JID Innovations

BackgroundPrurigo nodularis (PN) is a chronic, pruritic, inflammatory skin disease characterized by hyperkeratotic nodules on the trunk and extremities. While there is growing research on the immunological basis of PN, the neuropathic and structural components of PN lesions are unknown. ObjectiveTo determine the inflammatory, neuropathic, and structural pathways in PN compared to atopic dermatitis (AD). MethodsLesional and non-lesional skin biopsies were collected from 13 PN and 6 AD patients. mRNA and protein expression in biopsies was determined using RNA-Sequencing and immunohistochemistry (IHC), respectively. Differentially expressed genes (DEGs) were identified using the DESeq2 R package and pathway level enrichment was determined using Gene Set Enrichment Analysis. IHC expression was quantified with QuPath followed by statistical comparison with the Students t-test and Mann-Whitney U. ResultsCompared to lesional AD, lesional PN had greater mRNA expression of MMPs, OSM, NGF, IL1{beta}, CXCL2, CXCL5, CXCL8, and insulin-like growth factors, and lower expression of CCL13, CCL26, EPHB1, and collagens. Compared to non-lesional AD, non-lesional PN showed upregulation of keratin-family genes. GSEA revealed that lesional PN had greater keratinization, cornified envelope, myelin sheath, TGF-beta signaling, extracellular matrix disassembly, metalloendopeptidase activity, and neutrotrophin-TRK receptor signaling, while non-lesional PN had higher keratin filament, extracellular structure organization, extracellular matrix disassembly, and angiogenesis. IHC showed increased dermal nerve growth factor (NGF) expression in lesional PN compared to lesional AD (p=0.038), and greater epidermal NGF compared to dermal NGF in non-lesional PN (p=0.014). LimitationsSingle, tertiary care center. ConclusionsPN demonstrated increased neurotrophic and extracellular matrix (ECM) remodeling signatures compared to AD, possibly explaining the morphological differences in their lesions. These signatures may therefore be important components of the PN pathogenesis and may serve as therapeutic targets.

BackgroundAtopic dermatitis (AD) is a chronic skin disease that typically occurs in early childhood. In this cross-sectional case-control study, our objective was to employ machine learning approaches to identify novel clusters of protective or susceptibility features associated with AD. Methods and FindingsWe utilised an integrated dataset comprising previously established environmental, cytokine, antibody, and gene expression data from AmaXhosa children, both healthy and with AD, living in either rural or urban settings of South Africa, aged 12-36 months. The applied machine learning methods included the GeneSelectR workflow to identify a subset of relevant genes, the calculation of SHAP values to explain the machine learning output, and the use of DIABLO to integrate the datasets for a comprehensive analysis. Key findings included the identification of a protective cluster of environmental features primarily found in the rural setting, which were correlated with plasma cytokine levels and with expression of autophagy-related genes. Additionally, we identified AD susceptibility clusters where levels of allergen-specific and total IgE antibodies correlated with the cytokines MCP-4 and TARC. Lastly, we identified an RNA-Seq feature signature specific to the disease endotype. ConclusionsThe application of various machine learning methods enabled the identification of significant factors associated with AD in a complex, multi-modular dataset, making the output explainable and potentially informing targeted interventions and improved diagnostic criteria.

Background: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease associated with clinical, psychosocial, and economic burden. Accurate severity assessment is essential for guiding treatment escalation and monitoring disease activity, yet clinician-based scoring systems such as the Eczema Area and Severity Index (EASI) are limited by subjectivity and considerable inter- and intra-rater variability. Erythema, a key driver of AD severity grading, is particularly prone to inconsistent evaluation due to differences in ambient lighting, device quality, skin tone, and rater experience, underscoring the need for objective, reproducible assessment tools. Objective: To develop and validate an artificial intelligence (AI) pipeline for grading erythema, excoriation, and lichenification severity in AD from clinical photographs. The study evaluated the level of agreement between AI severity ratings in each category against dermatologists, non-specialists, and a consensus reference standard, with erythema as the primary outcome of interest. Methods: A two-stage AI pipeline was developed using EfficientNet B7 convolutional neural networks (CNNs). The first CNN was trained as a binary AD classifier on 451 AD and 601 non-AD images for lesion detection and segmentation. The second CNN was trained on 173 dermatologist-annotated AD images which were scored on a 0-3 ordinal scale for erythema, excoriation, and lichenification. This CNN had a downstream feature extraction algorithms such red channel contrast for erythema, Law's E5L5 for excoriation, and S5L5 texture maps for lichenification. In a cross-sectional validation study, 41 independent test images were scored by two blinded dermatologists and two blinded physicians. AI predictions were compared to individual rater groups and mode-derived consensus scores using weighted Cohen's kappa, classification accuracy, confusion matrices, and error direction analyses. Results: On internal validation, the severity CNN achieved 84% overall accuracy (averaged across all three attributes), 86% sensitivity, 87% specificity, and a macro-averaged area under the receiver operating characteristic curve (AUC) of 0.90. In the external comparison with blinded human raters, erythema agreement between the AI and dermatologist consensus was substantial (accuracy 80.7%; kappa = 0.68), with no large (>2-point) misclassifications. Physician consensus agreement was lower (accuracy 54.8%; kappa = 0.34), reflecting greater variability among primary care physicians (non-specialists). For excoriation, AI-dermatologist agreement was moderate (accuracy 72.4%; kappa = 0.62); for lichenification, agreement was similar (accuracy 71.4%; kappa = 0.59). Across all features, disagreements were predominantly between adjacent severity categories. The AI was able to generate erythema severity grades for images of darker skin tones that dermatologists typically would not rate and were marked as "unable to assess". Limitations: The validation set was small (41 images), severe cases (score 3) were underrepresented, one rater participated in both training annotation and validation scoring, and sample size was insufficient for robust stratification by skin tone or body site. Conclusion: The AI pipeline demonstrated dermatologist-level accuracy for erythema scoring, consistent moderate agreement for excoriation and lichenification, and a potential advantage in assessing erythema on darker skin tones. These findings support its potential as a standardized, objective tool for AD severity assessment. Prospective validation in larger, more diverse cohorts is warranted.

BackgroundAtopic dermatitis (AD) is a common atopic disease worldwide and dupilumab, a monoclonal antibody directing to the IL4/IL13 signalling, is emerging as an effective therapy for AD. Recently, there is report describing differences in AD severity and treatment responses to dupilumab among different geographic regions, but the underlying mechanisms remain unresolved. Patients ancestral backgrounds represent one of the key differences among various geographic areas. Their implications in variability regarding diseases and treatment responses are gaining more and more recognitions. MethodsWe aimed to delineate the potential ancestry-associated differences in AD and treatment responses to dupilumab. We thoroughly surveyed Gene Expression Omnibus (GEO) for transcriptomic dataset in the context of AD and dupilumab treatment involving individuals of diverse ancestral backgrounds and carried out comparative analyses for samples from different ancestral groups. ResultsOnly one transcriptomic dataset was found for biopsy specimens from lesion and non-lesion skin from AD patients of self-reported White and Asian ancestral backgrounds. Despite comparable clinical phenotypes, Gene Set Enrichment Analysis revealed that skin samples from White AD patients exhibited upregulated IL4 & IL13 signalling from baseline to up to 4-week post dupilumab treatment, relative to Asian ones. ConclusionsThis is the first study of its kind to unravel the ancestry-related differences in AD and dupilumab treatment responses. These findings might be instrumental to future clinical patient stratification, risk assessment and guide personalized medicine treatment options for dupilumab.

Psoriasis is a chronic inflammatory skin disease influenced by both genetic and environmental factors. Despite extensive research, its precise etiology remains unclear, posing significant challenges to understanding and treatment. The disease pathogenesis involves self-reactive T cells and immune-related cytokines. Genome-wide association studies have identified various susceptibility loci for immune-related diseases, but the underlying mechanisms remain only partially understood. Recent discoveries of critical signaling pathways, biological processes, and immune cell involvement have expanded our knowledge and offer hope for improved therapeutic strategies. This study aimed to enhance our understanding of psoriasis and proposes novel therapeutic approaches by employing integrated bioinformatics to identify signaling pathways and biological processes as potential disease markers. Presenting a systematic review and taking a meta-analytical approach to transcriptomic profiles, this investigation examined differential gene expression patterns across 44 studies involving 975 samples comparing lesional psoriasis, non-lesional psoriasis, and healthy controls. Consensus transcriptome signatures revealed a significant association between immune-related genes and psoriasis pathogenesis. Functional enrichment analysis identified several enriched pathways related to immunity and immune system processes. Comparison of these findings with the existing literature indicated that some immune-related genes were already known, while others are novel in the context of psoriasis. Additionally, novel gene analysis demonstrated psoriasis involvement in pathways such as gluconeogenesis, the FoxO signaling pathway, and mitophagy. This integrative approach confirmed classic genetic associations while uncovering novel gene expression patterns and pathways relevant to psoriasis. Notably, the disruption of the gluconeogenesis pathway emerged as a critical finding. These insights enhance our understanding of psoriasis pathophysiology and pave the way for targeted therapies, offering improved management options for affected individuals.

Palmoplantar pustulosis (PPP) and dyshidrotic eczema (DE) are chronic vesiculopustular dermatoses with overlapping clinical presentations but distinct underlying biology. Although comparative transcriptomic and proteomic analyses between PPP and DE have been reported, they remain limited in number and scope, with no comprehensive understanding of their distinct molecular signatures. Moreover, their molecular mechanisms remain unclear, and currently available therapeutic options are limited. To clarify disease-specific epidermal programs underlying vesicle formation, we conducted Visium HD spatial transcriptomic analysis of FFPE lesional skin samples obtained from patients with PPP and DE, followed by immunohistochemical validation against normal palmoplantar skin controls. Spatial clustering identified a keratinocyte subpopulation adjacent to vesicles that exhibited distinct transcriptional programs in the two diseases. In PPP, vesicle-associated keratinocytes demonstrated marked downregulation of aquaporin-3 (AQP3) and E-cadherin, together with strong, spatially localized activation of JAK-STAT3 signaling. Conversely, DE exhibited diffuse AQP3 expression and more homogeneous activation of JAK-STAT3 signaling throughout the epidermis. These results indicate that, although PPP and DE share inflammatory pathways, they differ substantially in their spatial molecular architecture. Reduced AQP3 expression and localized STAT3 activation may contribute to vesicle formation in PPP, supporting our previous hypothesis that implicates intraepidermal sweat leakage as a pathogenic mechanism in PPP. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/723901v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@19c7591org.highwire.dtl.DTLVardef@eab29aorg.highwire.dtl.DTLVardef@73c2e2org.highwire.dtl.DTLVardef@1ffc02f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Melanoma develops as the result of complex interactions between sun exposure and genetic factors. Data on the relationship between sunlight and melanoma from prospective studies are scant, and the combination of ultraviolet exposure data collected before melanoma diagnosis and genetic information is rarer still. We aimed to quantify the association between ambient and personal UV exposure in relation to risk of incident melanoma (invasive; invasive+in situ) in a large population-based prospective study of men and women (n=38,833) residing in a high ambient UV setting, and to examine potential gene-environment interactions. During a median follow-up time of 4.4 years, 782 (1.5%) participants developed cutaneous melanoma (316 invasive, 466 in situ). Country of birth, age at migration and sunburns during all periods of life were significantly associated with melanoma risk. Histories of keratinocyte cancer and of other actinic lesions were both strongly associated with melanoma risk. An interaction with polygenic risk is possible; among people at low risk, markers of cumulative sun exposure were associated with melanoma. In contrast, among people at high polygenic risk, markers of high-level early life ambient exposure were associated with melanoma. Polygenic risk scores can assist in identifying individuals for whom sunlight exposure is most relevant.

BackgroundAtopic dermatitis and food allergies affect a growing swath of the population and there is consensus that their development is determined by a confluence of inherent and environmental factors. Of the numerous influences identified, a significant proportion of them are readily accessible from birth, thereby potentially opening a path for risk stratification from birth. The CARE study aims to harness this knowledge, coupled with advances in machine learning predictive modeling, to effectively determine whether a neonate is at-risk for developing atopic dermatitis or food allergies from birth. Methods & DesignThe CARE study is a prospective observational study of neonates recruited 1-5 days following birth from the neonatal ward of participating medical centers. Upon recruitment, trans-epidermal water loss measurements will be taken from neonates and their biological parents, and a survey will be administered to parents to record various environmental, historic and lifestyle elements that may contribute to or protect against the development of atopic dermatitis and food allergy. Follow-up questionnaires will be administered at ages 6, 12 and 24 months. Atopic dermatitis outcome measures, primarily a modified version of the UK Working Party diagnostic criteria for atopic eczema, will be assessed at 6, 12 and 24 months and food allergy outcome measures will be assessed at 12 and 24 months of age. DiscussionThe data generated from the CARE trial will serve to validate the notion that easily-accessible measures of risk can enable risk stratification from birth for infants at-risk of developing atopic dermatitis and food allergies. Trial Registrationwww.clinicaltrial.gov NCT04325451, prospectively registered on March 27, 2020

Vitiligo, a chronic autoimmune skin disorder characterized by selective epidermal melanocyte loss, lacks a well-defined mechanism for this phenomenon. Our study offers compelling insights into vitiligo pathogenesis by revealing disruptions in the basement membrane zone (BMZ) architecture. We observed branched, fragmented, and multilayered lamina densa, accompanied by elevated dermal fibroblast numbers and notable matrix metalloproteinase 2 (MMP2) overexpression. Vitiliginous skin extracts exhibited significant active MMP2 upregulation. To establish a direct link, we intradermally injected MMP2-overexpressing fibroblasts into K14-SCF transgenic mice, resulting in vitiligo-like skin and melanocyte loss, effectively reversed by coadministering MMP2 inhibitors. These groundbreaking findings highlight the pivotal role of disorganized BMZ in vitiligo, proposing MMP2 overexpression in dermal fibroblasts as a potential key contributor. Enhancing our understanding of vitiligos mechanisms, this research opens avenues for innovative therapeutic strategies against this challenging autoimmune skin disorder. TeaserDisrupted skin architecture and MMP2 in dermal fibroblasts hold the key to a potential breakthrough against this puzzling autoimmune disease vitiligo.

Cutaneous squamous cell carcinoma (cSCC) poses significant clinical challenges due to its rising incidence and potential for metastasis. Histopathologic risk stratification is further limited by substantial inter-observer variability. Unsupervised AI approaches based on content-based image retrieval offer scalable and interpretable decision support for diagnostic pathology. The objective of this study was to evaluate the use of image retrieval within histopathology atlases to stratify cSCC tumor differentiation from whole-slide images (WSIs), while comparing different patch selection and feature extraction strategies. This retrospective study included 552 archived WSIs comprising 385 well-differentiated, 102 moderately differentiated, and 66 poorly differentiated cases collected across Mayo Clinic sites in Arizona, Florida, and Minnesota. Image atlases were constructed using multiple patch aggregation strategies (Mosaic, Collage, and Montage) and deep learning encoders (KimiaNet, PathDino, and H-Optimus-0). A leave-one-WSI-out evaluation framework was used to assess differentiation classification performance using accuracy, specificity, sensitivity, and F1 score. Mosaic combined with KimiaNet achieved the highest Top-1 accuracy (74.9%) and specificity (92.6%), while Mosaic with H-Optimus-0 yielded the best Top-5 accuracy (79.0%) and macro-F1 score (62.6%). Collage combined with KimiaNet produced the highest Top-5 specificity (99.5%). The generalizability of the evaluated AI models varied across hospitals, reflecting differences in imaging protocols, staining practices, and patient populations. Overall, unsupervised image search and retrieval provides effective, annotation-free support for cSCC differentiation and has the potential to enhance dermatopathology workflows when appropriate combinations of patch selection and feature ex-traction methods are employed.

Autoimmune blistering diseases (AIBDs) are rare, chronic disorders of the skin and mucous membranes, with a broad spectrum of clinical manifestations and morphological lesions. Considering that 1) diagnosis of AIBDs is a challenging task, owing to their rarity and heterogeneous clinical features, and 2) misdiagnoses are common, and the resulting diagnostic delay is a major factor in their high mortality rate, patient prognosis stands to benefit greatly from the development of a computer-aided diagnostic (CAD) tool for AIBDs. Artificial intelligence (AI) research into rare skin diseases like AIBDs is severely underrepresented, due to a variety of factors, foremost a lack of large-scale, uniformly curated imaging data. A study by Julia S. et al. finds that, as of 2020, there exists no machine learning studies on rare skin diseases [1], despite the demonstrated success of AI in the field of dermatology. Whereas previous research has primarily looked to improve performance through extensive data collection and preprocessing, this approach remains tedious and impractical for rarer, under-documented skin diseases. This study proposes a novel approach in the development of a deep learning based diagnostic aid for AIBDs. Leveraging the visual similarities between our imaging data with pre-existing repositories, we demonstrate automated classification of AIBDs using techniques such as transfer learning and data augmentation over a convolutional neural network (CNN). A three-loop process for training is used, combining feature extraction and fine-tuning to improve performance on our classification task. Our final model retains an accuracy nearly on par with dermatologists diagnostic accuracy on more common skin cancers. Given the efficacy of our predictive model despite low amounts of training data, this approach holds the potential to benefit clinical diagnoses of AIBDs. Furthermore, our approach can be extrapolated to the diagnosis of other clinically similar rare diseases.

BackgroundCommon diagnostic tools for atopic dermatitis (AD) often perform worse in skin-of-colour (SOC) populations. The objective of this review is to map the prevalence, validation, and effectiveness of clinician-based and patient-reported tools for diagnosing AD in SOC groups across all ages. MethodsThis review followed PRISMA-ScR guidelines and searched Embase, Scopus, PubMed, MEDLINE, Web of Science, and MedRxiv for articles published January 2015 through December 2023. Eligible studies were observational, randomized, or review articles evaluating clinician-rated scales or patient-reported measures with self-identified race or ethnicity. We excluded non-English publications, case reports/series, guidelines, editorials, and studies lacking stratification. After de-duplication, two reviewers screened titles, abstracts, and full texts with conflicts resolved by a third reviewer. Data extraction captured study design, population demographics, tools evaluated, and key findings on accuracy and reliability in SOC cohorts. Results28 articles (total n = 20 332) met inclusion criteria. 24 assessed clinician-rated scales, most often EASI (n = 16), SCORAD (n = 10), and o-SCORAD (n = 8). These tools frequently underestimate AD severity in Fitzpatrick IV-VI skin types. Five studies examined alternative clinician tools (vIGA-AD, IGAxBSA). Rajka-Langeland and ADSI scores were each assessed once. Patient-reported outcomes (PROs) were dominated by POEM (n = 17), which had only 14% SOC participants during initial validation. PO-SCORAD (a PRO based on SCORAD) was also assessed (n = 10). Nine newer PRO tools (RECAP, ADCT, PSAAD, ADSEQ, CEQ, DFI, CADIS, QoLIAD, PIQoL-AD) appeared in single studies. Adjunctive measures and technological approaches (body-surface area alone, photo guides, AI-assisted analysis, remote assessment) featured in five studies but lack multi-center validation. ConclusionsMost diagnostic tools remain validated in lighter-skinned cohorts and underrepresent SOC populations. Patient-reported measures show promise but require wider validation. Adjunctive and technology-driven methods may improve equity but need rigorous testing. Future research should prioritize multiethnic cohorts, age-specific validation, and consensus-driven adaptation of both clinician and patient-reported tools to ensure reliable assessment for all skin types.

Backgroundfollowing a first cutaneous squamous cell carcinoma (CSCC), one-third of patients develop new primaries, escalating their risk of metastasis and poor outcomes. However, current follow-up strategies are not risk-stratified, representing a critical gap in patient management. Objectiveto develop and validate a prognostic model to quantify individualized absolute risk of a first metachronous CSCC after an index tumor, accurately accounting for the high competing risk of mortality in this typically elderly population. Methodswe conducted a nationwide, population-based cohort study of 11,737 patients with a first histologically confirmed CSCC (Netherlands Cancer Registry, 2007-2008) with up to 10 years of follow-up. Data on subsequent tumors was retrieved via linkage to the Automated National Pathological Anatomy Archive (Palga). A Fine-Gray competing-risk model was developed using routinely available clinical and pathological predictors (age, sex, hematologic malignancy, basal cell carcinoma (BCC) and actinic keratosis (AK) history, presence of synchronous CSCC, primary tumor location, and differentiation). Model performance was assessed 10-fold cross-validation, quantifying discrimination (time-dependent C-index) and calibration. Resultsduring follow-up, 3,288 (28%) developed a first metachronous CSCC. The model identified key predictors: markers of cumulative UV-exposure (included AK history, [≥]5 prior BCCs), and immunosuppression (chronic lymphocytic leukaemia/small lymphocytic leukaemia). Male sex, presence of synchronous CSCC at baseline were also associated with higher risk. While discrimination was modest (cross-validated 5-year C-index: 0.64), the model demonstrated excellent calibration. Conclusionsthis competing-risk model provides individualized, well-calibrated absolute risk estimates for a first metachronous CSCC. Based on routinely available clinical features, it offers insight into how established predictors shape risk in this high-susceptibility population. External validation and the identification of novel predictors are necessary to further refine the model and support personalized dermatologic care.

The incidence of cutaneous malignancies is increasing worldwide, presenting an important public health burden. Cohort studies can provide high quality data on the epidemiology of these cancers, and are invaluable for deriving measures of disease burden used to inform prevention, diagnosis and treatment. We conducted a systematic review of the literature to summarise the characteristics of cohort studies that have published one or more papers describing the epidemiology of melanoma and/or keratinocyte cancers. Eligible studies were population-based cohort studies that have published findings on incidence or etiology of melanoma or keratinocyte cancer (including associations with phenotypic, environmental, and genetic factors). We excluded clinical cohorts focused on survivorship outcomes. We searched MEDLINE 1950 (U.S. National Library of Medicine, Bethesda, MD, USA), the ISI Science Citation Index (1990 to 31 July 2025) and the reference lists of retrieved articles, imposing no language restrictions. We identified 22 eligible cohort studies, 20 of which had published on melanoma, and 16 on keratinocyte cancer. Nine were conducted in the United States, eleven in Europe, and two in Australia. There was substantial variability in terms of cohort size, risk factor information recorded at baseline, and other data collected (e.g., health services, genetic). Only three studies were specifically designed to examine skin cancers as study endpoints, and only two cohorts pre-specified both melanoma and keratinocyte cancer endpoints. Our summary provides a resource for skin cancer researchers conducting investigations into the causes, burden and prevention of these important cancers.

Since the end of 2019, COVID-19 pandemic caused by the SARS-CoV-2 emerged globally. The angiotensin-converting enzyme 2 (ACE2) on the cell surface is crucial for SARS-COV-2 entering into the cells. We use SARS-COV-2 pseudo virus and humanized ACE2 mice to mimic the possible transmitting of SARS-COV-2 through skin based on the data we found that skin ACE2 level is associated with skin pre-existing cutaneous conditions in human and mouse models and inflammatory skin disorders with barrier dysfunction increased the penetration of topical FITC conjugated spike protein into the skin. Our study indicated the possibility that the pre-existing cutaneous conditions could increase the risk for SARS-COV-2 infection. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/181297v4_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@14cef60org.highwire.dtl.DTLVardef@1f78c65org.highwire.dtl.DTLVardef@1224834org.highwire.dtl.DTLVardef@1b27475_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundRhododendrol (RD) is a phenolic compound that was first developed as a skin-lightening agent that occasionally induces skin depigmentation. Although it has been shown that RD induces melanocyte death in vitro, it is still not fully understood why melanocytes are gone by RD in vivo. ObjectiveThis research aimed to investigate how melanocytes are eliminated in the animal model by RD. MethodsOn the backs of black guinea pigs (JY-4) with epidermal melanocytes in the basal layer, 30% RD was administered topically three times per day, five days per week. Skin tissues were collected sequentially and histologically analyzed. ResultsOn day 21, L* values in the RD-applied skin were significantly higher than in the vehicle-applied skin. From day 1 to day 7, the number of TRP1-positive melanocytes and melanin in the basal layer decreased, but no TUNEL-positive melanocytes were identified. On the other hand, an accumulation of melanin was newly found in the dermis. Immunohistochemical staining identified several melanocytes in the upper dermis or spinous layer, away from the basement membrane. An investigation of the epidermal-dermal interface showed a structural anomaly in a portion of the basement membrane with elevated MMP2 expression and increased dermal fibroblasts. The application of the MMP2 inhibitor Ilomastat abolished the basement membrane abnormality by RD. ConclusionThese findings suggest that RD-induced alterations in basement membrane structure may contribute to melanocyte detachment and loss, which is the cause of skin depigmentation not only in RD-induced vitiligo but also in vitiligo.

Chronic exposure to ultraviolet (UV) radiation, known as photoaging, accelerates skin aging by inducing molecular, histological, and functional changes. This study established a mouse model using SKH-1 hairless mice to investigate chronic UV-induced photoaging over eight weeks. SKH-1 hairless mice were exposed to a combination of UVA and UVB, and the progression of skin damage was monitored through physical, histological, and molecular parameters, with a focus on erythema, transepidermal water loss, and collagen and hyaluronan (HA) metabolism. Significant reductions in HA content and alterations in DNA repair markers, such as {gamma}H2AX, were observed, highlighting the impact of chronic UV exposure on skin structure and function. Reactive adipogenesis and increased epidermal thickness were noted, reflecting adaptive responses to UV-induced damage. By investigating these parameters over the evaluation period, we provide a comprehensive time-course analysis of the progressive impact of UV-induced photoaging, offering insights into the underlying mechanisms and potential therapeutic targets to prevent or delay photoaging.

BackgroundTargeted treatments for non-communicable chronic inflammatory skin diseases like eczema and psoriasis offer significant potential for effective therapy. However, therapeutic success requires an accurate diagnosis, which is challenging due to their overlapping clinical and histological features. ObjectiveWe aimed at assessing the diagnostic performance of both a manual (MC) and fully automated (PsorX-LabDisk) RT-qPCR test based on the expression of NOS2 and CCL27 compared with conventional dermatopathological evaluation in differentiating psoriasis from eczema. MethodsSeventy-three FFPE skin samples of psoriasis and eczema were randomly selected and evaluated histopathologically (H&E-stained sections) by 14 dermatopathologists to assess interobserver variability, quantified using Cohens and Fleiss {kappa}. To confirm that the observed variability was not cohort- or rater-specific, a validation cohort (n=72) from an independent institution was assessed by three dermatopathologists under identical conditions. For molecular analysis, both manual (MC) and automated NOS2/CCL27-based RT-qPCR (PsorX-LabDisk) workflows were applied. Diagnostic performance (sensitivity, specificity, accuracy) of histopathological and molecular analyses were determined against reference diagnoses. ResultsDermatopathological evaluation demonstrated only fair agreement (Fleiss {kappa} = 0.31) in both study and validation cohort. The mean diagnostic accuracy of dermatopathology was 76.9%, with a sensitivity of 70% and specificity of 81.6%. In comparison, MC and the PsorX-LabDisk achieved sensitivities of both 92.9%, specificities of 82.2% and 84.4%, and accuracies of 87.7% and 86.3%, respectively. In diagnostically ambiguous cases, molecular testing maintained high accuracy (>86%), clearly outperforming dermatopathology, which showed near-random agreement and low accuracy (61.7%). ConclusionsBoth MC and PsorX-LabDisk provide a reliable, examiner-independent complement to dermatopathology for differentiating psoriasis and eczema. By reducing diagnostic ambiguity, it enhances clinical confidence and supports more precise and timely therapeutic decisions in inflammatory skin disease management. Key pointsO_ST_ABSHigh interobserver variability in dermatopathologyC_ST_ABSAcross two independent cohorts, dermatopathological evaluation by multiple dermatopathologists showed only fair to no agreement, highlighting substantial subjectivity and diagnostic uncertainty in distinguishing psoriasis from eczema based solely on morphology. Superior accuracy of molecular diagnosticsBoth the manual (MC) and fully automated NOS2/CCL27-based RT-qPCR (PsorX-LabDisk) assays outperformed dermatopathology, achieving sensitivities around 93 % and overall accuracies around 88 %, demonstrating that molecular testing provides a more consistent and objective diagnostic approach. Robust performance in ambiguous casesIn diagnostically challenging samples with low dermatopathological consensus, the PsorX-LabDisk maintained high diagnostic accuracy (>86 %), outperforming expert evaluation. These results underscore its potential as a reliable, examiner-independent tool supporting precise diagnosis and optimized treatment selection in clinical practice. Capsule SummaryBoth MC and PsorX-LabDisk molecular assay outperformed dermatopathology in differentiating psoriasis from eczema, offering an objective, reproducible, and clinically practical tool that enhances diagnostic confidence and guides targeted treatment in inflammatory skin diseases.

Chronic pruritus on non-lesional skin (CPNL) is a hallmark of chronic pruritus of undetermined origin (CPUO) and characterized by persistent pruritus without visible skin lesions. While atopic dermatitis (AD) is well known to involve epidermal barrier disruption, the pathophysiology of CPNL remains poorly understood. Our goal was to compare stratum corneum (SC) morphology and its role as a functional epidermal barrier, the severity of the symptoms, the relationship between persistent itch and primary skin and scratch lesions, and selected inflammatory markers in patients with CPNL, AD, and healthy controls. We assessed corneocyte morphology and corneodesmosome density in skin samples using atomic force microscopy (AFM) and fluorescence microscopy (FLM). Transepidermal water loss (TEWL) and tissue hemoglobin index (THI) were used to evaluate epidermal barrier integrity and skin blood perfusion. Symptom severity was assessed using the Worst and Average Itch-Numerical Rating Scale (NRS), Scratch Sign Score (SSS), and patient-reported sleep disturbance. AD patients demonstrated structural differences in the SC, including reduced corneocyte area, clustering of corneocytes, absence of intermediate filaments, and relocated corneodesmosomes, along with elevated TEWL, THI values and IgE serum levels. In contrast, patients with CPNL displayed corneocyte morphology and skin barrier parameters like healthy controls, despite reporting high itch intensity and frequent sleep disturbance in clinical interviews. These findings indicate that CPNL is not triggered by a disrupted epidermal barrier but may represent different mechanisms.

Cutaneous squamous cell carcinoma (cSCC) represents a growing public health burden, with incidence projected to increase 23-29% over the coming decade. Topical immunoprevention strategies targeting the PD-L1/PD-1 and TLR4 axes have demonstrated preclinical efficacy, yet optimal intervention timing in humans remains undefined. To address this gap, single-cell RNA sequencing was performed on matched sun-protected (SP), sun-damaged (SD), and actinic keratosis (AK) biopsies from the same individuals, along with independent cSCC cases. Immune checkpoint and innate inflammatory signals were detectable as early as SD skin, prior to histologically confirmed dysplasia. Monotonically increasing expression of CD274 (PD-L1), CTLA4, PDCD1, CD27, and STAT1, alongside progressive TLR4-MYD88 innate immune signaling, was revealed through pseudobulk data analysis, with earliest upregulation at the SD stage. Fuzzy c-means trajectory clustering identified cell-typespecific programs across dendritic cells, macrophages, T cells, fibroblasts, endothelial cells, and keratinocytes. Dendritic cells shifted from early inflammatory antigen-presenting programs toward late PD-L1/IFN-regulatory states; macrophages showed monotonically increasing TLR4-associated myeloid activation; and T cells defined a "hot but exhausted" microenvironment in established cSCC. These findings identify SD and AK as biologically active stages for topical immunoprevention and provide a cellular roadmap for PD-L1/PD-1 and TLR4 blockade strategies.