Integrated bioinformatics analysis and experimental validation identifies CPE as a potential biomarker and therapeutic target for skin aging.

Ageing is an inevitable biological process characterized by progressive decline in physiological functions. It is a complex natural phenomenon that will cause structural and functional decline. Despite substantial progress in understanding the mechanism of ageing, both predictive biomarkers and preventive therapies remain limited. Using Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning techniques, we identified Carboxypeptidase E (CPE) as a pivotal marker of skin ageing, based on ageing-related bulk transcriptome and single-cell transcriptome data. Next, our investigation reveals downregulation of CPE in replicative, UVA-induced, and H2O2-induced senescent human dermal fibroblast cells (HDFs). Furthermore, shRNA-mediated CPE knockdown induced HDFs senescence, and overexpression of CPE delayed HDFs senescence. Moreover, downregulated CPE inhibits collagen synthesis and induces inflammation, highlighting its potential as a therapeutic target for skin ageing. In conclusion, our study demonstrated that CPE functions as a predictor and optional target for therapeutic intervention of skin ageing.

Downregulated SPESP1-driven fibroblast senescence decreases wound healing in aged mice.

BACKGROUND: Human dermal fibroblasts (HDFs) are essential in the processes of skin ageing and wound healing. However, the underlying mechanism of HDFs in skin healing of the elderly has not been well defined. This study aims to elucidate the mechanisms of HDFs senescence and how senescent HDFs affect wound healing in aged skin. METHODS: The expression and function of sperm equatorial segment protein 1 (SPESP1) in skin ageing were evaluated via in vivo and in vitro experiments. To delve into the potential molecular mechanisms by which SPESP1 influences skin ageing, a combination of techniques was employed, including proteomics, RNA sequencing, immunoprecipitation, chromatin immunoprecipitation and liquid chromatography-mass spectrometry analyses. Clearance of senescent cells by dasatinib plus quercetin (D+Q) was investigated to explore the role of SPESP1-induced senescent HDFs in wound healing. RESULTS: Here, we define the critical role of SPESP1 in ameliorating HDFs senescence and retarding the skin ageing process. Mechanistic studies demonstrate that SPESP1 directly binds to methyl-binding protein, leading to Decorin demethylation and subsequently upregulation of its expression. Moreover, SPESP1 knockdown delays wound healing in young mice and SPESP1 overexpression induces wound healing in old mice. Notably, pharmacogenetic clearance of senescent cells by D+Q improved wound healing in SPESP1 knockdown skin. CONCLUSIONS: Taken together, these findings reveal the critical role of SPESP1 in skin ageing and wound healing, expecting to facilitate the development of anti-ageing strategies and improve wound healing in the elderly.

Targeting the STAT3/IL-36G signaling pathway can be a promising approach to treat rosacea.

BACKGROUND: Rosacea is an inflammatory skin disorder characterized by the release of inflammatory mediators from keratinocytes, which are thought to play a crucial role in its pathogenesis. Despite an incidence of approximately 5.5%, rosacea is associated with a poor quality of life. However, as the pathogenesis of rosacea remains enigmatic, treatment options are limited. OBJECTIVES: To investigate the pathogenesis of rosacea and explore new therapeutic strategies. METHODS: Transcriptome data from rosacea patients combined with immunohistochemical staining were used to investigate the activation of STAT3 in rosacea. The role of STAT3 activation in rosacea was subsequently explored by inhibiting STAT3 activation both in vivo and in vitro. The key molecules downstream of STAT3 activation were identified through data analysis and experiments. Dual-luciferase assay and ChIP-qPCR analysis were used to validate the direct binding of STAT3 to the IL-36G promoter. DARTS, in combination with experimental screening, was employed to identify effective drugs targeting STAT3 for rosacea treatment. RESULTS: STAT3 signaling was hyperactivated in rosacea and served as a promoter of the keratinocyte-driven inflammatory response. Mechanistically, activated STAT3 directly bind to the IL-36G promoter region to amplify downstream inflammatory signals by promoting IL-36G transcription, and treatment with a neutralizing antibody (α-IL36γ) could mitigate rosacea-like inflammation. Notably, a natural plant extract (pogostone), which can interact with STAT3 directly to inhibit its activation and affect the STAT3/IL36G signaling pathway, was screened as a promising topical medication for rosacea treatment. CONCLUSIONS: Our study revealed a pivotal role for STAT3/IL36G signaling in the development of rosacea, suggesting that targeting this pathway might be a potential strategy for rosacea treatment.

Twin defect-rich Pt ultrathin nanowire nanozymes alleviate inflammatory skin diseases by scavenging reactive oxygen species.

Nanozymes with superior antioxidant properties offer new hope for treating oxidative stress-related inflammatory skin diseases. However, lacking sufficient catalytic activity or having complex material designs limit the application of current metallic nanozymes in inflammatory skin diseases. Here, we report a simple and effective twin-defect platinum nanowires (Pt NWs) enzyme with multiple mimetic enzymes and broad-spectrum ROS scavenging capability for the treatment of inflammatory skin diseases in mice (including psoriasis and rosacea). Pt NWs with simultaneous superoxide dismutase, glutathione peroxidase and catalase mimetic enzyme properties exhibit cytoprotective effects against ROS-mediated damage at extremely low doses and significantly improve treatment outcomes in psoriasis- and rosacea-like mice. Meanwhile, these ultrasmall sizes of Pt NWs allow the nanomaterials to effectively penetrate the skin and do not produce significant biotoxicity. Therefore, Pt NWs have potential applications in treating diseases related to oxidative stress or inflammation.

Correction: A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study.

[This corrects the article DOI: 10.2196/23415.].

Epidemiology and disease burden of androgenetic alopecia in college freshmen in China: A population-based study.

OBJECTIVE: To evaluate the epidemiology and disease burden of androgenetic alopecia (AGA) in college freshmen in China. METHODS: This population-based cross-sectional survey was carried out among 9227 freshmen of two comprehensive universities in two cities of China (Changsha and Xiamen) from September 2018 to October 2018. Questionnaires covering basic issues, surrounding demographic information, history of diseases, living habits, comorbidities, etc. were completed online in a self-reported manner Dermatological examination was performed by certified dermatologists. The disease burden of AGA, which includes health-related quality of life, symptoms of anxiety, symptoms of depression and quality of sleep, was measured by EQ-5D-3L, PHQ-2, GAD-2 and PSQI, respectively. RESULTS: The prevalence of AGA in college freshmen in China was 5.3/1000. Male was significantly associated with higher prevalence of AGA (7.9/1000, P<0.01) while female with lower risk of AGA (OR = 0.29, P = 0.002). There was no significant association between BMI and AGA, nor predilection of AGA in the Han nationality or the other ethnic minorities. Annual household income or parental highest educational level exerted no significant influence on the prevalence of AGA. Rosacea (OR = 3.22, P = 0.019) was significantly associated with higher prevalence of AGA while acne seemed not to be related to AGA. The scores of EQ-5D, GAD-2, PHQ-2 and PSQI were not significantly different between students with and without AGA. CONCLUSION: The onset of AGA in Chinese college freshmen differ between genders and was significantly associated with rosacea.

A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study.

BACKGROUND: Rosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis. OBJECTIVE: The objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea. METHODS: In this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50. RESULTS: The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists. CONCLUSIONS: The findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist.