Predictive Models of Carcinoma Cell Delamination from Heterogeneous Populations of Epithelial and Mesenchymal Cells
Matthew Lazzara, Associate Professor, Chemical Engineering (UVA Engineering), Shayn Peirce-Cottler, Professor, Biomedical Engineering (UVA School of Medicine), Dr.Todd Bauer, Professor, Surgery (UVA School of Medicine)
Over 50,000 patients are diagnosed every year with pancreatic ductal carcinoma (PDAC), which has a 5-year survival rate of only 8%. One of the main challenges in treating PDAC is that the cancer has already metastasized to other locations in more than half of PDAC patients at the time of diagnosis. Understanding the cellular interactions and behaviors that lead to metastases could lead to improved treatment options for these patients. In this project, experts from the Departments of Chemical Engineering, Biomedical Engineering, and Surgery are using computational models of tumor cell behavior and interactions in conjunction with experiments to better understand what drives metastasis and how to stop it.
Precision Oncology for Colorectal Cancer Using EHR Data
Dr. Matthew Reilley, Assistant Professor, Medicine-Hematology/Oncology (UVA School of Medicine), Timothy Clark, Associate Professor, Public Health Sciences (UVA School of Medicine), Malathi Veeraraghavan, Professor, Electrical and Computer Engineering (UVA Engineering), Jianhui Zhou, Associate Professor, Statistics (College and Graduate School of Arts and Sciences)
Colorectal cancer is the third most common and the second most lethal cancer in the United States, and despite significant efforts to develop novel therapeutics for treatment, standard chemotherapy has remained unchanged. There is evidence that better customizing chemotherapy dosing for each patient could improve survival and reduce side effects, but this has proven very difficult to implement for the drug most often used to treat colorectal cancer. This team will combine expertise from the departments of Medicine, Public Health Sciences, Electrical and Computer Engineering and Statistics to build mathematical models from electronic health records and genomic information that can help guide dosing in individual patients.
Drug Development and Global Health
Comparative genomics of pathogenic Cryptosporidium for pan-genus drug development
William Petri, Professor, Medicine – Infectious Disease & International Health (UVA School of Medicine), Jason Papin, Professor, Biomedical Engineering (UVA Engineering/UVA School of Medicine)
Microscopic parasites inflict a major global health burden, causing both diarrheal and respiratory infections. In the worst cases, infections can result in a developmental delay in children. Unfortunately, without a vaccine, both prevention and treatment of disease remains challenging. Cryptosporidium is a particularly problematic parasite because it contaminates drinking water and is resistant to chlorine treatment. Researchers from the UVA departments of Biomedical Engineering and Infectious Disease & International Health plan to use computational models of the parasite’s metabolic pathways alongside clinical data to identify new drug targets for the treatment of Cryptosporidium infection.
Aiding Undernutrition in Children
Computational Imaging to Predict Intestinal Mucosal Alterations in Children in Virginia
Undernutrition in children can lead to permanent physical and cognitive damage or even death. It affects 20% of children under the age of 5 in low- and middle-income countries but is also a problem in Virginia, where the percentage of babies with low birth is higher than the national average, even though the percentage of children living in poverty is less than that of the nation. It turns out that the amount of food children eat may not be the only important factor. With funding from the Gates Foundation, scientists at UVA are finding that gastrointestinal infections can also limit the ability of children to process food and absorb nutrients.
In this project, collaborators from UVA’s Department of Pediatric Gatroenterology and Department of Systems and Information Engineering will develop better methods to identify children who can’t absorb nutrients effectively, so they can be treated. These methods have the potential to improve pediatric nutrition and growth in Virginia and beyond.
The researchers published a paper about their work June 14, 2019, in the OPEN ACCESS JOURNAL JAMA OPEN NETWORK.
More at UVAToday: UVA Scientists Use Machine Learning to Improve Gut Disease Diagnosis
Improved Drug Dosing
Delivering Improved Anemia Outcomes in End Stage Renal Disease by Leveraging EMR Data in a Predictive Dosing Algorithm
Brendan Bowman, Assistant Professor, Medicine – Nephrology (UVA School of Medicine), Emaad Abdel-Rahman, Professor, Medicine – Nephrology (UVA School of Medicine), Donald Brown, Professor, Systems & Information Engineering (UVA Engineering)
End Stage Renal Disease (ESRD) is diagnosed when the kidney’s ability to cleanse toxins from blood falls below 15% of normal and can only be treated by kidney transplant or dialysis. While dialysis is a life-saving treatment for ESRD patients, it also has side effects, including a reduction in red blood cell counts called anemia. Anemia is treated using red blood cell stimulating medications (called ESAs), but these drugs are expensive, and individualizing the dose is difficult because it can take several weeks to see the full effects of a change in dose.
The collaborators from the UVA Division of Nephrology and the Department for Systems and Information Engineering, specifically the Data Sciences Institute, aim to provide personalized dosing of ESAs in dialysis patients using a variety of applied data analytics techniques. Using this proposed dosing algorithm could, in turn, lead to important cost savings in the healthcare system and more consistent red blood cell levels for patients. The UVA dialysis system cares for over 950 patients with ESRD, over 80% of whom receive UVA School of Medicine form of ESA treatment and would greatly benefit from more personalized medicine.
Engineering-Medicine Partnership on Sleep and Cardiovascular Research
HYOJUNG KANG, Assistant Professor, Systems & Information Engineering (UVA Engineering), YOUNGHOON KWON, Assistant Professor, Medicine-Cardiovascular (UVA School of Medicine), JEONGOK LOGAN, Assistant Professor, Nursing – Acute & Specialty Care (SON), JENNIFER LOBO, Assistant Professor, Public Health Sciences (UVA School of Medicine), MIN-WOONG SOHN, Associate Professor, Public Health Sciences (UVA School of Medicine)
Healthy sleep helps our bodies maintain normal homeostasis, while impaired sleep produces a wide range of adverse health outcomes, including increased risk for cardiovascular disease (CVD). Specifically, obstructive sleep apnea, inUVA School of Medicinenia, abnormal sleep duration and poor sleep quality are each known to increase the risk of CVD-related morbidity and mortality.
This truly interdisciplinary team, with experts from the UVA DEPARTMENT OF SYSTEMS AND INFORMATION ENGINEERING, DEPARTMENT OF MEDICINE, DEPARTMENT OF PUBLIC HEALTH SCIENCES and the SCHOOL OF NURSING, aims to harness a unique database of over 5000 sleep studies to examine the relationship between sleep and cardiovascular disease and to find previously unknown relationships important to predictive and precise medical care.