明胶
聚己内酯
材料科学
再生(生物学)
锌
膜
纳米颗粒
复合数
生物医学工程
化学工程
复合材料
纳米技术
医学
化学
冶金
聚合物
有机化学
生物化学
工程类
细胞生物学
生物
作者
Gina Prado-Prone,Phaedra Silva-Bermúdez,Maasoomeh Bazzar,Maria Letizia Focarete,Sandra E. Rodil,Ximena Vidal-Gutiérrez,Jorge A. García-Macedo,Victor I. García‐Pérez,Cristina Velasquillo,Argelia Almaguer‐Flores
标识
DOI:10.1088/1748-605x/ab70ef
摘要
The bacterial colonization of absorbable membranes used for guided tissue regeneration (GTR), as well as their rapid degradation that can cause their rupture, are considered the major reasons for clinical failure. To address this, composite membranes of polycaprolactone (PCL) and gelatin (Gel) loaded with zinc oxide nanoparticles (ZnO-NPs; 1, 3 and 6 wt% relative to PCL content) were fabricated by electrospinning. To fabricate homogeneous fibrillar membranes, acetic acid was used as a sole common solvent to enhance the miscibility of PCL and Gel in the electrospinning solutions. The effects of ZnO-NPs in the physico-chemical, mechanical and in vitro biological properties of composite membranes were studied. The composite membranes showed adequate mechanical properties to offer a satisfactory clinical manipulation and an excellent conformability to the defect site while their degradation rate seemed to be appropriate to allow successful regeneration of periodontal defects. The presence of ZnO-NPs in the composite membranes significantly decreased the planktonic and the biofilm growth of the Staphylococcus aureus over time. Finally, the viability of human osteoblasts and human gingival fibroblasts exposed to the composite membranes with 1 and 3 wt% of ZnO-NPs indicated that those membranes are not expected to negatively influence the ability of periodontal cells to repopulate the defect site during GTR treatments. The results here obtained suggest that composite membranes of PCL and Gel loaded with ZnO-NPs have the potential to be used as structurally stable GTR membranes with local antibacterial properties intended for enhancing clinical treatments.
科研通智能强力驱动
Strongly Powered by AbleSci AI