Dietary Ceramide Prepared from Soy Sauce Lees Improves Skin Barrier Function in Hairless Mice

Dietary sphingolipids such as glucosylceramide and sphingomyelin are known to improve the skin barrier function of damaged skin. In this study, we focused on free-ceramide prepared from soy sauce lees, which is a byproduct of soy sauce production. The effects of dietary soy sauce lees ceramide on the skin of normal mice were evaluated and compared with those of dietary maize glucosylceramide. We found that transepidermal water loss value was significantly suppressed by dietary supplementation with soy sauce lees ceramide as effectively as or more effectively than maize glucosylceramide. Although the content of total and each subclass of ceramide in the epidermis was not significantly altered by dietary sphingolipids, that of 12 types of ceramide molecules, which were not present in dietary sources, was significantly increased upon ingestion of maize glucosylceramide and showed a tendency to increase with soy sauce lees ceramide intake. In addition, the mRNA expression of ceramide synthase 4 and involucrin in the skin was downregulated by sphingolipids. This study, for the first time, demonstrated that dietary soy sauce lees ceramide enhances skin barrier function in normal hairless mice, although further studies are needed to clarify the molecular mechanism

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches.

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images with cancerous locations identified by radiologists and pathologists. First, 307 prostate MR images were classified using a well-established deep neural network without locational information of cancers. Subsequently, we assessed whether the deep learning-focused regions overlapped the radiologist-identified targets. Furthermore, pathologists provided histopathological diagnoses on 896 pathological images, and we compared the deep learning-focused regions with the genuine cancer locations through 3D reconstruction of pathological images. The area under the curve (AUC) for MR images classification was sufficiently high (AUC = 0.90, 95% confidence interval 0.87-0.94). Deep learning-focused regions overlapped radiologist-identified targets by 70.5% and pathologist-identified cancer locations by 72.1%. Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning. Deep learning algorithms can achieve highly accurate image classification without necessarily identifying radiological targets or cancer locations. Deep learning may find clues that can help a clinical diagnosis even if the cancer is not visible

The SuperKEKB Has Broken the World Record of the Luminosity

The SuperKEKB broke the world record of the luminosity in June 2020 in the Phase 3 operation. The luminosity has been increasing since then and the present highest luminosity is 4.65 x 10³⁴ cm⁻²s⁻¹ with β_{y}^{*} of 1 mm. The increase of the luminosity was brought with an application of crab waist, by increasing beam currents and by other improvements in the specific luminosity. In this paper, we describe what we have achieved and what we are struggling with. Finally, we mention a future plan briefly

Blast Furnace: Most Efficient Technologies for Greenhouse Emissions Abatement

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