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.

We identify SMARCD2 (SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily D, member 2), also known as BAF60b (BRG1/Brahma-associated factor 60b), as a critical regulator of myeloid differentiation in humans, mice, and zebrafish. Studying patients from three unrelated pedigrees characterized by neutropenia, specific granule deficiency, myelodysplasia with excess of blast cells, and various developmental aberrations, we identified three homozygous loss-of-function mutations in SMARCD2. Using mice and zebrafish as model systems, we showed that SMARCD2 controls early steps in the differentiation of myeloid–erythroid progenitor cells. In vitro, SMARCD2 interacts with the transcription factor CEBPε and controls expression of neutrophil proteins stored in specific granules. Defective expression of SMARCD2 leads to transcriptional and chromatin changes in acute myeloid leukemia (AML) human promyelocytic cells. In summary, SMARCD2 is a key factor controlling myelopoiesis and is a potential tumor suppressor in leukemia.

IκBα point mutants accumulate at higher levels compared with truncation mutants and are associated with more severe disease and greater impairment of canonical and noncanonical NF-κB activity in patients with AD EDA-ID.

Purpose. Inborn errors of IFN-γ-mediated immunity underlie Mendelian Susceptibility to Mycobacterial Disease (MSMD), which is characterized by an increased susceptibility to severe and recurrent infections caused by weakly virulent mycobacteria, such as Bacillus Calmette–Guérin (BCG) vaccines and environmental, nontuberculous mycobacteria (NTM). Methods. In this study, we investigated four patients from four unrelated consanguineous families from Isfahan, Iran with disseminated BCG disease. We evaluated the patients’ whole blood cell response to IL-12 and IFN-γ, IL-12Rβ1 expression on T-cell blasts, and sequenced candidate genes. Results. We reported four patients from Isfahan, Iran, ranging from 3 months to 26 years old, who had impaired IL-12 signaling. All patients suffered from BCG infectious diseases. One of them presented mycobacterial osteomyelitis as a form of infection. By Sanger sequencing, we identified three different types of homozygous mutations in IL12RB1. Expression of IL-12Rβ1 was completely abolished in the four patients with IL12RB1 mutations. Conclusions. IL-12Rβ1 deficiency was found in the four MSMD Iranian families tested. It is the first report of an Iranian case with S321X mutant IL-12Rβ1 protein. Mycobacterial osteomyelitis is another type of location of mycobacterial infection in an IL-12Rβ1-deficient patient, notified for the first time in this study.