The present study investigates the relationship between psoriasis and diseases such as health anxiety, depression, and somatosensory amplification. The participating patients (n=117, including 60 psoriasis patients and 57 controls) filled out the Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Somatosensory Amplification Scale (SSAS), and Health Anxiety Inventory (HAI) questionnaires. The mean scores from SSAS, HAI, and BDI were significantly higher in the psoriasis group than in the control group (p<0.05 in all comparisons). When the group medians of BAI were evaluated, the differences were not statistically significant, although BAI medians were higher in the patient group. Furthermore, a moderate correlation was found between the involvement of specific areas (especially the scalp and face) and SSAS scores. Patients with psoriasis score highly in depression, health anxiety, and somatosensory amplification, and there was a moderate correlation between specific body area involvement (especially the scalp and face) and SSAS score. The results of this study seem to indicate that psychiatric assessment and treatment approaches should be included in the treatment of such chronic skin diseases as psoriasis that follow a life-long remission and relapse pattern.

Background/Objectives: Psoriasis is a chronic, inflammatory, systemic skin disease. Although topical and systemic drugs with proven effectiveness are used in the treatment, ozone therapy is also applied as a treatment option based on clinical personal experience and with limited published knowledge. In this project, the aim was to evaluate the effectiveness of major ozone therapy in psoriasis patients together with biomarkers in serum. Methods: A total of 26 psoriasis patients and 19 healthy controls were included in the study. The disease severity was evaluated by the psoriasis area severity index score and grouped as mild, moderate/severe. Serum tumor necrosis factor alpha (TNF-α), interleukin 1-beta (IL-1β), high-sensitivity C-reactive protein (Hs-CRP), sialic acid, and Sialic acid binding Ig-like Lectin-14 (Siglec-14) levels were investigated in controls and psoriasis patients. Results: Psoriasis area severity index (PASI) score decreased significantly in psoriasis patients after ozone autohemotherapy application (p < 0.005). The values of IL-1β, sialic acid, and Siglec-14 after treatment in healthy subjects were statistically significantly higher than in psoriasis patients. It was found that Hs-CRP and Siglec-14 decreased in all patients after treatment, Hs-CRP decreased more significantly in mild psoriasis patients, and Siglec-14 decreased in both mild and moderate-severe groups (p < 0.05). Conclusions: Our research results suggest that ozone autohemotherapy has clinical efficacy in psoriasis patients, inflammation also has a role in the mechanism of action, and its effectiveness in treatment can be evaluated with inflammation markers.

Aim: Although dermoscopy has improved diagnostic accuracy for melanocytic lesions and melanoma in particular, inter-observer variability remains a clinical concern. This study evaluates the potential of an artificial intelligence (AI) ensemble to support dermatologists in a seven-class skin lesion classification task (including melanoma, basal cell carcinoma, actinic keratosis/intraepithelial carcinoma (AKIEC) and several benign lesions), using clinical and dermoscopic images. The comparison is intentionally restricted to an image-only, standardized classification setting and is not intended to represent full dermatologic diagnostic practice.Material and Methods: A dataset comprising more than 25,000 clinical and dermoscopic images obtained from the ISIC Archive and HAM10000 databases was used. A four-model ensemble deep learning architecture (ResNet-50, VGG-16, InceptionV3, and EfficientNet-B4) was developed using weighted voting aggregation. Dermatologists evaluated only the images and did not have access to patient age, sex, Fitzpatrick skin type, lesion site, duration, evolution, symptoms, prior treatments or palpation findings; the reader arm therefore reflects standardized image classification, not routine clinical practice. Model performance was compared with the dermatology panel on the same held-out image set using standard diagnostic metrics (sensitivity, specificity, F1, AUC-ROC). Grad-CAM was used to visualize model decision regions. Results: The AI ensemble achieved an overall seven-class accuracy of 94.2%, with an overall sensitivity of 93.8% (95% CI: 91.4-95.7) and specificity of 94.5% (95% CI: 92.3-96.2). The melanoma-specific sensitivity was 94.1%. On the same image-only test set, twelve board-certified dermatologists reached a mean accuracy of 88.5% (95% CI: 86.1-90.9). Ensemble modeling outperformed individual networks, and Grad-CAM outputs consistently mapped onto clinically relevant lesion regions.Conclusion: These findings should not be interpreted as AI being clinically superior to dermatologists, but as a comparative evaluation of standardized image-classification performance under an image-only setting. AI-assisted systems may serve as supportive tools in dermatology by assisting early detection, optimizing triage in high-volume settings and aiding teledermatology decision-making, complementing — not replacing — clinical expertise.