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Background: Patients with primary sclerosing cholangitis (PSC) possess autoantibodies against biliary epithelial cells. However, the target molecules remain unknown. Methods: The sera of patients with PSC and controls were subjected to enzyme-linked immunosorbent assays to detect autoantibodies using recombinant integrin proteins. Integrin αvβ6 expression in the bile duct tissues was examined using immunofluorescence. The blocking activity of the autoantibodies was examined using solid-phase binding assays. Results: Anti-integrin αvβ6 antibodies were detected in 49/55 (89.1%) patients with PSC and 5/150 (3.3%) controls (P < 0.001), with a sensitivity and specificity of 89.1% and 96.7%, respectively, for PSC diagnosis. When focusing on the presence or absence of IBD, the proportion of the positive antibodies in PSC with IBD was 97.2% (35/36) and that in PSC alone was 73.7% (14/19) (P = 0.008). Integrin αvβ6 was expressed in bile duct epithelial cells. Immunoglobulin (Ig)G from 15/33 patients with PSC blocked integrin αvβ6-fibronectin binding through an RGD (Arg–Gly–Asp) tripeptide motif. Conclusions: Autoantibodies against integrin αvβ6 were detected in most patients with PSC; anti-integrin αvβ6 antibody may serve as a potential diagnostic biomarker for PSC. [ABSTRACT FROM AUTHOR]

Atomization of a liquid jet in an immiscible liquid–liquid system is essential for maintaining safety in the nuclear industry. The Japan Atomic Energy Agency has developed a method to evaluate the behavior of molten fuel entering a shallow coolant pool using numerical and experimental simulations in an immiscible liquid–liquid system, focusing on hydrodynamic interaction between the fuel and pool. However, the atomization behavior associated with the spreading of the wall-impinging liquid jet immediately after entering a shallow pool in the system is quite different from jet-breakup regimes in a deep pool, and there are a few reports. Such atomization behavior can change subsequences in severe accidents in the nuclear industry, and the investigation of the atomization behavior of the wall-impinging liquid jet is significant. Herein, we investigate the atomization behavior of a wall-impinging liquid jet in terms of droplet formation and its flow field using numerical simulation and a dispersed-phase tracking method. The results show that the atomization behavior comprises droplet formation with three patterns. We obtain droplet properties immediately after droplet formation and develop theoretical boundaries enclosing two regions for droplet formation using dimensionless numbers. We characterize the patterns by comparing them with the theoretical regions and elucidate the droplet formation mechanisms of each source. Moreover, we establish the relationship between droplet formation as the local behavior and atomization as the whole behavior of the jet. Thus, this paper clarifies the atomization mechanisms in the liquid film flow of the wall-impinging liquid jet in a shallow pool. [ABSTRACT FROM AUTHOR]

Objectives: A videofluoroscopic swallowing study (VFSS) is conducted to detect aspiration. However, aspiration occurs within a short time and is difficult to detect. If deep learning can detect aspirations with high accuracy, clinicians can focus on the diagnosis of the detected aspirations. Whether VFSS aspirations can be classified using rapid-prototyping deep-learning tools was studied. Methods: VFSS videos were separated into individual image frames. A region of interest was defined on the pharynx. Three convolutional neural networks (CNNs), namely a Simple-Layer CNN, Multiple-Layer CNN, and Modified LeNet, were designed for the classification. The performance results of the CNNs were compared in terms of the areas under their receiver-operating characteristic curves (AUCs). Results: A total of 18,333 images obtained through data augmentation were selected for the evaluation. The different CNNs yielded sensitivities of 78.8%–87.6%, specificities of 91.9%–98.1%, and overall accuracies of 85.8%–91.7%. The AUC of 0.974 obtained for the Simple-Layer CNN and Modified LeNet was significantly higher than that obtained for the Multiple-Layer CNN (AUC of 0.936) (p < 0.001). Conclusions: The results of this study show that deep learning has potential for detecting aspiration with high accuracy. [ABSTRACT FROM AUTHOR]