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Tatsuya Ogawa, 1 Yosuke Ishitsuka, 1 Yoshiyuki Nakamura, 1 Naoko Okiyama, 1 Rei Watanabe, 1, 2 Yasuhiro Fujisawa, 1 Manabu Fujimoto 1, 2 1Department of Dermatology, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan; 2Department of Dermatology, Osaka University Graduate School of Medicine, Suita, Osaka, JapanCorrespondence: Yosuke IshitsukaDepartment of Dermatology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, JapanTel +81-29-853-3128Fax +81-29-853-3217Email yosuke.ishitsuka@md.tsukuba.ac.jpIntroduction: The stratum corneum protects against the entry of pathogens, allergens, and irritants while preventing dehydration. The Kelch-like erythroid cell-derived protein with cap-n-collar homology-associated protein 1 (KEAP1)/NF-E2-related factor 2 (NRF2) system maintains skin barrier homeostasis. Aggregated evidence suggests that NRF2-mediated antioxidative response is hardwired into the stratified squamous epithelia. Honey and chamomile have long been regarded as natural antioxidants. Nonetheless, it is still unclear whether they activate the KEAP1/NRF2 system in the epidermis and could promote epidermal barrier recovery.Methods: To address the abovementioned issue, we explored the antioxidative property of honey/chamomile extract by using non-cell-based KEAP1-inhibition assay and cultured human epidermal keratinocytes.Results: Herein we report that the extract inhibited KEAP1-NRF2 interaction and induced keratinocyte production of antioxidant small proline-rich protein.Conclusion: Our results may offer an opportunity to develop cosmetic products that boost NRF2-mediated antioxidative/antiaging, epidermis-intrinsic bio-responses.Keywords: NRF2, honey, chamomile, keratinocyte

Lakes are central components of the inland water system distinct from, yet inextricably connected to, river networks. Currently, existing network‐scale biogeochemistry research, although robust, typically treats each of these components separately or reductively. Here, we incorporate lake morphometry into a fully connected stream/lake network for the Connecticut River watershed and model potential evasion of terrestrially sourced headwater CO2 as transported through the network, ignoring in‐stream production. We found that approximately 25%–30% of total potential soil CO2 evasion occurs in lakes, and percent evasion is inversely related to streamflow. A lake's ability to evade CO2 is controlled by residence time and size: most lakes with residence time over 7 days or surface area greater than 0.004 km2 evade functionally all terrestrial CO2 entering from upstream, precluding further downstream transport. We conclude that lakes are important for soil CO2 degassing and that this coupled river/lake approach is promising for CO2 studies henceforth. Plain Language Summary: River networks are both delivery systems and active transformers of constituents (sediment, nutrients, biota, heat) as they carry them to the sea. An important and overlooked component of these networks are lakes: lakes dominate total water storage of surface water in river networks and have vastly different hydraulics than rivers. Despite this knowledge, most greenhouse gas (GHG) research addresses lakes and rivers separately or reductively. To address this, we built a fully connected model for over 98,000 rivers and lakes in the Connecticut River watershed and found that lakes emit substantially more carbon dioxide than rivers per unit (potentially 25%–30% of the total emission from the system), and that almost all lakes emit almost all the carbon dioxide that enters their waters. These findings demonstrate the importance of connected lakes to drainage network GHG exchange and corroborate the need to better integrate lakes into our understandings of GHG emissions from freshwater systems in a formalized framework. Key Points: We model potential evasion of terrestrially sourced headwater CO2 from over 98,000 river and lake units in the Connecticut River watershedLakes are responsible for approximately 25%–30% of potential CO2 evasion, as influenced by streamflow and stream orderLake CO2 evasion efficiency is a function of residence time and size, where larger lakes evade functionally 100% of CO2 from upstream [ABSTRACT FROM AUTHOR]

Summary: Background: Application of deep‐learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremely large. Objectives: To determine whether deep‐learning technology could be used to develop an efficient skin cancer classification system with a relatively small dataset of clinical images. Methods: A deep convolutional neural network (DCNN) was trained using a dataset of 4867 clinical images obtained from 1842 patients diagnosed with skin tumours at the University of Tsukuba Hospital from 2003 to 2016. The images consisted of 14 diagnoses, including both malignant and benign conditions. Its performance was tested against 13 board‐certified dermatologists and nine dermatology trainees. Results: The overall classification accuracy of the trained DCNN was 76·5%. The DCNN achieved 96·3% sensitivity (correctly classified malignant as malignant) and 89·5% specificity (correctly classified benign as benign). Although the accuracy of malignant or benign classification by the board‐certified dermatologists was statistically higher than that of the dermatology trainees (85·3% ± 3·7% and 74·4% ± 6·8%, P < 0·01), the DCNN achieved even greater accuracy, as high as 92·4% ± 2·1% (P < 0·001). Conclusions: We have developed an efficient skin tumour classifier using a DCNN trained on a relatively small dataset. The DCNN classified images of skin tumours more accurately than board‐certified dermatologists. Collectively, the current system may have capabilities for screening purposes in general medical practice, particularly because it requires only a single clinical image for classification. What's already known about this topic? Several computer‐aided classification systems have been introduced that achieve high sensitivity for melanoma detection; however, low specificity was a trade‐off for high sensitivity.The application of deep‐learning technology to skin cancer classification could potentially improve the sensitivity and specificity of skin cancer screening.The number of training images required for such a system is thought to be extremely large, and compiling large datasets for rare skin conditions is difficult. What does this study add? Our deep convolutional neural network (DCNN), trained on only 4867 images from 1842 patients, classified images of skin tumours into 14 different diagnoses more accurately than board‐certified dermatologists.The fluctuation range was only ± 3·2% by fivefold cross‐validation, showing the robustness of the system.Our DCNN system requires only a single image and provides 96·3% sensitivity and 89·5% specificity in the detection of skin cancer; however, it should be validated in a prospective clinical study before use for screening purposes in general medical practice. Linked Editorial:Janda and Soyer. Br J Dermatol 2019; 180:247–248. Plain language summary available online Respond to this article [ABSTRACT FROM AUTHOR]

CD8 resident memory T cells with interleukin 17A-producing potential are accumulated in disease-naïve nonlesional sites of psoriasis possibly in correlation with disease duration Although CD103 SP - sp CD8 T cells also showed augmented IL-17A production both in lesional and DN nonlesional skin (lesional skin: 2-7 ± 2-5%; DN nonlesional skin: 5-3 ± 6-3%; normal skin: 0-1 ± 0-1%), the CD103 SP + sp CD8 T SB RM sb fraction was a significantly larger source of IL-17A (Fig. c). In CD8 T cells, this IL-17A/IFN- production ratio in CD103+CD8 TRM (left) and CD103-CD8 T cells (right) from DN nonlesional skin is shown according to disease duration (n = 12). [Extracted from the article]

The article present a case study of several patients with anti-melanoma differentiation associated protein 5 (MDA5) antibody associated dermatomyositis. It mentions the diagnosis of clinically amyopathic dermatomyositis (CADM) essentially depends on cutaneous manifestations; and also mentions the recognition of distinctive rashes for anti-MDA5 antibody positive DM can greatly aid in distinguishing from other DM subsets.

The article discusses a study to determine the role of skin resident memory T‐cell population in systemic sclerosis (SSc) that is characterized by tissue fibrosis, vascular damage, and dysregulation of the immune system. Topics discussed include dysfunction of regulatory T cells with lower CD69 expression, immunofluorescence analysis of SSc, and humoral factors from the SSc T cells.