Biomaterials regulate macrophages and promote regeneration function, which is a new hot pot in tissue engineering and regenerative medicine. The research based on macrophage materials biology has appeared happy future, but related research on regulating macrophages and promoting tissue regeneration is still in its infancy. The surface roughness of biomaterials is one of the important factors affecting macrophage behavior. Previous study also found that the surface roughness of many biomaterials regulating macrophage polarization, but not including mineralized collagen (MC). In this study, we designed and fabricated MC with different roughness and investigated the influence of MC with different roughness on macrophages. In the study, we found that on the rough surface of MC, macrophages exhibited M1 phenotype-amoeboid morphology and high-level secretory of inflammatory factor (tumor necrosis factor-α and interleukin-6), while smoother surface exhibited M2 phenotype. These data will be beneficial to understand the mechanism deeply and enrich biomaterials tissue regeneration theory, provide a new train of thought biomaterials inducing tissue regeneration and repair and guide the optimum design of new biomaterials, development and reasonable applications.

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This study discusses a method for quantitative quality assessment for the simplification of linear features. Considering the multi-scale nature of linear features, this paper combines the improved Douglas-Peucker method without threshold and the multiway tree model to construct a weighted hierarchical linear feature representation model called the Douglas-Peucker Multiway Tree (DMC-tree). Subsequently, the uncertainty computation is conducted from the root of the DMC-Tree top-down level by level to obtain the quality indexes. Then, the quality index of the whole linear feature is obtained by combining the indexes of every layer together with their weights. The results of the presented method are compared with those of the length ratio method and the Hausdorff distance method. The results show the advantages of the presented method over the others, including (1) its sensitivity to feature points of multiple scales, (2) the quantitative characteristics of the indexes, and (3) the finer granularity in assessment.