Development of nanotechnology-based drug delivery systems with olive vegetable oil for cutaneous application

ABSTRACT Liquid-Crystalline Systems represent active compounds delivery systems that may be able to overcome the physical barrier of the skin, especially represented by the stratum corneum. To obtain these systems, aqueous and oily components are used with surfactants. Of the different association structures in such systems, the liquid-crystalline offer numerous advantages to a topical product. This manuscript presents the development of liquid-crystalline systems consisting, in which the oil component is olive oil, its rheological characterizations, and the location of liquid crystals in its phase map. Cytotoxic effects were evaluated using J-774 mouse macrophages as the cellular model. A phase diagram to mix three components with different proportions was constructed. Two liquid crystalline areas were found with olive oil in different regions in the ternary diagram with two nonionic surfactants, called SLC1 (S1) and SLC2 (S2). These systems showed lamellar liquid crystals that remained stable during the entire analysis time. The systems were also characterized rheologically with pseudoplastic behavior without thixotropy. The texture and bioadhesion assays showed that formulations were similar statistically (p < 0.05), indicating that the increased amount of water in S2 did not interfere with the bioadhesive properties of the systems. In vitro cytotoxic assays showed that formulations did not present cytotoxicity. Olive oil-based systems may be a promising platform for skin delivery of drugs

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost