Functional Genomics In Vivo Reveal Metabolic Dependencies of Pancreatic Cancer Cells

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Seroprevalence of human T lymphtropic virus (HTLV) among tissue donors in Iranian tissue bank

Iranian Tissue Bank prepares a wide range of human tissue homografts such as; heart valve, bone, skin, amniotic membrane and other tissues for different clinical applications. The purpose of this study was to determine the seroprevalence of HTLV in tissue donors. About 1,548 tissue donors were studied during a 5-years period by ELISA assays. HTLV_1,2—antibodies were tested for all of donors with other tests upon American Association of Tissue Banks (AATB) standards. About 25 (1.61%) out of 1,548 tissue donors were HTLV positive that 17 donors were male and 8 were female (female/male ratio was approximately 47%). Regarding to the prevalence of HTLV among tissue donors and importance of cell and tissue safety and quality assurance, we recommend that all blood, cell and tissue banks should be involved both routine serological methods and other complementary tests such as polymerase chain reaction (PCR) for diagnosis of HTLV.     

Optimization of Culture Conditions for Fermentation of Soymilk Using Lactobacillus casei by Response Surface Methodology

Soymilk was fermented with Lactobacillus casei, and statistical experimental design was used to investigate factors affecting viable cells of L. casei, including temperature, glucose, niacin, riboflavin, pyridoxine, folic acid and pantothenic acid. Initial screening by Plackett-Burman design revealed that among these factors, temperature, glucose and niacin have significant effects on the growth of L. casei. Further optimization with Box-Behnken design and response surface analysis showed that a second-order polynomial model fits the experimental data appropriately. The optimum conditions for temperature, glucose and niacin were found to be 15.77 °C, 5.23 and 0.63 g/L, respectively. The concentration of viable L. casei cells under these conditions was 8.23 log₁₀ (CFU/mL). The perfect agreement between the observed values and the values predicted by the equation confirms the statistical significance of the model and the model’s adequate precision in predicting optimum conditions.