International Journal of Dermatology最新文献

Atopic dermatitis (AD), or eczema, is an inflammatory skin disease related to environmental factors. As a heterogeneous disease, it presents with complex phenotypes and endotypes. A variety of intrinsic and extrinsic factors can promote the development of AD. While there has been extensive discussion on environmental exposure at the population and community levels, discourse on exposome at individual levels in AD remains insufficient. For example, allergens, microorganisms, parasites, dietary factors, and psychological factors such as stress and anxiety play important roles in AD development. Microorganisms, in particular, exhibit altered composition and diversity on the skin of AD patients, influencing skin barrier integrity and immune responses. The impact of certain microorganisms, such as fungi and viruses, on AD has garnered increasing attention because of their important role in maintaining skin homeostasis. Dietary factors, including sugar intake and histamine-rich foods, may modulate AD risk and severity, although findings are controversial. Allergens, particularly house dust mite allergens, and aeroallergens, exacerbate AD symptoms by promoting inflammation and barrier dysfunction. Since AD is often the first step in the atopic march, its primary prevention measures are crucial. Some preventive measures involving microorganisms, diet, and moisturizers remain controversial. Effective preventive strategies necessitate a clear understanding of the complex mechanisms of AD, especially host-microbe-environment interactions. This review summarizes recent advances in understanding various risk and protective factors, as well as primary prevention measures for AD.

Pilomatrix carcinoma (PMC), also known as pilomatrical carcinoma, is a rare, malignant tumor of the hair follicle that may arise from a benign pilomatricoma or de novo and carries a high risk of recurrence and metastasis. This study sought to investigate and compare surgical modalities for PMC, including reported outcomes and recurrence rates. In July 2024, a systematic review of PubMed, Embase, and Web of Science databases on surgical modalities and PMC was conducted. Randomized controlled trials and observational studies were included. The search yielded 257 articles; after the application of inclusion and exclusion criteria, 50 articles were included: four case series and 46 case reports with a total of 101 cases. There were 17 cases treated with Mohs micrographic surgery (MMS) with no recurrences. There were 84 cases treated with surgical excision with an overall recurrence rate of 34.5%. The mean follow-up time was 7.5 months for MMS and 7.6 months for surgical excision. MMS showed excellent outcomes for PMC with no recurrences compared with a recurrence rate of 34.5% for surgical excision (P < 0.0025). No recurrences or metastases were found on follow-up reported in all 17 cases treated with MMS in the literature, compared with a recurrence rate of 34.5% for the 84 cases of surgical excision. Due to the importance of early surgical intervention and attaining true negative margins to prevent recurrence and metastasis, MMS should be considered for the treatment of PMC, especially where tissue sparing is essential.

Background: Ex vivo confocal laser scanning microscopy (EVCM) is an emerging imaging modality that enables near real-time histology of whole tissue samples. However, the adoption of EVCM into clinical routine is partly limited because the recognition of modality-specific diagnostic features requires specialized training. Therefore, we aimed to build a machine learning algorithm for the detection of basal cell carcinoma (BCC) in images acquired using EVCM and, in turn, facilitate the examiner's decision-making process.

Methods: In this proof-of-concept study, histologically confirmed BCC fresh tissue samples were used to generate 50 EVCM images to train and assess a convolutional neural network architecture (MobileNet-V1) via tenfold cross-validation.

Results: Overall sensitivity and specificity of the model for detecting BCC and tumor-free regions on EVCM images compared to expert evaluation were 0.88 and 0.85, respectively. We constructed receiver operator characteristic and precision-recall curves from the aggregated tenfold cross-validation to assess the model's performance; the area under the curve was 0.94 and 0.87, respectively. Subsequently, the performance of one of the selected machine learning models was assessed with 19 new EVCM images of tumor-containing (n = 10) and 9 tumor-free (n = 9) skin tissue. A sensitivity of 0.83 and a specificity of 0.92 were achieved for the BCC group. The specificity for the tumor-free control group was 0.98.

Conclusion: The deep learning model developed in our study holds the potential to assist the diagnostic decision-making process and diminish the training time of novices by depicting tumor-positive regions in EVCM images.