Automatic segmentation of kidney and kidney tumors using the cascaded dense network combined with cLSTM in CT scan

In this study, we develop the cascaded deep neural network model for automatic segmentation of the kidney and kidney tumors in CT scans. We used the fully dense network (to extract inner-slice image features) combined with bi-cLSTM (to extract inter-slice image features) for segmentation of kidney and kidney tumors. The whole CT scan is preprocessing for resizing (256*256) and intensity normalization (clipping between -600 and 400 and then normalizing between 0 and 1), and then entered into 1 st neural network (growth factor = 8, and the consecutive slices = 3) for segmentation of kidney. And the output of 1 st network is resized into 96*96, and entered into 2 nd neural network, which is the same architecture of 1 st network excepting growth factor = 16, and the consecutive slices = 5. Our cascaded deep neural network showed the dice scores of 0.932 (train), and 0.884 (validation) for segmentation of kidney and the dice scores of 0.845 (train), and 0.696 (validation) for segmentation of kidney tumors

Toward an Evaluation Model of User Experiences on Virtual Reality Indoor Bikes

This paper deals with deriving a model or framework to evaluate user experiences (UX) of virtual reality (VR) systems, especially, VR indoor bikes which are under construction. Recently, VR is one of the most appealing areas attracting people’s interests around the world. Many products armed with it increasingly emerge on the market, and it is expected that the use of VR systems will continue to increase sharply in the future. However, UX of such products cannot be evaluated appropriately at the moment due to a lack of proper evaluation models. In a broad sense, UX that may stem from human machine interface in ergonomics covers affect, usability, and user value in spite of some differences in definition among the researchers. While evaluations of UX on the products without VR have been overall justifiably performed, UX has been evaluated neither systematically nor strictly on the products with VR. Through the analyses of expert reviews, we newly identify an additional component and its elements, and modify some elements of the three existing components for evaluating UX on the VR systems. As a result, we propose a comprehensive evaluation model of UX, which consists of four factors: usability, affect, user value, and presence feeling. In addition, we determine the components and their elements for specific VR indoor bikes similarly through the analyses of expert surveys and focus-group discussions, which results in developing a questionnaire for users. Finally, along with the questionnaire, we propose a specific evaluation model for VR indoor bikes

Spontaneous Regression of Pulmonary and Adrenal Metastases Following Percutaneous Radiofrequency Ablation of a Recurrent Renal Cell Carcinoma

The spontaneous regression of metastatic lesions from renal cell carcinoma (RCC) is extremely rare, but may be encountered following cytoreductive treatments. We report a case of a recurrent RCC with multiple metastatic lesions which spontaneously regressed after undergoing radiofrequency ablation of the renal tumor

REGNET: Mining context-specific human transcription networks using composite genomic information

Background: Genome-wide expression profiles reflect the transcriptional networks specific to the given cell context. However, most statistical models try to estimate the average connectivity of the networks from a collection of gene expression data, and are unable to characterize the context-specific transcriptional regulations. We propose an approach for mining context-specific transcription networks from a large collection of gene expression fold-change profiles and composite gene-set information.Results: Using a composite gene-set analysis method, we combine the information of transcription factor binding sites, Gene Ontology or pathway gene sets and gene expression fold-change profiles for a variety of cell conditions. We then collected all the significant patterns and constructed a database of context-specific transcription networks for human (REGNET). As a result, context-specific roles of transcription factors as well as their functional targets are readily explored. To validate the approach, nine predicted targets of E2F1 in HeLa cells were tested using chromatin immunoprecipitation assay. Among them, five (Gadd45b, Dusp6, Mll5, Bmp2 and E2f3) were successfully bound by E2F1. c-JUN and the EMT transcription networks were also validated from literature.Conclusions: REGNET is a useful tool for exploring the ternary relationships among the transcription factors, their functional targets and the corresponding cell conditions. It is able to provide useful clues for novel cell-specific transcriptional regulations. The REGNET database is available at http://mgrc.kribb.re.kr/regnet.open0

Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI

IntroductionWe aimed to predict platinum sensitivity using routine baseline multimodal magnetic resonance imaging (MRI) and established clinical data in a radiomics framework.MethodsWe evaluated 96 patients with ovarian cancer who underwent multimodal MRI and routine laboratory tests between January 2016 and December 2020. The patients underwent diffusion-weighted, contrast-enhanced T1-weighted, and T2-weighted MRI. Subsequently, 293 radiomic features were extracted by manually identifying tumor regions of interest. The features were subjected to the least absolute shrinkage and selection operators, leaving only a few selected features. We built the first prediction model with a tree-based classifier using selected radiomics features. A second prediction model was built by combining the selected radiomic features with four established clinical factors: age, disease stage, initial tumor marker level, and treatment course. Both models were built and tested using a five-fold cross-validation.ResultsOur radiomics model predicted platinum sensitivity with an AUC of 0.65 using a few radiomics features related to heterogeneity. The second combined model had an AUC of 0.77, confirming the incremental benefits of the radiomics model in addition to models using established clinical factors.ConclusionOur combined radiomics-clinical data model was effective in predicting platinum sensitivity in patients with advanced ovarian cancer

MR Imaging and Histopathologic Findings of A Case of Cerebral Ganglioneurocytoma

We report a case of ganglioneurocytoma manifesting as a complex partial seizure in a young adult male. MR images depicted a well-marginated cystic mass with a heterogeneous solid portion abutting the dura in the parietal lobe. The solid portion showed minimal heterogeneous enhancement, and pressure erosion of the overlying calvarium had occurred. Following gross total resection, the clinical outcome was satisfactory, with no further seizures, and during the five-year follow-up period, the tumor did not recur

Decreased in vivo glutamate/GABA ratio correlates with the social behavior deficit in a mouse model of autism spectrum disorder

Abstract To diagnose autism spectrum disorder (ASD), researchers have sought biomarkers whose alterations correlate with the susceptibility to ASD. However, biomarkers closely related to the pathophysiology of ASD are lacking. Even though excitation/inhibition (E/I) imbalance has been suggested as an underlying mechanism of ASD, few studies have investigated the actual ratio of glutamate (Glu) to γ-aminobutyric acid (GABA) concentration in vivo. Moreover, there are controversies in the directions of E/I ratio alterations even in extensively studied ASD animal models. Here, using proton magnetic resonance spectroscopy (1H-MRS) at 9.4T, we found significant differences in the levels of different metabolites or their ratios in the prefrontal cortex and hippocampus of Cntnap2−/− mice compared to their wild-type littermates. The Glu/GABA ratio, N-acetylaspartate (NAA)/total creatine (tCr) ratio, and tCr level in the prefrontal cortex were significantly different in Cntnap2−/− mice compared to those in wild-type mice, and they significantly correlated with the sociability of mice. Moreover, receiver operating characteristic (ROC) analyses indicated high specificity and selectivity of these metabolites in discriminating genotypes. These results suggest that the lowered Glu/GABA ratio in the prefrontal cortex along with the changes in the other metabolites might contribute to the social behavior deficit in Cntnap2−/− mice. Our results also demonstrate the utility of 1H-MRS in investigating the underlying mechanisms or the diagnosis of ASD

Fabrication of a Multi-Walled Nanotube (MWNT) Ionic Liquid Electrode and Its Application for Sensing Phenolics in Red Wines

A multi-walled nanotube (MWNT) ionic liquid was prepared by the immobilization of 1-butylimidazole bromide onto an epoxy group on a poly(glycidyl methacrylate)-grafted MWNT, which was synthesized by radiation-induced graft polymerization of glycidyl methacrylate onto MWNT in an aqueous solution. Subsequently, a MWNT ionic liquid electrode was fabricated by hand-casting MWNT ionic liquid, tyrosinase, and chitosan solution as a binder on indium tin oxide (ITO) glass. The sensing ranges of the MWNT ionic liquid electrode with immobilized tyrosinase was in the range of 0.01-0.08 mM in a phosphate buffer solution. The optimal conditions such as pH, temperature, and effects of different phenolic compounds were determined. The total phenolic compounds of three commercial red wines were also determined on the tyrosinase-immobilized biosensor