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Abstract It is partially unknown whether the immune response to severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection persists with time. To address this issue, we detected the presence of SARS-CoV-2 antibodies in different groups of individuals previously diagnosed with COVID-19 disease (group 1 and 2), or potentially exposed to SARS-CoV-2 infection (group 3 and 4), and in a representative group of individuals with limited environmental exposure to the virus due to lockdown restrictions (group 5). The primary outcome was specific anti-SARS-CoV-2 antibodies in the different groups assessed by qualitative and quantitative analysis at baseline, 3 and 6 months follow-up. The seroconversion rate at baseline test was 95% in group 1, 61% in group 2, 40% in group 3, 17% in group 4 and 3% in group 5. Multivariate logistic regression analysis revealed male gender, close COVID-19 contact and presence of COVID-19 related symptoms strongly associated with serological positivity. The percentage of positive individuals as assessed by the qualitative and quantitative tests was superimposable. At the quantitative test, the median level of SARS-CoV-2 antibody levels measured in positive cases retested at 6-months increased significantly from baseline. The study indicates that assessing antibody response to SARS-CoV-2 through qualitative and quantitative testing is a reliable disease surveillance tool.

Abstract A central problem in most data-driven personalized medicine scenarios is the estimation of heterogeneous treatment effects to stratify individuals into subpopulations that differ in their susceptibility to a particular disease or response to a specific treatment. In this work, with an illustrative example on type 2 diabetes we showed how the increasing ability to access and analyzed open data from randomized clinical trials (RCTs) allows to build Machine Learning applications in a framework of personalized medicine. An ensemble machine learning predictive model is first developed and then applied to estimate the expected treatment response according to the medication that would be prescribed. Machine learning is quickly becoming indispensable to bridge science and clinical practice, but it is not sufficient on its own. A collaborative effort is requested to clinicians, statisticians, and computer scientists to strengthen tools built on machine learning to take advantage of this evidence flow.

Abstract Deferasirox (DFX) is the newest among three different chelators available to treat iron overload in iron-loading anaemias, firstly released as Dispersible Tablets (DT) and more recently replaced by Film-Coated Tablets (FCT). In this retrospective observational study, pharmacokinetics, pharmacodynamics, and safety features of DFX treatment were analyzed in 74 patients that took both formulations subsequently under clinical practice conditions. Bioavailability of DFX FCT compared to DT resulted higher than expected [Cmax: 99.5 (FCT) and 69.7 (DT) μMol/L; AUC: 1278 (FCT) and 846 (DT), P