Intracellular distributing and interferon-γ secretion of human interleukin-18 in BxPC-3 cells.

OBJECTIVE: To investigate the characteristics of interleukin-18 (IL-18) in vitro, explore IL-18, interferon-γ (IFN-γ) and interleukin-2 (IL-2) secretive activity in BxPC-3 line cells with interleukin-18 mutants. METHODS: Human IL-18 full-length gene (hIL-18-F) and the hIL-18 presumed mature protein gene (hIL-18-M) were inserted into the expression vector pEGFP-N1, to construct recombinant plasmids as Mu0, Mu1, Mu2, Mu3, and Mu4, and the recombinant plasmids were then transferred into BxPC-3 line cells. There are significant differences between Mu1, Mu2 and the pEGFP-C1 control group (P<0.05) by 3-(4,5-dimethiazol- 2-yl)- 2,5-diphenyltetrazolium bromide (MTT) for a proliferation assay, and the fluorescence of the Mu1 and Mu 2 appeared targeted to the membranous region in the BxPC-3 cells after transfected 24h by confocal laser scanning microscope (OLSM).To characterize the intracellular distribution of hIL-18, recombinant IL-18 were each fused to the enhanced green fluorescent protein gene, and expressed in BxPC-3 cells. RESULTS: Results showed that the Mu1 tended to the membranous region in BxPC-3 cells, this indicates that the N-terminal former amino acid peptide helped ChIL-18 target to BxPC-3 cellS membranes. ELISA results demonstrated that IFN-γ and IL-18 secreted levels of BxPC-3 cells transfecting with recombinant plasmid showed an significant difference (P<0.01); refers to IL-2 expression, the two BxPC-3 cells groups transfecting with recombinant plasmid have no significant function (P>0.05). CONCLUSIONS: The results showed that hIL-18 and hIL-18 presumed mature protein can induce the secretion of IFN-γ in BxPC-3 cells, and increase the expression of IL-18, but they have no effects on IL-2.

Interactive medical image segmentation via a point-based interaction.

Due to low tissue contrast, irregular shape, and large location variance, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, a novel method is presented for interactive medical image segmentation with the following merits. (1) Its design is fundamentally different from previous pure patch-based and image-based segmentation methods. It is observed that during delineation, the physician repeatedly check the intensity from area inside-object to outside-object to determine the boundary, which indicates that comparison in an inside-out manner is extremely important. Thus, the method innovatively models the segmentation task as learning the representation of bi-directional sequential patches, starting from (or ending in) the given central point of the object. This can be realized by the proposed ConvRNN network embedded with a gated memory propagation unit. (2) Unlike previous interactive methods (requiring bounding box or seed points), the proposed method only asks the physician to merely click on the rough central point of the object before segmentation, which could simultaneously enhance the performance and reduce the segmentation time. (3) The method is utilized in a multi-level framework for better performance. It has been systematically evaluated in three different segmentation tasks, including CT kidney tumor, MR prostate, and PROMISE12 challenge, showing promising results compared with state-of-the-art methods.

Scalable and low-cost fabrication of hydrophobic PVDF/WS2 porous membrane for highly efficient solar steam generation.

Solar steam generation based on the light-to-heat conversion via photothermal materials has been considered as one of emerged technologies for utilizing solar energy to produce clean water. Here, a hydrophobic PVDF/WS2 porous membrane for highly efficient solar steam generation was prepared by a scalable and low-cost method. The WS2 photothermal materials were fabricated through a simple ball milling, and then a non-solvent induced phase inversion method was used to fabricate the porous PVDF/WS2 membrane. The PVDF/WS2 evaporator could absorb the sunlight of 90.58% from UV to NIR region due to the multiscattering of the porous structure and the synergistic effect of WS2 and seawater. Moreover, the PVDF/WS2 evaporator exhibits the hydrophobic properties. Taking the advantages mentioned above, our evaporator could manifest the evaporation rate of 4.15 kgm-2h-1 with the solar thermal efficiency of 94.2% under 3 sun irradiation, as well as an outstanding durability upon continuous running. Also, the evaporator shows both the excellent seawater desalination and sewage treatment ability. Outdoor experiments illustrate that the evaporator has practical applications under a natural sunlight condition. The numerous advantages of our PVDF/WS2 evaporator, including the high solar-thermal efficiency, the outstanding durability, and the simple and scalable manufacture process, may provide a potential photothermal material for the commercial solar desalination application and wastewater treatment.

Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor Segmentation in CT Images.

Due to the unpredictable location, fuzzy texture and diverse shape, accurate segmentation of the kidney tumor in CT images is an important yet challenging task. To this end, we in this paper present a cascaded trainable segmentation model termed as Crossbar-Net. Our method combines two novel schemes: (1) we originally proposed the crossbar patches, which consists of two orthogonal non-squared patches (i.e., the vertical patch and horizontal patch). The crossbar patches are able to capture both the global and local appearance information of the kidney tumors from both the vertical and horizontal directions simultaneously. (2) With the obtained crossbar patches, we iteratively train two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded training manner. During the training, the trained sub-models are encouraged to become more focus on the difficult parts of the tumor automatically (i.e., mis-segmented regions). Specifically, the vertical (horizontal) sub-model is required to help segment the mis-segmented regions for the horizontal (vertical) sub-model. Thus, the two sub-models could complement each other to achieve the self-improvement until convergence. In the experiment, we evaluate our method on a real CT kidney tumor dataset which is collected from 94 different patients including 3,500 CT slices. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior performance of our method on the Dice similarity coefficient, true positive fraction, centroid distance and Hausdorff distance. Moreover, to exploit the generalization to other segmentation tasks, we also extend our Crossbar-Net to two related segmentation tasks: (1) cardiac segmentation in MR images and (2) breast mass segmentation in X-ray images, showing the promising results for these two tasks. Our implementation is released at https: //github.com/Qianyu1226/Crossbar-Net.

A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling.

In this paper, we investigate if the MR prostate segmentation performance could be improved, by only providing one-point labeling information in the prostate region. To achieve this goal, by asking the physician to first click one point inside the prostate region, we present a novel segmentation method by simultaneously integrating the boundary detection results and the patch-based prediction. Particularly, since the clicked point belongs to the prostate, we first generate the location-prior maps, with two basic assumptions: (1) a point closer to the clicked point should be with higher probability to be the prostate voxel, (2) a point separated by more boundaries to the clicked point, will have lower chance to be the prostate voxel. We perform the Canny edge detector and obtain two location-prior maps from horizontal and vertical directions, respectively. Then, the obtained location-prior maps along with the original MR images are fed into a multi-channel fully convolutional network to conduct the patch-based prediction. With the obtained prostate-likelihood map, we employ a level-set method to achieve the final segmentation. We evaluate the performance of our method on 22 MR images collected from 22 different patients, with the manual delineation provided as the ground truth for evaluation. The experimental results not only show the promising performance of our method but also demonstrate the one-point labeling could largely enhance the results when a pure patch-based prediction fails.

Effects of different sources of copper on Ctr1, ATP7A, ATP7B, MT and DMT1 protein and gene expression in Caco-2 cells.

Copper sulfate (CuSO4), micron copper oxide (micron CuO) and nano copper oxide (nano CuO) at different concentrations were, respectively, added to culture media containing Caco-2 cells and their effects on Ctr1, ATP7A/7B, MT and DMT1 gene expression and protein expression were investigated and compared. The results showed that nano CuO promoted mRNA expression of Ctr1 in Caco-2 cells, and the difference was significant compared with micron CuO and CuSO4. Nano CuO was more effective in promoting the expression of Ctr1 protein than CuSO4 and micron CuO at the same concentration. Nano CuO at a concentration of 62.5 µM increased the mRNA expression levels of ATP7A and ATP7B, and the difference was significant compared with CuSO4. The addition of CuSO4 and nano CuO to the culture media promoted the expression of ATP7B proteins. CuSO4 at a concentration of 125 µM increased the mRNA expression level of MT in Caco-2 cells, and the difference was significant compared with nano CuO and micron CuO. Nano CuO at a concentration of 62.5 µM inhibited the mRNA expression of DMT1, and the difference was significant compared with CuSO4 and micron CuO. Thus, the effects of CuSO4, micron CuO and nano CuO on the expression of copper transport proteins and the genes encoding these proteins differed considerably. Nano CuO has a different uptake and transport mechanism in Caco-2 cells to those of CuSO4 and micron CuO.

A reporter for dsRNA response in Neurospora crassa.

In the filamentous fungus Neurospora, the production of dsRNA can elicit a dsRNA-induced transcriptional response similar to the interferon response in vertebrates. However, how fungi sense the expression of dsRNA and activate gene expression is unknown. In this study, we established a dsRNA response reporter system in Neurospora crassa. Using the dsRNA-activated RNA-dependent RNA polymerase gene rrp-3 promoter, we created an expression construct (pRRP-3::Myc-Al-1) and introduced it into al-1(KO) mutant. The test dsRNA efficiently induced pRRP-3::Myc-Al-1 expression in the al-1(KO) mutant, resulting in conidia color switching from white to yellow. These results confirm that the dsRNA response is regulated at the transcriptional level and this reporter system can be used for future studies in dsRNA response in filamentous fungi.