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The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Maskne, a newly coined term deriving from the contraction of mask-related acne, is a form of mechanical acne resulting from continuous textile–skin adherence and friction. Prolonged mask use heats up the face environment, thus modifying skin microbiota and sebum production. Although effective prevalence is unknown, since the pandemic began and the prolonged use of masks was extended to the general population, maskne has been a frequent topic of consultation among dermatologists. This term has been successfully introduced into common language, with more than 200,000 hashtags on social media, where it is also possible to find "home remedies" that can worsen this dermatosis. The aim of this paper is to discuss the pathogenesis, address clinicians on the correct differential diagnoses among facial dermatoses, and move towards the correct therapy. [ABSTRACT FROM AUTHOR]