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To investigate the feasibility, validity and security of a new robot platform called KD-SR-01 and compare relative merits of KD-SR-01 robotic partial nephrectomy (KD-RPN) and 3D-Laparoscopic Partial Nephrectomy (3D-LPN).Twelve porcine models were randomly and equally divided for KD-RPN and 3D-LPN. The perioperative outcomes, physical and mental workload of the surgeon were compared. Physical workload was evaluated with surface electromyography. Mental workload was evaluated with National Aeronautics and Space Administration Task Load Index (NASA-TLX).All surgeries were performed successfully. The average docking time of KD-RPN was 6 min. The estimated blood loss was lower in KD-RPN (p0.05). There were no differences in all the other operative variables (p0.05). KD-RPN showed advantages in physical workload and was significantly superior in mental workload (p0.05).KD-RPN is technically feasible, valid and safe in porcine models. It's comparable from operative perspective while KD-RPN had advantages over 3D-LPN from ergonomic perspective.

Purpose To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation. Methods A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder-decoder network was designed as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification for 12 different joint structures. The 3D fully connected CRF was applied to regularize contextual relationship among voxels within the same tissue class and between different classes. The 3D simplex deformable modeling refined the output from 3D CRF to preserve the overall shape and maintain a desirable smooth surface for joint structures. The method was evaluated on 3D fast spin-echo (3D-FSE) MR image data sets. Quantitative morphological metrics were used to evaluate the accuracy and robustness of the method in comparison to the ground truth data. Results The proposed segmentation method provided good performance for segmenting all knee joint structures. There were 4 tissue types with high mean Dice coefficient above 0.9 including the femur, tibia, muscle, and other non-specified tissues. There were 7 tissue types with mean Dice coefficient between 0.8 and 0.9 including the femoral cartilage, tibial cartilage, patella, patellar cartilage, meniscus, quadriceps and patellar tendon, and infrapatellar fat pad. There was 1 tissue type with mean Dice coefficient between 0.7 and 0.8 for joint effusion and Baker's cyst. Most musculoskeletal tissues had a mean value of average symmetric surface distance below 1 mm. Conclusion The combined CNN, 3D fully connected CRF, and 3D deformable modeling approach was well-suited for performing rapid and accurate comprehensive tissue segmentation of the knee joint. The deep learning-based segmentation method has promising potential applications in musculoskeletal imaging.

Upper tract urothelial carcinomas (UTUCs) are rare entities that are usually diagnosed at advanced stages. Research on UTUC pathobiology and clinical management has been hampered by the lack of models accurately reflecting disease nature and diversity. In this study, a modified organoid culture system is used to generate a library of 25 patient‐derived UTUC organoid lines retaining the histological architectures, marker gene expressions, genomic landscapes, and gene expression profiles of their parental tumors. The study demonstrates that the responses of UTUC organoids to anticancer drugs can be identified and the model supports the exploration of novel treatment strategies. This work proposes a modified protocol for generating patient‐derived UTUC organoid lines that may help elucidate UTUC pathophysiology and assess the responses of these diseases to various drug therapies in personalized medicine.

Objectives To investigate whether ureteroscopy (URS) before radical nephroureterectomy (RNU) for upper tract urothelial carcinomas (UTUCs) has an impact on oncological outcomes. Patients and Methods We performed a systematic literature search of PubMed, Web of Science, and EMBASE for citations published prior to September 2017 that described URS performed on patients with UTUC and conducted a standard meta-analysis on survival outcomes. Results Our meta-analysis included eight eligible studies containing 3975 patients. The results were as follows: cancer-specific survival (CSS; hazard ratio [HR] 0.76, 95% confidence interval [CI] 0.59–0.99; P = 0.04), overall survival (OS; HR 0.76, 95% CI 0.48–1.21; P = 0.24), recurrence-free survival (RFS; HR 0.89, 95% CI 0.69–1.14; P = 0.37), metastasis-free survival (MFS; HR 1.06, 95% CI 0.82–1.36; P = 0.66), and intravesical recurrence-free survival (IRFS; HR 1.51, 95% CI 1.29–1.77; P < 0.001). When excluding previous bladder tumour history, the result for IRFS was a HR of 1.81 (95% CI 1.53–2.13; P < 0.001). Conclusions This meta-analysis indicated that URS before RNU did not have a negative impact on CSS, OS, RFS, or MFS in patients with UTUC. However, patients were at higher risk of intravesical recurrence after RNU when they had undergone URS before RNU. Further studies are needed to assess the effects of post-URS intravesical chemotherapy on intravesical recurrence.

Purpose To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. Methods A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. Results The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. Conclusion The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Osteoporosis, a common and serious bone disorder affecting aged people and postmenopausal women, is characterized by osteoclast overactivity. One therapeutic strategy is suppressing the bone resorption function of hyperactive osteoclasts, but there is no effective drug in clinical practice so far. Herein, it is demonstrated that fullerenols suppress the bone resorption of osteoclasts by inhibiting ruffled borders (RBs) formation. The RBs formation, which is supported by well-aligned actin bundles (B-actins), is a critical event for osteoclast bone resorption. To facilitate this function, osteoclast RBs dynamics is regulated by variable microenvironments to bundle F-actins, protrude cell membrane, and so on. B-actin perturbation by fullerenols is determined here, offering an opportunity to regulate osteoclast function by destroying RBs. In vivo, the therapeutic effect of fullerenols on overactive osteoclasts is confirmed in a mouse model of lipopolysaccharide-induced bone erosion. Collectively, the findings suggest that fullerenols adhere to F-actin surfaces and inhibit RBs formation in osteoclasts, mainly through hampering Ca2+ from bundling F-actins, and this is likely due to the stereo-hindrance effect caused by adherent fullerenols.