Atopic dermatitis (AD) affects up to 20% of children and adults worldwide. To gain a deeper understanding of the pathophysiology of AD, we conducted a large-scale transcriptomic study of AD with deeply sequenced RNA-sequencing samples using long (126-bp) paired-end reads. In addition to the comparisons against previous transcriptomic studies, we conducted in-depth analysis to obtain a high-resolution view of the global architecture of the AD transcriptome and contrasted it with that of psoriasis from the same cohort. By using 147 RNA samples in total, we found striking correlation between dysregulated genes in lesional psoriasis and lesional AD skin with 81% of AD dysregulated genes being shared with psoriasis. However, we described disease-specific molecular and cellular features, with AD skin showing dominance of IL-13 pathways, but with near undetectable IL-4 expression. We also demonstrated greater disease heterogeneity and larger proportion of dysregulated long noncoding RNAs in AD, and illustrated the translational impact, including skin-type classification and drug-target prediction. This study is by far the largest study comparing the AD and psoriasis transcriptomes using RNA sequencing and demonstrating the shared inflammatory components, as well as specific discordant cytokine signatures of these two skin diseases.

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany.

This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists.

Summary Background Mycosis fungoides (MF) and Sézary Syndrome (SS) are the most common cutaneous T‐cell lymphomas. MF/SS is accompanied by considerable morbidity from pain, itching and disfigurement. Aim To identify factors associated with poorer health‐related quality of life (HRQoL) in patients newly diagnosed with MF/SS. Methods Patients enrolled into Prospective Cutaneous Lymphoma International Prognostic Index (PROCLIPI; an international observational study in MF/SS) had their HRQoL assessed using the Skindex‐29 questionnaire. Skindex‐29 scores were analysed in relation to patient‐ and disease‐specific characteristics. Results The study population consisted of 237 patients [60·3% male; median age 60 years, (interquartile range 49–70)], of whom 179 had early MF and 58 had advanced MF/SS. In univariate analysis, HRQoL, as measured by Skindex‐29, was worse in women, SS, late‐stage MF, those with elevated lactate dehydrogenase, alopecia, high modified Severity Weighted Assessment Tool and confluent erythema. Linear regression models only identified female gender (β = 8·61; P = 0·003) and alopecia (β = 9·71, P = 0·02) as independent predictors of worse global HRQoL. Item‐level analysis showed that the severe impairment in symptoms [odds ratio (OR) 2·14, 95% confidence interval (CI) 1·19–3·89] and emotions (OR 1·88, 95% CI 1·09–3·27) subscale scores seen in women was caused by more burning/stinging, pruritus, irritation and greater feelings of depression, shame, embarrassment and annoyance with their diagnosis of MF/SS. Conclusions HRQoL is significantly more impaired in newly diagnosed women with MF/SS and in those with alopecia. As Skindex‐29 does not include existential questions on cancer, which may cause additional worry and distress, a comprehensive validated cutaneous T‐cell lymphoma‐specific questionnaire is urgently needed to more accurately assess disease‐specific HRQoL in these patients. What's already known about this topic? Cross‐sectional studies of mixed populations of known and newly diagnosed patients with mycosis fungoides (MF)/Sézary syndrome (SS) have shown significant impairment in health‐related quality of life (HRQoL). Previous studies on assessing gender‐specific differences in HRQoL in MF/SS are conflicting. More advanced‐stage disease and pruritus is associated with poorer HRQoL in patients with MF/SS. What does this study add? This is the first prospective study to investigate HRQoL in a homogenous group of newly diagnosed patients with MF/SS. In patients newly diagnosed with MF/SS, HRQoL is worse in women and in those with alopecia and confluent erythema. MF/SS diagnosis has a multidimensional impact on patient HRQoL, including a large burden of cutaneous symptoms, as well as a negative impact on emotional well‐being.