Cloning and expression of CD19, a human B-cell marker in NIH-3T3 cell line

CD19 is a pan B cell marker that is recognized as an attractive target for antibody-based therapy of B-cell disorders including autoimmune disease and hematological malignancies. The object of this study was to stably express the human CD19 antigen in the murine NIH-3T3 cell line aimed to be used as an immunogen in our future study. Total RNA was extracted from Raji cells in which high expression of CD19 was confirmed by flow cytometry. Synthesized cDNA was used for CD19 gene amplification by conventional PCR method using Pfu DNA polymerase. PCR product was ligated to pGEM-T Easy vector and ligation mixture was transformed to DH5 alpha competent bacteria. After blue/white selection, one positive white colony was subjected to plasmid extraction and direct sequencing. Then, CD19 cDNA was sub-cloned into pCMV6-Neo expression vector by double digestion using Kpnl and HindIII enzymes. NIH-3T3 mouse fibroblast cell line was subsequently transfected by the construct using Jet-PEI transfection reagent. After 48 hours, surface expression of CD19 was confirmed by flow cytometry and stably transfected cells were selected by G418 antibiotic. Amplification of CD19 cDNA gave rise to 1701 bp amplicon confirmed by alignment to reference sequence in NCBI database. Flow cytometric analysis showed successful transient and stable expression of CD19 on NIH-3T3 cells [29 and 93%, respectively]. Stable cell surface expression of human CD19 antigen in a murine NIH-3T3 cell line may develop a proper immunogene which raises specific anti-CD19 antibody production in the mice immunized sera

Comparison of trial and error and genetic algorithm in neural network development for estimating Farinograph properties of wheat-flour dough

Background and Objectives: rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. Our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors

Materials and Methods: one hundred samples of white flour were collected from different provinces of Iran. Seven physicochemical properties of flour and Farinogram parameters of dough were selected as neural network's inputs and outputs, respectively. Trial-and-error and genetic algorithm [GA] were applied for developing an artificial neural network [ANN] with an optimized structure. Feed-forward neural networks with a back-propagation learning algorithm were employed. Sensitivity analyses were conducted to explore the ability of inputs in changing the Farinograph properties of dough

Results: the optimal neural network is an ANN-GA that evolves a four-layer network with eight nodes in the first hidden layer and seven neurons in the second hidden layer. The average of normalized mean square error, mean absolute error and correlation coefficient in estimating the test data set was 0.222, 0.124 and 0.953, respectively. According to the results of sensitivity analysis, gluten index was selected as the most important physicochemical parameter of flour in characterization of dough's Farinograph properties

Conclusions: an ANN is a powerful method for predicting the Farinograph properties of dough. Taking advantages of performance criteria proved that the GA is more powerful than trial-and-error in determining the critical parameters of ANN's structure, and improving its performance