Background and purpose:Brucine is one of the active components from Strychnos nux vomica,with signifi cant analgesic,anti-inflammatory and platelet-aggregating inhibitory properties.Due to its cytotoxic effect,the anti-tumor effect of brucine has increasingly been appreciated.In this study,we investigated the impact of brucine on A549 cells proliferation,apoptosis as well as the underlying mechanisms.Methods:MTT assay was used to examine the cell viability,flow cytometric analysis and fluorescent microscope were applied to examine cell apoptosis,ELISA method was used to examine the effect of brucine on PGE2 release from A549 cells and RT-PCR analysis was used to measure mRNA content,western blotting analysis was used to measure protein expression and luciferase activity was detected to examine the effect of brucine on COX-2 promoter activity.Results:Brucine was able to suppress the proliferation of A549 cells and induce cell apoptosis to time-dependent and dose-dependent manner.To understand the mechanisms,COX-2 was identifi ed to be an important target molecule involved in the apoptosis induced by brucine because brucine could suppress the COX-2 mRNA,protein expressions as well as PGE2 release in A549 cells in a timedependent manner.Furthermore,overexpression of COX-2 abrogated brucine-induced cell apoptosis,in contrast,when A549 cells were transfected with COX-2 siRNA,the apoptotic effect of brucine was dramatically enhanced.Further analysis revealed that brucine was able to suppress COX-2 transcriptional activation.Conclusion:Brucine was able to induce lung cancer apoptosis via downregulation of COX-2.

Objective To establish the chromatography fingerprint of Radix Scrophulariae by RP-HPLC.Methods HPLC was applied in this study.Kromasil KR100-5C18(4.6 mm? 250 mm,5 ? m)column and DAD detector were used with a mixture of methanol and 0.1 % methanoic acid as mobile phase in a gradient mode.Results The chemical substances of Radix Scrophulariae were optimally separated.Conclusion This method is simple,accurate with good reproducibility,thereby can be used specifically for the quality control of Radix Scrophulariae.

OBJECTIVETo study the molecular mechanism of cyclooxygenase-2 (COX-2), one of effective ingredient of brucine, in inducing non-small cell lung cancer cell apoptosis.

METHODCOX-2 promoter, transcription factor deletion mutants and COX-2 mRNA 3'-UTR-containing report plasmids were transfected with Renillia to non-small cell lung cancer A549 cell, in order to detect the activity of report gene luciferase and minimum cis-acting element of COX-2 promoter inhibited by brucine. The influence of brucine on IkappaB phosphorylation and the nuclear translocation of p65 were detected by immunoblotting assay.

RESULTBrucine significantly suppressed LPS-induced COX-2 promoter activation, but revealed minor impact on COX-2 mRNA stability. NF-kappaB in the vicinity of COX-2 promoter-262 was an important cis-acting element of brucine for inhibiting the activity of COX-2 promoter. Brucine was found to inhibit the phosphorylation of IkappaBalpha as well as the nuclear translocation of p65.

CONCLUSIONBrucine can improve A549 cells apoptosis by inhibiting the activity of NF-kappaB and the subsequent COX-2 gene expression.

Biological Transport ; drug effects ; Carcinoma, Non-Small-Cell Lung ; genetics ; metabolism ; Cell Line, Tumor ; Cyclooxygenase 2 ; genetics ; Humans ; NF-kappa B ; metabolism ; Phosphorylation ; drug effects ; Promoter Regions, Genetic ; drug effects ; genetics ; RNA Stability ; drug effects ; Strychnine ; analogs & derivatives ; pharmacology

Objective To establish the fingerprint for the dry stem in Dendrobium candidum by RP-HPLC.Methods Kromasil○RKR100-5 C18 column(250 mm?4.6 mm,5 ?m) was used with a mixture of acetonitrile-0.4% phosphorus acid as mobile phase in a gradient mode and the data were analyzed with ″Computer Aided Similarity Evaluation″ software.Results The chemical constituents of D.candidum were optimally separated,among which 15 fingerprint peaks in common were confirmed.Conclusion This method is simple,accurate with good reproducibility,and can be used specifically for the quality control of D.candidum.

Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.