In infants and toddlers, the identification of subdural fluid collections on neuroimaging warrants inclusion of AHT in the differential diagnosis. However, in infants with a history of congenital macrocephaly, and an otherwise negative AHT workup, an accidental mechanism for the formation of subdural collections should be considered, especially when co-occurring with an arachnoid cyst.In infants and toddlers, the identification of subdural fluid collections on neuroimaging warrants inclusion of AHT in the differential diagnosis. However, in infants with a history of congenital macrocephaly, and an otherwise negative AHT workup, an accidental mechanism for the formation of subdural collections should be considered, especially when co-occurring with an arachnoid cyst.Recent insights suggest that the osteochondral interface plays a central role in maintaining healthy articulating joints. Uncovering the underlying transport mechanisms is key to the understanding of the cross-talk between articular cartilage and subchondral bone. PCB chemical research buy Here, we describe the mechanisms that facilitate transport at the osteochondral interface. Using scanning electron microscopy (SEM), we found a continuous transition of mineralization architecture from the non-calcified cartilage towards the calcified cartilage. This refurbishes the classical picture of the so-called tidemark; a well-defined discontinuity at the osteochondral interface. Using focused-ion-beam SEM (FIB-SEM) on one osteochondral plug derived from a human cadaveric knee, we elucidated that the pore structure gradually varies from the calcified cartilage towards the subchondral bone plate. We identified nano-pores with radius of 10.71 ± 6.45 nm in calcified cartilage to 39.1 ± 26.17 nm in the subchondral bone plate. The extracted pore sizes were used to construct 3D pore-scale numerical models to explore the effect of pore sizes and connectivity among different pores. Results indicated that connectivity of nano-pores in calcified cartilage is highly compromised compared to the subchondral bone plate. Flow simulations showed a permeability decrease by about 2000-fold and solute transport simulations using a tracer (iodixanol, 1.5 kDa with a free diffusivity of 2.5 × 10-10 m2/s) showed diffusivity decrease by a factor of 1.5. Taken together, architecture of the nano-pores and the complex mineralization pattern in the osteochondral interface considerably impacts the cross-talk between cartilage and bone.Shear-mediated platelet activation (SMPA) in the "free flow" is the net result of a range of cell mechanobiological mechanisms. Previously, we outlined three main groups of mechanisms including 1) mechano-destruction - i.e. additive platelet (membrane) damage; 2) mechano-activation - i.e. activation of shear-sensitive ion channels and pores; and 3) mechano-transduction - i.e. "outside-in" signaling via a range of transducers. Here, we report on recent advances since our original report which describes additional features of SMPA. A clear "signature" of SMPA has been defined, allowing differentiation from biochemically-mediated activation. Notably, SMPA is characterized by mitochondrial dysfunction, platelet membrane eversion, externalization of anionic phospholipids, and increased thrombin generation on the platelet surface. However, SMPA does not lead to integrin αIIbβ3 activation or P-selectin exposure due to platelet degranulation, as is commonly observed in biochemical activation. Rather, downregulation odiovascular therapeutic devices. Background and Objective Recent studies in deep learning reveal that the U-Net stands out among the diverse set of deep models as an effective network structure, especially for imaging inverse problems. Initially, the U-Net model was developed to solve segmentation problems for biomedical images while using an annotated dataset. In this paper, we will study a novel application of the U-Net structure for the important inverse problem of MRI reconstruction. Deep networks are particularly efficient for the speed-up of the MR image reconstruction process by decreasing the data acquisition time, and they can significantly reduce the aliasing artifacts caused by the undersampling in the k-space. Our aim is to develop a novel and efficient cascaded U-Net framework for reconstructing MR images from undersampled k-space data. The new framework should have improved reconstruction performance when compared to competing methodologies. In this paper, a novel cascaded framework utilizing the U-Net as a sub-block is beibaseline reconstruction method from the fastMRI package. The use of the projection-based updated data consistency layer also leads to improved quantitative (including SSIM, PSNR, and NMSE results) and qualitative results when compared to the use of the conventional data consistency layer.The proposed cascaded U-Net configuration results in an improved reconstruction performance when compared to the CNN, the cascaded CNN, and also the singular U-Net structures, where the singular U-Net forms the baseline reconstruction method from the fastMRI package. The use of the projection-based updated data consistency layer also leads to improved quantitative (including SSIM, PSNR, and NMSE results) and qualitative results when compared to the use of the conventional data consistency layer. Association rule mining has been adopted to medical fields to discover prescribing patterns or relationships among diseases and/or medications; however, it has generated unreasonable associations among these entities. This study aims to identify the real-world profile of disease-medication (DM) associations using the modified mining algorithm and assess its performance in reducing DM pseudo-associations. We retrieved data from outpatient records between January 2011 and December 2015 in claims databases maintained by the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan. The association rule mining's lift (Q-value) was adopted to quantify DM associations, referred to as Q for the original algorithm and as Q for the modified algorithm. One thousand DM pairs with positive Q -values (Q ) and negative or no Q -values (Q or Q ) were selected as the validation dataset, in which two pharmacists assessed the DM associations. A total of 3,120,449 unique DM pairs were identified, of which there were 333,347 Q Q pairs and 429,931 Q Q pairs.