Trend Estimation of Blood Glucose Level Fluctuations Based on Data Mining

We have fabricated calorie-calculating software that calculates and records the total calorific food intake by choosing a meal menu selected using a computer mouse. The purpose of this software was to simplify data collection throughout a person's normal life, even if they were inexperienced computer operators. Three portable commercial devices have also been prepared a blood glucose monitor, a metabolic rate monitor and a mobile-computer, and linked into the calorie-calculating software. Time-course changes of the blood glucose level, metabolic rate and food intake were measured using these devices during a 3 month period. Based on the data collected in this study we could predict blood glucose levels of the next morning (FBG) by modeling using data mining. Although a large error rate was found for predicting the absolute value, conditions could be found that improved the accuracy of the predicting trends in blood glucose level fluctuations by up to 90 %. However, in order to further improve the accuracy of estimation it was necessary to obtain further details about the patients' life style or to optimise the input variables that were dependent on each patient rather than collecting data over longer periods

Application of QSAR analysis to organic anion transporting polypeptide 1a5 (Oatp1a5) substrates

Organic anion transporting polypeptide 1a5, Slco1a5 (previously called Oatp3, Slc21a7) is a multispecific transmembrane transport protein that belongs to the OATP/SLCO superfamily of solute carriers. It is expressed in several epithelial barriers such as the small intestine and the choroid plexus where it might play an important role in the disposition of numerous endogenous and exogenous organic compounds. Since the molecular basis of the multispecificity of Oatp1a5 is not known and the three-dimensional structure not solved yet, we used three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques to obtain topological information on the substrate binding site of the protein. We aligned a heterogeneous data set of 18 Oatp1a5 substrates using the Genetic Algorithm Similarity Program (GASP) and performed comparative molecular field analysis (CoMFA) using this alignment. This resulted in a reasonable QSAR model including steric and electrostatic fields with a leave-one-out cross-validated r(cv)2 value of 0.705 and a no-cross-validated regression coefficient r2 value of 0.949. Based on the derived model we identified new potential Oatp1a5 substrates and confirmed their predicted apparent affinity values experimentally