伤口愈合
炎症
生物相容性
细胞因子
肿瘤坏死因子α
促炎细胞因子
血管生成
免疫学
基质金属蛋白酶
医学
材料科学
癌症研究
内科学
冶金
作者
Aqib Iqbal Dar,Shiwani Randhawa,Mohini Verma,Amitabha Acharya
标识
DOI:10.1021/acsami.3c08166
摘要
Due to impaired wound healing, millions of acute and chronic wound cases with increased morbidity have been recorded in the developed countries. The primary reason has been attributed to uncontrolled inflammation at the wound site, which makes healing impossible for years. The use of red blood cell (RBC) ghosts or erythrocyte membranes for different theranostic applications has gained significant attention in recent years due to their biocompatibility and biomimicking properties. Our study builds upon this concept by presenting a new approach for creating an improved and controlled inflammatory response by employing RBC ghost encapsulated tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) modified AuNPs (gold nanoparticles) for accelerating the wound healing at early postinjury stage (∼48 h). The results suggested that the developed GTNFα-IL6@AuNPs created a controlled and time dependent TNF-α response and showed increased reactive oxygen species generation at ∼12 h. Further, proper M1/M2 functional transition of macrophages was observed in macrophages at different time intervals. The expression results suggested that the levels of wound healing biomarkers like transforming growth factor-β (1.8-fold) and collagen (2.4-fold) increased while matrix metalloproteinase (3–8-fold) levels declined at later stages, which possibly increased the cell migration rate of NP treated cells to ∼90%. Hence, we are here reducing the timeline of the inflammatory phase of wound healing by actually creating a controlled inflammatory response at an early postinjury stage and further assisting in regaining the ability of cells for wound remodelation and repair. We intend that this new approach has the potential to improve the current treatment strategies for wound healing and skin repair under both in vitro and in vivo conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI