The Impact of Green Physical Crosslinking Methods on the Development of Sericin-Based Biohydrogels for Wound Healing.
Maria C ArangoNatalia Jaramillo-QuicenoJose David BadiaAmparo CháferJosep Pasqual CerisueloCatalina Álvarez-LópezPublished in: Biomimetics (Basel, Switzerland) (2024)
Silk sericin (SS)-based hydrogels show promise for wound healing due to their biocompatibility, moisture regulation, and cell proliferation properties. However, there is still a need to develop green crosslinking methods to obtain non-toxic, absorbent, and mechanically strong SS hydrogels. This study investigated the effects of three green crosslinking methods, annealing treatment (T), exposure to an absolute ethanol vapor atmosphere (V.E), and water vapor (V.A), on the physicochemical and mechanical properties of SS and poly (vinyl alcohol) (PVA) biohydrogels. X-ray diffraction and Fourier-transform infrared spectroscopy were used to determine chemical structures. Thermal properties and morphological changes were studied through thermogravimetric analysis and scanning electron microscopy, respectively. The water absorption capacity, mass loss, sericin release in phosphate-buffered saline (PBS), and compressive strength were also evaluated. The results showed that physical crosslinking methods induced different structural transitions in the biohydrogels, impacting their mechanical properties. In particular, V.A hydrogen presented the highest compressive strength at 80% deformation owing to its compact and porous structure with crystallization and bonding sites. Moreover, both the V.A and T hydrogels exhibited improved absorption capacity, stability, and slow SS release in PBS. These results demonstrate the potential of green physical crosslinking techniques for producing SS/PVA biomaterials for wound healing applications.
Keyphrases
- wound healing
- electron microscopy
- tissue engineering
- physical activity
- cell proliferation
- high resolution
- mental health
- drug delivery
- magnetic resonance imaging
- drug release
- oxidative stress
- cell cycle
- magnetic resonance
- extracellular matrix
- machine learning
- high glucose
- endothelial cells
- replacement therapy
- risk assessment
- highly efficient
- artificial intelligence
- human health
- deep learning
- low cost
- bone regeneration