Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network

Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperature and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted for the cultivations using a Fractional Factorial (FF) design experiments for different variables. This novel concept of combining the optimization and modeling presented different optimal conditions for the mixture of B. bifidum and L. acidophilus growth from their one variable at-a-time (OVAT) optimization study. Through these statistical tools, the product yield (cell mass) of the mixture of B. bifidum and L. acidophilus was increased. Regression coefficients (R2) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.08 and 0.3%, respectively. The optimum conditions for the maximum biomass yield were at temperature 38°C, pH 6.5, inoculum volume 1.60 mL, inoculum age 30 h, carbon content 42.31% (w/v), and nitrogen content 14.20% (w/v). The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN

The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This concept of combining the optimization and modeling presented different optimal conditions for L. acidophilus growth from their original optimization study. Through these statistical tools, the product yield (cell mass) of L. acidophilus was increased. Regression coefficients (R²) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.06 and 0.2%, respectively. The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

The aim of this work was to optimize the biomass production by Bifidobacterium bifidum 255 using the response surface methodology (RSM) and artificial neural network (ANN) both coupled with GA. To develop the empirical model for the yield of probiotic bacteria, additional carbon and nitrogen content, inoculum size, age, temperature and pH were selected as the parameters. Models were developed using » fractional factorial design (FFD) of the experiments with the selected parameters. The normalized percentage mean squared error obtained from the ANN and RSM models were 0.05 and 0.1 percent, respectively. Regression coefficient (R²) of the ANN model showed higher prediction accuracy compared to that of the RSM model. The empirical yield model (for both ANN and RSM) obtained were utilized as the objective functions to be maximized with the help of genetic algorithm. The optimal conditions for the maximal biomass yield were 37.4 °C, pH 7.09, inoculum volume 1.97 ml, inoculum age 58.58 h, carbon content 41.74 percent (w/v), and nitrogen content 46.23 percent (w/v). The work reported is a novel concept of combining the statistical modeling and evolutionary optimization for an improved yield of cell mass of B. bifidum 255.

No association between seropositivity for hepatitis C virus and lichen planus: a case control study.

BACKGROUND: The epidemiological association of lichen planus (LP) with hepatitis C virus (HCV) infection has been recorded from some countries and HCV RNA3 has been isolated from lesional skin in patients with LP and chronic HCV infection. The observed geographical differences regarding HCV infection and LP could be immuno-genetically related. AIM: To determine whether HCV has a causal relationship with LP. METHODS: Histopathologically proved cases of LP were subjected to antibody to HCV test by the Third Generation Enzyme Immunoassay Kit for the detection of antibody to HCV (Anti-HCV) in human serum or plasma. They were routinely screened in the virology department by the reagent kit, HIVASE 1 + 2, adopting the "direct sandwich principle" for the assay to detect antibodies to HIV-1 and/or HIV-2. There were 150 age and sex matched controls (not suffering from LP) and HIV-I and II negative, and negative for HCV. RESULTS: Of the 104 patients studied only 2 patients (1.92%) of generalized LP with disease duration of more than 3 months were found to be positive for antibodies to HCV. This was not a significant finding and no statistical methods, e.g. Chi square test etc. could be applied. CONCLUSION: Hepatitis C virus is not significant to the causation of LP in India.