Strain Measurement in Osteoarthritic Cartilage (SMOC)
Self-funded PhD students only
School of Pharmacy and Biomedical Sciences
Applications accepted all year round
The work on this project will investigate:
- the strain distribution in normal human articular cartilage obtained from bone cancer Specimens
- the strain distribution in human OA samples taken from the tibial plateau during total knee replacement
- the strain distribution in specimens taken at different time points (longitudinal study) fromanimal models that develop OA
Osteoarthritis (OA) affects more than 250 million people worldwide, impacts more than half of the population over the age of 65 and is predicted to increase 7-fold by 2030. But our understanding of the aetiology and pathogenesis of OA remains incomplete, despite numerous research studies over several decades. Treatments have also been largely unsuccessful.
Early OA is associated with early changes in the architecture and volume of subchondral bone, which has led many in the field to think of OA as a disease of the ‘whole joint.’
The focus on bone changes as the initial effector of the osteoarthritic process is influenced by studies proposing how pathogenesis of OA can be attributed to a primary alteration in surrounding bone, which leads to increased strains in the the overlying articular cartilage. This adversely affects chondrocyte function and cartilage matrix loss.
This hypothesis is supported by numerous studies which have demonstrated that changes in bone occur very early in the development of OA. However, cartilage and bone both have the capacity to respond to adverse biomechanical signals and, therefore, it is more likely that both tissues undergo structural and functional alterations during the initiation and evolution of OA. The extent, the interrelated effect on bone and cartilage, and the precise timing of these changes remains unknown.
The strain in the subchondral bone and in the cartilage will be investigated using high-resolution 3D X-ray computed tomography (XCT), using both adsorption and phase-contrast imaging. Specimens will be subjected to in situ mechanical loading and imaged at increasing incremental loads. The degree of strain will be determined using digital volume correlation (DVC) and its distribution related to the degree of damage using histology and immunohistochemistry, which will detect the breakdown of the cartilage matrix.
The University of Portsmouth is uniquely positioned to answer this research question with our exceptional imaging facilities available at the Zeiss Global Centre as well as world-leading experience in digital volume correlation in musculoskeletal research. The project will develop and train a PGR student in the large research area of osteoarthritis, but at the same time will utilise new techniques to address the research question.
The student will use and develop skills, which could be applied to other aspects of biomedical engineering giving them a number of potential career opportunities after completing the PhD.
- You'll need a good first degree from an internationally recognised university (minimum second class or equivalent, depending on your chosen course) or a Master’s degree in a relevant subject area
- In exceptional cases, we may consider equivalent professional experience and/or Qualifications
- English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0
How to apply
Please contact Professor Gordon Blunn (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you're ready to apply, you can use our online application form and select ‘Biomedical, Biomolecular and Pharmacy’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
Our ‘How to Apply’ page offers further guidance on the PhD application process.
Please note, to be considered for this self-funded PhD opportunity you must quote project code PHBM4820219 when applying.