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Summary: Background: As the coronavirus disease 2019 (COVID‐19) pandemic has spread, information about COVID‐19 and skin disease or related biologics is still lacking. Objectives: To identify the association between COVID‐19 and skin diseases or biologics. Methods: A nationwide claim dataset relevant to COVID‐19 in South Korea was analysed. This dataset included insurance claim data before and during COVID‐19 treatment and clinical outcomes. Claim data related to skin diseases and relevant biologics were analysed to determine the association of COVID‐19 with skin diseases and relevant biologics. Results: The dataset contained a total of 234 427 individuals (111 947 male and 122 480 female) who underwent COVID‐19 testing. Of them, 7590 (3·2%) were confirmed as having COVID‐19, and 227 (3·0%) confirmed patients died. Among various skin diseases and biologics, no significant increase in the presence of specific skin diseases or exposure to biologics was observed in the COVID‐19‐positive group, even after adjusting for or matching covariates. The presence of skin diseases and exposure to biologics also did not seem to affect clinical outcomes including mortality. Conclusions: Underlying skin diseases did not appear to increase susceptibility to COVID‐19 or mortality from COVID‐19. Considering the risks and benefits, biologics for dermatological conditions might be continuously used during the COVID‐19 pandemic. What is already known about this topic?There is concern about the association between coronavirus disease 2019 (COVID‐19) and skin diseases or relevant biologics.Clinical information about COVID‐19 and skin disease or related biologics is lacking. What does this study add?Underlying skin diseases or exposure to relevant biologics did not appear to increase susceptibility to COVID‐19 or mortality from COVID‐19.Biologics for dermatological conditions might be continuously used during the COVID‐19 pandemic. Linked Comment: Descalzo and Garcia‐Doval. Br J Dermatol 2021; 184:194. Plain language summary available online [ABSTRACT FROM AUTHOR]

Summary: Background: Deep convolutional neural networks (DCNNs) can classify skin diseases at a level equivalent to a dermatologist, but their performance in specific areas requires further research. Objective: To evaluate the performance of a trained DCNN‐based algorithm in classifying benign and malignant lip diseases. Methods: A training set of 1629 images (743 malignant, 886 benign) was used with Inception‐Resnet‐V2. Performance was evaluated using another set of 344 images and 281 images from other hospitals. Classifications by 44 participants (six board‐certified dermatologists, 12 dermatology residents, nine medical doctors not specialized in dermatology and 17 medical students) were used for comparison. Results: The outcomes based on the area under curve, sensitivity and specificity were 0·827 [95% confidence interval (CI) 0·782–0·873], 0·755 (95% CI 0·673–0·827) and 0·803 (95% CI 0·752–0·855), respectively, for the set of 344 images; and 0·774 (95% CI 0·699–0·849), 0·702 (95% CI 0·579–0·808) and 0·759 (95% CI 0·701–0·813), respectively, for the set of 281 images. The DCNN was equivalent to the dermatologists and superior to the nondermatologists in classifying malignancy. After referencing the DCNN result, the mean ± SD Youden index increased significantly for nondermatologists, from 0·201 ± 0·156 to 0·322 ± 0·141 (P < 0·001). Conclusions: DCNNs can classify lip diseases at a level similar to dermatologists. This will help unskilled physicians discriminate between benign and malignant lip diseases. What's already known about this topic?Deep convolutional neural networks (DCNNs) can classify malignant and benign skin diseases at a level equivalent to dermatologists.The lips are a unique feature in terms of histology and morphology.Previous studies of DCNNs have not investigated tumours on specific locations. What does this study add?This study shows that DCNNs can distinguish rare malignant and benign lip disorders at the same rate as dermatologists.DCNNs can help nondermatologists to distinguish malignant lip diseases. What are the clinical implications of this work?DCNNs can distinguish malignant and benign skin diseases even at specific locations such as the lips, as well as board‐certified dermatologists.Malignant lip diseases are rare and difficult for less trained doctors to differentiate them from benign lesions.This study shows that in dermatology, DCNN can help improve decision‐making processes for rare skin diseases in specific areas of the body. Plain language summary available online [ABSTRACT FROM AUTHOR]

Muscular dystrophy ( MD) is a genetically and clinically heterogeneous group of disorders. Here, we performed targeted sequencing of 18 limb-girdle MD ( LGMD)-related genes in 35 patients who were highly suspected of having MD. We identified one or more pathogenic variants in 23 of 35 patients (65.7%), and a genetic diagnosis was performed in 20 patients (57.1%). LGMD2B was the most common LGMD type, followed by LGMD1B, LGMD2A, and LGMD2G. Among the three major LGMD types in this group, LGMD1B was correlated with the lowest creatine kinase ( CK) levels and the earliest onset, whereas LGMD2B was correlated with the highest CK levels and the latest onset. Thus, next-generation sequencing-based gene panels can be a helpful tool for the diagnosis of MDs, particularly in young children and those displaying atypical symptoms. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is progressing rapidly, and its application in the field of medicine is increasing.1 AI algorithms have been shown to be as accurate as dermatologists in classifying various skin diseases based on clinical images.2,3 Furthermore, AI can assist non-dermatologists in classifying skin conditions.4,5 A recent international online survey reported that most dermatologists agree that AI will change in dermatology.6 However, AI-driven changes will primarily affect future generations of medical doctors. When asked to select areas of educational importance, 76% selected the practical application of AI in medicine to be the most important, followed by medical ethics of AI (61%), AI theory (59%) and AI programming (44%). [Extracted from the article]

We have studied the effects of total gas velocity during the growth of undoped GaN epilayers on (0 0 0 1) sapphire substrate at a low pressure of 300 Torr via a two-step growth condition in a horizontal MOCVD reactor. We changed the flow rates of H2 push gas into the main run line to change total gas velocity on the substrate during the GaN growth. The photoluminescence spectra of undoped GaN epilayers were strongly affected by the changes of the total gas velocity. In a total gas velocity range of 0.21–0.46 m/s under the two-step condition in our study, all samples showed the absence of yellow luminescence band, which was not affected by the changes of the growth temperature from 1080°C to 1020°C by a 30°C decrease. Instead, our samples showed the donor–acceptor pair (DAP) recombination transitions at the extreme conditions in our range of the flow rates of push gas, i.e. total gas velocity. The band-edge emission of I2 showed a blue-shift with increasing the total gas velocity in a lower velocity regime in this study. The presence of DAP transitions and the peak position of band-edge emission would be a useful indication to obtain a process window and to optimize the process variables during the growth of an undoped GaN epilayer. [Copyright &y& Elsevier]