Effectiveness and Safety of Long-Term Dupilumab Treatment in Elderly Patients with Atopic Dermatitis: A Multicenter Real-Life Observational Study.

OBJECTIVE: The objective of this study was to assess the effectiveness and safety of dupilumab in treating elderly patients with atopic dermatitis from baseline to 52 weeks. METHODS: A retrospective observational real-life study was conducted in a group of elderly patients with severe atopic dermatitis treated with dupilumab for 52 weeks. Inclusion criteria were: age ≥ 65 years; diagnosis of atopic dermatitis made by an expert dermatologist; Eczema Area and Severity Index ≥ 24; and a contraindication, side effects, or failure to respond to cyclosporine. The primary outcome was the mean percentage reduction in the Eczema Area and Severity Index score from baseline to week 52. Secondary measures included the mean percentage reduction in the Pruritus and Sleep Numerical Rating Scales and the Dermatology Life Quality Index, and the types and rates of adverse events from baseline to week 52. RESULTS: One hundred and five patients were eligible for the study. Flexural dermatitis was the most frequent clinical phenotype (63.8%). The coexistence of more than one clinical phenotype was found in 70/105 (66.6%) patients. We observed a reduction in all disease severity scores from baseline to week 52 (p < 0.001). Adverse events were recorded in 30/105 (28.6%) patients, with conjunctivitis and injection-site reaction the most frequent. CONCLUSIONS: In this study, dupilumab is an effective and safe treatment for the long-term management of atopic dermatitis in patients aged over 65 years.

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies.

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.