Diabetic wounds are susceptible to microbial infection. The treatment of these wounds requires a higher payload of growth factors. With this in mind, the strategy for this study was to utilize a novel payload comprising of Eudragit RL/RS 100 nanofibers carrying the bacterial inhibitor gentamicin sulfate (GS) in concert with recombinant human epidermal growth factor (rhEGF); an accelerator of wound healing. GS containing Eudragit was electrospun to yield nanofiber scaffolds, which were further modified by covalent immobilization of rhEGF to their surface. This novel fabricated nanoscaffold was characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction. The thermal behavior of the nanoscaffold was determined using thermogravimetric analysis and differential scanning calorimetry. In the in vitro antibacterial assays, the nanoscaffolds exhibited comparable antibacterial activity to pure gentemicin powder. In vivo work using female C57/BL6 mice, the nanoscaffolds induced faster wound healing activity in dorsal wounds compared to the control. The paradigm in this study presents a robust in vivo model to enhance the applicability of drug delivery systems in wound healing applications. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 641-651, 2018.

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Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

Tissue engineering technologies involving growth factors have produced one of the most advanced generations of diabetic wound healing solutions. Using this approach, a nanocomposite carrier was designed using Poly(d,l-lactide-co-glycolide) (PLGA)/Gelatin polymer solutions for the simultaneous release of recombinant human epidermal growth factor (rhEGF) and gentamicin sulfate at the wound site to hasten the process of diabetic wound healing and inactivation of bacterial growth. The physicochemical characterization of the fabricated scaffolds was carried out using scanning electron microscopy (SEM) and X-ay diffraction (XRD). The scaffolds were analyzed for thermal stability using thermogravimetric analysis and differential scanning calorimetry. The porosity, biodegradability, and swelling behavior of the scaffolds was also evaluated. Encapsulation efficiency, drug loading capacity, and in vitro drug release were also investigated. Further, the bacterial inhibition percentage and detailed in vivo biocompatibility for wound healing efficiency was performed on diabetic C57BL6 mice with dorsal wounds. The scaffolds exhibited excellent wound healing and continuous proliferation of cells for 12 days. These results support the applicability of such systems in rapid healing of diabetic wounds and ulcers.

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