In vitro growth, differentiation and biological characteristics of neural stem cells

OBJECTIVE： To summarize the biological characteristics of neural stem cells, and the separation, purification. differentiation and source of neural stem cells. DATA SOURCES ： An online search of Pubmed database was undertaken to identify English articles about the growth of neural stem cells in vitro published from January 2000 to October 2006 by using the keywords of ＂neural stem cells, bone marrow mesenchymal stem cells （BMSCs）, umbilical cord blood stem cells, embryonic stem cells （ESC）, separation methods, neural growth factor＂. And relevant articles published in IEEE/IEE Electronic Library （IEL） database, Springer Link database and Kluwer Online Journals were also searched, Chinese relevant articles published between January 2000 to October 2006 were searched with the same keywords in Chinese in Chinese journal full-text database. STUDY SELECTION ： The articles were primarily screened, and then the full-texts were searched. Inclusive criteria： （1） Articles relevant to the biological characteristics and classification of neural stem cells; （2） Articles about the source, separation and differentiation of the ESCs, BMSCs and umbilical cord blood stem cells. The repetitive studies and reviews were excluded. DATA EXTRACTION ： Thirty articles were selected from 203 relevant articles according to the inclusive criteria Articles were excluded because of repetition and reviews. DATA SYNTHESES ： Neural stem cells have the ability of self-renewing and high differentiation, and they are obtained from ESCs, nerve tissue, nerve system, BMSCs and umbilical cord blood stem cells. ESCs can be separated by means of mechanical dissociation is better than that of the trypsin digestion, BMSCs by density gradient centrifuge separation, hemolysis, whole-blood culture, etc., and umbilical cord blood stem ceils by Ficoil density gradient centrifugation, hydroxyethyl starch （HES） centrifugation sedimentation, etc. Neural growth factor （NGF） and other factors play an important role in the growth of NSCs, such as transforming growth factor （TGF） is an important player in repairing organs, NGF accelerates the process of growth, insulin-like growth factor serves importantly in the differentiation of stem cells into neuron-like cells. CONCLUSION ： As unipotent stem cells, NSCs have the abilities of self-renewal and potential of high differentiation. The method of mechanical dissociation is better than trypsin digestion in e separating ESCs. However, density gradient centrifuge separation is better than other methods in the separation of the BMSCs. NGF and other factors play an important role in the growth of NSCs.

Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy

Fused deposition modeling （FDM） is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This article empiri- cally reports the effects of the process parameters, i.e., the layer thickness, raster angle, raster width, air gap, part orientation, and their interactions on the accuracy of the length, width, and thicknes, of acrylonitrile-butadiene- styrene （ABSP 400） parts fabricated using the FDM tech- nique. It was found that contraction prevailed along the directions of the length and width, whereas the thickness increased from the desired value of the fabricated part. Optimum parameter settings to minimize the responses, such as the change in length, width, and thickness of the test specimen, have been determined using Taguchi＇s parameter design. Because Taguchi＇s philosophy fails to obtain uniform optimal factor settings for each response, in this study, a fuzzy inference system combined with the Taguchi philosophy has been adopted to generate a single response from three responses, to reach the specific target values with the overall optimum factor level settings. Further, Taguchi and artificial neural network predictive models are also presented in this study for an accuracy evaluation within the dimensions of the FDM fabricated parts, subjected to various operating conditions. The pre- dicted values obtained from both models are in good agreement with the values from the experiment data, with mean absolute percentage errors of 3.16 and 0.15, respectively. Finally, the confirmatory test results showed an improvement in the multi-response performance index of 0.454 when using the optimal FDM parameters over the initial values.