Unveiling the Immunomodulatory Potential of Phenolic Compounds in Food Allergies.

Food allergies are becoming ever more prevalent around the world. This pathology is characterized by the breakdown of oral tolerance to ingested food allergens, resulting in allergic reactions in subsequent exposures. Due to the possible severity of the symptoms associated with this pathology, new approaches to prevent it and reduce associated symptoms are of utmost importance. In this framework, dietary phenolic compounds appear as a tool with a not fully explored potential. Some phenolic compounds have been pointed to with the ability to modulate food allergies and possibly reduce their symptoms. These compounds can modulate food allergies through many different mechanisms, such as altering the bioaccessibility and bioavailability of potentially immunogenic peptides, by modulating the human immune system and by modulating the composition of the human microbiome that resides in the oral cavity and the gastrointestinal tract. This review deepens the state-of-the-art of the modulation of these mechanisms by phenolic compounds. While this review shows clear evidence that dietary supplementation with foods rich in phenolic compounds might constitute a new approach to the management of food allergies, it also highlights the need for further research to delve into the mechanisms of action of these compounds and decipher systematic structure/activity relationships.

Characterization of the Effect of a Novel Production Technique for 'Not from Concentrate' Pear and Apple Juices on the Composition of Phenolic Compounds.

The consumption of 'not-from-concentrate' (NFC) fruit juices can be a convenient and enjoyable way to incorporate the nutritional benefits and flavors of fruits into one's diet. This study will focus on the effect of production of juices from apple and pear fruits, by using centrifugal decanter and tangential filtration, on the profile of polyphenols as a valuable source of bioactive compounds. Likewise, by-products from the juice industry were characterized in order to understand the high-value-added potential based on their composition of polyphenols. Briefly, apple and apple juice showed great contents of chlorogenic acid (0.990 ± 0.021 mg/g of DW), the dihydrochalcone phloridzin (1.041 ± 0.062 mg/g of DW), procyanidins (0.733 ± 0.121 mg/g of DW) and quercetin derivatives (1.501 ± 0.192 mg/g of DW). Likewise, the most abundant compounds in pear and pear juices were chlorogenic acid (0.917 ± 0.021 mg/g of DW), caffeoylquinic acid (0.180 ± 0.029 mg/g of DW), procyanidins (0.255 ± 0.016 mg/g of DW) and quercetin derivatives (0.181 ± 0.004 mg/g of DW). Both temperature and tangential speed affect the amount of phenolic compounds in fruit juices, highlighting the need to control the technological process to obtain a more nutritious/healthier beverage. Overall, NFC juices arise as a better option when compared with concentrated juices. Furthermore, the higher yield of phenolic compounds found in fruit pomace clearly open new ways for upcycling this fruit by-product as a high-value-added ingredient.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges.

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.