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The European Health Data Space (EHDS) aspires to enable secure, interoperable, and decentralized health data usage across Europe. This paper explores legal and technical challenges in implementing EHDS goals, particularly for secondary data use. It highlights federated and swarm learning as promising yet complex solutions, requiring robust infrastructure, standardization, and regulatory clarity. We emphasize the need for coordinated legislative and technological advances to realize EHDS ambitions.

In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.

ObjectiveTo investigate socioeconomic differences in six perinatal health outcomes in Denmark in the first decade of the 21st century.DesignA population-based cohort study.SettingDanish national registries.ParticipantsA total of 646 829 live born children and 3076 stillborn children (≥22+0 weeks of gestation) born in Denmark from 2000 to 2009. We excluded children with implausible relations between birth weight and gestational age (n=644), children without information on maternal country of origin (n=138) and implausible values of maternal year of birth (n=36).Main outcome measuresWe investigated the following perinatal health outcomes: stillbirth, neonatal and postneonatal mortality, small-for-gestational age, preterm birth grated into moderate preterm, very preterm and extremely preterm, and congenital anomalies registered in the first year of life.ResultsMaternal educational level was inversely associated with all adverse perinatal outcomes. For all examined outcomes, the risk association displayed a clear gradient across the educational levels. The associations remained after adjustment for maternal age, maternal country of origin and maternal year of birth. Compared with mothers with vocational education, mothers with more than 15 years of education had an adjusted risk ratio for stillbirth of 0.64(95% CI 0.56 to 0.72). The corresponding adjusted risk ratios for neonatal mortality, postneonatal mortality, congenital anomalies, moderate preterm birth and small-for-gestational age were, respectively, 0.79(95% CI 0.67 to 0.93), 0.57(95% CI 0.42 to 0.78), 0.87(95% CI 0.83 to 0.91), 0.80(95% CI 0.77 to 0.83) and 0.83(95% CI 0.81 to 0.85).ConclusionSubstantial educational inequalities in perinatal health were still present in Denmark in the first decade of the 21st century.