Prepubertal and postpubertal vitiligo: a multivariate comparative study in 375 patients

Restraint stress promotes monobenzone-induced depigmentation in mice via the activation of glucocorticoid receptor/macrophage migration inhibitory factor signaling pathway

Psychological stress triggers onset and development of vitiligo in humans. However, the mechanism of psychological stress on vitiligo remains unclear. The study aims to investigate whether psychological stress promotes vitiligo and explore the underlying mechanism. A depigmentation mouse model induced by applying a skin-bleaching reagent monobenzone to dorsal skin and an in vitro HaCaT keratinocyte death model induced by monobenzone were employed to explore the effect of restraint stress, which mimics psychological stress, on depigmentation. The results indicated that restraint stress promoted vitiligo-related depigmentation, vacuolisation, spongiosis, CD8+ T lymphocyte infiltration, and loss of melanocytes in the skin. Restraint stress activated cutaneous NLR family containing pyrin domain protein 3 (NLRP3) inflammasome. In addition, restraint stress aggravated anxiety-like behaviors and increased levels of macrophage migration inhibitory factor (MIF) and corticosterone in the circulation, accompanied with decreasing the expression of cutaneous 8-oxoguanine DNA glycosylase (OGG1) in depigmentation mice. In vitro experiments demonstrated that activation of glucocorticoid receptor (GR) by cortisol upregulated NLRP3 expression dependent on MIF, and directly decreased the transcription of OGG1. Blockade of MIF reversed the NLRP3 signal in restraint stress-induced depigmentation mice. In conclusion, restraint stress promotes vitiligo-related depigmentation in mice via the activation of GR/MIF signaling pathway. The findings provide a theoretical basis for prevention and treatments of vitiligo with therapies of targeting GR, MIF, and OGG1.

Semi-Automatic Tool for Vitiligo Detection and Analysis

Vitiligo vulgaris is an autoimmune disease which causes a strong reduction of the cells producing melanin, which is the main skin pigment. This results in the growth of white patches on patients’ skin, which are more or less visible, depending on the skin phototype. Precise, objective and fast detection of vitiligo patches would be crucial to produce statistically relevant data on huge populations, thus giving an insight on the disease. However, few methods are available in literature. In the present paper, a semi-automatic tool based on image processing to detect facial vitiligo patches is described. The tool requires pictures to be captured under black light illumination, which enhances patches contrast with respect to healthy skin. The user is only required to roughly define the regions of interest and set a global threshold, thus, no specific image-processing skills are required. An adaptive algorithm then automatically discerns between vitiligo and healthy skin pixels. The tools also allow for a statistical data interpretation by overlapping the detected patches of all patients on a face template through an occurrence map. Preliminary results obtained on a small population of 15 patients allowed us to assess the tool’s performance. Patch detection was checked by an experienced dermatologist, who confirmed the detection for all the studied patients, thus supporting the effectiveness of the proposed tool.