PXR-Mediated Upregulation of CYP3A Expression by Herb Compound Praeruptorin C from Peucedanum praeruptorum

We recently reported that Praeruptorin C effectively transactivated the mRNA, protein expression, and catalytic activity of CYP3A4 via the CAR-mediated pathway, but whether and how PC could affect the expression and catalytic activity of CYP3A4 via PXR pathway remains unknown. Therefore, in this study, the effect of PC on the CYP3A gene expression was investigated in mice primary hepatocytes after knockdown of PXR by transient transfection of PXR siRNA, and the gene expression, protein expression, and catalytic activity of CYP3A4 in the LS174T cells with PXR overexpression were determined by real-time PCR, western blot analysis, and LC-MS/MS-based CYP3A4 substrate assay, respectively. We found that the level of CYP3a11 gene expression in mouse primary hepatocytes was significantly increased by praeruptorin C, but such an induction was suppressed after knockdown of pregnane X receptor by its siRNA. In PXR-overexpressed LS174T cells, PC significantly enhanced CYP3A4 mRNA, protein expression, and functional activity through PXR-mediated pathway; conversely, no such increase was found in the untransfected cells. These findings suggest that PC can significantly upregulate CYP3A level via the PXR-mediated pathway, and this should be taken into consideration to predict any potential herb-drug interactions between PC, Qianhu, and the other coadministered drugs

Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training

Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models. However, due to the large and unnecessary increase in the margin along adversarial directions, adversarial training causes heavy cross-over between natural examples and adversarial examples, which is not conducive to balancing the trade-off between robustness and natural accuracy. In this paper, we propose a novel adversarial training scheme to achieve a better trade-off between robustness and natural accuracy. It aims to learn a moderate-inclusive decision boundary, which means that the margins of natural examples under the decision boundary are moderate. We call this scheme Moderate-Margin Adversarial Training (MMAT), which generates finer-grained adversarial examples to mitigate the cross-over problem. We also take advantage of logits from a teacher model that has been well-trained to guide the learning of our model. Finally, MMAT achieves high natural accuracy and robustness under both black-box and white-box attacks. On SVHN, for example, state-of-the-art robustness and natural accuracy are achieved

Meta-analysis of quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesions

BACKGROUND: To determine, in a meta-analysis, the diagnostic performance of quantitative diffusion-weighted (DW) MR imaging in patients with breast lesions. METHODS: English and Chinese studies published prior to June 2009 to assess the diagnostic performance of quantitative DWI in patients with breast lesions were reviewed and summarized with reference to the inclusion and exclusion criteria. Methodological quality was assessed by using the quality assessment of diagnostic studies (QUADAS) instrument. Publication bias analysis was performed by using Comprehensive Meta-analysis version 2. Meta-Disc version 1.4 was used to describe primary results and explore homogeneity by Chi-square test and inconsistency index; to explore threshold effect by receiver operator characteristic (ROC) space and Spearman correlation coefficient; and to pool weighted sensitivity and specificity by fixed or random effect model. A summary ROC (sROC) curve was constructed to calculate the area under the curve (AUC). RESULTS: Of 65 eligible studies, 13 with 615 malignant and 349 benign lesions were included in the original meta-analysis, among which heterogeneity arising from factors other than threshold effect and publication bias was explored. Methodological quality was moderate. The pooled weighted sensitivity and specificity with corresponding 95% confidence interval (CI) in one homogenous subgroup of studies using maximum b = 1000 s/mm(2 )were 0.84 (0.80, 0.87) and 0.84 (0.79, 0.88) respectively. AUC of sROC was 0.9085. Sensitivity analysis demonstrated that the pooled estimates were stable and reliable. CONCLUSIONS: Quantitative DWI has a higher specificity to differentiate between benign and malignant breast lesions compared to that of contrast-enhanced MRI. However, large scale randomized control trials (RCTs) are necessary to assess its clinical value because of disunified diffusion gradient factor b and diagnosis threshold