Psychological stress is a crucial factor in the development of many skin diseases, and the stigma caused by skin disorders may further increase the psychological burden, forming a vicious cycle of psychological stress leading to skin diseases. Therefore, understanding the relationship between stress and skin diseases is necessary. The skin, as the vital interface with the external environment, possesses its own complex immune system, and the neuroendocrine system plays a central role in the stress response of the body. Stress-induced alterations in the immune system can also disrupt the delicate balance of immune cells and inflammatory mediators in the skin, leading to immune dysregulation and increased susceptibility to various skin diseases. Stress can also affect the skin barrier function, impair wound healing, and promote the release of pro-inflammatory cytokines, thereby exacerbating existing skin diseases such as psoriasis, atopic dermatitis, acne, and urticaria. In the present review, we explored the intricate relationship between stress and skin diseases from a neuroendocrine-immune interaction perspective. We explored the occurrence and development of skin diseases in the context of stress, the stress models for skin diseases, the impact of stress on skin function and diseases, and relevant epidemiological studies and clinical trials. Understanding the relationship between stress and skin diseases from a neuroendocrine-immune interaction perspective provides a comprehensive framework for targeted interventions and new insights into the diagnosis and treatment of skin diseases.

Air pollutants deteriorate the survival environment and endanger human health around the world. A large number of studies have confirmed that air pollution jeopardizes multiple organs, such as the cardiovascular, respiratory, and central nervous systems. Skin is the largest organ and the first barrier that protects us from the outside world. Air pollutants such as particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs) will affect the structure and function of the skin and bring about the development of inflammatory skin diseases (atopic dermatitis (AD), psoriasis), skin accessory diseases (acne, alopecia), auto-immune skin diseases (cutaneous lupus erythematosus(CLE) scleroderma), and even skin tumors (melanoma, basal cell carcinoma (BCC), squamous-cell carcinoma (SCC)). Oxidative stress, skin barrier damage, microbiome dysbiosis, and skin inflammation are the pathogenesis of air pollution stimulation. In this review, we summarize the current evidence on the effects of air pollution on skin diseases and possible mechanisms to provide strategies for future research.

Radiotherapy (RT) as a preoperative or postoperative adjuvant or primary treatment is the most common management modality for locally advanced cervical cancer. Radioresistance of tumor cells remains a major therapeutic problem. Consequently, we aimed to explore if the stem cell biomarkers SOX2 and OCT4 protein could be used to predict radioresistance in patients with locally advanced cervical squamous cell carcinoma (LACSCC). These 132 patients were divided into two groups (radiation-resistant and radiation-sensitive groups) according to progress-free survival (PFS). Using pretreatment paraffin-embedded tissues, we evaluated SOX2 and OCT4 expression using immunohistochemical staining. The percentage of overexpression of SOX2 and OCT4 in the radiation-resistant group was much higher than that in the radiation-sensitive group ( p<0.001 and p <0.001, respectively). The patients with high expression of SOX2 and OCT4 showed a shorter PFS than those with low expression. Our study suggests that the expression of SOX2 and OCT4 in tumor cells indicates resistance to radiotherapy and that these two factors were important predictors of poor survival in patients with LACSCC (hazard ratio [95% CI], 2.294 [1.013, 5.195] and 2.300 [1.050, 5.037], respectively; p=0.046 and p=0.037, respectively).

Background: Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. Objective: This study aimed to develop an image–artificial intelligence (AI)–based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. Methods: A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. Results: The proposed image-AI–based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. Conclusions: An image-AI–based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists’ accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

Autophagy, or self-eating, is an evolutionarily conserved process in which cytosol and organelles are sequestered within double-membrane vesicles that deliver the contents to the lysosome/vacuole for the degradation and recycling of cytoplasmic components in eukaryotes. It is well recognized that autophagy plays an important role in maintaining cellular homeostasis under physiological and pathophysiological con­ditions and the upregulation of autophagy may serve as an adaptive process to provide nutrients and energy when under stresses. Recently, studies have illustrated that autophagy is intricately related to skin diseases. This review provides a brief synopsis of the process of autophagy and aims to elucidate the roles of autophagy in different skin diseases and to highlight the need for increased research in the field.

Background: Skin and subcutaneous disease is the fourth-leading cause of the nonfatal disease burden worldwide and constitutes one of the most common burdens in primary care. However, there is a severe lack of dermatologists, particularly in rural Chinese areas. Furthermore, although artificial intelligence (AI) tools can assist in diagnosing skin disorders from images, the database for the Chinese population is limited. Objective: This study aims to establish a database for AI based on the Chinese population and presents an initial study on six common skin diseases. Methods: Each image was captured with either a digital camera or a smartphone, verified by at least three experienced dermatologists and corresponding pathology information, and finally added to the Xiangya-Derm database. Based on this database, we conducted AI-assisted classification research on six common skin diseases and then proposed a network called Xy-SkinNet. Xy-SkinNet applies a two-step strategy to identify skin diseases. First, given an input image, we segmented the regions of the skin lesion. Second, we introduced an information fusion block to combine the output of all segmented regions. We compared the performance with 31 dermatologists of varied experiences. Results: Xiangya-Derm, as a new database that consists of over 150,000 clinical images of 571 different skin diseases in the Chinese population, is the largest and most diverse dermatological data set of the Chinese population. The AI-based six-category classification achieved a top 3 accuracy of 84.77%, which exceeded the average accuracy of dermatologists (78.15%). Conclusions: Xiangya-Derm, the largest database for the Chinese population, was created. The classification of six common skin conditions was conducted based on Xiangya-Derm to lay a foundation for product research.