Prospective, multicenter validation of a platform for rapid molecular profiling of central nervous system tumors

Molecular data integration plays a central role in central nervous system (CNS) tumor diagnostics but currently used assays pose limitations due to technical complexity, equipment and reagent costs, as well as lengthy turnaround times. We previously reported the development of Rapid-CNS2, an adaptive-sampling-based nanopore sequencing workflow. Here we comprehensively validated and further developed Rapid-CNS2 for intraoperative use. It now offers real-time methylation classification and DNA copy number information within a 30-min intraoperative window, followed by comprehensive molecular profiling within 24 h, covering the complete spectrum of diagnostically and therapeutically relevant information for the respective entity. We validated Rapid-CNS2 in a multicenter setting on 301 archival and prospective samples including 18 samples sequenced intraoperatively. To broaden the utility of methylation-based CNS tumor classification, we developed MNP-Flex, a platform-agnostic methylation classifier encompassing 184 classes. MNP-Flex achieved 99.6% accuracy for methylation families and 99.2% accuracy for methylation classes with clinically applicable thresholds across a global validation cohort of more than 78,000 frozen and formalin-fixed paraffin-embedded samples spanning five different technologies. Integration of these tools has the potential to advance CNS tumor diagnostics by providing broad access to rapid, actionable molecular insights crucial for personalized treatment strategies

Active bi- and multistability in cooperative microactuator systems

The smart coupling of microactuators to cooperative microactuator systems enables new functionalities like active bi- and multistability requiring no external force for switching between stable states. This review explores different concepts of cooperative microactuator systems combining microactuation based on either the same or different transducer principles. The transducer principles comprise electrostatic, magnetic, dielectric elastomer and shape memory effects as well as combinations thereof. Thereby, active bi-/multistable switching is achieved via selective control of the microactuators using diverse control signals based on thermal, electrical or magnetic stimuli. The combination of the microactuators in confined space gives rise to various coupling effects and cross-sensitivities that need to be considered. In the following, the engineering aspects of material properties, microsystems design and fabrication, as well as experimental and numerical characterization of system performances and dependencies on design parameters will be discussed. The presented microactuator systems will be assessed with respect to their energy characteristics and critical forces for switching. Their application potential will be highlighted

Enhancing trust by a Keycloak-Flower integration for federated machine learning

Since its introduction, federated learning (FL) has attracted a lot of attention in the medical field, but its actual application in healthcare organisations remains limited. Flower is a leading FL framework known for its good documentation and wide application. To close security gaps, we propose to integrate Keycloak with gRPC and Flower to improve identity and access management. We have developed a lightweight Python module that integrates both and also validates the client's code with the server before execution. The system has been tested in a simple prototype, but further work and security testing is required for a complex evaluation

Federated learning for predictive analytics in weaning from mechanical ventilation

Mechanical ventilation is crucial for critically ill patients in ICUs, requiring accurate weaning and extubations timing for optimal outcomes. Current prediction models struggle with generalizability across datasets like MIMIC-IV and eICU-CRD. We propose a federated learning approach using XGBoost with bagging aggregation to improve weaning predictions while ensuring patient data privacy, compliant with GDPR and HIPAA. Using the OMOP Common Data Model, our method integrates machine learning techniques across three ICU databases, encompassing over 33,000 patients. Our model achieved robust performance with 77% AUC and 73% AUPRC. Planned pilot studies in Germany will further refine and validate our approach. This study demonstrates the potential of federated learning to enhance critical care by providing personalized, data-driven insights for ventilation management