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Emerging evidence indicates an important role for the breast cancer resistance protein (BCRP) in limiting brain penetration of substrate drugs. While in vitro transwell assays can provide an indication of BCRP substrate potential, the predictability of these assays in relation to in vivo brain penetration is still under debate. The present study examined the correlation of BCRP membrane protein expression level and transcellular transport activity across Madin-Darby canine kidney (MDCK) II monolayers. We expressed human BCRP or murine BCRP1 in MDCKII wild-type cells using BacMam2 virus transduction. The selective P-glycoprotein (P-gp) inhibitor LY335979 (1 M) was included in the transport medium to measure BCRP-mediated transcellular transport for P-gp and BCRP cosubstrates. The BCRP levels in membrane extracts from MDCKII-BCRP or MDCKII-Bcrp1 cells were quantified by liquid chromatography-tandem mass spectrometry. The results are summarized as follows: 1) the membrane protein expression levels correlate with the corrected efflux ratios of substrates for human BCRP and murine BCRP1 within the efflux ratios investigated; 2) we demonstrate good concordance in rank order between the BCRP and BCRP1-mediated efflux ratios for 12 drugs; and 3) we propose an approach to contextualize in vitro BCRP transport data of discovery compounds by comparing them to the in vitro and in vivo transport data of the reference drug dantrolene and taking into account interbatch variation in BCRP expression. This approach correctly predicted compromised brain penetration for 25 discovery compounds in rodents, which were BCRP substrates but not P-gp or weak P-gp substrates. These results suggest that BCRP-expressing MDCKII cells are useful in predicting the in vivo role of BCRP in brain penetration.

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Epidemics usually spread widely and can cause a great deal of loss to humans. In the real world, vaccination is the principal method for suppressing the spread of infectious diseases. The Susceptible-Infected-Susceptible (SIS) model suggests that voluntary vaccination may affect the spread of an epidemic. Most studies to date have argued that the infection rates of nodes in the SIS model are not heterogeneous. However, in reality, there exist differences in the neighbor network structure and the number of contacts, which may affect the spread of infectious diseases in society. As a consequence, it can be reasonably assumed that the infection rate of the nodes is heterogeneous because of the amount of contact among people. Here, we propose an improved SIS model with heterogeneity in infection rates, proportional to the degree of nodes. By conducting simulations, we illustrate that almost all vaccinated nodes have high degrees when the infection rate is positively correlated with the degree of a node. These vaccinated nodes can divide the whole network into many connected sub-graphs, which significantly slows down the propagation of an epidemic; the heterogeneity of infection rates has a strong inhibitory effect on epidemic transmission. On the other hand, when the infection rate is negatively related to the degrees of the infection rate nodes, it is difficult for most nodes to meet the inoculation conditions, and the number of inoculations is close to zero.

<abstract> <p>Delineation of the boundaries of the Left Ventricle (LV) in cardiac Magnetic Resonance Images (MRI) is a hot topic due to its important diagnostic power. In this paper, an approach is proposed to extract the LV in a sequence of MR images. In the proposed paper, all images in the sequence are segmented simultaneously and the shape of the LV in each image is supposed to be similar to that of the LV in nearby images in the sequence. We coined the novel shape similarity constraint, and it is called sequential shape similarity (SSS in short). The proposed segmentation method takes the Active Contour Model as the base model and our previously proposed Gradient Vector Convolution (GVC) external force is also adopted. With the SSS constraint, the snake contour can accurately delineate the LV boundaries. We evaluate our method on two cardiac MRI datasets and the Mean Absolute Distance (MAD) metric and the Hausdorff Distance (HD) metric demonstrate that the proposed approach has good performance on segmenting the boundaries of the LV.</p> </abstract>