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https://www.selleckchem.com/products/skf38393-hcl.html INTRODUCTION Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. METHODS We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated. RESULTS A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%-75%. DISCUSSION Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD. © 2020 The Authors. Alzheimer's & Dementia published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association.Social Media has changed the way that individuals interact with each other - it has brought considerable benefits, yet also some challenges. Social media in anatomy has enabled anatomists all over the world to engage, interact and form new collaborations that otherwise would not have been possible. In a relatively small discipline where individuals may be working as the only anatomist in an institution, having such a virtual community can be important. Social media is also being used as a means for anatomists to communicate with the current generation of students as well as members of the public. Posting appropriate content is o

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