Work Study (University of Toronto): Biomedical Research Project Assistant

Apply to Job 197267 through the University of Toronto’s Career & Co-Curricular Learning Network: https://clnx.utoronto.ca/

The Biomedical Research Project Assistant will work in a biomedical laboratory at the Lunenfeld Tanenbaum Research Institute supporting a multi-site, pre-clinical orthopaedics study. We are looking for a candidate with an interest in research, strong organizational skills and ability to work responsibly in a wet laboratory. The candidate will work collaboratively with our research team to ensure the successful execution of a pre-clinical study.

Core responsibilities include:

1. Management of schedules and documentation related to the study

2. Participation in lab analyses, for which the candidate will receive all necessary training

Students with science and engineering academic backgrounds are encouraged to apply. Students undertaking programs of study in the Department of Laboratory Medicine & Pathobiology, Biomedical Engineering, or related fields will be preferred. Experience required:

• Must have experience working in a wet laboratory

• Experience with project management an asset

Skills/competencies needed:

• Excellent attention to detail

• Excellent problem-solving skills

• A logical approach to work

• Strong communication skills

MEng Project: Image Classification for Chondrocyte Viability Subjected to Long-term Storage

Osteochondral allograft transplantation is a surgical repair technique that replaces damaged cartilage with osteochondral grafts, consisting of bone and cartilage, from a cadaveric donor. These grafts are stored for a minimum of 14 days before transplantation in order to complete routine screening and storage methods are used to ensure chondrocytes (cartilage cells) remain viable over this time. We have developed a fluorescent staining method to identify changes in chondrocyte metabolism concurrently with viability and recently developed a grading system to describe certain visual features. In this multidisciplinary project, the student will develop a machine learning algorithm to classify chondrocytes using an existing bank of images and participate in bench top experiments to gather data at additional storage time points. The results from this project will contribute to understanding how chondrocytes respond to prolonged storage and support efforts to increase the quantity and quality of fresh osteochondral allografts available for clinical use.

MEng Project: Understanding Non-invasive Measurements of Cartilage Quality using FEA

Electroarthrography (EAG) is a method by which electrical signals produced by cartilage are measured non-invasively through electrodes placed on skin surrounding an articular joint, analogous to the collection of other bio-potentials such as electrocardiography. The electrical signals produced by cartilage result from the way this highly specialized tissue responds to weight bearing and are directly linked to cartilage quality. In our lab experiments on joint explants, we simulate joint loading and collect EAG measurements from 6 to 8 skin electrodes at one of 3 levels of load, then we open the joint to directly measure cartilage quality. The EAG measurements are influenced by cartilage quality as well as other factors like load level, contact area, and joint angle. In this multidisciplinary project, the student will develop a joint specific finite element model as well as participate in performing experiments as described above. The results from this study will contribute to development of EAG into a clinical methodology that may contribute to the diagnosis and treatment of degenerative joint diseases.