Search Results (3)

In this study, the antioxidant, antimicrobial, mechanical, optical, and barrier attributes of Solanum lycocarpum starch bio-based edible films incorporated with a phenolic extract from jaboticaba peel were investigated. Aiming to determine the effect of the polymers and the phenolic extract on the properties of the films, a three-factor simplex-lattice design was employed, and the formulation optimization was based on the produced films’ antioxidant potential. The optimized formulation of the starch-PEJP film showed a reddish-pink color with no cracks or bubbles and 91% antioxidant activity against DPPH radical. The optimized starch-PEJP film showed good transparency properties and a potent UV-blocking action, presenting color variation as a function of the pH values. The optimized film was also considerably resistant and highly flexible, showing a water vapor permeability of 3.28 × 10⁻⁶ g m⁻¹ h⁻¹ Pa⁻¹. The microbial permeation test and antimicrobial evaluation demonstrated that the optimized starch-PEJP film avoided microbial contamination and was potent in reducing the growth of Escherichia coli, Staphylococcus aureus, and Salmonella spp. In summary, the active starch-PEJP film showed great potential as an environmentally friendly and halochromic material, presenting antioxidant and antimicrobial properties and high UV-protecting activity. Full article

The cytotoxic activity of four sets of camphorimine complexes based on the Cu(I), Cu(II), Ag(I), and Au(I) metal sites were assessed against the cisplatin-sensitive A2780 and OVCAR3 ovarian cancer cells. The results showed that the gold complexes were ca. one order of magnitude more active than the silver complexes, which in turn were ca. one order of magnitude more active than the copper complexes. An important finding was that the cytotoxic activity of the Ag(I) and Au(I) camphorimine complexes was higher than that of cisplatin. Another relevant aspect was that the camphorimine complexes did not interact significantly with DNA, in contrast with cisplatin. The cytotoxic activity of the camphorimine complexes displayed a direct relationship with the cellular uptake by OVCAR3 cells, as ascertained by PIXE (particle-induced X-ray emission). The levels of ROS (reactive oxygen species) formation exhibited an inverse relationship with the reduction potentials for the complexes with the same metal, as assessed by cyclic voltammetry. In order to gain insight into the toxicity of the complexes, their cytotoxicity toward nontumoral cells (HDF and V79 fibroblasts) was evaluated. The in vivo cytotoxicity of complex 5 using the nematode Caenorhabditis elegans was also assessed. The silver camphorimine complexes displayed the highest selectivity coefficients (activity vs. toxicity). Full article

Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities. Full article