Daily Cosmetic Research Analysis

BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer, requiring an early diagnosis and accurate margin definition to prevent functional and cosmetic complications. Traditional methods using clinical and dermoscopic images (C&D) often rely on biopsies and histology for final validation. Non-invasive techniques like LC-OCT, enabling 'digital biopsies', are promising alternatives, but remain underutilized due to the expertise required. The development of Artificial Intelligence (AI) algorithms is a promising approach to assist dermatologists in their diagnosis and support the broader adoption of such technologies. OBJECTIVE: We present a real-time AI assistant for BCC diagnosis with LC-OCT, which is, to date, the only real-time AI model across all dermatological imaging modalities. The study aims to quantify the model's effectiveness when used by dermatologists with different levels of expertise and compare its performance with traditional methods and unaided LC-OCT. METHODS: This multicenter, retrospective study involved 43 dermatologists in a double-rounded quiz on 200 equivocal BCC lesions. Diagnoses were first made on C&D images, then with LC-OCT or AI-assisted LC-OCT in a randomized manner. RESULTS: AI-assisted LC-OCT significantly improves dermatologists' diagnostic performance in detecting BCC (+25.8 points in sensitivity and +16.8 points in specificity compared to C&D), particularly benefiting those with less LC-OCT experience, effectively bridging a 2-year gap of expertise. These results highlight the potential for broader clinical adoption through AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes. CONCLUSION: These results support a broader adoption of LC-OCT use in clinical practice thanks to AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes. Basal cell carcinoma (BCC) is the most common skin cancer. Although rarely deadly, it can progressively invade surrounding tissues such as cartilage and bone. Early and accurate diagnosis is essential to avoid complications such as functional loss or significant cosmetic damage. This study includes clinical and LC‐OCT data from 200 suspicious lesions for BCC, collected across four hospitals in France, Spain, Italy and Belgium. Its goal is to retrospectively quantify the effect of real‐time AI assistance with LC‐OCT on the diagnostic efficacy of dermatologists with different levels of expertise with this technology. Forty‐three dermatologists evaluated the 200 lesions using traditional imaging methods (clinical photography and dermoscopy) as well as LC‐OCT, through a dedicated web application. They had to give a binary decision for BCC at two different stages, with traditional images alone and with LC‐OCT videos. Half of the time, LC‐OCT images were accompanied by an AI assistant. The analysis of the diagnostic results across the different scenarios demonstrated that LC‐OCT outperforms traditional imaging for diagnosing BCC in equivocal lesions. It also showed that AI assistance provides significant help to dermatologists and more particularly those with less experience in LC‐OCT, by improving their diagnostic precision. AI support could make LC‐OCT easier to interpret and improve BCC detection. This may reduce the need for skin biopsies, accelerate clinical decision‐making and improve patient care by guiding treatment selection and optimizing surgical procedures.

Twin-Chain (TC) networks are cryogels containing two polyvinyl alcohols (PVAs), whose pre-gel mixtures undergo a polymer-polymer phase-separation, forming micro-domains that act as porogens during the freezing process. As a result, TC gels show a sponge-like morphology, fundamental to improve adhesion and fluid transport at the gel-substrate interface for applications ranging from the cleaning of artworks to cosmetics and drug delivery. While the effects of freezing on single-polymer, monophasic PVA solutions have been widely investigated through scattering techniques (e.g. Small Angle X-ray, Neutron and Light scattering, SAXS, SANS and LS, respectively), the gelation kinetics of PVA mixtures characterized by a liquid-liquid phase separation has not yet been explored. In this work, the behavior of TC solutions at freezing was investigated through SANS. The scattering signal, acquired over time during the cryostructuration process and at the end of gelation, showed the evolution of the nanoscale domains as PVA crystallites (the gels "physical crosslinks") and mesh-sizes formed. Our results show that TC gelation significantly differs from that of homogeneous PVA solutions, being generally slower and forming domains of different size. The process depends on the polymers concentration, molecular weight and the chain-chain interactions, altogether defining the systems phase-separation behavior at room and sub-zero temperatures.

Coastal tourism and recreational activities contribute to the release of Personal Care Products, including sunscreens, which contain organic ultraviolet filters (oUVFs), increasingly recognised as contaminants of emerging concern in marine ecosystems. Oceanic islands offer natural laboratories for studying these compounds due to their isolated ecosystems and varying levels of human pressure. This study investigates the occurrence and distribution of oUVFs in seawater, sediments, and biota from three locations in the Madeira Archipelago with varying human influence, sampled during both high and low tourist seasons. Solid-phase extraction (SPE) and microwave-assisted extraction (MAE) were used as extraction methods, followed by UHPLC-MS/MS for identification and quantification. Eight of 11 target compounds were detected in at least one matrix. Total maximum concentrations reached 70.61 ng/L in seawater, 299.8 ng/g d.w. in algae, 472.2 ng/g d.w. in fish, and 651.33 ng/g d.w. in zooplankton. Detection frequencies and levels were highest at the site with the most significant anthropogenic pressure during the high tourist season. Zooplankton showed the highest accumulation levels, followed by herbivorous fish and red algae, while no oUVFs were detected in mesopredators and some invertebrates. Contamination was associated with proximity to shore and direct inputs linked with anthropogenic pressure. However, the oceanographic (e.g., currents, tides) and geological characteristics (rocky reefs) of oceanic islands must also be considered, as they can affect the environmental fate and distribution of oUVFs across different matrices. These findings highlight the need to monitor oUVFs in marine environments and identify susceptible species to improve ecological risk assessment and regulatory actions.