Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility of using three published convolution neural network (CNN) models built for optical flow (hereafter referred to as CNN-based tracking) by the computer vision community for breast ultrasound strain elastography. Elastographic datasets produced by finite element and ultrasound simulations were used to retrain three published CNN models: FlowNet-CSS, PWC-Net, and LiteFlowNet. After retraining, the three improved CNN models were evaluated using computer-simulated and tissue-mimicking phantoms, and in vivo breast ultrasound data. CNN-based tracking results were compared with two published two-dimensional (2D) speckle tracking methods: coupled tracking and GLobal Ultrasound Elastography (GLUE) methods. Our preliminary data showed that, based on the Wilcoxon rank-sum tests, the improvements due to retraining were statistically significant (p < 0.05) for all three CNN models. We also found that the PWC-Net model was the best neural network model for data investigated, and its overall performance was on par with the coupled tracking method. CNR values estimated from in vivo axial and lateral strain elastograms showed that the GLUE algorithm outperformed both the retrained PWC-Net model and the coupled tracking method, though the GLUE algorithm exhibited some biases. The PWC-Net model was also able to achieve approximately 45 frames/second for 2D speckle tracking data investigated.

Objectives End-stage renal disease (ESRD) patients are at an increased risk of needing total joint arthroplasty (TJA); however, both dialysis and renal transplantation might be potential predictors of adverse TJA outcomes. For dialysis patients, the high risk of blood-borne infection and impaired muscular skeletal function are threats to implants’ survival, while for renal transplant patients, immunosuppression therapy is also a concern. There is still no high-level evidence in the published literature that has determined the best timing of TJA for ESRD patients. Methods A literature search in MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (up to November 2019) was performed to collect studies comparing TJA outcomes between renal transplant and dialysis patients. Two reviewers independently conducted literature screening and quality assessments with the Newcastle-Ottawa Scale (NOS). After the data were extracted, statistical analyses were performed. Results Compared with the dialysis group, a lower risk of mortality (RR = 0.56, Cl = [0.42, 0.73], P < 0.01, I2 = 49%) and revision (RR = 0.42, CI = [0.30, 0.59], P < 0.01, I2 = 43%) was detected in the renal transplant group. Different results of periprosthetic joint infection were shown in subgroups with different sample sizes. There was no significant difference in periprosthetic joint infection in the small-sample-size subgroup, while in the large-sample-size subgroup, renal transplant patients had significantly less risk (RR = 0.19, CI = [0.13, 0.23], P < 0.01, I2 = 0%). For dislocation, venous thromboembolic disease, and overall complications, there was no significant difference between the two groups. Conclusion Total joint arthroplasty has better safety and outcomes in renal transplant patients than in dialysis patients. Therefore, delaying total joint arthroplasty in dialysis patients until renal transplantation has been performed would be a desirable option. The controversy among different studies might be partially accounted for that quite a few studies have a relatively small sample size to detect the difference between renal transplant patients and dialysis patients.

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Our primary objective of this work was to extend a previously published 2D coupled sub-sample tracking algorithm for 3D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3D coupled sub-sample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking (TM) phantom and in vivo breast ultrasound data. The performance of this 3D sub-sample tracking algorithm was compared with the conventional 3D quadratic sub-sample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3D sub-sample estimation algorithm can provide high-quality strain data (i.e. high correlation between the pre- and the motion-compensated post-deformation RF echo data and high contrast-to-noise ratio strain images), as compared to the conventional 3D quadratic sub-sample algorithm. Using the GPU implementation of the 3D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 seconds per volume [2.5 cm × 2.5 cm × 2.5 cm]).

Background and Aims: Extrachromosomal circular DNAs (eccDNAs) are prevalent in cancer genomes and emerge as a class of crucial yet less characterized oncogenic drivers. However, the structure, composition, genomewide frequency, and contribution of eccDNAs in HCC, one of the most fatal and prevalent cancers, remain unexplored. In this study, we provide a comprehensive characterization of eccDNAs in human HCC and demonstrate an oncogenic role of microRNA (miRNA)-17-92-containing eccDNAs in tumor progression.Approach and Results: Using the circle-sequencing method, we identify and characterize more than 230,000 eccDNAs from 4 paired samples of HCC tumor and adjacent nontumor liver tissues. EccDNAs are highly enriched in HCC tumors, preferentially originate from certain chromosomal hotspots, and are correlated with differential gene expression. Particularly, a series of eccDNAs carrying the miRNA-17-92 cluster are validated by outward PCR and Sanger sequencing. Quantitative PCR analyses reveal that miRNA-17-92-containing eccDNAs, along with the expression of their corresponding miRNAs, are elevated in HCC tumors and associated with poor outcomes and the age of HCC patients. More intriguingly, exogenous expression of artificial DNA circles harboring the miR-17-92 cluster, which is synthesized by the ligase-assisted minicircle