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Background. While all antihypertensive medications lower blood pressure, hemodynamic properties of various classes of antihypertensive medications may differ.Objective. To perform a meta-analysis to compare the hemodynamic properties of different classes of antihypertensive medications.Methods. Studies involving the treatment of hypertension using the effect of ACEIs,β-blockers, CCBs and thiazide diuretics on plasma volume (PV), cardiac output (CO) or stroke volume (SV) were searched using online databases prior to May 2011. Studies had to be written in the English language, studying human subjects with a single pharmacological agent (monotherapy), and with a minimum duration of 4 weeks.Results. Seventy-five (75) studies that enrolled a total of 1522 subjects were included. All four antihypertensive classes lowered blood pressure.β-blockers decreased heart rate; the other classes had no effect upon heart rate. ACEIs increased PV; the other classes had no effect upon PV.β-blockers and thiazide diuretics decreased CO while ACEIs and CCBs had no effect upon CO.β-blockers and CCBs increased SV, thiazide diuretics decreased SV, and ACEIs did not change SV.Conclusion. In the treatment of uncomplicated hypertension, the various classes of antihypertensive medications differ from each other in terms of their non-blood pressure lowering hemodynamic properties.

In this study, support vector machine (SVM) method combined with genetic algorithm (GA) for feature selection and conjugate gradient (CG) method for parameter optimization (GA-CG-SVM), has been employed to develop prediction models of human plasma protein binding rate (PPBR) and oral bioavailability (BIO). The advantage of the GA-CG-SVM is that it can deal with feature selection and SVM parameter optimization simultaneously. Five-fold cross-validation as well as independent test set method were used to validate the prediction models. For the PPBR, a total of 692 compounds were used to train and test the prediction model. The prediction accuracy by means of 5-fold cross-validation is 86% and that for the independent test set (161 compounds) is 81%. These accuracies are markedly higher over that of the best model currently available in literature. The number of descriptors selected is 29. For the BIO, the training set is composed of 690 compounds and external 76 compounds form an independent validation set. The prediction accuracy for the training set by using 5-fold cross-validation and that for the independent test set are 80% and 86%, respectively, which are better than or comparable to those of other classification models in literature. The number of descriptors selected is 25. For both the PPBR and BIO, the descriptors selected by GA-CG method cover a large range of molecular properties which imply that the PPBR and BIO of a drug might be affected by many complicated factors.