Estimation of the area under a curve via several B-spline-based regression methods and applications.

Estimating the area under a curve (AUC) is an important subject in many fields of medicine and science. The regression model using B-spline functions provides flexibility in curve fitting, making it suitable for AUC estimation with various types of nonlinear trends. Despite the versatility of the B-spline approach, comprehensive discussions regarding relevant AUC estimation techniques using B-spline functions and their comparison with existing methods cannot be found in extant literature. In this paper, we investigate AUC estimation using B-spline regression and B-spline regression with several penalties, as well as discuss corresponding inferences. We carry out an extensive Monte Carlo study to evaluate the performance of the proposed methods in various realistic pharmacokinetics and analytical chemistry data settings. We show that the proposed methods provide robust and reliable AUC estimation regardless of different types of nonlinear models from scientific and medical research areas. Our proposed method is appropriate for general AUC estimation since it does not require nonlinear model specifications and inference techniques corresponding to the specified model.

[Medical analytical toxicology in Poland in year 2012]. / Kliniczna analityka toksykologiczna w Polsce w 2012 roku.

There are 12 centers of acute poisoning treatment and 9 round the clock toxicological laboratories. Most of the laboratories access evidence of activity run by National Clinical Toxicology Consultant. The paper presents actual status of medical toxicology laboratories in Poland and summarizes activity of the laboratories in the year 2012. In 2012 toxicological laboratories reported 113,719 assays. There were diagnosed 63.8% men and 34.8% women. The toxicological laboratories determine most substances and markers of exposition to chemical compounds important for diagnosis and treatment of acute poisonings (i.e. ethanol, methanol, ethylene glycol, acetaminophen, salicylates, anticonvulsants, carboxyhemoglobin, methemoglobin). There is not possible to determine heavy metals, all medicines and "designed" drugs of abuse in all laboratories. Limited access to reference methods, that enable to confirm results obtained by screening methods (immunological cassette and strip tests) is also a problem.

Building optimal regression tree by ant colony system-genetic algorithm: application to modeling of melting points.

The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.