Particulate Matter and Its Molecular Effects on Skin: Implications for Various Skin Diseases.

Particulate matter (PM) is a harmful air pollutant composed of chemicals and metals which affects human health by penetrating both the respiratory system and skin, causing oxidative stress and inflammation. This review investigates the association between PM and skin disease, focusing on the underlying molecular mechanisms and specific disease pathways involved. Studies have shown that PM exposure is positively associated with skin diseases such as atopic dermatitis, psoriasis, acne, and skin aging. PM-induced oxidative stress damages lipids, proteins, and DNA, impairing cellular functions and triggering inflammatory responses through pathways like aryl hydrocarbon receptor (AhR), NF-κB, and MAPK. This leads to increased production of inflammatory cytokines and exacerbates skin conditions. PM exposure exacerbates AD by triggering inflammation and barrier disruption. It disrupts keratinocyte differentiation and increases pro-inflammatory cytokines in psoriasis. In acne, it increases sebum production and inflammatory biomarkers. It accelerates skin aging by degrading ECM proteins and increasing MMP-1 and COX2. In conclusion, PM compromises skin health by penetrating skin barriers, inducing oxidative stress and inflammation through mechanisms like ROS generation and activation of key pathways, leading to cellular damage, apoptosis, and autophagy. This highlights the need for protective measures and targeted treatments to mitigate PM-induced skin damage.

Automatic evaluation of Nail Psoriasis Severity Index using deep learning algorithm.

Nail psoriasis is a chronic condition characterized by nail dystrophy affecting the nail matrix and bed. The severity of nail psoriasis is commonly assessed using the Nail Psoriasis Severity Index (NAPSI), which evaluates the characteristics and extent of nail involvement. Although the NAPSI is numeric, reproducible, and simple, the assessment process is time-consuming and often challenging to use in real-world clinical settings. To overcome the time-consuming nature of NAPSI assessment, we aimed to develop a deep learning algorithm that can rapidly and reliably evaluate NAPSI, thereby providing numerous clinical and research advantages. We developed a dataset consisting of 7054 single fingernail images cropped from images of the dorsum of the hands of 634 patients with psoriasis. We annotated the eight features of the NAPSI in a single nail using bounding boxes and trained the YOLOv7-based deep learning algorithm using this annotation. The performance of the deep learning algorithm (DLA) was evaluated by comparing the NAPSI estimated using the DLA with the ground truth of the test dataset. The NAPSI evaluated using the DLA differed by 2 points from the ground truth in 98.6% of the images. The accuracy and mean absolute error of the model were 67.6% and 0.449, respectively. The intraclass correlation coefficient was 0.876, indicating good agreement. Our results showed that the DLA can rapidly and accurately evaluate the NAPSI. The rapid and accurate NAPSI assessment by the DLA is not only applicable in clinical settings, but also provides research advantages by enabling rapid NAPSI evaluations of previously collected nail images.

Genome-wide Association Study of Susceptibility Loci for Self-Reported Atopic Dermatitis and Allergic Rhinitis in the Korean Population.

BACKGROUND: Allergic diseases include atopic dermatitis (AD) and allergic rhinitis (AR), which are chronic, relapsing inflammatory disorders of the skin or mucosa that usually accompany immunoglobulin E-mediated immune responses. They are complex, multifactorial diseases with an etiology involving interactions between genetic and environmental factors. OBJECTIVE: We performed a genome-wide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with allergic diseases in the Korean population. METHODS: A total of 8,840 samples were obtained from the Korean Association Resource Consortium dataset of the Korean Genome and Epidemiology Study Ansan-Anseong cohort. The allergic disease phenotype was determined based on self-reported physician diagnoses. After quality control, 8,823 subjects with 877,242 variants remained for the final analysis. The GWAS was performed using logistic regression analysis in an additive model adjusted for age and sex. RESULTS: A total of 636 patients with allergic disease and 8,176 controls were analyzed. Three SNPs were associated with allergic disease at a level of genome-wide suggestive significance (p<1.0×10-5) in the Korean population: rs7275360, located in neural cell adhesion molecule 2; rs698195; and rs3750552, located in family with sequence similarity 189, member A2. These polymorphisms were on chromosomes 21q21.1, 7q31.1, and 9q21.12, respectively. CONCLUSION: We identified 3 novel SNPs significantly associated with allergic diseases in the Korean population. Further research is required to confirm the association between these novel SNPs and allergic disease in the Korean population and in other ethnicities.

Automated mass screening and association rules analysis for comorbidities of psoriasis: A population-based case-control study.

Patients with psoriasis frequently have comorbidities, which are linked to higher mortality rates. An in-depth investigation of comorbidities and their effects on health can help improve the management of patients with psoriasis. We conducted a comprehensive and unbiased investigation of comorbidities in patients with psoriasis and explored the pattern of association between comorbidities. A nationwide population-based study included 384 914 patients with psoriasis and 384 914 matched controls between 2011 and 2021. We used automated mass screening of all diagnostic codes to identify psoriasis-associated comorbidities and applied association rule analysis to explore the patterns of comorbidity associations in patients with psoriasis. Patients with psoriasis had an increased risk of autoimmunity-related diseases such as inflammatory arthritis, Crohn's disease, type 1 diabetes, and acute myocardial infarction. The comorbidities of patients with psoriasis with a history of cardiovascular events demonstrated strong interrelationships with other cardiovascular risk factors including type 2 diabetes mellitus, essential hypertension, and dyslipidemia. We also found comorbidities, such as malignant skin tumors and kidney and liver diseases, which could have adverse effects of anti-psoriasis therapy. In contrast, patients with psoriasis showed a decreased association with upper respiratory tract infection. Our results imply that comorbidities in patients with psoriasis are associated with the systemic inflammation of psoriasis and the detrimental effects of its treatment. Furthermore, we found patterns of associations between the cardiovascular risk factors and psoriasis. Mass screening and association analyses using large-scale databases can be used to investigate impartially the comorbidities of psoriasis and other diseases.

Association between cardio-cerebrovascular disease and systemic antipsoriatic therapy in psoriasis patients using population-based data: A nested case-control study.

The effect of antipsoriatic therapy on cardio-cerebrovascular disease (CCVD) is not well described. Thus, we performed a population-based nested case-control study to investigate the effect of systemic antipsoriatic therapy on CCVD in psoriasis patients. Using nationwide cohort data from the Korean National Health Insurance Claims database, newly diagnosed psoriasis patients were identified. Among the enrolled participants, postenrollment development of CCVD events (ischemic heart disease, myocardial infarction, cerebral infarction, and cerebral hemorrhage) was investigated. To evaluate the effect of systemic antipsoriatic therapy on CCVD risk, we calculated the proportion of the treatment period with systemic antipsoriatic therapy during the study period (PTP [%]: the sum of all systemic antipsoriatic therapy durations divided by total observation period). Among 251 813 participants, 6262 experienced CCVD events during the study period (CCVD group). Controls included 245 551 patients without CCVD history during the study period (non-CCVD group). The non-CCVD group had greater PTP than the CCVD group (CCVD 2.12 ± 7.92, non-CCVD 2.64 ± 9.64; P < 0.001). In multiple logistic regression analysis, PTP was inversely associated with the CCVD risk after adjusting for age, sex, diabetes, hypertension, and dyslipidemia. A 10% increase in PTP reduced CCVD risk by 0.96 (95% confidence interval 0.93 to 0.99). Reduced CCVD risk was robust for both conventional antipsoriatic therapy and biologics. Our study found that systemic antipsoriatic therapy use was inversely associated with CCVD risk in psoriasis patients. These findings suggested that systemic antipsoriatic therapy could reduce CCVD development in patients with psoriasis.

Evaluation of the area subscore of the Palmoplantar Pustulosis Area and Severity Index using an attention U-net deep learning algorithm.

Palmoplantar pustulosis (PPP) is a variant of pustular psoriasis involving the palms and soles. The severity of PPP is usually evaluated using the Palmoplantar Pustulosis Area and Severity Index (PPPASI). Among the components of the PPPASI, the area of the involved lesion is evaluated differently by raters, who generally make a rough estimate using the eye and not through a specific calculation. To overcome inconsistent evaluation of the area subscore of PPPASI by human raters, we developed and validated deep-learning-based algorithms to enable automated and reliable assessment of the area involved in PPP to provide clinical advantages. In this study, we developed a dataset of 611 images of the palms and soles of 153 patients with PPP. We evaluated the area of the lesion by dividing the number of pixels in the area involved in PPP by the number of pixels in the area of the palms or soles. Using attention U-net, we developed two convolutional neural network (CNN) models that can evaluate the percentage of the affected area (%) and subsequently assign a score ranging from 0 to 6. The area subscore of PPPASI evaluated by the deep-learning algorithm was same or differed by 1-point from the subscore of ground truth in 98.8% of the images. The intraclass correlation coefficient between the CNN and ground truth was 0.879, indicating good agreement. The accuracy and mean absolute error of the model were 66.7% and 0.344, respectively. In a Bland-Altman plot, most of the differences in the percentage of the affected area lay between the 95% confidence interval with a mean difference of 0 and a standard deviation of 0.2. The deep-learning algorithm can provide several clinical advantages by objectively evaluating the components of the PPPASI without concern for disagreement between clinicians. The algorithms further enable cumulative clinical data acquisition related to PPP severity.