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SigrafW is Windows-compatible software developed using the Microsoft® Visual Basic Studio program that uses the simplified Hill equation for fitting kinetic data from allosteric and Michaelian enzymes. SigrafW uses a modified Fibonacci search to calculate maximal velocity (V), the Hill coefficient (n), and the enzyme-substrate apparent dissociation constant (K). The estimation of V, K, and the sum of the squares of residuals is performed using a Wilkinson nonlinear regression at any Hill coefficient (n). In contrast to many currently available kinetic analysis programs, SigrafW shows several advantages for the determination of kinetic parameters of both hyperbolic and nonhyperbolic saturation curves. No initial estimates of the kinetic parameters are required, a measure of the goodness-of-the-fit for each calculation performed is provided, the nonlinear regression used for calculations eliminates the statistical bias inherent in linear transformations, and the software can be used for enzyme kinetic simulations either for educational or research purposes.Keywords: Enzyme kinetics, allosteric enzyme, Michaelian enzyme, nonlinear fitting.The accurate estimation of kinetic parameters is of fundamental importance for biochemical studies. The use of partially purified enzyme preparations and the apparently complex relationship between velocity and substrate concentration are perhaps the main reasons that encourage enzyme characterization to be carried out in a simplified manner. In addition, enzyme kinetics analyses are often difficult to comprehend and apply because of confusing theoretical explanations and excessive use of mathematical extrapolation. Furthermore, enzyme kinetics teaching requires the association of theoretical lectures with time consuming experiments and calculations. In addition, it is frequently difficult and expensive to obtain enzymes with a specific and known mechanism of action. As a consequence, most aspects of enzymatic kinetics are often superficially exploited for teaching purposes [1,2].According to their kinetic behavior enzymes are classified as Michaelian [3] or allosteric [4]. For allosteric enzymes, fitting and plotting of data are usually performed according the simplified Hill equation [5]:where v is the reaction rate for the substrate concentration [S], V is the maximal rate, and K is the enzyme-substrate complex dissociation constant. The plot of log (v/(V Ϫ v)) versus log [S] results in a straight line that allows the determination of the kinetic parameters after linear regression data treatment. However, fitting experimental data for allosteric enzyme kinetics using linear regression of the Hill plot can produce unreliable results due to the uncertainty of the estimates of V for the reaction. The Hill treatment has been successfully applied to steady-state kinetics by Monod and colleagues in their classical work on allosteric enzymes [6]. However, it is well known that there are limitations in the interpretation of Hill coefficients determined from steady-state kinetics as com...

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

BackgroundResearch on Genomic medicine has suggested that the exposure of patients to early life risk factors may induce the development of chronic diseases in adulthood, as the presence of premature risk factors can influence gene expression. The large number of scientific papers published in this research area makes it difficult for the healthcare professional to keep up with individual results and to establish association between them. Therefore, in our work we aim at building a computational system that will offer an innovative approach that alerts health professionals about human development problems such as cardiovascular disease, obesity and type 2 diabetes.MethodsWe built a computational system called Chronic Illness Surveillance System (CISS), which retrieves scientific studies that establish associations (conceptual relationships) between chronic diseases (cardiovascular diseases, diabetes and obesity) and the risk factors described on clinical records. To evaluate our approach, we submitted ten queries to CISS as well as to three other search engines (Google™, Google Scholar™ and Pubmed®;) — the queries were composed of terms and expressions from a list of risk factors provided by specialists.ResultsCISS retrieved a higher number of closely related (+) and somewhat related (+/-) documents, and a smaller number of unrelated (-) and almost unrelated (-/+) documents, in comparison with the three other systems. The results from the Friedman’s test carried out with the post-hoc Holm procedure (95% confidence) for our system (control) versus the results for the three other engines indicate that our system had the best performance in three of the categories (+), (-) and (+/-). This is an important result, since these are the most relevant categories for our users.ConclusionOur system should be able to assist researchers and health professionals in finding out relationships between potential risk factors and chronic diseases in scientific papers.

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