Alopecia universalis after drug reaction with eosinophilia and systemic symptoms (Dress).

Drug reaction with eosinophilia and systemic symptoms (DRESS) is a severe cutaneous adverse reaction characterized by a morbilliform cutaneous eruption, fever, lymphadenopathy, and multiorgan involvement. Alopecia universalis is a variant of alopecia areata characterized by complete loss of hair on the entire body. Herein, we report a case of alopecia universalis that presented after DRESS.

Artificial intelligence in dermatology.

Dermatology and medicine are producing data at an increasing rate that are progressively difficult to sort and manage. Artificial intelligence (AI) and machine learning are examples of tools that may have the capability to produce significant and meaningful results from these data. Currently, AI and machine learning have a variety of applications in medicine including, but not limited to, diagnostics, patient management, preventive medicine, and genomic analysis. Although the role of AI in dermatology is greater than ever, its use is still extremely limited. As AI is continually developed and implemented, it is essential that stakeholders understand AI terminology, applications, limitations, and projected uses in dermatology. With the continued development of AI technology, however, its implementation may afford greater dermatologist efficiency, greater increased patient access to dermatologic care, and improved patient outcomes.

What Looks Like a Clot But Is Not a Clot?: Cardiac Leiomyosarcoma Mimicking Pulmonary Embolism.

Primary cardiac tumors in the right ventricular outflow tract are often misdiagnosed as pulmonary embolism due to rarity and inadequate imaging characterization. Multimodality imaging offers advantages and facilitates subsequent diagnostics and management. We present a case of a woman with suspected submassive pulmonary embolism who was found to have pleomorphic leiomyosarcoma. (Level of Difficulty: Intermediate.).

The Burden of Skin and Subcutaneous Diseases in the United States From 1990 to 2017.

Importance: Skin and subcutaneous diseases affect the health of millions of individuals in the US. Data are needed that highlight the geographic trends and variations of skin disease burden across the country to guide health care decision-making. Objective: To characterize trends and variations in the burden of skin and subcutaneous tissue diseases across the US from 1990 to 2017. Design, Setting, and Participants: For this cohort study, data were obtained from the Global Burden of Disease (GBD), a study with an online database that incorporates current and previous epidemiological studies of disease burden, and from GBD 2017, which includes more than 90 000 data sources such as systematic reviews, surveys, population-based disease registries, hospital inpatient and outpatient data, cohort studies, and autopsy data. The GBD separated skin conditions into 15 subcategories according to incidence, prevalence, adequacy of data, and standardized disease definitions. GBD 2017 also estimated the burden from melanoma of the skin and keratinocyte carcinoma. Data analysis for the present study was conducted from September 9, 2019, to March 31, 2020. Main Outcomes and Measures: Primary study outcomes included age-standardized disability-adjusted life-years (DALYs), incidence, and prevalence. The data were stratified by US states with the highest and lowest age-standardized DALY rate per 100 000 people, incidence, and prevalence of each skin condition. The percentage change in DALY rates in each state was calculated from 1990 to 2017. Results: Overall, age-standardized DALY rates for skin and subcutaneous diseases increased from 1990 (821.6; 95% uncertainty interval [UI], 570.3-1124.9) to 2017 (884.2; 95% UI, 614.0-1207.9) in all 50 states and the District of Columbia. The degree of increase varied according to geographic location, with the largest percentage change of 0.12% (95% UI, 0.09%-0.15%) in New York and the smallest percentage change of 0.04% (95% UI, 0.02%-0.07%) in Colorado, 0.04% (95% UI, 0.01%-0.06%) in Nevada, 0.04% (95% UI, 0.02%-0.07%) in New Mexico, and 0.04% (95% UI, 0.02%-0.07%) in Utah. The age-standardized DALY rate, incidence, and prevalence of specific skin conditions differed among the states. New York had the highest age-standardized DALY rate for skin and subcutaneous disease in 2017 (1097.0 [95% UI, 764.9-1496.1]), whereas Wyoming had the lowest age-standardized DALY rate (672.9 [95% UI, 465.6-922.3]). In all 50 states and the District of Columbia, women had higher age-standardized DALY rates for overall skin and subcutaneous diseases than men (women: 971.20 [95% UI, 676.76-1334.59] vs men: 799.23 [95% UI, 559.62-1091.50]). However, men had higher DALY rates than women for malignant melanoma (men: 80.82 [95% UI, 51.68-123.18] vs women: 42.74 [95% UI, 34.05-70.66]) and keratinocyte carcinomas (men: 37.56 [95% UI, 29.35-49.52] vs women: 14.42 [95% UI, 10.01-20.66]). Conclusions and Relevance: Data from the GBD suggest that the burden of skin and subcutaneous disease was large and that DALY rate trends varied across the US; the age-standardized DALY rate for keratinocyte carcinoma appeared greater in men. These findings can be used by states to target interventions and meet the needs of their population.

Curated compendium of human transcriptional biomarker data.

One important use of genome-wide transcriptional profiles is to identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for clinical application. Data from thousands of translational-biomarker studies have been deposited in public repositories, enabling reuse. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated 45 publicly available, translational-biomarker datasets from a variety of human diseases. To increase the data's utility, we reprocessed the raw expression data using a uniform computational pipeline, addressed quality-control problems, mapped the clinical annotations to a controlled vocabulary, and prepared consistently structured, analysis-ready data files. These data, along with scripts we used to prepare the data, are available in a public repository. We believe these data will be particularly useful to researchers seeking to perform benchmarking studies-for example, to compare and optimize machine-learning algorithms' ability to predict biomedical outcomes.