Circular RNAs (circRNAs) have received increasing attention in cancer development. However, a substantial number of circRNAs still require characterization. The purpose of this study is to uncover novel circRNAs and their molecular mechanism in bladder cancer (BCa).
 
  A combinative strategy of extensive data mining and computational biology was employed to identify BCa-related circRNAs and explore their potential mechanisms of action.
 
  Three differentially expressed circRNAs (has_circ_0023642, has_circ_0047322, has_circ_0041151) were obtained from the microarray dataset (GSE92675). Four miRNAs (miR-616, miR-515-5p, miR-647, miR-1178) with potential binding sites with these three circRNAs were identified. Pathway analysis demonstrated that all four miRNAs were closely associated with some cancer-related pathways. Survival analysis indicated that these miRNAs might potentially play a role in tumor-suppressive functions in BCa. Subsequently, 181 overlapping genes were identified from 472 up-regulated genes in BCa (TCGA database), and 10,017 predicted target genes of the four miRNAs obtained. A circRNA-miRNA-mRNA network was constructed on the identified three circRNAs, four miRNAs, and 181 overlapping genes. Besides, six hub genes (
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  ) were identified from establishing a protein-protein interaction (PPI) network on the same overlapping genes. Furthermore, a circRNA-miRNA-hub gene sub-network was built to delineate the links among the differential circRNAs, miRNA, and hub genes.
 
  Our study provided significant insights into the molecular mechanisms that regulate the progression of BCa from the circRNA-miRNA-mRNA network view.
 Our study provided significant insights into the molecular mechanisms that regulate the progression of BCa from the circRNA-miRNA-mRNA network view.Despite the overall success of using artificial intelligence (AI) to assist radiologists in performing computer-aided patient diagnosis, it remains challenging to build good models with small datasets at individual sites. Because many medical images do not come with proper labelling for training, this requires radiologists to perform strenuous labelling work and to prepare the dataset for training. Placing such demands on radiologists is unsustainable, given the ever-increasing number of medical images taken each year. We propose an alternative solution using a relatively new learning framework. This framework, called federated learning, allows individual sites to train a global model in a collaborative effort. Federated learning involves aggregating training results from multiple sites to create a global model without directly sharing datasets. This ensures that patient privacy is maintained across sites. Furthermore, the added supervision obtained from the results of partnering sites improves the global model's overall detection abilities. This alleviates the issue of insufficient supervision when training AI models with small datasets. Lastly, we also address the major challenges of adopting federated learning.Pregnancy-related cerebrovascular disease is a serious complication of pregnancy and puerperium. The etiology and pathological mechanisms of cerebrovascular disease are complex, involving changes in the cardiovascular, endocrine, and immune systems. Vascular risk factors during pregnancy and puerperium may cause vasospasm and endothelial cell damage leading to cerebral ischemia, hemorrhage, posterior reversible encephalopathy syndrome (PRES), and reversible cerebral vasoconstriction syndrome. Arterial or venous obstruction may damage the blood-brain barrier (BBB) and impede venous return, resulting in cerebral edema, hemorrhage, and intracranial hypertension. Pregnancy with hypercoagulability may threaten the lives of both the mother and the developing fetus. With improvements in stroke treatment during pregnancy and puerperium, neuroradiologists have gained new insights into this problem. This article reviews the pathogenesis, imaging findings, and risk factors of stroke during pregnancy and puerperium, focusing on imaging diagnosis and prognostic assessment.This work describes a new method for diffusion-weighted (DW) magnetic resonance imaging (MRI) without susceptibility artifacts. The technique combines a DW spin-echo module and a single-shot stimulated echo acquisition mode (STEAM) MRI readout with undersampled radial trajectories and covers a volume by a gapless series of cross-sectional slices. In a first step, optimal coil sensitivities for all slices are obtained from a series of non-DW acquisitions by nonlinear inverse reconstruction with regularization to the image and coil sensitivities of a directly neighboring slice. In a second step, these coil sensitivities are used to compute all series of non-DW and DW images by linear inverse reconstruction with spatial regularization to a neighboring image.  https://www.selleckchem.com/products/tertiapin-q.html  Proof-of-principle applications to the brain (51 sections) and prostate (31 sections) of healthy subjects were realized for a protocol with two b-values and 6 gradient directions at 3 T. Including averaging the measuring times for studies of the brain at 1.0×1.0×3.0 mm3 resolution (b =1,000 s mm-2) and prostate at 1.4×1.4×3.0 mm3 resolution (b =600 s mm-2) were 2.5 min and 4.5 min, respectively. All reconstructions were accomplished online with use of a multi-GPU computer integrated into the MRI system. The resulting non-DW images, mean DW images averaged across directions and maps of the apparent diffusion coefficient confirm the absence of geometric distortions or false signal alterations and demonstrate diagnostic image quality. The novel method for DW STEAM MRI of a volume without susceptibility artifacts warrants extended clinical trials.Accurate and reproducible measurement of abdominal aortic aneurysm (AAA) size is an essential component of patient management, and most reliably performed at CT using a multiplanar reformat (MPR) strategy. This approach is not universal, however. This study aims to characterize the measurement error present in routine clinical assessment of AAAs and the potential clinical ramifications. Patients were included if they had AAA assessed by CT and/or MRI at two time points at least 6 months apart. Clinical maximal AAA diameter, assessed by non-standardized methods, was abstracted from the radiology report at each time point and compared to the reference aneurysm diameter measured using a MPR strategy. Discrepancies between clinical and reference diameters, and associated aneurysm enlargement rates were analyzed. Two hundred thirty patients were included, with average follow-up 3.3±2.5 years. When compared to MPR-derived diameters, clinical aneurysm measurement inaccuracy was, on average, 3.3 mm. Broad limits of agreement were found for both clinical diameters [-6.