To date, the involvement of α-Lactalbumin (α-LA) in the management of polycystic ovary syndrome (PCOS) refers to its ability to improve intestinal absorption of natural molecules like inositols, overcoming the inositol resistance. However, due to its own aminoacidic building blocks, α-LA is involved in various biological processes that can open new additional applications. A great portion of women with PCOS exhibit gastrointestinal dysbiosis, which is in turn one of the triggering mechanisms of the syndrome. Due to its prebiotic effect, α-LA can recover dysbiosis, also improving the insulin resistance, obesity and intestinal inflammation frequently associated with PCOS. Further observations suggest that altered gut microbiota negatively influence mental wellbeing. Depressive mood and low serotonin levels are indeed common features of women with PCOS. Thanks to its content of tryptophan, which is the precursor of serotonin, and considering the strict link between gut and brain, using α-LA contributes to preserving mental well-being by maintaining high levels of serotonin. In addition, considering women with PCOS seeking pregnancy, both altered microbiota and serotonin levels can induce later consequences in the offspring. Therefore, a deeper knowledge of potential applications of α-LA is required to transition to preclinical and clinical studies extending its therapeutic advantages in PCOS.
Objective Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies. Methods Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions. Results The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%. Conclusions Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.
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