Indian Journal of Dermatology Venereology & Leprology最新文献

Background Several traditional observational studies have reported an association between uveitis and psoriatic arthritis (PsA). However, the causal relationship between them remains unclear. Objective To investigate whether genetically predicted uveitis is related to the risk of PsA, and vice versa. Methods A two-sample bidirectional Mendelian randomisation (MR) design was employed, conducting a meta-analysis on data sourced from three distinct origins, followed by sensitivity analyses to ensure the robustness of the findings. Results MR analysis revealed a positive causal effect of uveitis on PsA. Meta-analysis results from three data sources revealed an odds ratio (OR) of 1.63, with a 95% confidence interval (CI) ranging from 1.22 to 2.19, and a statistically significant P-value of 0.001. Inverse MR results indicated a positive causal relationship between PsA and uveitis. Meta-analysis results: OR = 1.55, 95%CI = 1.07-2.24, P-value = 0.02. Limitations This study exclusively included individuals of European ancestry, thereby potentially limiting its generalisability to other populations, such as those of Asian or African descent. Secondly, Uveitis is a collective term for various intraocular inflammations, including anterior uveitis, intermediate uveitis, posterior uveitis, and pan uveitis. Conclusions The outcomes of our study indicate a significant association between uveitis and an elevated risk of PsA. Conversely, PsA is associated with an increased risk of uveitis. These findings add to the understanding of the complex relationship between uveitis and PsA, suggesting the possibility of mutual influence.

Background Syringoma, a benign skin tumour characterised by stromal fibrosis and epithelial encirclement, suffers from limited treatment options. Aim This study aimed to analyse the correlation between mast cell density and histological features of syringomas, particularly stromal fibrosis and epidermal pigmentation. Methods Immunohistochemical staining with CD117 and estrogen receptor (ER)-α was performed in 49 samples from 47 patients of syringoma to assess mast cell density and ER-α expression. Results The study revealed a female predominance (4.2:1), with a median age of 42 years (range, 21-80 years) and an onset at the age 28.5 years (5-74). All 49 cases were localised, primarily to the eyelid (67.3%), followed by the face (18.4%), vulva (8.2%), and axilla/extremities (6.1%). Epidermal thickness correlated directly with tumour thickness (r=0.652, p<0.01), which varied across anatomical sites. Mast cell density significantly correlated with basal pigmentation (r=0.338, p=0.02), stromal fibrosis (r=0.308, p=0.03), and stromal ER-α (r=0.249, p=0.03), suggesting their involvement in syringoma pathogenesis. Acanthosis, basal hyperpigmentation, and stromal fibrosis were prevalent in 83.7%, 83.7%, and 100% of patients, respectively. ER-α positivity was observed in the syringoma stromal area in 30 cases (61.2%), whereas positivity in the tumour cell area was confirmed in only 3 cases (6.1%). Limitations The retrospective nature of the analysis and the limited sample size may affect the generalisability of the findings. Conclusion Our findings highlight a significant correlation between mast cells and stromal fibrosis, as well as basal hyperpigmentation in syringomas. This suggests the potential involvement of mast cells in their pathomechanism. Furthermore, the correlation of mast cell density with stromal ER-α may explain the female predominance.

Genomic sequencing technologies have revolutionised the diagnostic approach to genodermatoses, facilitating precise diagnosis, offering unparalleled insights into pathogenesis, and guiding personalised treatment strategies. In this article, we provide an outline for clinical evaluation, how to choose the most suitable genetic test for different dermatological presentations and examine indications for single-gene tests, gene panels, whole exome, and whole genome sequencing. We then delve into sequencing technologies, including next-generation sequencing (NGS) and its various platforms, detailing their strengths, limitations, and clinical applications. By understanding these technologies, clinicians will be better equipped to interpret test results accurately and collaborate with genetic counsellors and laboratory experts effectively.