Cell-free scaffold from jellyfish Cassiopea andromeda (Cnidaria; Scyphozoa) for skin tissue engineering.

Disruption of the continuous cutaneous membrane in the integumentary system is considered a health problem of high cost for any nation. Several attempts have been made for developing skin substitutes in order to restore injured tissue including autologous implants and the use of scaffolds based on synthetic and natural materials. Current biomaterials used for skin tissue repair include several scaffold matrices types, synthetic or natural, absorbable, degradable or non-degradable polymers, porous or dense scaffolds, and cells capsulated in hydrogels or spheroids systems so forth. These materials have advantages and disadvantages and its use will depend on the desired application. Recently, marine organisms such as jellyfish have attracted renewed interest, because both its composition and structure resemble the architecture of human dermic tissue. In this context, the present study aims to generate scaffolds from Cassiopea andromeda (C. andromeda), with application in skin tissue engineering, using a decellularization process. The obtained scaffold was studied by infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), differential scanning calorimetry analysis (DSC), and scanning electron microscopy (SEM). Crystal violet staining and DNA quantification assessed decellularization effectiveness while the biocompatibility of scaffold was determined with human dermic fibroblasts. Results indicated that the decellularization process reduce native cell population leading to 70% reduction in DNA content. In addition, SEM showed that the macro and microstructure of the collagen I-based scaffold were preserved allowing good adhesion and proliferation of human dermic fibroblasts. The C. andromeda scaffold mimics human skin and therefore represents great potential for skin tissue engineering.

Efficient CT Image Reconstruction in a GPU Parallel Environment.

Computed tomography is nowadays an indispensable tool in medicine used to diagnose multiple diseases. In clinical and emergency room environments, the speed of acquisition and information processing are crucial. CUDA is a software architecture used to work with NVIDIA graphics processing units. In this paper a methodology to accelerate tomographic image reconstruction based on maximum likelihood expectation maximization iterative algorithm and combined with the use of graphics processing units programmed in CUDA framework is presented. Implementations developed here are used to reconstruct images with clinical use. Timewise, parallel versions showed improvement with respect to serial implementations. These differences reached, in some cases, 2 orders of magnitude in time while preserving image quality. The image quality and reconstruction times were not affected significantly by the addition of Poisson noise to projections. Furthermore, our implementations showed good performance when compared with reconstruction methods provided by commercial software. One of the goals of this work was to provide a fast, portable, simple, and cheap image reconstruction system, and our results support the statement that the goal was achieved.