DEVELOPMENT OF ORALLY DISINTEGRATING TABLETS OF MEMANTINE HYDROCHLORIDE-A REMEDY FOR ALZHEIMER’S DISEASE

Objective: The study is directed towards the development of an orally disintegrating drug delivery system of memantine hydrochloride which can be commercially exploited for the well-being of society for the treatment of Alzheimer’s disease, which is a most common form of dementia. Methods: Orally disintegrating immediate-release tablets of memantine hydrochloride were prepared and optimized for disintegration time and in vitro drug release. The top spray granulation method was used for the preparation of granules. Subsequently, these granules were compressed to tablets. The levels of diluent, disintegrant and taste-masking agents were optimized using the design of experiments. The resulting tablets were evaluated for disintegration time and in vitro drug release. The optimized formulation was subjected to accelerated stability study for 3 mo. Results: The optimized orally disintegrating tablet formulation exhibited a disintegration time of 2-3 min and complete drug release i.e. more than 85 % drug release within 10 min while performing in vitro drug release study. This is a prerequisite for faster action in the case of patients suffering from Alzheimer’s disease. Accelerated stability studies indicated good physical and chemical stability of the optimized formulation. Conclusion: Developed orally disintegrating tablet formulation of memantine hydrochloride could release the drug faster compared to conventional immediate-release tablets which is useful in paediatric, geriatric and psychiatric patients

Modeling of Late Blooming Phases and Precipitation Kinetics in Aging Reactor Pressure Vessel (RPV) Steels

The principle work at the atomic scale is to develop a predictive quantitative model for the microstructure evolution of RPV steels under thermal aging and neutron radiation. We have developed an AKMC method for the precipitation kinetics in bcc-Fe, with Cu, Ni, Mn and Si being the alloying elements. In addition, we used MD simulations to provide input parameters (if not available in literature). MMC simulations were also carried out to explore the possible segregation/precipitation morphologies at the lattice defects. First we briefly describe each of the simulation algorithms, then will present our results

Fast Association Tests for Genes with FAST

Gene-based tests of association can increase the power of a genome-wide association study by aggregating multiple independent effects across a gene or locus into a single stronger signal. Recent gene-based tests have distinct approaches to selecting which variants to aggregate within a locus, modeling the effects of linkage disequilibrium, representing fractional allele counts from imputation, and managing permutation tests for p-values. Implementing these tests in a single, efficient framework has great practical value. Fast ASsociation Tests (Fast) addresses this need by implementing leading gene-based association tests together with conventional SNP-based univariate tests and providing a consolidated, easily interpreted report. Fast scales readily to genome-wide SNP data with millions of SNPs and tens of thousands of individuals, provides implementations that are orders of magnitude faster than original literature reports, and provides a unified framework for performing several gene based association tests concurrently and efficiently on the same data. Availability: https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Hom

Modeling the Ductile Brittle Fracture Transition in Reactor Pressure Vessel Steels Using a Cohesive Zone Model Based Approach

Fracture properties of Reactor Pressure Vessel (RPV) steels show large variations with changes in temperature and irradiation levels. Brittle behavior is observed at lower temperatures and/or higher irradiation levels whereas ductile mode of failure is predominant at higher temperatures and/or lower irradiation levels. In addition to such temperature and radiation dependent fracture behavior, significant scatter in fracture toughness has also been observed. As a consequence of such variability in fracture behavior, accurate estimates of fracture properties of RPV steels are of utmost importance for safe and reliable operation of reactor pressure vessels. A cohesive zone based approach is being pursued in the present study where an attempt is made to obtain a unified law capturing both stable crack growth (ductile fracture) and unstable failure (cleavage fracture). The parameters of the constitutive model are dependent on both temperature and failure probability. The effect of irradiation has not been considered in the present study. The use of such a cohesive zone based approach would allow the modeling of explicit crack growth at both stable and unstable regimes of fracture. Also it would provide the possibility to incorporate more physical lower length scale models to predict DBT. Such a multi-scale approach would significantly improve the predictive capabilities of the model, which is still largely empirical

Ex-situ Studies of Captive Breeding of Ompok bimaculatus (Bloch, 1794) in Tripura

Broodstock (37 males and 83 females) of Ompok bimaculatus was sampled from Rivers Feni, Muhuri, Gomoti and also from Hurijala wetland of Tripura during November 2008 to February 2010. During sampling of fish species, water samples from different locations were also sampled in order to know the water quality characteristics of the habitat. The broodstock was provided specific live feed and water quality during acclimatization. The maturity cycle of the species was examined with gonad during the monsoon period. During breeding season the gravid females and males were identified with some specific phenotypic characteristics. Induced spawning was done under some particular aquaculture conditions only

BIG DATA MINING TOOLS FOR UNSTRUCTURED DATA: A REVIEW

Big data is a buzzword that is used for a large size data which includes structured data, semi-structured data and unstructured data. The size of big data is so large, that it is nearly impossible to collect, process and store data using traditional database management system and software techniques. Therefore, big data requires different approaches and tools to analyze data. The process of collecting, storing and analyzing large amount of data to find unknown patterns is called as big data analytics. The information and patterns found by the analysis process is used by large enterprise and companies to get deeper knowledge and to make better decision in faster way to get advantage over competition. So, better techniques and tools must be developed to analyze and process big data. Big data mining is used to extract useful information from large datasets which is mostly unstructured data. Unstructured data is data that has no particular structure, it can be any form. Today, storage of high dimensional data has no standard structure or schema, because of this problem has risen. This paper gives an overview of big data sources, challenges, scope and unstructured data mining techniques that can be used for big data