A comprehensive in vitro characterization of non-crosslinked, diverse tissue-derived collagen-based membranes intended for assisting bone regeneration.
PloS one(2024)
Abstract
Collagen-based membranes are class III-medical devices widely used in dental surgical procedures to favour bone regeneration. Here, we aimed to provide biophysical and biochemical data on this type of devices to support their optimal use and design/manufacturing. To the purpose, four commercial, non-crosslinked collagen-based-membranes, obtained from various sources (equine tendon, pericardium or cortical bone tissues, and porcine skin), were characterized in vitro. The main chemical, biophysical and biochemical properties, that have significant clinical implications, were evaluated. Membranes showed similar chemical features. They greatly differed in morphology as well as in porosity and density and showed a diverse ranking in relation to these latter two parameters. Samples highly hydrated in physiological medium (swelling-ratio values in the 2.5-6.0 range) and, for some membranes, an anisotropic expansion during hydration was, for the first time, highlighted. Rheological analyses revealed great differences in deformability (150-1500kPa G') also alerting about the marked variation in membrane mechanical behaviour upon hydration. Samples proved diverse sensitivity to collagenase, with the cortical-derived membrane showing the highest stability. Biological studies, using human-bone-derived cells, supported sample ability to allow cell proliferation and to prompt bone regeneration, while no relevant differences among membranes were recorded. Prediction of relative performance based on the findings was discussed. Overall, results represent a first wide panel of chemical/biophysical/biochemical data on collagen-based-membranes that 1) enhances our knowledge of these products, 2) aids their optimal use by providing clinicians with scientific basis for selecting products based on the specific clinical situation and 3) represents a valuable reference for optimizing their manufacturing.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined