The effect of single versus multiple sets on strength

Research has previously been divided on whether performing resistance training with a single set per training session is as effective for increasing strength as training with multiple sets. The purpose of this study was to determine the effect of single sets versus multiple sets on strength. Forty subjects were randomly assigned into 1 of 3 groups: control (C; n = 8), single set (SS; n = 14), or multiple sets (MS; n = 18) to perform 8 maximal knee extensions at 60 degrees .s(-1) on a Biodex System 3 isokinetic dynamometer twice a week for 8 weeks. The SS group performed 1 set while the MS group performed 3 sets. All groups were pre-, mid- (4 weeks), and posttested at 60 degrees x s(-1). Strength was expressed as peak torque (PT). A 3 x 3 x 2 (time x group x sex) mixed factor repeated measures analysis of variance (ANOVA) revealed no interaction involving sex, but there was an interaction of group by time. The MS group exhibited a significant (p < 0.05) increase in PT (pre = 171.39 +/- 61.98 Nm; mid = 193.08 +/- 66.23 Nm) between the pretest and the midtest while the SS (pre = 163.45 +/- 56.37 Nm; mid = 172.60 +/- 61.78 Nm) and C groups (pre = 135.997 +/- 54.31 Nm; mid = 127.66 +/- 53.12 Nm) did not change. Strength did not change between the midtest and the posttest for any group. It was concluded that performing 3 sets of isokinetic knee extensions was more effective than performing a single set for increasing peak torque. These results seem to indicate that for increasing strength of the quadriceps, performing multiple sets is superior to performing a single set of resistance exercise.     

Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors

P-Glycoprotein (P-gp, ABCB1) plays a significant role in determining the ADMET properties of drugs and drug candidates. Substrates of P-gp are not only subject to multidrug resistance (MDR) in tumor therapy, they are also associated with poor pharmacokinetic profiles. In contrast, inhibitors of P-gp have been advocated as modulators of MDR. However, due to the polyspecificity of P-gp, knowledge on the molecular basis of ligand-transporter interaction is still poor, which renders the prediction of whether a compound is a P-gp substrate/non-substrate or an inhibitor/non-inhibitor quite challenging. In the present investigation, we used a set of fingerprints representing the presence/absence of various functional groups for machine learning based classification of a set of 484 substrates/non-substrates and a set of 1935 inhibitors/non-inhibitors. Best models were obtained using a combination of a wrapper subset evaluator (WSE) with random forest (RF), kappa nearest neighbor (kNN) and support vector machine (SVM), showing accuracies >70%. Best P-gp substrate models were further validated with three sets of external P-gp substrate sources, which include Drug Bank (n=134), TP Search (n=90) and a set compiled from literature (n=76). Association rule analysis explores the various structural feature requirements for P-gp substrates and inhibitors.