Vitiligo is an autoimmune depigment disease results from extensive melanocytes destruction. The destruction of melanocyte is thought to be of multifactorial causation. Genome‐wide associated studies have identified single‐nucleotide polymorphisms in a panel of susceptible loci as risk factors in melanocyte death. But vitiligo onset can't be solely attributed to a susceptive genetic background. Oxidative stress triggered by elevated levels of reactive oxygen species accounts for melanocytic molecular and organelle dysfunction, a minority of melanocyte demise, and melanocyte‐specific antigens exposure. Of note, the self‐responsive immune function directly contributes to the bulk of melanocyte deaths in vitiligo. The aberrantly heightened innate immunity, type‐1‐skewed T helper, and incompetent regulatory T cells tip the balance toward autoreaction and CD8⁺ cytotoxic T lymphocytes finally execute the killing of melanocytes, possibly alarmed by resident memory T cells. In addition to the well‐established apoptosis and necrosis, we discuss several death modalities like oxeiptosis, ferroptosis, and necroptosis that are probably employed in melanocyte destruction. This review focuses on the various mechanisms of melanocytic death in vitiligo pathogenesis to demonstrate a panorama of that. We hope to provide new insights into vitiligo pathogenesis and treatment strategies by the review.

Vitiligo is an autoimmune cutaneous disease in which melanocytes are destroyed by CD8⁺ T cells resulting in disfiguring white spots. From the very beginning of the disease, oxidative stress plays a significant role in promoting the onset o...

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.

Vitiligo is a cutaneous depigmenting autoimmune disease caused by the extensive destruction of epidermal melanocytes. Convincing data has defined a critical role for oxidative stress in the pathogenesis of vitiligo. Oxeiptosis is a caspase-independent cell death modality that was reportedly triggered by oxidative stress and operative in pathogen clearance. However, whether oxeiptosis exists in oxidative stress-induced melanocytes demise in vitiligo remains undetermined. In the present study, we initially found that other cell death modalities might exist in addition to the well-recognized apoptosis and necroptosis in H₂O₂-treated melanocytes. Furthermore, AIFM1 was found to be dephosphorylated at Ser116 in oxidative stress-induced melanocytes death, which was specific to oxeiptosis. Moreover, KEAP1 and PGAM5, upstream of the AIFM1 in oxeiptosis, were found to operate in melanocytic death. Subsequently, the KEAP1-PGAM5-AIFM1 signaling pathway was proved to be involved in oxidative stress-triggered melanocytes demise through the depletion of KEAP1 and PGAM5. Altogether, our study indicated that oxeiptosis might occur in melanocytes death under oxidative stress and contribute to the pathogenesis of vitiligo.