Preparation and <Emphasis Type="Italic">In Vitro</Emphasis>/<Emphasis Type="Italic">In Vivo</Emphasis> Evaluation of Microparticle Formulations Containing Meloxicam

In this study, we have formulated chitosan-coated sodium alginate microparticles containing meloxicam (MLX) and aimed to investigate the correlation between in vitro release and in vivo absorbed percentages of meloxicam. The microparticle formulations were prepared by orifice ionic gelation method with two different sodium alginate concentrations, as 1% and 2% (w/v), in order to provide different release rates. Additionally, an oral solution containing 15 mg of meloxicam was administered as the reference solution for evaluation of in vitro/in vivo correlation (ivivc). Following in vitro characterization, plasma levels of MLX and pharmacokinetic parameters [elimination half-life (t _1/2), maximum plasma concentration (C _max), time for C _max (t _max)] after oral administration to New Zealand rabbits were determined. Area under plasma concentration–time curve (AUC_0–∞) was calculated by using trapezoidal method. A linear regression was investigated between released% (in vitro) and absorbed% (in vivo) with a model-independent deconvolution approach. As a result, increase in sodium alginate content lengthened in vitro release time and in vivo t _max value. In addition, for ivivc, linear regression equations with r ² values of 0.8563 and 0.9402 were obtained for microparticles containing 1% and 2% (w/v) sodium alginate, respectively. Lower prediction error for 2% sodium alginate formulations (7.419 ± 4.068) compared to 1% sodium alginate formulations (9.458 ± 5.106) indicated a more precise ivivc for 2% sodium alginate formulation.     

Water level and fish-mediated cascading effects on the microbial community in eutrophic warm shallow lakes: a mesocosm experiment

Information on the effects of water level changes on microbial planktonic communities in lakes is limited but vital for understanding ecosystem dynamics in Mediterranean lakes subjected to major intra- and inter-annual variations in water level. We performed an in situ mesocosm experiment in an eutrophic Turkish lake at two different depths crossed with presence/absence of fish in order to explore the effects of water level variations and the role of top-down regulation at contrasting depths. Strong effects of fish were found on zooplankton, weakening through the food chain to ciliates, HNF and bacterioplankton, whereas the effect of water level variations was overall modest. Presence of fish resulted in lower biomass of zooplankton and higher biomasses of phytoplankton, ciliates and total plankton. The cascading effects of fish were strongest in the shallow mesocosms as evidenced by a lower zooplankton contribution to total plankton biomass and lower zooplankton:ciliate and HNF:bacteria biomass ratios. Our results suggest that a lowering of the water level in warm shallow lakes will enhance the contribution of bacteria, HNF and ciliates to the plankton biomass, likely due to increased density of submerged macrophytes (less phytoplankton); this effect will, however, be less pronounced in the presence of fish.     

Cholesterol-related gene variants are associated with diabetes in coronary artery disease patients

Molecular Biology Reports - Coronary artery disease (CAD) which is a complex cardiovascular disease is the leading cause of death worldwide. The changing prevalence of the disease in different...     

Modeling of polygalacturonase enzyme activity and biomass production by <Emphasis Type="Italic">Aspergillus sojae</Emphasis> ATCC 20235

Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R ² of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R ² of 0.84 and biomass with an R ² value of 0.83, whereas the regression models predicted enzyme activity with an R ² of 0.84 and biomass with an R ² of 0.69.     

Size‐based interactions across trophic levels in food webs of shallow Mediterranean lakes

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