Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics. Data was curated through calls to members of the OATech Network+ (Abstract
Objectives
Methods
While the human embryonic, foetal and juvenile intervertebral disc (IVD) is composed of large vacuolated notochordal cells, these morphologically distinct cells are lost with skeletal maturity being replaced by smaller nucleus pulpous cells. Notochordal cells are thought to be fundamental in maintaining IVD homeostasis and, hence, their loss in humans may be a key initiator of degeneration, leading ultimately to back pain. Therefore, it is essential to understand the human notochordal cell phenotype to enable the development of novel biological/regenerative therapies. CD24+ notochordal cells and CD24- sclerotomal cells were sorted from enzymatically-digested human foetal spines (7.5–14 WPC, n=5) using FACS. Sorting accuracy was validated using qPCR for known notochordal markers and Affymetrix cDNA microarrays performed. Differential gene expression was confirmed (qPCR) and Interactive Pathway Analysis (IPA) performed.Background
Methods
Mesenchymal stem-cell based therapies have been
proposed as novel treatments for intervertebral disc degeneration,
a prevalent and disabling condition associated with back pain. The
development of these treatment strategies, however, has been hindered
by the incomplete understanding of the human nucleus pulposus phenotype
and by an inaccurate interpretation and translation of animal to
human research. This review summarises recent work characterising
the nucleus pulposus phenotype in different animal models and in
humans and integrates their findings with the anatomical and physiological
differences between these species. Understanding this phenotype
is paramount to guarantee that implanted cells restore the native
functions of the intervertebral disc. Cite this article:
