The aim of this study was to explore potential herb-drug interaction between baicalin and rosuvastatin, a typical substrate for organic anion-transporting polypeptide 1B1 (OATP1B1) related to different OATP1B1 haplotype groups. Eighteen unrelated healthy volunteers who were CYP2C9*1/*1 with different OATP1B1 haplotypes (six OATP1B1*1b/*1b, six OATP1B1*1b/*15, and six OATP1B1*15/*15) were selected to participate in this study. Rosuvastatin (20 mg orally) pharmacokinetics after coadministration of placebo and 50-mg baicalin tablets (three times daily orally for 14 days) were measured for up to 72 h by liquid chromatography-mass spectrometry in a two-phase randomized crossover study. After baicalin treatment, the area under the plasma concentration-time curve (AUC)(0-72) and AUC(0-infinity) of rosuvastatin decreased by 47.0+/-11.0% (P=0.001) and 41.9+/-7.19% (P=0.001) in OATP1B1*1b/*1b, 21.0+/-20.6% (P=0.035) and 23.9+/-8.66% (P=0.004) in OATP1B1*1b/*15, and 9.20+/-11.6% (P=0.077) and 1.76+/-4.89% (P=0.36) in OATP1B1*15/*15, respectively. Moreover, decreases of both AUC(0-72) and AUC(0-infinity) of rosuvastatin among different haplotype groups were significantly different (P=0.002 and <0.001). Baicalin reduces plasma concentrations of rosuvastatin in an OATP1B1 haplotype-dependent manner.

Micro particle image velocimetry (PIV) characterization of two-fluid flow in a microchannel was analysed focusing on the effect of differences in the refractive indices of the two fluids on the accuracy of the PIV data. In the present study, we first analysed the objective-imaging system used for two-fluid flow measurement, and then derived the precondition for measurement of a valid velocity profile across the two-fluid interface. A micro PIV experimental system was set up to measure the two-fluid flow inside a Y-shaped microchannel. Using this system, the two-fluid flow of two glycerol–water mixtures with different glycerol mass concentrations (ϕ) was measured for three cases (ϕ = 0 and ϕ = 0.2; ϕ = 0.1 and ϕ = 0.5; and ϕ = 0 and ϕ = 0.6). The resulting experimental data agreed well with numerical predictions.

Objective: To assess the extent that the genetic and environmental factors contribute to the phenotypic correlations between obesity traits and age at menarche (AAM), and also to examine the influence of AAM on obesity in both pre-and postmenopausal women. Methods: Five hundred and twelve pedigrees with 2667 Caucasian female subjects from two to four generations were recruited. Fat mass and lean mass (both in kg) were measured by dual-energy X-ray absorptiometry scanner. Body mass index (BMI) (kg/m 2 ) was calculated. We performed bivariate quantitative genetic analyses in the total sample containing 2667 Caucasian women. We also selected 206 unrelated premenopausal women and 140 unrelated postmenopausal women from the total sample, and computed the respective phenotypic correlation between obesity and AAM in these two subgroups. Results: For fat mass, lean mass and BMI, we detected their significant negative genetic correlations with AAM after adjustment for significant covariates, which were À0.3170 (Po0.001), À0.1721 (Po0.05) and À0.3665 (Po0.001), respectively. However, their environmental correlations with AAM were all nonsignificant (P40.05), ranging from À0.0016 to 0.0192. In the premenopausal subgroup, significant associations were observed between fat mass and AAM (r ¼ À0.231, Po0.01) as well as between BMI and AAM (r ¼ À0.257, Po0.01). In the postmenopausal subgroup, no such associations were observed. Conclusion: Our results for the first time suggested that significant phenotypic association between obesity phenotypes and AAM is mainly attributable to shared genetic rather than environmental factors, and AAM may have stronger effects on obesity phenotypes in pre-than in postmenopausal women.

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