In vivo evaluation of a mucoadhesive polymeric caplet for intravaginal anti-HIV-1 delivery and development of a molecular mechanistic model for thermochemical characterization

Context and objective: The aim of this study was to develop, characterize and evaluate a mucoadhesive caplet resulting from a polymeric blend (polymeric caplet) for intravaginal anti-HIV-1 delivery.

Materials and methods: Poly(lactic-co-glycolic) acid, ethylcellulose, poly(vinylalcohol), polyacrylic acid and modified polyamide 6, 10 polymers were blended and compressed to a caplet-shaped device, with and without two model drugs 3′-azido-3′-deoxythymidine (AZT) and polystyrene sulfonate (PSS). Thermal analysis, infrared spectroscopy and microscopic analysis were carried out on the caplets employing temperature-modulated DSC (TMDSC), Fourier transform infra-red (FTIR) spectrometer and scanning electron microscope, respectively. In vitro and in vivo drug release analyses as well as the histopathological toxicity studies were carried out on the drug-loaded caplets. Furthermore, molecular mechanics (MM) simulations were carried out on the drug-loaded caplets to corroborate the experimental findings.

Results and discussion: There was a big deviation between the T_g of the polymeric caplet from the T_g's of the constituent polymers indicating a strong interaction between constituent polymers. FTIR spectroscopy confirmed the presence of specific ionic and non-ionic interactions within the caplet. A controlled near zero-order drug release was obtained for AZT (20 d) and PSS (28 d). In vivo results, i.e. the drug concentration in plasma ranged between 0.012–0.332?mg/mL and 0.009–0.256?mg/mL for AZT and PSS over 1–28 d.

Conclusion: The obtained results, which were corroborated by MM simulations, attested that the developed system has the potential for effective delivery of anti-HIV-agents.     

Pilot Plant Study of Simultaneous Sewage Sludge Digestion and Metal Leaching

This research is related to a preindustrial pilot scale study of the performance of the simultaneous sewage sludge digestion and metal leaching (SSDML) process for decontamination and stabilization of sewage sludge. Ten batch tests were carried out in two 4?m3 bioreactors under various conditions of operation. Results indicated that the addition of 1.0 to 1.5?g?S0/L, which is the equivalent of approximately 30 to 50?kg?S0 per tons dry sludge, is sufficient to obtain conditions of acidity (pH<2.5) and oxido-reduction potential greater than 500 mV necessary for an effective solubilization of toxic metals. The final average of metal solubilization in the output sludge during the SSDML process varied in the following ranges: 25–78% Cd, 9–32% Cr, 48–100% Cu, 77–99% Mn, 15–53% Ni, 12–47% Pb, and 66–100% Zn. The N, P, and K contents were also preserved in the decontaminated sludge. Moreover, the use of low concentrations of elemental sulfur makes it possible to obtain decontaminated sludge with a low total sulfur content (1.4–1.5% S) compatible with agricultural use. The suspended solids removal calculated for the SSDML process was slightly lower, (2.5±0.4)% volatile suspended solids per day, than those reached using standard aerobic digestion. Finally, the SSDML process was found to be effective in removing bad odors and in the destruction (99–100%) of indicator bacteria.     

Swoop: A Web Ontology Editing Browser

In this paper, we describe Swoop, a hypermedia inspired Ontology Browser and Editor based on OWL, the recently standardized Web-oriented ontology language. After discussing the design rationale and architecture of Swoop, we focus mainly on its features, using illustrative examples to highlight its use. We demonstrate that with its Web-metaphor, adherence to OWL recommendations and key unique features, such as Collaborative Annotation using Annotea, Swoop acts as a useful and efficient Web Ontology development tool. We conclude with a list of future plans for Swoop, that should further increase its overall appeal and accessibility.     

Activation of extracellular matrix metalloproteinases in equine laminitis

Samples of connective tissue obtained from the hoof of six laminitic and eight non-laminitic adult horses were analysed zymographically to investigate whether connective tissue matrix metalloproteinases are activated or induced during laminitis. The activity or matrix metalloproteinases was substantially greater in the tissues from the laminitic horses than in the tissues from the non-laminitic horses. A comparison of the collagenolytic activity in the laminitic and control tissues showed that collagenolytic activities corresponding to the 92 kDa (P < 0.001), 72 kDa (P < 0.01) and 66 kDa (P < 0.01) bands were induced in the laminitic tissues.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Investigation of long term drift of an intraocular pressure sensor

Microsystem Technologies - “Zero drift” behavior of an optical intraocular pressure sensor is studied using an analytical model based on the deflection of a circular membrane. Results...     

An efficient neural-network model for real-time fault detection in industrial machine

Neural Computing and Applications - Induction machines have extensive demand in industries as they are used for large-scale production and, therefore, vulnerable to both electrical and mechanical...