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This review explores the potential health benefits and applications of phenolic secoiridoids derived from olive oil by-products in the prevention of Alzheimer’s disease (AD). As reviewed herein, polyphenols, such as epigallocatechin-3-gallate, epicatechin, and resveratrol, show in vitro and in vivo antioxidant, anti-inflammatory, and neuroprotective properties, and are particularly relevant in the context of AD, a leading cause of dementia globally. The olive oil industry, particularly in the Mediterranean region, produces significant amounts of waste, including leaves, pomace, and wastewater, which pose environmental challenges but also offer an untapped source of bioactive compounds. Despite promising in vitro and in vivo studies indicating that olive-derived polyphenols, such as oleuropein and hydroxytyrosol, may mitigate AD pathology, human clinical trials remain limited. The variability in extraction methods and the complex nature of AD further complicate research. Future studies should focus on standardizing the protocols and conducting robust clinical trials to fully assess the therapeutic potential of these compounds. This approach not only supports the development of new treatments for AD but also promotes environmental sustainability by valorizing olive oil industry waste.

Neurodegenerative diseases are a significant challenge to global healthcare, and oxidative stress plays a crucial role in their development. This paper presents a comprehensive analysis of the neuroprotective potential of olive oil, with a primary focus on its antioxidant properties. The chemical composition of olive oil, including key antioxidants, such as oleuropein, hydroxytyrosol, and oleocanthal, is systematically examined. The mechanisms by which these compounds provide neuroprotection, including counteracting oxidative damage and modulating neuroprotective pathways, are explored. The neuroprotective efficacy of olive oil is evaluated by synthesizing findings from various sources, including in vitro studies, animal models, and clinical trials. The integration of olive oil into dietary patterns, particularly its role in the Mediterranean diet, and its broader implications in neurodegenerative disease prevention are also discussed. The challenges in translating preclinical findings to clinical applications are acknowledged and future research directions are proposed to better understand the potential of olive oil in mitigating the risk of neurodegenerative conditions. This review highlights olive oil not only as a dietary component, but also as a promising candidate in preventive neurology, advocating for further investigation in the context of neurodegenerative diseases.

This review explores the critical role of advanced metabolomic methodologies in interpreting the complex relationship between the bioactive compounds in olive oil and their health benefits. Olive oil, the cornerstone of the Mediterranean diet, is renowned for its numerous health benefits, including anti-inflammatory, antioxidant, and cardioprotective properties. This review begins with an overview of the importance of olive oil for nutrition and health, followed by a detailed discussion of the key metabolites that contribute to its therapeutic potential. It further explores the evolution of analytical techniques in metabolomics from traditional methods to cutting-edge technologies, such as mass spectrometry and nuclear magnetic resonance spectroscopy, which have enhanced our understanding of the complex composition of olive oil. This review highlights how these advanced analytical findings correlate with the known health properties of olive oil and presents case studies in which these methodologies have led to new insights. Finally, we address the challenges in standardizing these techniques and discuss future research directions, underscoring the significance of metabolomics in nutritional science and public health. This comprehensive analysis aims to provide a deeper understanding of the health benefits of olive oil, informed by the latest advancements in analytical methodologies.

Food insecurity (FI) indicates a situation in which there is no regular access to food in satisfactory quantity and quality. To characterize FI in students from Portuguese higher education institutions, a study was conducted in a non-random sample of students, with an online questionnaire consisting of sociodemographic questions and the Portuguese version of the IF scale. There were 200 valid questionnaires and 27.5% of the participants were classified as having some degree of FI. Participants with FI reported fewer visits to the family’s home (p = 0.024) and less financial resources (p < 0.001). The results indicate that interventions are needed in this area.

This study estimates wave energy for the Moroccan Atlantic coast using SWAN, a third-generation wave model, covering a period of 30 years, from 1991 to 2020. The model is forced by the wind from the ERA-5 reanalysis dataset and uses boundary conditions generated by the WAVEWATCH III model. The significant wave height and period are used to obtain wave energy, which is analyzed at a regional scale. The mean wave energy density within the domain is assessed to be about 20 kW/m. Five specific locations are evaluated along the coast in order to determine the most energetic ones. The most energetic area of the Moroccan Atlantic coast is located at the center, between the cities of Agadir and Essaouira. Finally, the performance of six different wave energy converters is assessed through their power matrix for each of the five locations.

Apple and pear crops are very important to the rural economy of Portugal. Despite significant improvements in productivity and quality, due to the introduction of new management techniques, model-based decision support may further increase the revenue of the growers. Available simulation models of orchard growth and production are scarce and are often highly empirical. This study presents a mechanistic model for the simulation of productivity and fruit grade of apple and pear orchards under potential and water-limited conditions. The effects of temperature extremes and rain on fruit set are addressed. The model was validated on apple and pear datasets derived from extensive experiments conducted in Central and Southern Portugal. Model performance is high and depicts the effect of crop load on productivity and fruit-size grade and the distribution of both crops. A simulation example shows the relationship between productivity and average fruit size for a hypothetical six-year-olc apple orchard. The model herewith presented is a tool that can be used to estimate optimal crop load for maximum revenue and productivity, fruit size distribution, water use, and other variables relevant for pome fruit production.