Mehedi Hasan

Novel alginate-di-aldehyde cross-linked gelatin/nano-hydroxyapatite bioscaffolds for soft tissue regeneration

The present study describes the fabrication of a novel alginate-di-aldehyde (ADA) cross-linked gelatin (GEL)/nano-hydroxyapatite (nHAp) bioscaffold by lyophilization process. The physico-chemical properties of the scaffolds were evaluated in order to assess its suitability for tissue engineering application. ADA was prepared from periodate oxidation of alginate which facilitate the crosslinking between free amino group of gelatin and available aldehyde group of ADA through Schiffs base formation. nHAp was synthesized from waste egg-shells by wet chemical method. The synthesized HAp was found crystalline and nanosize (~45 nm) by XRD and TEM analysis respectively. Ca to P ratio of nHAp is 1.51 as observed by EDX confirmed the suitability of the scaffold for biomedical application. The crosslinked ADA increases thermal stability of scaffolds. Water uptake and degradation ability significantly reduced with the increase of nHAp in the scaffold due to the higher stiffness contributed by nHAp. SEM analysis revealed that the pore size and porosity of the scaffolds declines with the proliferation of nHAp in the scaffolds. XRD analysis of the scaffolds shows the increase of crystallinity in the composites due to incorporation of nHAp and ADA. Cytotoxicity of the all scaffolds were examined by normal kidney epithelial cells (Vero cells) and the results confirmed the non-toxicity of the scaffolds, which proved it is extremely cytocompatible. These tunable physical properties and enhance biocompatibility of prepared scaffold offer advance application in soft tissue regeneration and could be a promising candidate for biomedical application.

GSCS – Graph Stream Classification with Side Information

With the popularity of applications like Internet, sensor network and social network, which generate graph data in stream form, graph stream classification has become an important problem. Many applications are generating side information associated with graph stream, such as terms and keywords in authorship graph of research papers or IP addresses and time spent on browsing in web click graph of Internet users. Although side information associated with each graph object contains semantically relevant information to the graph structure and can contribute much to improve the accuracy of graph classification process, none of the existing graph stream classification techniques consider side information. In this paper, we have proposed an approach, Graph Stream Classification with Side information (GSCS), which incorporates side information along with graph structure by increasing the dimension of the feature space of the data for building a better graph stream classification model. Empirical analysis by experimentation on two real life data sets is provided to depict the advantage of incorporating side information in the graph stream classification process to outperform the state of the art approaches. It is also evident from the experimental results that GSCS is robust enough to be used in classifying graphs in form of stream.