Archives of Dermatological Research最新文献

Quantifying acne lesions from photographs is an essential component of acne evaluation. Algorithms are available to automate this process, but they can be easily disrupted by common image noise such as blur or illumination issues. We propose a new automated acne lesions classification method that is capable of withstanding common image corruptions by adapting the memory classifier technique and introducing new expert-defined acne visual features to characterize acne lesions. We then integrate the memory classification algorithm into an end-to-end framework to automatically detect lesions, classify them, and assign an acne severity grade from an image of acne-affected skin. On a dataset consisting of 4658 primary lesions (comedones, papules/pustules, and nodules) taken from 140 lateral facial images from a retrospective de-identified cohort of 63 pediatric and 35 adult patients, the proposed method achieved 87.58% overall classification accuracy. Its performance was similar after adding common noise perturbations to the images. Embedding the classification algorithm within an end-to-end framework to count lesions and grade acne severity from an input image resulted in a mean square error of 0.99 for overall acne severity (against the clinicians’ baseline on an 8-pt severity scale). The results show that the proposed algorithm is both accurate in classifying acne lesion types and capable of withstanding noise perturbations in images. Furthermore, they demonstrate its potential use for automating lesion counting and acne severity grading in research and clinical settings.

Dermatomyositis (DM) and polymyositis (PM) comprise a group of autoimmune conditions known as idiopathic inflammatory myopathies (IIM) which can be paraneoplastic. While DM and PM have been associated with malignancy in international cohorts, the strength of this association remains unclear in a nationally representative U.S. cohort. To characterize the association of DM and PM with malignancy, we analyzed 208 DM and 171 PM patients with their matched morphea controls from the NIH’s All of Us program, a large U.S. sample that may reduce variation seen in smaller regional studies. Morphea patients were selected as controls as they often undergo comparable management though typically with lower intensity immunosuppressive treatment relative to DM/PM, thus providing an analysis of malignancy risk attributable to IIM itself rather than chronic immune dysfunction. Our study found that while there was no difference in time to onset of malignancy between DM or PM and their matched control, both DM (p = 0.0304) and PM (p = 0.0380) were associated with increased risk for malignancy compared to their matched controls. Five-year malignancy incidence was 11.9% in DM and 15.9% in PM patients. These findings expand evidence for the association of malignancy with IIM and specifically highlight increased risk among DM/PM patients in a nationally representative U.S. cohort. A thorough understanding of the nuanced relationship between malignancy and autoimmunity remain to be clarified in larger prospective studies.

Dermoscopy improves early detection of pigmented and non-pigmented skin cancers, yet interpretation remains operator dependent and variable. Artificial intelligence (AI) systems trained on dermoscopic images offer scalable decision support, but evidence is dispersed across datasets, model types, and reference standards, limiting clinical translation. This systematic review aimed to evaluate the diagnostic accuracy of artificial intelligence (AI)-based algorithms for dermoscopic image analysis across various skin conditions and to compare different models based on dataset characteristics, training strategies, and diagnostic reference standards. We systematically searched PubMed, Scopus, Cochrane Library, and Web of Science up to May 2025 using comprehensive search strategies. Studies were included if they applied AI techniques to dermoscopic images for diagnostic purposes and reported performance metrics. Risk of bias was assessed using the QUADAS-2 tool. Data were synthesized qualitatively due to methodological heterogeneity. Ninety-six studies met the inclusion criteria, covering a wide range of AI approaches. Convolutional Neural Networks (CNNs) were the most common model type and generally showed variable diagnostic performance, with sensitivity ranging from 0.69 to 1.00 and specificity from 0.36 to 0.98. Hybrid and ensemble models, especially those incorporating EfficientNet, DenseNet, and ResNet architectures, often outperformed classical machine learning approaches. Key limitations included variability in datasets, inconsistent ground truth references, and lack of standardized reporting. Few studies involved prospective real-world validation. AI-based algorithms for dermoscopic image analysis particularly CNNs and ensemble frameworks, have diagnostic potential, however, variability in study design and evaluation limits their current clinical applicability. Future efforts must focus on standardization, external validation, and transparent algorithm development to support clinical integration.

Teledermoscopy (TDS) offers an efficient alternative to face-to-face assessment of suspicious skin lesions. Although diagnostic accuracy in TDS is relatively high, further improvement is desirable to reduce the risk of overlooked skin cancers and unnecessary interventions of benign lesions. Image enhancement (IE), i.e. digital modification of different parameters of an image, may potentially improve visualisation and thereby diagnostic accuracy. Several tools for IE were reviewed through two workshops and two were eventually selected for the study: Contrast Limited Adaptive Histogram Equalisation (CLAHE) for contrast enhancement and Gamma Correction for brightness adjustment. A retrospective randomized clinical study was performed in which 1997 TDS cases were distributed to 13 assessors with varying dermoscopic experience, who assessed lesions either with access to IE-tools (IE-group) or without (control group). Diagnostic accuracy for malignant vs. benign and melanoma-group vs. non-melanoma-group lesions was compared using chi-square tests. A generalized linear mixed model (GLMM) with assessor as a random intercept and adjustment for image quality was applied to account for assessor-dependent variance. Management recommendations were analyzed using both graded intervention levels relative to a gold standard and a stricter harm–benefit framework, assessing inappropriately under-management of malignant and over-management of benign lesions. No significant differences were found between the groups regarding malignant/benign classification. For melanoma differentiation, specificity was slightly, but significantly lower in the IE group compared to the control group (86.3; 95% confidence interval (CI) 83.8–88.5 vs. 89.9%; 95% CI 87.6–91.8, respectively, p = 0.02) while sensitivity remained unchanged. GLMM analyses confirmed these findings. No significant differences were observed in appropriateness of management recommendations or diagnostic confidence. Use of the IE tools CLAHE and Gamma Correction did not improve diagnostic performance in TDS assessments. These findings do not support routine clinical use of these IE tools for the purpose of improving diagnostic assessment of skin lesions.

Mohs micrographic surgery (MMS) is a specialized skin-sparing technique used to treat high-risk skin cancers and those in cosmetically sensitive areas. While MMS is considered the gold-standard for skin cancer treatment, access to MMS providers across the United States is inconsistent. The present study utilizes data from the Medicare Physician & Other Practitioners – by Provider and Service (MPOP), which provides publicly available information about services and procedures performed by medical professionals for Medicare Part B beneficiaries, to characterize the geographic distribution of and factors associated with access to MMS care across the United States. Our findings identify a complex relationship between urban and rural MMS access, and higher median household incomes and lower unemployment rates in counties with a higher density of Mohs providers. Taken together, our study provides an initial atlas characterizing geographic and demographic variation in MMS procedures and will be complemented by future studies with additional data.

Photoaging, a premature skin aging caused by exposure to UVA radiation, is a significant dermatological problem driven by oxidative stress and inflammatory responses. The radio-resistant extremophile Deinococcus radiodurans is the source of the oxy-carotenoid Deinoxanthin, which has emerged as a noteworthy antioxidant with possible photoaging therapeutic uses. This investigation employed an integrated network pharmacology analysis to explain the possible molecular targets and biological mechanisms of deinoxanthin toward photoaging. Intersection analysis between deinoxanthin-related and photoaging-related targets showed 47 overlapping targets, suggesting potential molecular candidates for exploring deinoxanthin therapeutic efficacy. The protein-protein interaction network was built from these targets and identified eight core targets: TNF-α, PPARG, IL-6, NFκB1, MAPK3, CASP3, EGFR, and ESR1. Docking analysis exhibited strong binding affinities of DRE with the top three core targets: TNF-α, MAPK3, and NFκB1. The dynamics simulation and binding free energy study with over 100 nanoseconds further decoded the stability of deinoxanthin-MAPK3 and deinoxanthin-TNF-α complexes, showing stable conformations, while the deinoxanthin-NFκB1 complex showed significant fluctuations, indicating potential instability. Cytotoxicity assays confirm that Deinoxanthin-rich extract (DRE) concentrations up to 15 µg/mL did not harm NIH/3T3 cells. Western blot analysis further confirmed that the DRE pre-treatment significantly reduced upregulated TNF-α, MAPK3, and NFκB1 levels in UVA-exposed cells, showcasing its protective effects towards photoaging by modulating signaling pathways and inhibiting oxidative stress.

Investigating the pivotal role of Matrix Metalloproteinase-1 (MMP-1) unveils its significant impact on the skin aging process, a complex interplay of intrinsic genetic factors and external environmental stressors, including photoaging. As a key enzyme in collagen degradation, MMP-1 significantly influences the loss of skin integrity and elasticity over time. Delving into the architecture and role of MMP-1, as well as the regulatory mechanisms controlling its activity, is crucial for devising effective anti-aging strategies. The potential of MMP-1 inhibitors, including small molecules, peptides, and natural compounds, is emphasized for their ability to diminish aging signs and enhance skin appearance. Moreover,the application of MMP-1 inhibitors in chronic wound healing illustrates their extensive therapeutic capabilities beyond anti-aging. The compilation of current studies highlights the urgent need for innovative MMP-1 inhibitors and combination therapies to improve clinical outcomes and safety profiles. Through the synthesis of a series of studies, it is hoped to pave the way for groundbreaking advancements in anti-aging strategies, making significant contributions to the fields of dermatology and cosmeceutical sciences.

Kaposi sarcoma (KS) is a vascular tumor caused by human herpesvirus 8 (HHV-8) and is traditionally classified into classic, endemic, epidemic, and iatrogenic subtypes. However, recent reports describe a fifth KS type occurring in human immunodeficiency virus (HIV)-negative, immunocompetent men who have sex with men (MSM), which has emerged as a distinct entity with evolving characterization. We conducted a retrospective review of KS cases diagnosed at our institution between 2000 and 2024 to evaluate the clinical and epidemiologic features of KS in HIV-negative, immunocompetent adult men whose presentations do not align with existing subtype classifications. Patients were excluded if they were over 50 years old, from endemic regions, HIV-positive, or immunosuppressed. Twelve patients met the inclusion criteria, with a median age of 42 years. Seven (58.3%) identified as MSM and five (41.7%) as non-MSM. Histories of sexually transmitted infections were frequent among MSM patients, though not significantly different from the non-MSM subgroup. Pre-exposure prophylaxis (PrEP) use after KS diagnosis was significantly more likely among MSM than non-MSM patients. Lesions most commonly affected the lower extremities, and initial management varied from observation to excision, with mixed clinical responses at follow-up. Notably, we identified five HIV-negative, immunocompetent, non-MSM patients with KS who lacked clear alignment with known subtypes, a finding that suggests current KS classifications may not fully capture the clinical spectrum of disease among these patients. Further investigation is warranted to clarify the pathogenesis of KS, identify transmission dynamics, and explore potentially unidentified risk factors in this population.