IntroductionIn light of the SARS-CoV-2 pandemic, protecting vulnerable groups has become a high priority. Persons at risk of severe disease, for example, those receiving immunosuppressive therapies for chronic inflammatory cdiseases (CIDs), are prioritised for vaccination. However, data concerning generation of protective antibody titres in immunosuppressed patients are scarce. Additionally, mRNA vaccines represent a new vaccine technology leading to increased insecurity especially in patients with CID.ObjectiveHere we present for the first time, data on the efficacy and safety of anti-SARS-CoV-2 mRNA vaccines in a cohort of immunosuppressed patients as compared with healthy controls.Methods42 healthy controls and 26 patients with CID were included in this study (mean age 37.5 vs 50.5 years). Immunisations were performed according to national guidelines with mRNA vaccines. Antibody titres were assessed by ELISA before initial vaccination and 7 days after secondary vaccination. Disease activity and side effects were assessed prior to and 7 days after both vaccinations.ResultsAnti-SARS-CoV-2 antibodies as well as neutralising activity could be detected in all study participants. IgG titres were significantly lower in patients as compared with controls (2053 binding antibody units (BAU)/mL ±1218 vs 2685±1102). Side effects were comparable in both groups. No severe adverse effects were observed, and no patients experienced a disease flare.ConclusionWe show that SARS-CoV-2 mRNA vaccines lead to development of antibodies in immunosuppressed patients without considerable side effects or induction of disease flares. Despite the small size of this cohort, we were able to demonstrate the efficiency and safety of mRNA vaccines in our cohort.

Highlights d Low avidity and broad cross-reactivities of pre-existing SARS-CoV-2 memory T cells d Strong CCCoV-specific memory CD4 + T cell responses in all analyzed individuals d SARS-CoV-2-specific CD4 + T cells in COVID-19 patients lack cross-reactivity to CCCoVs d Low avidity and clonality of SARS-CoV-2-specific T cell responses in severe COVID-19

Highlights d SARS-CoV2 infection elicits dynamic changes of circulating cells in the blood d Severe COVID-19 is characterized by increased metabolically active plasmablasts d Elevation of IFN-activated megakaryocytes and erythroid cells in severe COVID-19 d Cell-type-specific expression signatures are associated with a fatal COVID-19 outcome

In an analysis of serum samples from more than 500 patients with IBD, we observed a negative correlation between serum levels of tryptophan and disease activity. Increased levels of tryptophan metabolites-especially of quinolinic acid-indicated a high activity of tryptophan degradation in patients with active IBD. Tryptophan deficiency could contribute to development of IBD or aggravate disease activity. Interventional clinical studies are needed to determine whether modification of intestinal tryptophan pathways affects the severity of IBD.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.