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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. Here, we generate interfacial scaffolds by the spatially controlled removal of mineral content from trabecular bone using a chelating solution. We controlled the degree and location of the mineral interface, producing scaffolds that support cell growth, while maintaining the hierarchical structure of these tissues. We characterized the structural and compositional gradients across the scaffold using X-ray diffraction, microcomputed tomography (μCT), and Raman microscopy, revealing the presence of mineral gradients on the scale of 20 -40 μm. Using these data, we generated a model showing the dependence of mineral removal as function of time in the chelating solution and initial bone morphology, specifically trabecular density. These scaffolds will be useful for interfacial tissue engineering, with application in the fields of orthopedics, developmental biology, and cancer metastasis to bone.

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. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

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