Angelina Passaro

Microalgae represent a growing innovative source of nutraceuticals such as carotenoids and phenolic compound which are naturally present within these single-celled organisms or can be induced in response to specific growth conditions. The presence of the unfavourable allelic variant in genes involved in the control of oxidative stress, due to one or more SNPs in gene encoding protein involved in the regulation of redox balance, can lead to pathological conditions such as insulin resistance, which, in turn, is directly involved in the pathogenesis of type 2 diabetes mellitus. In this review we provide an overview of the main SNPs in antioxidant genes involved in the promotion of insulin resistance with a focus on the potential role of microalgae-derived antioxidant molecules as novel nutritional tools to mitigate oxidative stress and improve insulin sensitivity.

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.