Our findings suggest that the colored grains of maize landraces studied may hold promise for the development of grain-based functional foods or natural colorants regarding their carotenoid and anthocyanin contents and as genetic resource in breeding programs.

Nanotechnology is an exciting emerging field with multiple applications in skin regeneration. Nanofibers have gained special attention in skin regeneration based on their structural similarity to the extracellular matrix. A wide variety of polymeric nanofibers with distinct properties have been developed and tested as scaffolds for skin regeneration. Besides providing support for tissue repair, nanofibrous materials can act as delivery systems for drugs, proteins, growth factors, and other molecules. Moreover, the morphology, biodegradability, and other functionalities of nanofibrous materials can be controlled towards specific conditions of wound healing. Other nanostructured drug delivery systems, such as nanoparticles, micelles, nanoemulsions, and liposomes, have been used to improve wound healing at different stages. These nanoscale delivery systems have demonstrated several benefits for the wound healing process, including reduced cytotoxicity of drugs, administration of poorly water-soluble drugs, improved skin penetration, controlled release properties, antimicrobial activity, and protection of drugs against light, temperature, enzymes or pH degradation, as well as stimulation of fibroblast proliferation and reduced inflammation.

This work aims at discriminating flours of 26 maize landraces from southern Brazil, by using the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy and chemometrics (principal components analysis -PCA). PCA applied to the FTIR spectra in the 3-600 (whole spectrum) and 1650-1500 cm )1 (fingerprint region of proteins) spectral windows clearly discriminated the Amarela˜o landrace. Quantitative and semi-qualitative analysis of proteins showed a wide range among the fractions, mainly of prolamine (13.47-28.43 g Kg )1 ) and glutelin (5.57-30.98 g Kg )1 ) contents. Pixurum 6, Pixurum 5, and MPA1 landraces are of superior nutritional value for their albumin, globulin, and glutelin contents. PCA of the spectral dataset in the fingerprint region to carbohydrates (1200-950 and 1065-950 cm )1 ) also including commercial standards of amylose and amylopectin was able in separating the Moroti genotype, which grouped with the amylopectin standard. Thus, ATR-FTIR and PCA showed to be useful tools for the quick screening and discrimination of maize with distinct chemical composition.

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.