Changes in epidermal thickness and their correlation with clinical characteristics in patients with vitiligo.

Vitiligo is an autoimmune disorder characterized by epidermal melanocyte damage, with the typical clinical manifestation of white patches of skin. Keratinocytes, which work in concert with melanocytes to maintain the structural and functional integrity of the skin, are implicated in the progression of vitiligo. Recent studies have reported abnormal keratinocyte proliferation and epidermal thickening in some patients with vitiligo; however, the relationship between these changes and the clinical characteristics of vitiligo remains unclear. We assessed the changes in epidermal thickness in patients with vitiligo and their correlation with clinical characteristics. Compared to the non-lesional skins, the stratum corneum, viable epidermis, and full epidermis in the lesional skins were all significantly thicker. The thickness of the stratum corneum in the head, neck, and trunk was greatly lower than that in the extremities. The thickness of the stratum corneum in the sun-exposed area was higher than that in the sun-protected area, whereas the thickness of the viable epidermis decreased. In conclusion, our study found that the epidermis in the lesional skins of patients with vitiligo was significantly thickened, especially in the sun-exposed areas and extremities.

Demographic and clinical characteristics and risk factors of stereoacuity in convergence insufficiency-type intermittent exotropia.

Purpose: To investigate characteristics and risk factors of poor stereoacuity of Convergence insufficiency-type Intermittent Exotropia (CI-type X(T)). Design: Observational, cross-sectional study. Methods: The medical records of 615 CI-type X(T) and 222 basic-type intermittent exotropia (X(T)) were enrolled from January 2018 to January 2022. The characteristics were compared between the two types, and the associations between clinical factors and poor stereoacuity were examined using logistic regression. Results: Compared with basic-type X(T), earlier surgery age, shorter misalignment duration, and the smaller distance exodeviation were observed in CI-type X(T). The CI-type X(T) demonstrated better sensory status and lower incidence of ocular muscle dysfunction than did the basic-type X(T). The surgery age between 6 and 12 years (odds ratio [OR], 0.595; compared with ≤6 years) was inversely associated with poor near stereoacuity, whereas duration more than 4 years (OR, 2.474), amblyopia (OR, 4.057), large distance exodeviation (>60PD: OR, 2.462) and anisometropia (>2.00D: OR, 3.874) were positively associated with poor near stereoacuity. The onset age older than 6 years (6-9 years: OR, 0.397; >9 years: OR, 0.317) was associated with better distance stereoacuity, whereas large distance exodeviation (>60PD: OR, 23.513), and dominant eye best corrected visual acuity (BCVA) worsen than 0.20 (OR, 2.987) were positively associated with poor distance stereoacuity. Conclusion: CI-type X(T) declined surgery early, with small distance exodeviation, better sensory status, and low incidence of ocular muscle dysfunction. A strong dose-dependent link between early onset age, long misalignment duration, worse dominant eye BCVA, distance exodeviation, amblyopia, anisometropia, and poor stereoacuity was confirmed.

Serum cytokine profiles predict response to systemic glucocorticoid in active vitiligo.

Introduction: Vitiligo is an immune-related skin disease. Cytokines regulate immune response and inflammation and are involved in the pathogenesis of vitiligo. Aim: To assess the serum levels of pro-inflammatory cytokines pre- and post- systemic glucocorticoid treatment in patients with active vitiligo. Material and methods: We measured serum cytokine levels using the enzyme-linked immunosorbent assay in 31 patients with active vitiligo before and after treatment. All patients received systemic glucocorticoid (compound betamethasone injection) in combination with topical halometasone cream and tacrolimus ointment for 3 months. Twenty healthy controls were also examined. The cytokines measured included TNF-α, IL-1ß, IL-6, IFN-γ, IL-2, IL-17, IL-10, IL-8, and CXCL10. Results: The serum levels of TNF-α, IL-1ß, IL-6, IFN-γ, IL-2, IL-17, IL-8, and CXCL10 were significantly higher, and levels of IL-10 were lower in vitiligo patients compared to controls. Additionally, serum IFN-γ (r = 0.378; p = 0.036), IL-17 (r = 0.426; p = 0.017), and CXCL10 (r = 0.514; p = 0.003) showed a positive correlation with affected body surface area in vitiligo patients. After 3 months of systemic glucocorticoid treatment, the levels of IL-1ß, IFN-γ, IL-2, IL-17, and CXCL10 in responders were significantly decreased and nearly restored to normal levels. The IL-10 level was also increased in response to treatment. In contrast, the non-responder group had persistently high IL-6, IL-17, IL-8, and CXCL10 levels, and negligible changes in TNF-α, IL-1ß, IFN-γ, IL-2, and IL-10. Conclusions: Our study indicated that the levels of inflammatory cytokines were significantly ameliorated in the glucocorticoid responder group. Altered cell-mediated immunity may contribute to the resistance in vitiligo. The cytokines such as TNF-α, IL-1ß, IFN-γ and IL-2 could serve as therapeutic targets for managing glucocorticoid-resistant vitiligo.

Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability.

Vitiligo is a hypopigmented skin disease characterized by the loss of melanin. The progressive nature and widespread incidence of vitiligo necessitate timely and accurate detection. Usually, a single diagnostic test often falls short of providing definitive confirmation of the condition, necessitating the assessment by dermatologists who specialize in vitiligo. However, the current scarcity of such specialized medical professionals presents a significant challenge. To mitigate this issue and enhance diagnostic accuracy, it is essential to build deep learning models that can support and expedite the detection process. This study endeavors to establish a deep learning framework to enhance the diagnostic accuracy of vitiligo. To this end, a comparative analysis of five models including ResNet (ResNet34, ResNet50, and ResNet101 models) and Swin Transformer series (Swin Transformer Base, and Swin Transformer Large models), were conducted under the uniform condition to identify the model with superior classification capabilities. Moreover, the study sought to augment the interpretability of these models by selecting one that not only provides accurate diagnostic outcomes but also offers visual cues highlighting the regions pertinent to vitiligo. The empirical findings reveal that the Swin Transformer Large model achieved the best performance in classification, whose AUC, accuracy, sensitivity, and specificity are 0.94, 93.82%, 94.02%, and 93.5%, respectively. In terms of interpretability, the highlighted regions in the class activation map correspond to the lesion regions of the vitiligo images, which shows that it effectively indicates the specific category regions associated with the decision-making of dermatological diagnosis. Additionally, the visualization of feature maps generated in the middle layer of the deep learning model provides insights into the internal mechanisms of the model, which is valuable for improving the interpretability of the model, tuning performance, and enhancing clinical applicability. The outcomes of this study underscore the significant potential of deep learning models to revolutionize medical diagnosis by improving diagnostic accuracy and operational efficiency. The research highlights the necessity for ongoing exploration in this domain to fully leverage the capabilities of deep learning technologies in medical diagnostics.

From mice to men: An assessment of preclinical model systems for the study of vitiligo.

Vitiligo is an autoimmune skin disease of multiple etiology, for which there is no complete cure. This chronic depigmentation is characterized by epidermal melanocyte loss, and causes disfigurement and significant psychosocial distress. Mouse models have been extensively employed to further our understanding of complex disease mechanisms in vitiligo, as well as to provide a preclinical platform for clinical interventional research on potential treatment strategies in humans. The current mouse models can be categorized into three groups: spontaneous mouse models, induced mouse models, and transgenic mice. Despite their limitations, these models allow us to understand the pathology processes of vitiligo at molecule, cell, tissue, organ, and system levels, and have been used to test prospective drugs. In this review, we comprehensively evaluate existing murine systems of vitiligo and elucidate their respective characteristics, aiming to offer a panorama for researchers to select the appropriate mouse models for their study.

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.

OBJECTIVE: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. METHODS: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. CONCLUSIONS: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.

Influences of vitiligo-associated characteristics on the occurrence of diabetes mellitus: Interactive analysis of a cross-sectional study.

The risk of diabetes mellitus (DM) in vitiligo patients is higher than that in non-vitiligo population. Our goal was to explore the influencing factors for DM in vitiligo patients. A matched-pair design of 107 cases with DM and 428 controls without DM was conducted among vitiligo patients in Xijing hospital from January 2010 to October 2021. The baseline characteristics of patients were analysed based on standard descriptive statistics. The vitiligo-associated characteristics were analysed by logistic regression to identify influencing factors of DM. Interaction analysis was performed to explore the additive interactions between vitiligo-associated characteristics and baseline characteristics. After adjustment for the baseline characteristics, the severity of vitiligo [odds ratio (OR) = 2.47, 95% confidence interval (CI): 1.47-4.14] and onset age of vitiligo (OR = 0.98, 95% CI: 0.97-0.99) had a significant correlation with occurrence of DM. The severity of vitiligo had additive interaction with family history of diabetes [relative excess risk due to interaction (RERI) = 132.51 (95% CI: 5.51-1100.20), attributable proportion (AP) = 0.91 (95% CI: 0.17-0.95), synergy index (S) = 11.53 (95% CI: 1.32-100.5)] and with smoking history [RERI = 6.54 (95% CI: 0.67-19.83), AP = 0.64 (95% CI: 0.04-0.80), S = 3.48 (95% CI: 1.17-10.36)]. Earlier onset age of vitiligo and greater BSA involvement might be two independent risk factors for DM in vitiligo patients. Interaction assessment identified the severity of vitiligo as additive interaction factors with diabetes family history and with smoking history for the DM occurrence.

Circulatory levels of alarmins in patients with non-segmental vitiligo: Potential biomarkers for disease diagnosis and activity/severity assessment.

Background: Non-segmental vitiligo (NSV) is an autoimmune skin disorder that is difficult to determine disease activity/severity and thus to treat. Alarmins have emerged as promising biomarkers in various diseases, so further confirmation of their potential roles in NSV would be of considerable value. With the present work, we aimed to determine the serum levels of alarmins in patients with NSV, correlate these alarmins with disease activity and severity, and analyze the predictive value of the combination of these markers. Methods: 104 NSV patients and 56 healthy controls were enrolled at the Xijing Hospital of Fourth Military Medical University between September 1, 2018, and June 30, 2019. The serum levels of alarmins (including IL-33, IL-1α, S100A9, S100A12, S100B, and HMGB1) were measured with enzyme-linked immunosorbent assays. The predictive performance of these biomarkers was evaluated with the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and other representative statistics. Results: A total of 104 patients with NSV (mean [SD] age, 34.2 [13.0] years; 62 [59.6%] male) and 56 healthy controls (mean [SD] age, 34.8 [13.5] years; 34 [60.7%] male) were enrolled. For vitiligo diagnosis, S100B had the highest sensitivity (92.31%), whereas HMGB1 had the highest specificity (85.71%); the combination of IL-1α, S100B, S100A9, and HMGB1 increased the AUC value to 0.925, with a sensitivity of 87.50% and a specificity of 85.71%. Multivariate logistic regression analysis showed S100B (OR, 1.019; 95% CI, 1.002-1.038; P =0.03), S100A9 (OR, 1.002; 95% CI, 1.001-1.003; P<0.001), and HMGB1 (OR, 1.915; 95% CI, 1.186-3.091; P =0.008) were significantly associated with vitiligo activity. S100A9 had the highest accuracy in discriminating patients at the active stage from the stable stage, with an AUC value of 0.827. The combination of these alarmins had an AUC value of 0.860 to assess disease activity, with a sensitivity of 90.00% and a specificity of 72.97%. Furthermore, S100B (r=0.61, P <0.001), S100A9 (r=0.33, P <0.001), and HMGB1 (r = 0.51, P <0.001) levels were positively correlated with the affected body surface area (BSA) in NSV patients. Conclusions: Serum S100B, S100A9, and HMGB1 might be biomarkers for diagnosing and assessing the activity/severity of NSV, either used alone or in combination.

Th1-like Treg in vitiligo: An incompetent regulator in immune tolerance.

Vitiligo is an autoimmune skin disease resulting from epidermal melanocyte destruction mediated by CD8+T cells that breach the self-tolerance. Regulatory T cells (Tregs) are critical for keeping the CD8+T cells in check, but the deficiency of Tregs leading to the immune disequilibrium in vitiligo remains undefined. In the present study, we used RNA-sequencing (RNA-seq) to acquire the transcriptome data of Tregs from vitiligo patients and healthy controls, respectively. Further flow cytometry analysis and immunofluorescence assays substantiated the phenotype of Th1-like Tregs in vitiligo. CD8+T cell-/vitiligo serum-Treg co-culture assays and chemotaxis assays were used to functionally examine this subset of Tregs. As a result, RNA-seq, flow cytometry, and immunofluorescence all indicated the transition of bona fide Treg to the Th1-like T-bet+IFN-γ+Treg in vitiligo patients. Besides, these Th1-like Tregs exhibited significantly dampened suppression on the proliferation and activation of CD8+T cells and a markedly higher tendency to be chemoattracted by CXCL10 and CXCL16. More interestingly, vitiligo serum could even elicit bona fide Tregs of healthy controls to adopt the Th1-like phenotype and manifest impaired suppression. To conclude, Tregs from vitiligo patients are functionally disturbed and the Th1-skewed inflammatory microenvironment in the serum of vitiligo patients is responsible for the generation of Th1-like Tregs. We provide a clinical exploitable strategy that in addition to simply replenishing the bona fide Treg or promoting the homing of Treg to the skin, the normalization of the Th1-skewed inflammatory environment in vitiligo patients and targeting the incompetent Th1-like Tregs might be critical in the future treatment of vitiligo.