Synthetic Health Data: Real Ethical Promise and Peril.

Researchers and practitioners are increasingly using machine-generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while-potentially-mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it.

How Machine Learning is Powering Neuroimaging to Improve Brain Health.

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan.

Mineral Distribution Spatially Patterns Bone Marrow Stromal Cell Behavior on Monolithic Bone Scaffolds.

Interfaces between soft tissue and bone are characterized by transitional gradients in composition and structure that mediate substantial changes in mechanical properties. For interfacial tissue engineering, scaffolds with mineral gradients have shown promise in controlling osteogenic behavior of seeded bone marrow stromal cells (bMSCs). Previously, we have demonstrated a 'top-down' method for creating monolithic bone-derived scaffolds with patterned mineral distributions similar to native tissue.

Top-down Fabrication of Spatially Controlled Mineral-Gradient Scaffolds for Interfacial Tissue Engineering.

Materials engineering can generally be divided into "bottom-up" and "top-down" approaches, where current state-of-the-art methodologies are bottom-up, relying on the advent of atomic-scale technologies. Applying bottom-up approaches to biological tissues is challenging due to the inherent complexity of these systems. Top-down methodologies provide many advantages over bottom-up approaches for biological tissues, given that some of the complexity is already built into the system.