Objective: In this study, chitosan/alginate-ellagic acid sustained-release microspheres were prepared, and the effect of sustained-release microspheres on preadipocyte adipogenic differentiation was analyzed. Methods: Chitosan/alginate-ellagic acid microspheres were prepared and identified by scanning electron microscopy (SEM) and infrared spectroscopy (IR). The drug release rates were measured at pH 6.8, 7.0, 7.2, 7.4 to determine sustained release of ellagic acid from microspheres. The effects of 0.1, 1, 10 mg/L chitosan/alginate-ellagic acid microsphere on 3T3-F442A preadipocyte proliferation were determined by Methyl thiazolyl tetrazolium assay (MTT), and cell morphology was checked by hematoxylin/ eosin staining (HE staining). The effect of chitosan/alginate-ellagic acid microspheres on preadipocyte adipogenic differentiation was also determined by Oil red O staining, and lipogenesis was measured by isopropanol extraction. The molecular mechanism was investigated by detecting the mRNA expression of CCAAT/enhancer binding protein alpha (C/EBPα) and peroxisome proliferatorsactivated receptor gamma (PPARγ). Results: Chitosan/alginate-ellagic acid sustained-release microspheres were successfully prepared, and the inhibition of proliferation and adipogenic differentiation of preadipocytes was found to be dosedependent. The mechanism of differentiation inhibition was found to be closely related to the expression of transcription factor C/EBPα and PPARγ. Conclusion: Chitosan/alginate can be used as a good material to prepare ellagic acid sustained-release microspheres, and these microspheres can be used for treating the obesity.

Brain MRI images that are acquired from the scanner will be having the non-brain tissues like skull, cerebrospinal fluid, Dura as the integral part of the image. All such unwanted elements does considerable impact on the estimation of the volume of the damaged region from resultant segmented image, Hence all such unwanted components from the brain MR image are be eliminated for accurate results. In this paper we had proposed a computationally efficient approach called Structural Augmentation which uses distance measures and morphological operation over a threaded bitmap image to eliminate the undesired region from the brain tissues. On applying the above said procedure well before the segmentation of the MR image, the evaluation seems to be meticulous. The end results of the proposed approach are proven to be superior in term of the accuracy and precision over conventional approaches.