Background: Short‐term exercise may reduce disease activity in symptomatic dermographism (SD), but its prevalence and short‐ and long‐term effects remain unclear and understudied. This study aims to assess the impact of both short‐term and regular long‐term exercise programs on disease activity in patients with SD.Methods: We performed a short‐term exercise test to assess the disease activity and the critical friction threshold (CFT) using the FricTest before (SDE1) and 10 min after (SDE2) this test on 34 SD patients. Afterward, we asked the patients to carry on a 1‐month regular long‐term exercise program according to the World Health Organization's physical activity recommendations. At the end of this 1‐month period, we performed the short‐term exercise test using the FricTest before (SDE3) and 10 min after (SDE4) the exercise test.Results: Before a 1‐month regular exercise program, 32 of 34 patients (94.1%) showed a reduction in the critical friction threshold after the short‐term exercise test (SDE1; 1.95 ± 0.88 vs. SDE2; 0.81 ± 0.86). After a 1‐month regular exercise program, 29 of 34 patients (85%) showed a reduction in SD symptoms with short‐term exercise test and the FricTest scores were significantly decreased (SDE3; 1.57 ± 0.80 vs. SDE4; 1.01 ± 0.83). After the 1‐month regular exercise program, a statistically significant increase was seen in the patients’ UCT scores and quality of life.Conclusions: Our findings show that short‐term exercise improves SD symptoms, while long‐term regular exercise programs reduce disease symptoms and improve UCT scores and quality of life.

The global incidence of skin cancer is rising, emphasizing the need for early detection tools. Artificial intelligence (AI) models, including multimodal systems like ChatGPT-4o, can analyze visual data to assist clinicians in diagnosis. This study evaluated ChatGPT-4o's diagnostic accuracy in detecting melanoma and non-melanoma skin cancers from macroscopic and dermoscopic images. Ninety patients with histopathologically confirmed lesions were included. For each patient, macroscopic images were first uploaded to ChatGPT-4o, followed by combined upload of macroscopic and dermoscopic images. ChatGPT-4o was instructed to provide a preliminary diagnosis and three differential diagnoses for each lesion. Accuracy was assessed at four levels: Level 1: preliminary diagnosis using macroscopic images; Level 2: preliminary diagnosis using macroscopic and dermoscopic images; Level 3: three differential diagnoses using macroscopic images; Level 4: three differential diagnoses using macroscopic and dermoscopic images. Overall Level 1 accuracy was 73.3%, with Level 2, Level 3, and Level 4 accuracies of 66.6%, 75%, and 76.6%, respectively. Dermoscopic images improved accuracy for squamous cell carcinoma (72.7% vs 81.8%, p = 1.00), reduced overall and basal cell carcinoma accuracy (73.3% vs 66.6%, p = 0.180 and 79.6% vs 67.8%, p = 0.065, respectively), and did not affect malignant melanoma (84.6% vs 84.6%) or lentigo maligna (0% vs 0%). Statistical analysis revealed that the addition of dermoscopic images did not significantly influence diagnostic accuracy, either overall or within individual diagnostic categories. The model recommended biopsy for all lesions, suggesting potential as a supportive diagnostic tool. ChatGPT-4o showed variable diagnostic accuracy for melanoma and non-melanoma skin cancers. Dermoscopic images reduced performance for certain diagnostic categories. These misclassifications highlight the potential for unnecessary interventions and patient anxiety underscoring that AI-based systems should serve as supportive aids rather than standalone diagnostic tools.

Background: Demodex mites are believed to contribute to the pathogenesis of both rosacea and demodicosis. While standardized skin surface biopsy (SSSB) remains the gold standard for detecting Demodex density, it is invasive and time-consuming. Ultraviolet (UV) dermoscopy, a noninvasive technique that visualizes fluorescent structures, may offer a rapid alternative for identifying Demodex proliferation. Objectives: To investigate the relationship between Demodex folliculorum and Demodex brevis counts obtained by SSSB and fluorescent dots detected under UV dermoscopy, and to evaluate the diagnostic value of these findings in rosacea and demodicosis. Methods: Fifty-four patients with rosacea or demodicosis were evaluated using both UV dermoscopy and SSSB from the same facial region. Orange and bright blue fluorescent dots were recorded dermoscopically, and mite density and species were quantified via SSSB. Correlation analyses and receiver operating characteristic (ROC) curve analysis were performed to assess diagnostic performance. Results: A significant positive correlation was found between total Demodex count and bright blue fluorescent dots (r = 0.343; P = 0.013), but not with orange dots (r = –0.065; P = 0.649). Only D. folliculorum showed significant correlation with blue and total fluorescent dots. ROC analysis identified > 11 bright blue dots as the optimal threshold for predicting Demodex positivity, with a sensitivity of 82.1% and specificity of 71.4% (area under the curve 0.793; P = 0.003). Conclusions: Bright blue fluorescent dots observed under UV dermoscopy correlate significantly with Demodex density, particularly D. folliculorum. A threshold of > 11 blue dots demonstrated good diagnostic accuracy and may serve as a practical, noninvasive alternative or adjunct to SSSB in clinical settings. Orange fluorescence was not associated with Demodex density and most likely reflects unrelated sebaceous or microbial activity.