Publications by authors named "S C Motta"

Aims: Postmenopausal women experience an increase in cardiovascular, sexual, and emotional changes that can significantly impact their quality of life. Although phytoestrogens offer therapeutic benefits, our understanding of resveratrol's effects remains limited. This study aims to evaluate the effects of using resveratrol on the lipid profile of postmenopausal women.

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Graphene oxide (GO) is an amphiphilic and versatile graphene-based nanomaterial that is extremely promising for targeted drug delivery, which aims to administer drugs in a spatially and temporally controlled manner. A typical GO nanocarrier features a polyethylene glycol coating and conjugation to an active targeting ligand. However, it is challenging to accurately model GO dots, because of their intrinsically complex and not unique structure.

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This study investigated the efficacy of using probiotics on the performance and health parameters of sows and their litters. A randomized block design was used with 584 sows and 292 replications, with two dietary treatments: the control group (basal diet without probiotics) and the probiotic group (basal diet supplemented with 400 g/ton of a probiotic composed of () 541 and () 516). Feed intake was evaluated throughout the experimental period.

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In this work, 37 enological tannins of different classes were studied to investigate whether linear sweep voltammetry (LSV) could be a method to determine the family of a sample and its antioxidant capacity. A "wholistic" approach was used, combining LSV data with nuclear magnetic resonance (NMR), polyphenol quantification (Folin-Ciocalteu method and gravimetric analysis), antiradical activity (DPPH assay), and reducing capacity (FRAP assay). Voltammetric data were processed with statistical techniques and the results show the clustering of tannins in three different classes: ellagitannins, gallotannins, and condensed tannins.

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The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit.

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