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.

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.

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.

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.

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.