Marlise A. dos Santos

Polysaccharide Nanoparticles for Protein and Peptide Delivery

Finding adequate carriers for protein and peptide delivery has become an urgent need, owing to the growing number of macromolecules identified as having therapeutic potential. Nanoparticles have emerged in the field as very promising vehicles and much work has been directed to testing the capacity of different materials to compose the matrix of these carriers. Natural materials and, specifically, polysaccharides have been taking the forefront of the challenge, because of several favoring properties that include the higher propensity to exhibit biodegradability and biocompatibility, and also the high structural flexibility. The majority of works found in the literature regarding polysaccharide nanoparticles uses very popular materials like chitosan or hyaluronic acid. This review is aimed at describing and exploring the potential of polysaccharides that are not so well known or that are less explored. For those, the main properties will be described, together with an overview of the reported applications as nanoparticle matrix materials.

Polysaccharide nanoparticles for protein and Peptide delivery: exploring less-known materials.

Finding adequate carriers for protein and peptide delivery has become an urgent need, owing to the growing number of macromolecules identified as having therapeutic potential. Nanoparticles have emerged in the field as very promising vehicles and much work has been directed to testing the capacity of different materials to compose the matrix of these carriers. Natural materials and, specifically, polysaccharides have been taking the forefront of the challenge, because of several favoring properties that include the higher propensity to exhibit biodegradability and biocompatibility, and also the high structural flexibility. The majority of works found in the literature regarding polysaccharide nanoparticles uses very popular materials like chitosan or hyaluronic acid. This review is aimed at describing and exploring the potential of polysaccharides that are not so well known or that are less explored. For those, the main properties will be described, together with an overview of the reported applications as nanoparticle matrix materials.

Towards designing for equity: active citizen participation in eHealth

Purpose – Reducing inequity in accessing healthcare among rural and remote populations remains a problem. Internationally, eHealth is now touted as a potential solution, with a range of diverse approaches and impacts. Yet, the equity gains of implementing eHealth are often not realized due to a lack of effective strategies for citizen participation. The purpose of this paper is to present the background to, and results of, a multidisciplinary eHealth assistance project in a remote region of the Brazilian Amazon, highlighting the importance of citizen participation within planning processes.Design/methodology/approach – The project was conducted in three phases – pre‐mission, mission, and post‐mission. Discussions were held between health teams and local community leaders, and were coordinated by government health organizations in partnership with the Amazon State University. A multidisciplinary team visited five remote communities in the Brazilian Amazon, where participants underwent clinical assessment u...

Development of a skateboarding trick classifier using accelerometry and machine learning

Introduction: Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU) use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN). Methods: State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE). The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation). Results: The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion: Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.