Atopic dermatitis, cognitive function and psychiatric comorbidities across early childhood and adolescence in a population-based UK birth cohort.

Background: Atopic dermatitis (AD) may affect cognitive function, but studies are limited and inconsistent. The effect of AD severity on cognition remains underexplored and few previous studies have examined clinically validated or repeated measures of cognition throughout childhood.

Objectives: To evaluate the relationship of AD activity and severity with validated measures of general cognition in a longitudinal birth cohort.

Rapid resolution of diffuse warts following initiation of dupilumab for severe atopic dermatitis.

Cutaneous warts are an exceedingly common cutaneous viral infection for which existing treatment options are often painful, expensive, and only marginally effective. Extensive warts may occur in the setting of primary immunodeficiencies, wherein they can co-occur with other diseases of immune dysfunction, such as atopic dermatitis (AD). Dupilumab, an IL-4 receptor α (IL-4Rα)-blocking monoclonal antibody, is a biologic agent recently approved for the treatment of moderate-to-severe eczema.

No Association of filaggrin copy number variation and atopic dermatitis risk in White and Black Americans.

Atopic dermatitis (AD) is a chronic, inflammatory skin condition with a multifactorial pathophysiology. The filaggrin gene (FLG) has particularly been implicated given loss of function (LoF) mutations in this gene lead to skin barrier dysfunction and such mutations can increase a patient's likelihood of developing AD. FLG has intragenic copy number variation (CNV), which impacts the total amount of filaggrin produced.

Untapping the potential of utilizing electronic medical records to identify patients with atopic dermatitis: an algorithm using ICD-10 codes.

Atopic Dermatitis (AD) is a chronic, inflammatory skin condition that imposes an enormous personal and economic burden in the United States. Due to the ubiquity of the use of electronic medical records (EMR) in the United States, utilizing such data is critically important to studying common dermatologic diseases, such as AD. Our goal was to create a simple-to-use algorithm applied to EMR data to accurately identify AD patients thereby making it possible to efficiently use EMR data to ascertain and then study individuals with AD.