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In this article, we analyze the impact of ion dynamics in the sheath of argon DC plasma discharges at moderate pressures (13 , 65 , and 130 Pa), where the ion mean free path is smaller than the sheath width. Our analysis reveals that the evolution of the ion temperature profile plays a major role in regulating the sheath formation process, influencing plasma species density and ion fluid velocity at the cathode. Through meticulous comparison of simulation data from one-dimensional self-consistent fluid models with Particles-In-Cell 1D3V (one dimension in space and three dimensions in velocity) kinetic models, we demonstrate the necessity of considering ion-neutral collisions in fluid models to accurately simulate the glow discharge. In particular, we emphasize the necessity of self-consistent ion temperature profile calculations, particularly in the sheath region. Notably, even at moderate neutral gas pressures, the ion temperature within the cathode sheath can significantly exceed background gas temperature. Kinetic simulations demonstrate the role of ion-neutral collisions in the progressive spreading of ion velocities in directions perpendicular to the cathode sheath electric field. [ABSTRACT FROM AUTHOR]

Background: Clinical information that distinguishes invasive nail unit melanoma from nail unit melanoma in situ before surgery would aid physicians in the decision‐making process and estimating prognosis. However, limited information is available on the detailed demographic and dermoscopic features of invasive nail unit melanoma and nail unit melanoma in situ for differential diagnosis. Objective: This study aimed to investigate the demographic data and dermoscopic features of invasive nail unit melanoma and nail unit melanoma in situ and establish a predictive model for differentiating these two forms of nail unit melanoma. Methods: A retrospective observational study of ninety‐seven patients diagnosed with nail unit melanoma (59 in situ and 38 invasive cases) in four healthcare centres in South Korea (three tertiary referral hospitals and one second referral hospital) from March 2014 to December 2019. Results: A multivariable analysis revealed that ulcer (odds ratio = 21.6, confidence interval = 2.1–219.8, P = 0.009), total melanonychia (odds ratio = 17.6, confidence interval = 3.0–104.0, P = 0.002), nail plate destruction (odds ratio = 10.9, confidence interval = 2.0–59.4, P = 0.006) and polychromia (odds ratio = 5.3, confidence interval = 1.36–20.57, P = 0.016) were distinctive dermoscopic features of invasive nail unit melanoma. A predictive model with scores ranging from 0 to 6 points demonstrated a reliable diagnostic value (C‐statistic = 0.902) in differentiating invasive nail unit melanoma from nail unit melanoma in situ. Conclusions: Invasive nail unit melanoma and nail unit melanoma in situ have different dermoscopic features. A predictive model based on morphologic dermoscopic features could aid in differentiating invasive nail unit melanoma from nail unit melanoma in situ. [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]

Summary: Nuchal‐type fibroma (NTF) is a rare, benign, tumour‐like fibrous proliferation of unknown pathogenesis. Despite the name, approximately one‐third of cases occur at extranuchal sites. We report a rare case of NTF of the sacral region, an extremely uncommon extranuchal site. As NTF is often misdiagnosed because of its rarity and histopathological similarity with other fibrous tumours, we present this case to increase understanding of this rare disease entity. Click here for the corresponding questions to this CME article. [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]

A letter to the editor is presented on fixed drug eruption due to side effects of acyclovir and valacyclovir.

Background: Basal cell carcinoma (BCC) is by far the most common cancer in white populations. In addition, recent reports have demonstrated an increasing incidence of BCC in Korea. We have observed a significant number of early-onset BCC cases in which the disease occurred in patients younger than 50 years., Objective: To investigate the clinicopathological characteristics of early-onset BCC in an Asian population, specifically in Koreans., Methods: One hundred and five patients with early-onset BCC were enrolled from a total of 1047 BCC patients who underwent surgery between January 1997 and December 2014 (942 patients over the age of 50 years were designated as the control group)., Results: Early-onset BCC accounted for 10.03% of all 1047 cases and the incidence over time displayed an incremental trend. The early-onset group displayed similar results as the control group, with a predominance of female BCC patients and the majority of tumours displaying the following characteristics: small in size, occurring in sun-exposed areas and belonging to the noduloulcerative clinical subtype and nodular histopathological subtype. In comparison with a previous study in a Western population, the incidence of the disease in non-exposed areas of the body, as well as the proportion of tumours of the superficial histological subtype, were lower in Asian patients., Conclusion: Although the clinicopathological characteristics of BCC are well-known, these characteristics have not been determined for early-onset BCC in an Asian population. Therefore, this study is the first report on early-onset BCC in Asians, specifically in a Korean patient group., (© 2016 European Academy of Dermatology and Venereology.)