Improved biopharmaceutical properties of carvedilol employing α-tocopheryl polyethylene glycol 1000 succinate-based self-emulsifying drug delivery system

The main objective of this study was to develop a self-emulsifying drug delivery system (SEDDS) of carvedilol (CAR) with improved oral absorption and hepatoprotective properties. SEDDS-CAR was prepared based on d-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) and physicochemically characterized. Pharmacokinetic behaviors after the oral administration of CAR samples in rats were evaluated to clarify the possible enhancement of the oral absorption of CAR. The hepatoprotective effects of orally dosed CAR samples were assessed in a rat model of acute hepatic injury induced by carbon tetrachloride (CCl₄). SEDDS-CAR showed the immediate formation of fine micelles with a mean droplet size of 84?nm when introduced in aqueous media. SEDDS-CAR improved the dissolution behavior of CAR in distilled water as evidenced by at least five-fold higher solubility than the equilibrium solubility of CAR. After the single oral administration of SEDDS-CAR (10?mg-CAR/kg) in rats, enhanced CAR exposure was observed with an increase of AUC_0–∞ showing a 2.5-fold increase compared with crystalline CAR. In CCl₄-treated rats, orally dosed SEDDS-CAR (10?mg-CAR/kg, p.o.) led to 91.8 and 91.2% reductions of ALT and AST, respectively; however, crystalline CAR was found to be less effective. From these findings, SEDDS-CAR might be an efficacious oral dosage option for enhancing the hepatoprotective potential of CAR.     

Droplet generation in a microchannel with a controllable deformable wall

We report the droplet generation behavior of a microfluidic droplet generator with a controllable deformable membrane wall using experiments and analytical model. The confinement at the droplet generation junction is controlled by using external pressure, which acts on the membrane, to generate droplets smaller than junction size (with other parameters fixed) and stable and monodispersed droplets even at higher capillary numbers. A non-dimensional parameter, i.e., controlling parameter K _p, is used to represent the membrane deformation characteristics due to the external pressure. We investigate the effect of the controlled membrane deformation (in terms of K _p), viscosity ratio λ and flow rate ratio r on the droplet size and mobility. A correlation is developed to predict droplet size in the controllable deformable microchannel in terms of the controlling parameter K _p, viscosity ratio λ and flow rate ratio r. Due to the deflection of the membrane wall, we demonstrate that the transition from the stable dripping regime to the unstable jetting regime is delayed to a higher capillary number Ca (as compared to rigid droplet generators), thus pushing the high throughput limit. The droplet generator also enables generation of droplets of sizes smaller than the junction size by adjusting the controlling parameter.     

Studies on blends of polyethylene terephthalate with bisphenol-a-polycarbonate and polypropylene

Moldability and mechanical properties of polyethylene terephthalate (PET) under normal molding conditions were found to improve significantly when it was blended with bisphenol-A-polycarbonate (PC) and polypropylene (PP) to form ternary polymer blend systems. DSC results of these blends revealed that the PET and PC components formed a miscible blend while PP being incompatible with them, formed a separate phase. PP was also found to form a sleeve around the PET-PC miscible phase and, thereby, showed a skin-core type of morphology. Variations of mechanical properties with varying amounts of PP was measured keeping the ratio of PET and PC constant. Tensile and flexural properties of the blends decrease with the amount of PP. Notched impact strength increases up to a certain level of PP and then decreases, while the unnotched values decrease gradually. The effect of annealing on the mechanical properties of these blends have been discussed on the basis of the increased crystallinity of some of the components.     

Automated emotion recognition employing a novel modified binary quantum‐behaved gravitational search algorithm with differential mutation

The present paper proposes a supervised learning based automated human facial emotion recognition strategy with a feature selection scheme employing a novel variation of the gravitational search algorithm (GSA). The initial feature set is generated from the facial images by using the 2‐D discrete cosine transform (DCT) and then the proposed modified binary quantum GSA with differential mutation (MBQGSA‐DM) is utilized to select a sub‐set of features with high discriminative power. This is achieved by minimising the cost function formulated as the ratio of the within class and interclass distances. The overall system performs its final classification task based on selected feature inputs, utilising a back propagation based artificial neural network (ANN). Extensive experimental evaluations are carried out utilising a standard, benchmark emotion database, that is, Japanese Female Facial Expresssion (JAFFE) database and the results clearly indicate that the proposed method outperforms several existing techniques, already known in literature for solving similar problems. Further validation has also been carried out on a facial expression database developed at Jadavpur University, Kolkata, India and the results obtained further strengthen the notion of superiority of the proposed method.