Suchergebnisse

Recombinant vesicular stomatitis virus-Zaire Ebola virus (rVSV-ZEBOV) is the most advanced Ebola virus vaccine candidate and is currently being used to combat the outbreak of Ebola virus disease (EVD) in the Democratic Republic of the Congo (DRC). Here we examine the humoral immune response in a subset of human volunteers enrolled in a phase 1 rVSV-ZEBOV vaccination trial by performing comprehensive single B cell and electron microscopy structure analyses. Four studied vaccinees show polyclonal, yet reproducible and convergent B cell responses with shared sequence characteristics. EBOV-targeting antibodies cross-react with other Ebolavirus species, and detailed epitope mapping revealed overlapping target epitopes with antibodies isolated from EVD survivors. Moreover, in all vaccinees, we detected highly potent EBOV-neutralizing antibodies with activities comparable or superior to the monoclonal antibodies currently used in clinical trials. These include antibodies combining the IGHV3-15/IGLV1-40 immunoglobulin gene segments that were identified in all investigated individuals. Our findings will help to evaluate and direct current and future vaccination strategies and offer opportunities for novel EVD therapies.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine 1,2 . Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes 3 . However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation 4,5 . 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.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2 . Patients with leukaemia can be identifed 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 confdentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifers 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 classifers outperform those developed at individual sites. In addition, Swarm Learning completely fulfls local confdentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine(1,2). Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes(3). However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation(4,5). 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.