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Multiscale/Multimodal Structure-Function Analysis of Intervertebral Disc Degeneration & Regeneration

Thursday, April 25, 2019

9:00 AM-11:00 AM

BIOMED PhD Research Proposal

Multiscale and Multimodal Structure-Function Analysis of Intervertebral Disc Degeneration and Regeneration

Beth G. Ashinsky, MD/PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Robert L. Mauck, PhD
Mary Black Ralston Professor for Education and Research In Orthopaedic Surgery
Department: Orthopaedic Surgery
Perelman School of Medicine
University of Pennsylvania

Lin Han, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Intervertebral disc (IVD) degeneration and associated back pain place a significant burden on the population. IVD degeneration is a progressive cascade of cellular, compositional and structural changes, which results in a loss of disc height, disorganization of extracellular matrix architecture, herniation of the nucleus pulposus, tears in the annulus fibrosus, and remodeling of the boney and cartilaginous endplates. These changes often occur concomitantly, which renders it difficult to determine which factor causes the initiation of degeneration. Further, the assessment of the subcomponents and interfacial regions of the IVD have been largely qualitative to date, and the changes that occur with degeneration are not yet well understood.

Current therapies to restore native structure and function of the IVD are limited, thus, clinical treatments are focused on alleviation of symptoms. Tissue engineering provides a promising treatment option and has the potential to restore both the structure and function of native discs, however, multi-scale predictors of functional restoration do not exist.

Given that the field has not yet established the most important features to define degeneration or successful regeneration, and that most extant tools are qualitative in nature, our overall goal is to establish quantitative microscale and macroscale outcomes that define the spectrum of degeneration and regeneration. In Aim 1 of this proposal we will develop a quantitative dataset of the structural and functional features of degeneration over time in an in vivo rabbit model to discern the most predictive properties of degeneration. We will employ similar analyses in Aim 2, using human cadaveric discs of differing degenerative states. In particular, the outcomes from Aims 1 & 2 will be assessed using clinically relevant diagnostic tools. With these distinct quantitative datasets, we will develop a machine learning approach to identify the primary contributors to disc degeneration. In order advance and optimize a tissue-engineered disc replacement, in Aim 3, we will apply these predictive models to determine which engineered disc design modifications best promote optimal regeneration.

The results from this research will identify the most significant contributors to disc degeneration and regeneration, advancing new treatment strategies towards clinical use. Overall, this work has the potential to significantly improve the diagnosis and clinical treatment of back pain associated with disc degeneration.

Contact Information

Ken Barbee

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