Effect of phytosterols in the treatment of hypercholesterolemia in adults – systematic review with meta-analysis / Efeitos dos fitoesteróis no tratamento da hipercolesterolemia em adultos – revisão sistemática com metanálise

Increased plasma total cholesterol (TC) and LDL-cholesterol (low-density lipoprotein) are considered risk factors for coronary disease. Phytosterols are among the dietary options for decreasing serum concentrations of TC and LDL-c by up to 15%.To evaluate the scientific evidence on the use of phytosterols in the treatment of hypercholesterolemia in adults.A systematic meta-analysis of the Medline, Embase, Web of Service, VHL, PUBMED, Scopus, Cochrane Library and LILACS databases was performed between November and December 2016. The PICO strategy was used. Inclusion criteria were randomized clinical trials with adults of both sexes using phytosterols longer than 4 weeks intervention. Exclusion criteria were animal and in vitro studies, humans less than 18 years old and individuals with other diseases (cancer, metabolic syndromes, diabetes mellitus, hypertension, renal disease, liver diseases). The risk of bias was assessed by two reviewers. The primary outcomes investigated were TC and LDL-cholesterol. Statistical analyses were conducted using the RevMan 5.3. The standardized effect size was used to estimate the standardized mean difference and 95% CI of TC and LDL-c.Twenty-seven randomized controlled trials were included in this systematic review and 26 studies in the meta-analysis of TC and LDL-cholesterol. The meta-analyzes showed an association with the reduction in plasma TC (-2.54 [-3.04; -2.03]) and LDL-c (-2.8 [-2.63; -1.53]) after intervention with the vegetable esters.The consumption of 1.5 to 2.0 g/day of phytosterols promotes reduction of TC and LDL-c in hypercholesterolemic individuals, regardless of the way they are consumed.

Web-of-Service Software Reusability Prediction using Heterogenous Ensemble Classifier

WoS computing environment is expected to have numerous parallel computing engines. Presently, software professionals or developers often want to reuse existing software components to exhibit a task with time-efficient and cost effective solutions. However, software component reusability in uncontrolled manner leads to failure, premature shutdown and software smells or aging. This paper develops a novel evolutionary computing assisted ensemble classification system for WoS software reusability prediction. This applies different base learners such asNaïve Bayes (NB), Linear Regression (LR), Decision Tress (DT),Logarithmic Regression (LOGR),and Support Vector Machine (SVM),Multivariate Adaptive Regression Spline (MARS). Once training the base learners, the outputs of each classifier have been processed with majority vote.The computation in conjunction with weighted sum enabled final labelling of each software class. The performance results affirmed that the present work ensemble classifier has better performance with respect to base classifiers.