Fabrication of Asymmetric Polysaccharide Composite Membranes as Guided Bone Regeneration Materials

Periodontal regeneration can be achieved by guided tissue and guided bone regeneration (GTR/GBR) membranes, which act as a physical barrier to exclude migration of connective and epithelium, favoring the repopulation of periodontal ligament cells. Asymmetric polysaccharide GBR membranes with two different surfaces were developed in this study. Positive chitosan (CS), negative hyluronaic acid (HA) and konjac glucomannan (KGM) were composited by electrostatic interaction, forming smooth and dense membranes as upper surface to inhibit the ingrowth of cells from gingiva. The lower porous and coarse surface was obtained by gel freeze-drying and mineralization to improve the regeneration of the bone tissue. The performance of the membranes was characterized by Infrared Radiation (IR), X-ray diffraction (XRD), scanning electron microscope (SEM), tensile strength and biological evaluation. It was found that the composite membranes with chitosan content of 56.7 wt%in the dry state possess the highest tensile strength, with elongation 10 times more higher than that of the pure CS ones. Additionaly, open pores with diameter of 10-100 µm and homogenouse distributed nano-hydroxyapatite (HAP) were investigated on the coarse part. Cell studies demonstrated that the porous surface promoted the growth of the preosteoblast. Overall, the composite membranes may be useful for regeneration of periodontal regeneration

Polyurethane/Liquid Crystal Microfibers with pDNA Polyplex Loadings for the Optimal Release and Promotion of HUVEC Proliferation

Fiber structures with connected pores resemble the natural extracellular matrix (ECM) in tissues, and show high potential for promoting the formation of natural functional tissue. The geometry of composite fibers produced by electrospinning is similar to that of the living-tissue ECM, in terms of structural complexity. The introduction of liquid crystals does not affect the morphology of fibers. The composite mat shows better hydrophilicity, with higher content of liquid crystal. At the same time, the higher the content of liquid crystal, the lower the modulus and tensile strength, and the higher the breaking energy and the elongation at break. Additionally, the factors affecting fibers are also investigated in this study. The addition of liquid crystals to the fibers’ matrix can slow down the release of pDNA, which is the most common vehicle for genetic engineering, and the encapsulation of pDNA polymer into the fiber matrix can maintain biological activity. The continued release of the pDNA complex was achieved in this study through liquid crystals, and the effective release is controllable. In addition, the integration of liquid crystals into fibers with pDNA polymers can cause a faster transfection rate and promote HUVEC (Human Umbilical Vein Endothelial Cells) growth. It is possible to combine electrospun fibers containing LC (liquid crystal) with pDNA condensation technology to achieve the goal of a sustained release. The production of inductable tissue-building equipment can manipulate the required signals at an effective level in the local tissue microenvironment

Integrating multiple cues into adaptive hierarchical segmentation for high-resolution remote-sensing images

National Natural Science Foundation of China [40971245]Image segmentation has been, and still is, a hot research topic in computer vision and pattern recognition. However, few existing segmentation algorithms are suitable for all objects presented in high-resolution remote-sensing (HRRS) images, because the relevant methods often implement segmentation in the same mode for the whole image rather than considering the different characteristics of various objects. Therefore, this article proposes an adaptive hierarchical segmentation framework for HRRS images by integrating multiple cues (e.g. intensity, texture and boundary). This two-stage framework first analyses the class of region presented in the study image, then according to this analysis, partitions each region class by adaptively utilizing the proper segmentation method with the most representative features. The distinctive characteristics of this framework are that the first stage simplifies the problem before using the segmentation method, and the second stage guarantees that the segmentation is carried out with the representative cues and corresponding suitable method for these cues. The performance of the proposed segmentation framework is demonstrated through a complete set of experimental results and substantiated using quantitative criteria