IntroductionIn light of the SARS-CoV-2 pandemic, protecting vulnerable groups has become a high priority. Persons at risk of severe disease, for example, those receiving immunosuppressive therapies for chronic inflammatory cdiseases (CIDs), are prioritised for vaccination. However, data concerning generation of protective antibody titres in immunosuppressed patients are scarce. Additionally, mRNA vaccines represent a new vaccine technology leading to increased insecurity especially in patients with CID.ObjectiveHere we present for the first time, data on the efficacy and safety of anti-SARS-CoV-2 mRNA vaccines in a cohort of immunosuppressed patients as compared with healthy controls.Methods42 healthy controls and 26 patients with CID were included in this study (mean age 37.5 vs 50.5 years). Immunisations were performed according to national guidelines with mRNA vaccines. Antibody titres were assessed by ELISA before initial vaccination and 7 days after secondary vaccination. Disease activity and side effects were assessed prior to and 7 days after both vaccinations.ResultsAnti-SARS-CoV-2 antibodies as well as neutralising activity could be detected in all study participants. IgG titres were significantly lower in patients as compared with controls (2053 binding antibody units (BAU)/mL ±1218 vs 2685±1102). Side effects were comparable in both groups. No severe adverse effects were observed, and no patients experienced a disease flare.ConclusionWe show that SARS-CoV-2 mRNA vaccines lead to development of antibodies in immunosuppressed patients without considerable side effects or induction of disease flares. Despite the small size of this cohort, we were able to demonstrate the efficiency and safety of mRNA vaccines in our cohort.

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.

SummaryBackground GP2015 is a proposed etanercept biosimilar. Objectives To demonstrate equivalent efficacy, and comparable safety and immunogenicity of GP2015 and the etanercept originator (ETN, Enbrel â ) in patients with

Adipose tissue is an active endocrine organ contributing to the regulation of multiple metabolic pathways via self-produced bioactive products called adipokines. These adipokines are key players in the pathogenesis of metabolic syndrome and cardiovascular diseases. Co-occurrence of obesity and psoriasis could lead to interactions of both diseases in which adipokines, at least in part, are involved and may contribute to associated comorbidities of psoriasis. Until today numerous adipokines have been identified of which the most important ones are discussed in the following within the context of obesity, chronic inflammation and their possible role in the pathogenesis of psoriasis. Adipokines could serve as a missing link in the causal relationship between psoriasis and comorbidities and may provide a biomarker for disease severity, risk of comorbidities and treatment success.

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.