Sex differences in blood pressure regulation
Should men and women suffering from high blood pressure be prescribed the same medications?
Hypertension is a global health challenge with known sexual dimorphism in pathophysiology and in responses to drug treatments, yet men and women are typically treated with the same approach. We conduct model simulations to gain mechanistic insights into how sex differences in the renin-angiotensin system and in renal phenotype impact kidney function in hypertension.
Simulating drug interactions in diabetes
Described as an "economic tsunami" by Diabetes Canada, diabetes has been projected to incur an annual cost of $16 billion in Canada by 2020. We seek to understand the sex differences and drug-drug interactions of key anti-hypertensive treatment (ACE inhibitors and ARB) and anti-hyperglycemic treatment (specifically, SGLT2 inhibitors).
A known complication of ACEi and ARB is hyperkalemia (high plasma potassium concentration). The impact of SGLT2i on potassium balance is controversial. Only one of the SGLT2i (canagliflozin) but not the other three (dapagliflozin, empagliflozin, ertugliflozin) mentions an increased risk of hyperkalemia in its official monograph. To what extent does SGLT2i modify the risk of hyperkalemia in diabetic patients with preserved versus impaired kidney function? What are the potential drug interactions between SGLT2i and ACEi/ARB? And are there sex differences?
Image processing, data-driven mechanistic modeling
A major goal of our research program is to transform Canada's health care system by designing, implementing and iteratively evaluating a data-informed digital health services model for chronic conditions, such as diabetes and polycystic kidney disease. Our group collaborate with clinician scientists to create novel analytic approaches using large databases to develop and validate tools for predicting serious health outcomes.
To improve disease diagnosis, we apply image processing to MRI scans, and we build data-driven mechanistic models that integrate large multidimensional datasets (which, in the case of diabetes, may include kidney physiology, MRI markers, IVGTT, metabolomics, CGM, kidney biomarkers, sex steroids and growth factors). The ultimate goal is to create novel analytic approaches using large databases to develop and validate tools for predicting serious health outcomes, for both populations and individuals with chronic diseaes.
To augment the impact of this data-informed process, our team collaborate with cybersecurity experts to develop a secured health data platform that enables timely access to clinical information.
Mitochondrial bioenergeics, metabolism, and aging
Bioenergetics is a field in biochemistry and cell biology that concerns energy flow through living systems. How do living organisms acquire and transform energy in order to perform biological work? How do the thousands of different cellular processes, such as cellular respiration and the many other metabolic and enzymatic processes, interact and lead to theproduction and utilization of energy in forms such as adenosine triphosphate (ATP) molecules. The study of metabolic pathways is thus essential to bioenergetics.
Aging, long considered to be solely the result of wear and tear, is in fact regulated by specific genetic and metabolic pathways. We use mathematical modeling to answer important questions: How do simple changes in the environment (e.g. dietary restriction) can drastically extend lifespan? How do several of these signaling pathways control longevity in response to changes in the surroundings?
A circadian rhythm is a roughly 24 hour cycle in the physiological processes of living beings, including plants, animals, fungi and cyanobacteria. There are many examples of circadian rhythms, including the sleep-wake cycle, the body-temperature cycle, and the cycles in which a number of hormones are secreted.
We are particularly interested in the role of the circadian rhythm in diseases and treatment. It has long been recognized that normal glucose‐induced insulin secretion in humans varies across the day–night cycle. How may the disruption of the circadian oscillation of glucose metabolism contribute to the development of type 2 diabetes?
Blood pressure exhibits a circadian rhythm and drops significantly at night time, in what is called a "dipper pattern." Shift workers, however, often show a non-dipper pattern. Does shift work have adverse effects on blood pressure, paritcularly in patients with hypertension? This is an important question, since shift workers make great use of health care services, as they are associated with increased cardiovascular morbidity and mortality.
Network analysis of eye gaze patterns
Our group has developed a new autism spectrum disorder (ASD) detection technique that distinguishes different eye-gaze patterns to help doctors more quickly and accurately detect ASD in children. This technique is based on how individuals with ASD visually explore and scan a person’s face differently from neurotypical individuals. An infrared device interprets and identifies the locations on the stimuli at which an individual is looking via emission and reflection of wave from the iris. We then apply network analysis (centrality measures) to evaluate the varying degree of importance the individual placed on different facial features. These measures are significantly different between individuals with ASD and neurotypical individuals.
Eye tracking techniques such as this have many applications, including diagnosing social anxiety, revealing an individual's level of interest (in commercial products, in other people), confidence level, etc.
Data for Good: How to improve water quality rhythms
This is a collaborative project with the Basu lab. Together, we utilize tools from environmental engineering and data science to explore pressing water quality issues. Our research is motivated by these questions: What can we learn from existing datasets? What are the emergent patterns in watershed hydrologic and biogeochemical responses across spatial and temporal scales? Why do these patterns emerge? What are the underlying processes that contribute to these patterns? How do humans interact with the landscapes to modify these patterns in space and time?
The last 100 years have seen a more than threefold increase in global population accompanied by agricultural intensification and dramatic changes in land use. Human activities have greatly accelerated nitrogen (N) and phosphorus (P) cycles, causing excess N and P leaching into surface water that has led to increased algal blooms, eutrophication and drinking water contamination. To mitigate and manage water pollution it is critically important to understand and model the drivers of water quality degradation at regional and global scales. The last decade has seen an exponential increase in the availability of high temporal resolution data from water quality sensors, as well as remote sensing and other spatial datasets, providing exciting opportunities for data scientists and environmental modelers to understand spatially and temporally varying drivers and controls. The objective of the project is to analyze these different datasets, with novel data science and machine learning techniques -- such as Random Forest Models, the Long Short-Term Memory Model (LSTM) that uses a type of Recurrent Neural Network Structure and learns from sequential data -- to undertake detailed multi-scale assessments of drivers of water quality degradation and to confirm generalizable water quality principles at the regional and global scales. Datasets explored will include surface water and groundwater N and P data, remotely sensed chlorophyll-a data in reservoirs and lakes, and soil moisture datasets across the landscape, as well as various land use and land management datasets. Project goal is to integrate these datasets, and develop machine learning models and tools to analyze them.