Patients with hepatitis C virus (HCV)-related cirrhosis treated with direct-acting antivirals (DAA) are still at risk of developing hepatocellular carcinoma (HCC). We investigated the accuracy of non-invasive scoring systems (NSS) for the prediction of de novo HCC development in patients treated with DAA on long-term follow-up (FU). We analyzed data from 575 consecutive patients with cirrhosis and no history of HCC who achieved a sustained virologic response (SVR) to DAA therapy. NSS (i.e., Forns index, APRI, FIB-4, ALBI, and aMAP) were calculated at 3 months after the end of therapy. Performance for de novo HCC prediction was evaluated in terms of area under the curve (AUC) and Harrell’s C-index. During a median FU of 44.9 (27.8–58.6) months, 57 (9.9%) patients developed de novo HCC. All five NSS were associated with the risk of de novo HCC. At multivariate analysis, only the ALBI score resulted in being significantly and independently associated with de novo HCC development (adjusted hazard ratio = 4.91, 95% CI 2.91–8.28, p < 0.001). ALBI showed the highest diagnostic accuracy for the detection of de novo HCC at 1-, 3-, and 5-years of FU, with AUC values of 0.81 (95% CI 0.78–0.85), 0.71 (95% CI 0.66–0.75), and 0.68 (95% CI 0.59–0.76), respectively. Consistently, the best predictive performance assessed by Harrell’s C-statistic was observed for ALBI (C-index = 0.70, 95% CI 0.62–0.77). ALBI score may represent a valuable and inexpensive tool for risk stratification and the personalization of an HCC surveillance strategy for patients with cirrhosis and previous history of HCV infection treated with DAA.

This study was carried out at the Dermatologic Clinic of the University of Turin, Italy, to assess the effectiveness and safety of adjuvant therapy in patients who received either targeted therapy (TT: dabrafenib + trametinib) or immunotherapy (IT: nivolumab or pembrolizumab) for up to 12 months. A total of 163 patients participated, including 147 with stage III and 19 with stage IV with no evidence of disease. The primary outcomes were relapse-free survival (RFS), distant metastasis-free survival (DMFS), and overall survival (OS). At 48 months, both TT and IT approaches yielded comparable outcomes in terms of RFS (55.6–55.4%, p = 0.532), DMFS (58.2–59.8%, p = 0.761), and OS (62.4–69.5%, p = 0.889). Whilst temporary therapy suspension was more common among TT-treated patients compared to IT-treated individuals, therapy discontinuation due to adverse events occurred at comparable rates in both groups. Predictors of relapse included mitoses, lymphovascular invasion, ulceration, and positive sentinel lymph nodes. Overall, the proportion of BRAF-mutated patients receiving IT stood at 7.4%, lower than what was observed in clinical trials.

Background: Hormonal fluctuations significantly influence skin physiology, affecting collagen production, sebum regulation, pigmentation, and tissue repair. Hormonal therapies are increasingly used in cosmetic dermatology to address age-related and hormone-dependent skin changes. Methods: This narrative review synthesizes the current literature on the mechanisms, clinical applications, and future directions of hormonal therapies in dermatologic aesthetics. Studies were selected through a comprehensive search on PubMed, Scopus, and Web of Science. Results: Estrogens, androgens, progesterone, and other hormones act on skin through specific receptors, modulating fibroblast, sebocyte, and melanocyte activity. Clinical applications include hormone-based strategies for anti-aging, acne, melasma, alopecia, and postmenopausal atrophy. Both systemic (e.g., HRT) and topical (e.g., clascoterone, phytoestrogens) approaches are discussed. Safety concerns, including systemic absorption and off-label use, require careful evaluation. Emerging technologies such as SERMs, nanocarriers, and regenerative combinations suggest promising future avenues. Conclusions: Hormonal therapies offer a biologically rational and increasingly evidence-based tool in cosmetic dermatology. Responsible integration into clinical practice depends on personalized approaches, ethical prescribing, and further research on long-term safety and efficacy.

Breslow thickness (BT) is the most powerful prognostic indicator in cutaneous melanoma, yet histopathological measurement exhibits some limitations such as interobserver variability and diagnostic delays. Preoperative clinical assessment demonstrates 30% misclassification rates. This narrative review synthesizes evidence on deep learning models for non-invasive BT estimation from dermoscopic images. Convolutional neural networks (ResNet, EfficientNet, Vision Transformers) with transfer learning from ImageNet achieve up to 75–79% accuracy and AUC 0.76–0.85 on single-center datasets. Preprocessing techniques (hair removal, color normalization, data augmentation) and interpretability methods (Grad-CAM, LIME) enhance clinical applicability. However, external validation reveals performance degradation. The clinically critical thickness range (0.4–1.0 mm) demonstrates poor discrimination. Significant dataset bias exists: most training data represents lighter skin phototypes, resulting in an underrepresentation of darker skin types. AI models function as complementary decision-support tools rather than replacements for histopathology. Prospective clinical trials validating clinical utility are lacking, and regulatory approval pathways are undefined. Research priorities include diverse public datasets with balanced skin tone representation, the adoption of threshold-weighted loss functions to prioritize accuracy at the 0.8 mm surgical cut-off, multi-institutional external validation, prospective randomized trials, federated learning frameworks, and regulatory engagement. Only rigorous, equitable research can translate AI from proof-of-concept to clinically reliable tools benefiting all melanoma patients.

Psoriasis is a chronic immune-mediated skin disease with highly variable responses to systemic therapies. Emerging evidence highlights the microbiome as a potential modulator of drug efficacy and toxicity. Gut bacteria can enzymatically metabolize drugs, such as methotrexate, altering bioavailability and therapeutic outcomes, while microbial metabolites—including short-chain fatty acids, branched-chain amino acids, and tryptophan derivatives—shape host immunity and barrier integrity, influencing drug action. Baseline microbial signatures have been linked to treatment response, potentially predicting anti-TNF or IL-17 inhibitor efficacy. Systemic therapies themselves reshape microbial communities: IL-17 blockade induces broad shifts in gut and skin microbiota, whereas cyclosporine and anti-TNF agents exert subtler effects. Small molecules such as apremilast and fumarates may reduce fungal overgrowth and influence microbial composition, whereas data on JAK/TYK2 inhibitors remain limited. Notably, current evidence exhibits a literature bias toward the gut microbiota, while the roles of the oral and skin axes remain understudied. Adjunctive microbiome-directed interventions, including probiotics and fecal microbiota transplantation, have demonstrated potential to enhance treatment outcomes by promoting anti-inflammatory taxa and restoring barrier function. Despite these promising findings, current evidence is heterogeneous, often limited by small sample sizes, short follow-up, and variable methodology. Integrating pharmacomicrobiomics data with clinical, genetic, and multi-omics profiling could enable precision medicine approaches in psoriasis, allowing therapy selection tailored to individual microbial and metabolic signatures. Future research should focus on longitudinal, multicenter studies to identify actionable microbial biomarkers, clarify mechanistic interactions between drugs, microbes, and host immunity, and evaluate microbiome-targeted adjuncts in randomized trials. Understanding the bidirectional crosstalk between systemic therapies and the microbiome may transform psoriasis management, improving efficacy, reducing adverse events, and enabling durable, personalized responses.

Background/Objectives: Melanoma is an aggressive skin cancer with increasing incidence worldwide. Dermoscopy has revolutionized early melanoma detection, but most studies have focused on the general adult population. This study aims to analyze dermoscopic and histological differences in melanoma across age groups, evaluating whether specific patterns vary between younger and older patients. Methods: This retrospective study included 285 histopathological confirmed melanomas diagnosed at the Dermatology Clinic of the University of Turin between November 2021 and April 2024. Patients were stratified by age (<40 vs. ≥40 years), and statistical analyses (Chi-square, logistic regression) assessed differences in dermoscopic, histopathological, and anatomical parameters. Results: Younger patients showed a higher prevalence of growth-related features (e.g., pseudopods, OR = 5.43; asymmetric globules, OR = 2.33) and a thicker Breslow index (mean = 1.05 mm). Older patients exhibited more regression-associated signs (scar-like depigmentation, OR = 0.15; peppering, OR = 0.39), greater lesion size, and solar elastosis. Dermoscopic regression significantly predicted histological regression, with age-stratified analysis revealing peppering as a predictor in younger patients (p = 0.015) and scar-like depigmentation in older ones (p = 0.012). Conclusions: Melanoma exhibits distinct dermoscopic features depending on patient age, with growth-associated patterns being more common in younger individuals and regressive patterns predominating in older patients. These findings highlight the importance of age-specific diagnostic considerations in melanoma detection, potentially improving early diagnosis and patient outcomes.