Two new phenol compounds from roots of Ardisia crenata.

Two new phenols, ardisiphenol I (1) and ardisiphenol J (2), along with three known compounds (3-5) were isolated and identified from the roots of Ardisia crenata var. bicolor. Their structures were elucidated by means of spectroscopic techniques. Structurally, new compounds 1 and 2 have rare side chain with seven carbons. All compounds were tested for antibacterial activity, and compound 5 showed moderate antibacterial activity against Enterococcus faecalis. This work provided the isolation and structural identification of all the compounds in detail, and shed a new light on its further research.

bFGF-Regulating MAPKs Are Involved in High Glucose-Mediated ROS Production and Delay of Vascular Endothelial Cell Migration.

High blood sugar is a symptom of diabetes mellitus (DM). Vascular endothelial cells (VECs) directly contact the blood and are damaged when blood sugar levels are high. However, the molecular mechanism underlying this process remains elusive. To analyze the effects of DM on migration, we simulated DM by applying high glucose (HG) to the human VEC. HG delayed cell migration and induced phosphorylation of MAPKs (JNK and ERK). By contrast, in presence of bFGF, cell migration was promoted and MAPK phosphorylation levels were reduced. Furthermore, treatment with JNK and ERK inhibitors rescued HG-mediated delay of cell migration. Molecular and cell biological studies demonstrated that HG increased ROS production, whereas treatment with bFGF or JNK/ERK inhibitors blocked HG-induced ROS accumulation. Addition of MnTMPyP, a ROS scavenger, reduced HG-induced ROS production and accelerated cell migration, suggesting that the influence of HG on bFGF-MAPK signaling causes accumulation of ROS, which in turn regulate cell migration. This is the first study to elucidate the molecular mechanism of HG-mediated VEC migration; these findings could facilitate the development of novel therapies for DM.

High-glucose inhibits human fibroblast cell migration in wound healing via repression of bFGF-regulating JNK phosphorylation.

One of the major symptoms of diabetes mellitus (DM) is delayed wound healing, which affects large populations of patients worldwide. However, the underlying mechanism behind this illness remains elusive. Skin wound healing requires a series of coordinated processes, including fibroblast cell proliferation and migration. Here, we simulate DM by application of high glucose (HG) in human foreskin primary fibroblast cells to analyze the molecular mechanism of DM effects on wound healing. The results indicate that HG, at a concentration of 30 mM, delay cell migration, but not cell proliferation. bFGF is known to promote cell migration that partially rescues HG effects on cell migration. Molecular and cell biology studies demonstrated that HG enhanced ROS production and repressed JNK phosphorylation, but did not affect Rac1 activity. JNK and Rac1 activation were known to be important for bFGF regulated cell migration. To further confirm DM effects on skin repair, a type 1 diabetic rat model was established, and we observed the efficacy of bFGF on both normal and diabetic rat skin repair. Furthermore, proteomic studies identified an increase of Annexin A2 protein nitration in HG-stressed fibroblasts and the nitration was protected by activation of bFGF signaling. Treatment with FGFR1 and JNK inhibitors delayed cell migration and increased Annexin A2 nitration levels, indicating that Annexin A2 nitration is modulated by bFGF signaling via activation of JNK. Together with these results, our data suggests that the HG-mediated delay of cell migration is linked to the inhibition of bFGF signaling, specifically through JNK suppression.

Overexpression of a SNARE protein AtBS14b alters BR response in Arabidopsis.

BACKGROUND: N-ethyl-maleimide sensitive factor adaptor protein receptor (SNAREs) domain-containing proteins were known as key players in vesicle-associated membrane fusion. Genetic screening has revealed the function of SNAREs in different aspects of plant biology, but the role of many SNAREs are still unknown. In this study, we have characterized the role of Arabidopsis Qc-SNARE protein AtBS14b in brassinosteroids (BRs) signaling pathway. RESULTS: AtBS14b overexpression (AtBS14b ox) plants exhibited short hypocotyl and petioles lengths as well as insensitivity to exogenously supplied BR, while AtBS14b mutants did not show any visible BR-dependent morphological differences. BR biosynthesis enzyme BR6OX2 expression was slightly lower in AtBS14b ox than in wild type plants. Further BR-mediated repression of BR6OX2, CPD and DWF4 was inhibited in AtBS14b ox plants. AtBS14b-mCherry fusion protein localized in vesicular compartments surrounding plasma membrane in N. benthamiana leaves. In addition, isolation of AtBS14b-interacting BR signaling protein, which localized in plasma membrane, showed that AtBS14b directly interacted with membrane steroid binding protein 1 (MSBP1), but did not interact with BAK1 or BRI1. CONCLUSION: These data suggested that Qc-SNARE protein AtBS14b is the first SNARE protein identified that interacts with MSBP1, and the overexpression of AtBS14b modulates BR response in Arabidopsis.

Propofol pharmacokinetics in China: a multicentric study.

OBJECTIVE: A multicenter population pharmacokinetics study of propofol was performed to establish a new population model. MATERIALS AND METHODS: Three thousand two hundred and fifty-nine blood samples of 220 participants were measured by HPLC-UV or HPLC-FLU or GC-MS. Target-controlled infusion after single bolus or continuous infusion was applied for propofol anesthesia. The samples were taken from 2 to 1500 min. The concentration-time profiles were analyzed by nonlinear mixed effect model (NONMEM) with first order estimation method. The inter-individual variability and the residual variability were described by exponential model and constant coefficient variation model. The stepwise modeling strategy using PsN was applied for covariate modeling. The criteria of forward addition and backward elimination were (α = 0.01 and α = 0.005, χ(2), df = 1). The final model was evaluated by bootstrap using PDx and visual predictive check using PsN. 500 bootstraps and 1000 simulation were run. RESULT: The propofol population model was described by 3-compartment model with inter-individual variability of CL, V(1), Q(2,) and Q(3) describing by exponential model. The inter-individual variability of V(2), V(3) were not included because it is reported that the parameter was near its boundary. The typical value of CL, V1, Q2, V2, Q3 and V3 were 1.28 L · min(-1), 10.1 × (age/44)-0.465 × (1 + 0.352 × sex) L, 0.819 L · min(-1), 36.0 L, 0.405 × (bodyweight/60)1.58 L · min(-1) and 272 L, respectively. Coefficients of inter-individual variability of CL, V1, Q2 and Q3 were 30.5%, 35.6%, 43.7% and 66.9%, respectively, and the coefficients of variation of HPLC-UV, GC-MS and HPLC-FLU were 13.3%, 16.9% and 24.2%, respectively. The bootstrap evaluation showed that the final model parameter estimates were within ± 3.39% compared with bootstrap median. The curves of observations percentiles were distributed within the corresponding 95 prediction percentiles by the visual predictive check. CONCLUSION: The three-compartment model with first-order elimination could describe the pharmacokinetics of propofol fairly well. The involved fixed effects are age, body weight and sex. The population model was evaluated to be stable by bootstrap and visual predictive check.

[Population pharmacokinetic modeling and evaluation of propofol from multiple centers].

In order to successfully develop the effective population pharmacokinetic model to predict the concentration of propofol administrated intravenously, the data including the concentrations across both distribution and elimination phases from five hospitals were analyzed using nonlinear mixed effect model (NONMEM). Three-compartment pharmacokinetic model was applied while the exponential model was used to describe the inter-individual variability and constant coefficient model to the intra-individual variability, accordingly. Covariate effect including the body weight on the parameter CL, V1, Q2, V2, Q3 and V3 were investigated. The performance of final model was assessed by Bootstrapping, goodness-of-fit and visual predictive checking (VPC). The context-sensitive half-times and the infusion rates necessary to maintain the concentration of 1 microg x mL(-1) were simulated to six subpopulations. The results were as follows: the typical value of CL, V1, Q2, V2, Q3 and V3 were 0.965 x (1 + 0.401 x VESS) x (BW/59)(0.578) L x min(-1), 13.4 x (AGE/45)(-0.317) L, 0.659 x (1 + GENDER x 0.385) L x min(-1), 28.8 L, 0.575 x (1 + GENDER x 0.367) x (1 - 0.369 x VESS) L x min(-1) and 196 L respectively. Coefficients of the inter-individual variability of CL, V1, Q2, V2, Q3 and V3 were 29.2%, 46.9%, 35.2%, 40.4%, 67.0% and 49.9% respectively, and the coefficients of residual variability were 24.7%, 16.1% and 22.5%, the final model indicated a positive influence of a body weight on CL, and also that a negative correlation of age with V1. Q2 and Q3 in males were higher than those in females at 38.5% and 36.7%. The CL and Q3 were 40.1% increased and 36.9% decreased in arterial samples compared to those in venous samples. The determination coefficient of observations (DV)-individual predicted value (IPRED) by the final model was 0.91 which could predict the propofol concentration fairly well. The stability and the predictive performance were accepted by Bootstrapping, the goodness-of-fit and VPC. The context-sensitive half-times and infusion rates necessary to maintain the concentration of 1 microg x mL(-1) were different obviously among the 6 sub-populations obviously. The three-compartment model with first-order elimination could describe the pharmacokinetics of propofol fairly well. The involved fixed effects are age, body weight, gender and sampling site. The simulations in 6 subpopulations were available in clinical anesthesia. The propofol anesthesia monitor care could be improved by individualization of pharmacokinetic parameter estimated from the final model.