Phenolic compounds represent a group of secondary metabolites that serve essential functions in plants. Beyond their positive impact on plant, these phenolic metabolites, often referred to as polyphenols, possess a range of biological properties that can promote skin health. Scientific research indicates that using phenolics derived from plants topically can be advantageous, but the activity and stability highly depend on storage of the source material and the extract method. These compounds have the ability to relieve symptoms and hinder the progression of different skin diseases. Because they come from natural sources and have minimal toxicity, phenolic compounds show potential in addressing the causes and effects of skin aging, skin diseases, and various types of skin damage, such as wounds and burns. Hence, this review provides an extensive information of the particular crops from which by-product phenolic compounds can be sourced, but pointing the need investigation according to a proper storage plant material and the choice of the best extracting method, along with an examination of their specific functions and the mechanisms by which they act to protect skin.

Coagulation is the most sensitive step in drinking water treatment. Underdosing may not yield the required water quality, whereas overdosing may result in higher costs and excess sludge. Traditionally, the coagulant dosage is set based on bath experiments performed manually. Therefore, this test does not allow real-time dosing control, and its accuracy is subject to operator experience. Alternatively, solutions based on machine-learning (ML) have been evaluated as a computer-aided alternative. Despite these advances, there is open debate on the most suitable ML method applied to the coagulation process, capable of the most highly accurate prediction. This study addresses this gap, where a comparative analysis between ML methods was performed. As a research hypothesis, a novel data-driven fuzzy inference system (FIS) should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Although ML methods have been widely investigated, only a few studies report hybrid neuro-fuzzy systems applied to coagulation. Thus, to the best of our knowledge, this is the first study thus far to address the accuracy of this novel data-driven FIS for such application. The novel FIS provided the smallest error (0.86), indicating a promising alternative tool for real-time and highly accurate coagulant dosing in drinking water treatment.

Mucopolysaccharidoses (MPSs) are rare inherited lysosomal storage diseases (LSDs) caused by deficient activity in one of the enzymes responsible for glycosaminoglycans lysosomal degradation. MPS II is caused by pathogenic mutations in the IDS gene, leading to deficient activity of the enzyme iduronate-2-sulfatase, which causes dermatan and heparan sulfate storage in the lysosomes. MPS VI has dermatan sulfate lysosomal storage, owing to a deficiency of arylsulfatase B, due to pathogenic mutations in the ARSB gene. Alterations in the immune system of MPSs mouse models have been described but the data is still scarce concerning MPSs patients. Herein, we study different leukocyte populations in MPS II and VI disease patients. MPS VI, but not MPS II patients, have a decrease percentage of Natural Killer (NK) cells and monocytes when compared with controls. No alterations were identified in the percentage of T, invariant NKT and B cells in both groups of MPS disease patients. However, we uncovered alterations in the naïve versus memory status of both helper and cytotoxic T cells in MPS VI disease patients compared to control group. Indeed, MPS VI disease patients have a higher frequency of naïve T cells and consequently lower frequency of memory T cells than control subjects. Altogether, these results reveal MPS VI disease-specific alterations in some leukocyte populations, suggesting that the type of substrate accumulated and/or enzyme deficient in the lysosome may have a particular effect on the normal cellular composition of the immune system.