The main function of the skin is to protect the body from the external environment. However, the skin can undergo inflammatory processes, due to genetic, hormonal, or environmental factors. When the defense system is overloaded, there is an increase in pro-inflammatory mediators and reactive oxygen species (ROS), which results in skin disorders. Among the substances used to treat these inflammatory processes, many natural substances with anti-inflammatory and antioxidant properties are being studied: nature is yet an abundant source to obtain diverse pharmacological actives. The treatment of skin diseases is usually focused on topical application, as it reduces the risk of systemic side effects and prevents drug degradation by first-pass metabolism. Thus, the properties of drug delivery vehicles can facilitate or inhibit its permeation. Due to the hydrophobic nature of the skin, a promising strategy to improve dermal drug penetration is the use of lipid-based nanoparticles, such as nanostructured lipid carriers (NLC). Therefore, in this review, we present NLC as a tool to improve dermal administration of natural substances with anti-inflammatory properties.

Background: Androgenetic alopecia (AGA) is a prevalent genetic condition that can affect both male and female, and is considered the most frequent form of hair loss. Traditional scales and methods of classifying AGA are basically qualitative. Objective: This work aims to propose a quantitative scale to classify AGA in order to assist hair transplantation surgery. Methods: Based on whole hairless and thinning areas that needs to receive follicular units in a hair transplantation procedure, basic equations to support the scale are proposed. Additionally, the study involves simulations that apply the classification system and compare its results with those of qualitative methods. Results: The PRECISE scale utilizes a range of 0–10, using 30 cm² as the measured standard of a bald area. For hair transplantation, 1500 follicular units (FU) are recommended for each score in the PRECISE scale. Technological and manual methods to measure the hairless and thinning areas are presented and discussed. This new quantitative classification, combined with different and complementary methods of measurement of hairless and thinning areas endorse the understanding of the clinical condition by the patient and the planning of a surgery procedure. Conclusion: The developed PRECISE scale brings a different way of classifying Androgenetic alopecia (AGA), through an essentially quantitative evaluation. It can be used to elaborate the best strategy for the hair transplantation surgery and to improve the outcomes. Level of Evidence V: This journal requires that authors assign a level of evidence to each article. For a full description of these evidence-based medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Neste manuscrito abordamos o uso potencial da Inteligência Artifical (IA) no ensino, na prevenção, na detecção precoce, no diagnóstico e no tratamento das úlceras em pessoas com Diabetes Mellitus por meio de uma discussão estruturada em 4 eixos, sendo eles: Eixo1. Ensino e Informação; Eixo 2.Prevenção; Eixo 3. Detecção Precoce e Diagnóstico; Eixo 4. Tratamento. No qual relatamos as principias e mais atuais pesquisas em cada eixo. Conclui-se que a IA possui um futuro promissor no ensino e informação, na prevenção, na detecção precoce da lesão, no diagnóstico e no tratamento de UPD, com benefícios diretos para os sistemas de saúde, para os pacientes e para os profissionais que prestam assistência às feridas de pessoas com diabetes. Contudo, é ressaltamos que será necessário adaptação e empenho coletivo pelos membros da sociedade para o uso dos potenciais benefícios da IA e deste modo ocorrer uma real melhoria no cuidado de úlceras em pessoas com Diabetes Mellitus favorecida pela IA.