A simple, precise and accurate assay for the determination of 6-methoxy-2-naphthylacetic acid (6-MNA), an active metabolite of nabumetone in human plasma, was developed and validated using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The analyte (6-MNA) and propranolol (internal standard, IS) were extracted from 200 microL aliquot of human plasma via solid-phase extraction employing HLB Oasis cartridges and separated on a Discovery HS C18 (50 x 4.6 mm, 5 microm) column. Detection of analyte and IS was done by tandem mass spectrometry with a turbo ion spray interface operating in positive ion and multiple reaction monitoring acquisition mode. The total chromatographic runtime was 3.0 min with retention time for 6-MNA and IS at 1.97 and 1.26 min, respectively. The method was validated over a dynamic linear range of 0.20-60.00 microg/mL for 6-MNA with mean correlation coefficient r > or = 0.9986. The intra-batch and inter-batch precision (%CV) across five validation runs (lower limit of quantiation, low-, medium- and high-quality controls and upper limit of quantitation) was less than 7.5%. The accuracy determined at these levels was within -5.8 to +0.2% in terms of percentage bias. The method was successfully applied for a bioequivalence study of 750 mg nabumetone tablet formulation in 12 healthy Indian male subjects under fasted condition.
Data mining refers to the process of analyzing the data from different perspectives and summarizing it into useful information that is mostly used by the different users for analyzing the data as well as for preparing data sets. A data set is collection of data that is present in the tabular form. Preparing data set involves complex SQL queries, joining tables and aggregate functions. Traditional RDBMS manages the tables with vertical format and returns one number per row. It means that it returns a single value output which is not suitable for preparing a data set. This paper mainly focused on k means clustering algorithm which is used to partition data sets after horizontal aggregations and a small description about the horizontal aggregation methods which returns set of numbers instead of one number per row. This paper consists of three methods that is SPJ, CASE and PIVOT methods in order to evaluate horizontal aggregations. Horizontal aggregations results in large volumes of data sets which are then partitioned into homogeneous clusters is important in the system. This can be performed by k means clustering algorithm.
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