OALib Journal期刊

ISSN: 2333-9721



匹配条件: “Indurkhya Arpna” ,找到相关结果约5条。
Novel application of mixed hydrotropic solubilization technique in the formulation and evaluation of hydrotropic solid dispersion of aceclofenac
Maheshwari Rajesh,Indurkhya Arpna
Asian Journal of Pharmaceutics , 2010,
Abstract: In the present investigation, newly developed mixed hydrotropic solid dispersion (HSD) technology precludes the use of organic solvent and also decreases the individual concentration of hydrotropic agents, simultaneously decreasing their toxic potential. ′Mixed-hydrotropic solubilization′ technique is the phenomenon to increase the solubility of poorly water-soluble drugs in the aqueous solution containing blends of hydrotropic agents, which may give synergistic enhancement effect on solubility of poorly water-soluble drugs and to reduce concentrations of each individual hydrotropic agent to minimize their toxic effects due to high concentration of hydrotropic agents. Maheshwari has made HSD of paracetamol using urea. In the present study, the aqueous solution of hydrotropic blend (20% urea and 10% sodium citrate) has been found to increase aqueous solubility of poorly water-soluble drug, aceclofenac. This mixedhydrotropic blend was used to prepare solid dispersion of aceclofenac. The prepared solid dispersions have been characterized by IR and XRD studies. They have been studied for dissolution rate enhancement effect. The prepared solid dispersions were found very stable (chemically).
Reaction Factoring and Bipartite Update Graphs Accelerate the Gillespie Algorithm for Large-Scale Biochemical Systems
Sagar Indurkhya,Jacob Beal
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0008125
Abstract: ODE simulations of chemical systems perform poorly when some of the species have extremely low concentrations. Stochastic simulation methods, which can handle this case, have been impractical for large systems due to computational complexity. We observe, however, that when modeling complex biological systems: (1) a small number of reactions tend to occur a disproportionately large percentage of the time, and (2) a small number of species tend to participate in a disproportionately large percentage of reactions. We exploit these properties in LOLCAT Method, a new implementation of the Gillespie Algorithm. First, factoring reaction propensities allows many propensities dependent on a single species to be updated in a single operation. Second, representing dependencies between reactions with a bipartite graph of reactions and species requires only storage for reactions, rather than the required for a graph that includes only reactions. Together, these improvements allow our implementation of LOLCAT Method to execute orders of magnitude faster than currently existing Gillespie Algorithm variants when simulating several yeast MAPK cascade models.
Arpna patial,Purnima verma
International Research Journal of Pharmacy , 2012,
Abstract: The present investigation explores a method to analyze colchicine in phosphate buffer saline pH 6.4. Based on the spectrophotometric characteristics of colchicine, λmax selected was 353.8 nm. Analytical parameters validated were linearity, accuracy, precision, LOD, LOQ and robustness. The developed method demonstrated exemplary linearity in phosphate buffer saline pH6.4 (R2 = 0.995) for concentration of 6-22 μg/ml with linear equation of y=0.037x+0.005. LOD and LOQ obtained were found to be i.e. 0.145μg/ml and 0.483μg/ml respectively. All validation parameters were observed to be within acceptable range, as recommended by ICH guidelines. The above results confirmed the validity of proposed method for routine estimation of colchicine in phosphate buffer saline pH6.4.
Performance analysis of modified algorithm for finding multilevel association rules
Arpna Shrivastava,R. C. Jain
Computer Science , 2013, DOI: 10.5121/cseij.2013.3401
Abstract: Multilevel association rules explore the concept hierarchy at multiple levels which provides more specific information. Apriori algorithm explores the single level association rules. Many implementations are available of Apriori algorithm. Fast Apriori implementation is modified to develop new algorithm for finding multilevel association rules. In this study the performance of this new algorithm is analyzed in terms of running time in seconds.
Rule-based Machine Learning Methods for Functional Prediction
S. M. Weiss,N. Indurkhya
Computer Science , 1995,
Abstract: We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.

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