Cosmetics

Post-oncologic skin is subject to multiple structural and functional impairments following chemotherapy and radiotherapy, including delayed epidermal turnover, compromised barrier integrity, and mitochondrial dysfunction. These changes can lead to persistent dryness, heightened reactivity, impaired regeneration, and reduced patient quality of life. In this context, topical dermocosmetic strategies are essential not only for improving comfort and hydration, but also for supporting key cellular pathways involved in mitochondrial protection and oxidative stress reduction. Despite the promise of natural antioxidant actives, their cutaneous efficacy is often limited by poor stability, low bioavailability, and insufficient penetration of the stratum corneum. The use of nanocarriers promotes deeper skin penetration, protects oxidation-prone antioxidant compounds, and enables a controlled and prolonged release profile. This review summarizes the current evidence (2020–2025) on skin delivery systems designed to enhance the efficacy, stability, and skin penetration of antioxidants. Knowledge gaps and future directions are outlined, highlighting how rationally engineered delivery systems for mitochondria-targeted actives could contribute to safer, more effective strategies for post-oncologic skin regeneration.

Background/Objectives: Understanding how plant-derived extracts influence the rheological and sensory behavior of emulsions is crucial for developing stable and consumer-appealing formulations. Although walnut leaf extract (Juglans regia L.) is recognized for its bioactive properties, its structural impact on cosmetic emulsions has not been systematically characterized. This study aimed to investigate the effect of increasing walnut leaf extract concentration on the rheological profile, mechanical integrity during application, and sensory performance of oil-in-water creams. Methods: Four emulsion formulations (F1–F4) containing 0%, 1%, 3%, and 5% walnut leaf extract were prepared using Olivem 1000 and Olivem 300 as emulsifiers. Rheological measurements included amplitude sweep, flow curve, frequency sweep, and thixotropy tests to assess viscoelasticity, flow behavior, and recovery. A sensory evaluation was conducted by trained panelists to correlate rheological parameters with perceived product attributes. Results: All formulations exhibited pseudoplastic, shear-thinning behavior in well-structured cosmetic emulsions during application. The addition of walnut extract significantly modified rheological responses: at 1% concentration, an increase in storage modulus (G′) and shear-thinning ratio (η₀/η∞) indicated structural reinforcement and improved spreadability, whereas higher concentrations (3–5%) led to structural softening and faster thixotropic recovery. The frequency sweep revealed a concentration-dependent shift from elastic- to viscous-dominant behavior. Sensory analysis confirmed these trends, with higher extract levels reducing stickiness and greasiness while enhancing absorption. Conclusions: Walnut leaf extract shows a concentration-dependent influence on the rheological behavior of the emulsions, strengthening the network structure at low levels while promoting softening and faster structural recovery at higher concentrations. The strong correlation between rheological and sensory parameters underscores the potential of walnut extract as a multifunctional ingredient for designing well-structured, non-greasy, and consumer-preferred cosmetic creams.

Introduction: Artificial intelligence (AI) has developed into an increasingly important tool in dermatology. While new technologies integrated within laser devices are emerging, there is a lack of data on the applicability of publicly available AI models. Methods: The prospective study used an online questionnaire where participants evaluated diagnosis and treatment for 25 dermatological cases shown as pictures. The same questions were given to AI models: ChatGPT-4o, Claude Sonnet 4, Gemini 2.5 Flash, and Grok-3. Results: Dermatologists outperformed AI in diagnostic accuracy (suspected primary diagnosis-SD 75.6%) in pooled dermatologists vs. pooled AI (SD 57.0%), with laser specialists achieving the highest accuracy (SD 82.0%) and residents the lowest (SD 66.0%). There was a high heterogeneity across AI models. Gemini approached dermatologist performance (SD 72.0%), while Claude showed a low accuracy (SD 40.0%). While AI models reached near 100% accuracy in some classic/common diagnoses (e.g., acne, rosacea, spider angioma, infantile hemangioma), their accuracy dropped to near 0% on rare or context-dependent cases (e.g., blue rubber bleb nevus syndrome, angiosarcoma, hirsutism, cutaneous siderosis). Inter-rater agreement was high among laser experts in terms of diagnostic accuracy and treatment choice. Agreement between residents and AI models was highest for diagnostic accuracy and treatment choice, while it was lowest between experts and AI models. Conclusions: Before AI-based tools can be integrated into daily practice, particularly regarding diagnosis and appropriate laser treatment recommendations, specific supervised medical training of the AI model is necessary, as open-source platforms currently lack the ability to contextualize presented data.