Different types of scaffolds for reconstruction of the urinary tract by tissue engineering.

INTRODUCTION: Tissue engineering is an important and expanding field in reconstructive surgery. The ideal biomaterial for urologic tissue engineering should be biodegradable and support autologous cell growth. We examined different scaffolds to select the ideal material for the reconstruction of the bladder wall by tissue engineering. MATERIALS AND METHODS: We seeded mouse fibroblasts and human keratinocytes in a co-culture model on 13 different scaffolds. The cell-seeded scaffolds were fixed and processed for electron microscopy, hematoxylin and eosin stain, and immunohistochemistry. Cell density and epithelial cell layers were evaluated utilizing a computer-assisted optical measurement system. RESULTS: Depending on the growth pattern, scaffolds were classified into the following three distinct scaffold types: carrier-type scaffolds with very small pore sizes and no ingrowth of the cells. This scaffold type induces a well-differentiated epithelium. Fleece-type scaffolds with fibers and huge pores. We found cellular growth inside the scaffold but no epithelium on top of it. Sponge-type scaffolds with pores between 20 and 40 microm. Cellular growth was observed inside the scaffold and well-differentiated epithelium on top of it. CONCLUSION: To our knowledge, this is the first time three distinct scaffold types have been reported. All types supported the cell growth. The structure of the scaffolds affects the pattern of cell growth.

Interpretation of surface EMGs in children with cerebral palsy: An initial study using a fuzzy expert system.

Surface EMG detected simultaneously at different muscles has become an important tool for analysing the gait of children with cerebral palsy (CP), as it offers essential information about muscular coordination. However, the interpretation of surface EMG is a difficult task that assumes extensive knowledge and experience. As such, this noninvasive procedure is not frequently used in the general clinical routine. An Artificial Intelligence (AI) system for interpreting surface EMG signals and the resulting muscular coordination patterns could overcome these limitations. To support such interpretation, an expert system based on fuzzy inference methodology was developed. The knowledge-base of the system implemented 15 rules, from which the fuzzy inference methodology performs a prediction of the effectiveness of the muscular coordination during gait. Our aim was to assess the feasibility and value of such an expert system in clinical applications. Surface EMG signals were recorded from the tibialis anterior, soleus muscle, and gastrocnemius muscles of children with CP to assess muscular coordination patterns of ankle movement during gait. Nineteen children underwent 114 surface EMG measurements. Simultaneously, the gait cycles of each patient were determined using foot switches and videotapes. From the EMG signals, the effectiveness of the ankle movement was predicted by the expert system, and predictions were classified using a three-point ordinal scale. In 91 cases (80%), the clinical findings matched the predictions of the expert system. In 23 cases (20%) the predictions of the expert system differed from the clinical findings with 12 cases revealing worse and 11 cases revealing better results in comparison to the clinical findings. As this study is a first attempt to verify the feasibility and correctness of this expert system, the results are promising. Further study is required to assess the correlation with the kinematic data and to include the whole leg.