Pustular dermatoses represent a heterogeneous group of conditions frequently encountered in dermatologic practice. The differential diagnosis of pustular dermatoses is broad, encompassing an array of inflammatory, autoimmune, infectious, and genetic etiologies, each with characteristic clinical features and pathophysiology. This article, part of a continuing medical education series, dives into the evolving landscape of these conditions, delivering up-to-date insights into their epidemiology, pathogenesis, clinical presentations, diagnostic advances, and emerging management strategies. Part I will provide an overview of inflammatory and autoimmune etiologies. Part II will focus on infectious, genetic, and transient neonatal and infantile pustular eruptions. This continuing medical education series will help practitioners expand their differential diagnosis, highlight subtle clues that characterize the clinical presentations of these conditions, and spotlight cutting-edge developments that are reshaping management paradigms for some of these entities.

Figure S5: Evaluation of SETDB1 knockdown in human melanoma cells, related to Figure 4:

Pustular dermatoses represent a heterogeneous group of conditions frequently encountered in dermatologic practice. The differential diagnosis of pustular dermatoses is broad, encompassing an array of inflammatory, autoimmune, infectious, and genetic etiologies, each with characteristic clinical features and pathophysiology. This article, part of a continuing medical education series, dives into the evolving landscape of these conditions, delivering up-to-date insights into their epidemiology, pathogenesis, clinical presentations, diagnostic advances, and emerging management strategies. Part II will focus on infectious, genetic, and transient neonatal and infantile pustular eruptions. This continuing medical education series will help practitioners expand their differential diagnosis, highlight clues that characterize the clinical presentation of these conditions, and spotlight cutting-edge developments that are reshaping management paradigms for some of these entities.

Granuloma annulare (GA) is an inflammatory skin disease typically characterized by an erythematous eruption consisting of papules and annular plaques. GA can have a severe impact on quality of life, especially when widespread. GA can be difficult to treat and often recurs after treatment is stopped; there remain no Food and Drug Administration‑approved therapies. In recent years, there has been significant progress in understanding the epidemiology, disease associations, and molecular pathogenesis of GA. Patients with GA are more likely to have hyperlipidemia, diabetes mellitus, and autoimmune diseases including thyroiditis, rheumatoid arthritis, and systemic lupus erythematosus. These associations further underscore the importance of recognizing GA and evaluating for comorbid disease. Accurate diagnosis of GA requires distinguishing it from its clinical and histologic mimics, some of which share annular morphology or granulomatous inflammation. Molecular work has suggested a T cell‑mediated pathogenesis and identified key cytokines and other signals that drive macrophage accumulation and activation in tissue. Emerging therapies that block these cytokine signals are showing promise in the clinic. In this article, we provide an updated overview of the epidemiology, disease associations, clinical and histopathologic characteristics, molecular pathogenesis, and treatment of GA.

Figure S2: CRISPR screen guide distributions, related to Figure 1

List of primers used to perform RT-qPCR on YUDOSO cells related to Figure S6.

Figure S3: Validation and characterization of Setdb1-/- cell lines, tumors, and microenvironments, related to Figure 2.

Figure S6: Characterization of possible SKO tumor antigens, related to Figure 4.

Ranked CRISPR screen dropout hits, related to Figure 1. Analysis was performed using MaGeCK.

Differential expression of SKO vs YRG cells, by RNAseq.

Figure S4: Evaluation of differences in SKO tumor gene expression and microenvironment profiles, related to Figure 3:

Differential expression of SKO vs YRG cells ± IFNAR blockade by RNAseq.

IFN-I signaling is a hallmark of discoid lupus erythematosus (DLE), but routine tissue-based assays to detect this pathway are limited. Re-analysis of public transcriptomic data identified ISG15 and IFI6 as candidate IFN markers, ranking among the top differentially expressed genes in DLE datasets. We performed RNA in situ hybridization for both markers on archival specimens from DLE (n =11), other inflammatory dermatoses (n = 11; atopic dermatitis, psoriasis, lichen planus), and controls (n = 8). A total of 81 slides were scored (0-4+) in epidermal and dermal compartments. RNA in situ hybridization showed strong staining in basal keratinocytes and dermis of DLE, with deep dermal positivity in 89% of specimens with sufficient depth for analysis. Deep staining was absent in controls and other dermatoses. DLE demonstrated significantly higher staining intensity than inflammatory controls (P < .001). RNA in situ hybridization for ISG15 and IFI6 identifies IFN activation in DLE, distinguishing it from other dermatoses and providing objective molecular evidence applicable to routine specimens.

Background Leukocytoclastic vasculitis (LCV) and microvascular occlusion (MVO) are distinct histopathologic patterns underlying dermatologic diagnoses of purpura. This study explores the potential of attention-based artificial intelligence (AI) models to enhance diagnostic accuracy and provide interpretable insights in differentiating these conditions, serving as a proof of concept for the application of explainable AI in dermatopathology.Methods We compared the performance of two attention-based AI models, clustering-constrained-attention multiple-instance learning (CLAM) and attention multiple instance learning (MIL), in analyzing whole slide images of LCV and MVO cases. The models were trained and evaluated using a cohort of 69 biopsies. Performance metrics included precision, recall, accuracy, AUROC, and F1 score. Attention-based heatmaps were generated to highlight diagnostic regions and reveal histopathologic patterns.Results The CLAM model outperformed the attention MIL model across all evaluation metrics. Generated heatmaps effectively highlighted key diagnostic regions, including subtle areas of occlusion in the superficial papillary dermis of MVO cases.Conclusions This study demonstrates the potential of attention-based AI models to improve diagnostic accuracy and provide interpretable insights in differentiating LCV and MVO. The use of explainable AI and heatmaps offers a valuable tool for pathologists, enhancing their ability to identify and understand subtle histopathologic patterns.