectiveness of burn wound healing treatments by prolonging the lifespan of the material. In this study, a composite hydrogel combining hyaluronic acid (HA) and konjac glucomannan (KGM), referred to as HAKGM hydrogel, was synthesized using the Schiff-base reaction. The research focused on investigating the impact of varying KGM concentrations on key properties such as gelation time, swelling ratio, biodegradation, mechanical characteristics, and physico-chemical properties of the HAKGM hydrogel composite. Notably, the HAKGM hydrogel composite exhibited notable antibacterial and biofilm activities against tested microorganisms, underscoring its potential for infection control in burn wound management. Furthermore, cytotoxicity assessments on NIH/3T3 cells revealed minimal harm caused by the HAKGM hydrogel composite, suggesting its biocompatibility. Fluorescence imaging showcased the attachment and infiltration of fibroblasts into the hydrogel matrix over 24, 48, and 72 h of cell growth, indicating its ability to support cellular proliferation and tissue regeneration. Finally, in a full-thickness skin defect model, the injectable HAKGM hydrogel composite demonstrated significant efficacy in reducing burn wound recovery time. These findings highlight the potential utility of the novel HAKGM hydrogel composite as an advanced solution for burn wound dressings, offering enhanced healing properties and facilitating improved patient outcomes.Graphical abstract
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Hydrogels: synthesis, properties, applications · Polysaccharides Composition and Applications · Electrospun Nanofibers in Biomedical Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
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