污污草莓视频

1st Place

Universal Antigen Encoding of T-Cell Activation from High-dimensional Single-Cell Dynamics

Liamo Pennimpede, Abe Arafat, Ryan Reszetnik, and Romen Poirier (all from Electrical, Computer, and Software Engineering)

We have built an application to help cancer researchers speed up the drug development process. The application leverages a fully customised, unsupervised, machine learning model. Our supervisors are at Mila in Montreal and the National Cancer Institute in Washington, D.C. The use of our solution allows cancer researchers to test a high number of neoantigens at a very low cost, and very quickly. Then, only top candidates move on to be validated using the more costly mouse-model, before finally moving on to human trials.

2nd Place

Multi-Camera Surgical Tool Localization Setup for Image-Guided Neurosurgery

George Sideris, Justin Cree, Andrew Stirling, and Mamadou Ly (all from Mechanical Engineering)

Our project included the design (both mechanical and software) and fabrication of a multi-camera surgical tool localization setup for image-guided neurosurgery. Image-guided neurosurgery entails the modelling of critical structures in the brain, such as tumors and blood vessels, and the tracking of surgical tools in relation to these models. This tracking enables the surgeons to visualize the position of their tools in relation to the critical structures in real-time, allowing them to plan tumor removal approaches before performing a craniotomy (analogous to 'x-ray' vision). Our solution has the potential to greatly improve the user experience of neurosurgeons by limiting any obstructions to their view or mobility in comparison to state-of-the-art tool localization systems. Further, its greatly reduced cost will enable image-guided surgery technologies to become accessible to many hospitals within Canada and around the world which lack the funding for currently available systems.

3rd Place

Multiplexed Colorimetric Biosensor on Contact-Mode CMOS Image Sensor

Laura Camila Penuela Cardenas, Nassib Jr. Hassouna, Young chae Han, Lan Anh Huynh, and Mary Wan (all from Bioengineering)

We have designed and built a colorimetric biosensor on a contact-mode CMOS image sensor for the monitoring of chronic kidney disease (CKD) via the multiplex detection of sweat CKD biomarkers (e.g., uric acid, creatinine, chloride). Our proposed solution offers end users the potential to monitor their own progression of CKD from the comfort of their home.

1st Place

elleFA

Maya DeCruz, Anita Kriz, Zoe Goldberger, Grace Reszetnik, and Alexandra Magliocco (all from Bioengineering)
At elleFA, we are working to develop a solution that can address the shortcomings in the diagnosis and treatment of endometriosis. In all, our centralized platform is composed of three goals (1) screen for endometriosis biomarkers correlated with the disease with our at home test kit (2) track biomarkers, symptoms, and intervention responses over time with our app and (3) connect doctors and patients with quantitative results and qualitative symptoms. To screen, we are developing an at-home lateral flow assay (LFA) kit, the same technology used to make pregnancy tests. With this kit, women can quickly see their urine levels of prognostic biomarkers that are correlated with endometriosis in the comfort of their homes. Throughout their treatment journey, they can continue to use this kit to evaluate the progression of their disease. To further ease patients in the tracking of their disease management, we are simultaneously developing an app in which patients can record symptoms, screening results, and responses to treatments in a novel format endorsed by the stakeholders we have partnered with. Our third and final goal will be met through the app by organizing various patient information in a format that is easy for both the physicians and the patient to understand. To facilitate communication between the two parties, we will provide resources through the app that will make monitoring efficient, direct and simple.

2nd Place

Orthosis Device for Low Back Pain (LBP)

Maria Calderbank, Emilie Davignon, Louis Tan, and Roseline Theroux (all from Mechanical Engineering)

The main objective of our Capstone Design Project was to design and fabricate a back supportive device that reduces low back pain (LBP) by increasing intra-abdominal pressure (IAP) during forward bending. Our orthosis incorporates an abdominal belt that automatically tightens relative to the extension of the spine, via a linkage mechanism. This prototype was designed for labor intensive workers with mature spines suffering from chronic LBP. The cost of one prototype is $227.23CAD. The device has been validated by preliminary results from clinical trial, showing that it increases IAP by 36.12% during bending.

3rd Place

Re-Design of an Aircraft Seat Table

Mavesa Nguyen, Amelia Duguay, Prune Huguet, and Valentin Sutyushev (all from Mechanical Engineering)

Our design solution is not only desirable from a human and environmental perspective but also technically feasible and economically viable. The tablet holder feature solves the problem of neck cramps and discomfort from holding devices for extended periods during flight. Additionally, the added convenience means you can use your tray table for other activities, like reading or working, without having to constantly hold onto your device. The 3D printing technology used for manufacturing eliminates the need for expensive molds, reducing costs and increasing environmental sustainability compared to the costs associated with manufacturing and replacing traditional aircraft tray tables. It is maintenance-free, and easy to replace and install on aircraft seats.

Our redesigned Aircraft Tray Table is the perfect solution for airlines looking to reduce costs and improve the in-flight experience for their passengers, making it a desirable solution for the industry. And for travelers, it's a game changer - personalized entertainment and a comfortable way to use their devices during long flights.

Reconstructing the Esophageal Tumour Microenvironment: Development of an ECM Hydrogel to Host Tumour Spheroids

Madison Santos, Ariel Corsano, and Isabelle Dummer (all from Bioengineering)

Mimicking the mechanical properties of the tumour microenvironment (TME) is essential in cancer models, as mechanical changes impact the ability of drugs to reach the tumour core. However, in vitro cell monolayer models lack the three-dimensional aspect of the TME, and in vivo small animal tumour xenografts introduce non-human factors into tumour growth. We propose a 3D in vitro tumour model that mirrors the biomechanical and pathophysiological characteristics of esophageal cancer. We have formulated a material incorporating structural proteins and retaining physical and biomolecular cues to promote native cell proliferation. The compositional and structural material characterization was used to appropriately tune the material mechanical properties to imitate esophageal tissue mechanics. To this end, alginate-gelatin hydrogels of varying concentrations have been synthesized and characterized by rheometry. Decellularized extracellular matrix (dECM) was selected as a bioactive additive for the tumour models and was obtained by enzymatic decellularization of porcine gastric tissue. The biochemical composition of the dECM was evaluated by spectrophotometric assays to confirm successful decellularization and structural protein content. Cancer cell viability and expansion in spheroid culture were evaluated within the new material. Overall, this biologically derived spheroid tumour model is expected to improve the accuracy of existing tumour models, which will increase understanding of esophageal cancer proliferation, mechanics, and immune response, which will be used to inform treatment plans and patient outcomes.