Genetic Differences in the Immediate Transcriptome Response to Stress Predict Risk-Related Brain Function and Psychiatric Disorders

Depression risk is exacerbated by genetic factors and stress exposure; however, the biological mechanisms through which these factors interact to confer depression risk are poorly understood. One putative biological mechanism implicates variability in the ability of cortisol, released in response to stress, to trigger a cascade of adaptive genomic and non-genomic processes through glucocorticoid receptor (GR) activation. Here, we demonstrate that common genetic variants in long-range enhancer elements modulate the immediate transcriptional response to GR activation in human blood cells. These functional genetic variants increase risk for depression and co-heritable psychiatric disorders. Moreover, these risk variants are associated with inappropriate amygdala reactivity, a transdiagnostic psychiatric endophenotype and an important stress hormone response trigger. Network modeling and animal experiments suggest that these genetic differences in GR-induced transcriptional activation may mediate the risk for depression and other psychiatric disorders by altering a network of functionally related stress-sensitive genes in blood and brain

Turnaround time prediction for clinical chemistry samples using machine learning

Objectives: Turnaround time (TAT) is an essential performance indicator of a medical diagnostic laboratory. Accurate TAT prediction is crucial for taking timely action in case of prolonged TAT and is important for efficient organization of healthcare. The objective was to develop a model to accurately predict TAT, focusing on the automated pre-analytical and analytical phase. Methods: A total of 90,543 clinical chemistry samples from Erasmus MC were included and 39 features were analyzed, including priority level and workload in the different stages upon sample arrival. PyCaret was used to evaluate and compare multiple regression models, including the Extra Trees (ET) Regressor, Ridge Regression and K Neighbors Regressor, to determine the best model for TAT prediction. The relative residual and SHAP (SHapley Additive exPlanations) values were plotted for model evaluation. Results: The regression-tree-based method ET Regressor performed best with an R2 of 0.63, a mean absolute error of 2.42 min and a mean absolute percentage error of 7.35%, where the average TAT was 30.09 min. Of the test set samples, 77% had a relative residual error of at most 10%. SHAP value analysis indicated that TAT was mainly influenced by the workload in pre-analysis upon sample arrival and the number of modules visited. Conclusions: Accurate TAT predictions were attained with the ET Regressor and features with the biggest impact on TAT were identified, enabling the laboratory to take timely action in case of prolonged TAT and helping healthcare providers to improve planning of scarce resources to increase healthcare efficiency

Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL

Summary: Platform trials bring the promise of making clinical research more efficient and more patient centric. While their use has become more widespread, including their prominent role during the COVID-19 pandemic response, broader adoption of platform trials has been limited by the lack of experience and tools to navigate the critical upfront planning required to launch such collaborative studies. The European Union-Patient-cEntric clinicAl tRial pLatform (EU-PEARL) initiative has produced new methodologies to expand the use of platform trials with an overarching infrastructure and services embedded into Integrated Research Platforms (IRPs), in collaboration with patient representatives and through consultation with U.S. Food and Drug Administration and European Medicines Agency stakeholders. In this narrative review, we discuss the outlook for platform trials in Europe, including challenges related to infrastructure, design, adaptations, data sharing and regulation. Documents derived from the EU-PEARL project, alongside a literature search including PubMed and relevant grey literature (e.g., guidance from regulatory agencies and health technology agencies) were used as sources for a multi-stage collaborative process through which the 10 more important points based on lessons drawn from the EU-PEARL project were developed and summarised as guidance for the setup of platform trials. We conclude that early involvement of critical stakeholder such as regulatory agencies or patients are critical steps in the implementation and later acceptance of platform trials. Addressing these gaps will be critical for attaining the full potential of platform trials for patients. Funding: Innovative Medicines Initiative 2 Joint Undertaking with support from the European Union’s Horizon 2020 research and innovation programme and EFPIA