Lunanos Inc. and Ryerson University's School of Graphic Communications Management are seeking to collaborate on a six-month project to develop an innovative colour-changing indicator label that will be used in healthcare facilities to track the cleaning of various equipment and surfaces to reduce the frequency of healthcare-associated infections (HAI). In Canada, 1 in 10 patients admitted to a hospital picks up an infection and it is estimated that 30-50% of these cases are preventable through better infection prevention and control.

Paramedics provide essential emergency care services for Canadians. However, in providing this service, paramedics are exposed to many highly demanding task and situations. Further, these high demands are often referred to when paramedics suffer pain, discomfort and even injury. While efforts are underway to lessen these demands where possible, due to the nature of paramedic work it is not feasible to eliminate all high demand, potentially hazardous tasks.

Automation of medical diagnosis/detection process is very important in terms of enhancing diagnostic accuracy, increasing throughput, reducing cost, and training new staff. Our current goal is to go from the proof of concept stage (automatic recognition and classification of human blood images) to a complete working optimized prototype and to start testing it in an actual clinical lab environment with help from CLS. The prototype design will take into account user friendliness, high throughput, robustness, integration with existing lab work flow and reasonable cost.

The proposed project is aimed to provide information on the mechanism of premature calcification in the Transcatheter Aortic Valves through a complete investigation of the physicochemical factors influencing the process (chemical composition, pH, temperature, fluid dynamics, and the presence of foreign inorganic and organic substances). The outcome of the proposed research project would allow formulation of the test criterions for proper identification of the valve design prone to premature, flow-induced calcification.

Accurate monitoring of a patient’s vital signs – including body temperature, blood pressure, and pulse oxygenation – is central to the ability of clinicians to provide appropriate medical care. In spite of this, the standard equipment used to take these measurements is inefficient, inconvenient, and expensive. Adept Diagnostics will break into the medical device market with the development of novel wireless sensor systems to track these physiological variables continuously and inobtrusively.

Deep brain stimulation (DBS) is a complicated electrotherapeutic medical procedure which provides irreplaceable therapies and treatments for several nervous system disorders, such as Parkinson’s disease. Advancements in the electrodes designs will improve the delivered therapy and enhance the efficacy of the DBS system. The objective of this project is to begin preliminary designs for deep brain stimulation and targeting electrodes capable of providing enhanced functionalities and improved biocompatibility to address the limitations in conventional electrode technologies.

A versatile spine modeling toolkit would enable the automatic assessment of gross anomalies and the evaluation of limited regions and spine components such as vertebra and discs. The system would consist of modules for: 1) segmenting the spine; 2) modeling spinal components; 3) detecting, assessing and analyzing salient features; and 4) generating information for medical diagnosis, patient reporting and clinical management. Despite a clear need for a versatile spine assessment toolkit, none is commercially available today.

Peanut products are known to contain allergens capable of causing severe and life threatening allergic reactions. Classically, Ara h 1, Ara h 2, and Ara h 3 are considered to be the major peanut allergens, whereas Ara h 9 is the most prevalent allergen in Mediterranean countries. Current detection methods of peanut allergens are expensive and require centralized laboratories and highly trained personnel. Hence, there is an urgent need to develop rapid, simple, economic, and reliable assays for the detection of peanut allergens.

Bacterial cellulose (BC) is a natural polymer produced by certain bacteria in the form of nanaofiber. Being a natural nanobiomaterial, it has been investigated for a broad range of applications ranging from headphone diaphragm to wound dressing and medical implant. The biochemical process for BC production using the well established bacterium, A. xylinum, is limited by the inherent kinetics and oxygen availability resulting in a relatively modest yield and high production cost. In this project, we will be investigating a recently discovered facultative bacterium for BC production.

Brain wave technologies allow researchers and clinicians to monitor the brain at work. Our team has developed a new technology that uses electroencephalography (EEG) to provide an online record of different brain functions in a very short period of time. The brain waves recorded by this technology may change normally as time passes and we first need to understand how this happens in a healthy individual.