COVID-19 Rapid Response Pilot Grant Awardees
COVID-19 has emerged as a major global health threat evolving into a global pandemic. There is a critical need to develop new therapeutics and interventions to reduce the impact of the SARS-CoV-2 coronavirus on patients as well as those tangentially effected by the global pandemic. To help accomplish this, CCTS funded the following collaborative and multidisciplinary translational research studies.
“Real-Time Sensing of COVID-19”
Michael Caffrey, PhD, Associate Professor, Department of Biochemistry and Molecular Genetics, UIC College of Medicine
Igor Paprotny, PhD, Associate Professor, Department of Electrical and Computer Engineering, UIC College of Engineering
In the absence of vaccines or treatments, surveillance of the virus is particularly important. The long-term goal of this project is to develop a microfluidic sensing device to measure COVID-19 virus (SARS-CoV-2), aerosolized or from surface samples, in near real-time. The proposed sensing device will sample aerosols suspended in the ambient air or present on surfaces, concentrate and deposit the virus into a microfluidic system, label the virus using antibodies with fluorescent tags, and sense the presence of the virus by detecting the fluorescence emission. Applications of the proposed device, which are broad and of immediate need to the general public, include (1) real-time monitoring of viral load in ambient air, 2) continuous monitoring of public spaces for exposure potential to aerosolized or deposited virus (e.g. auditoriums, airplanes, hospitals), (3) as well as diagnosis of viral shedding in exhaled breath prior to the onset of symptoms.
“Systematic design of complementary libraries of peptides with a high affinity to the mutating SARS-CoV-2 spike protein”
Petr Kral, PhD, Professor, Department of Chemistry, UIC College of Liberal Arts and Sciences
Lela Vukovic, PhD, Assistant Professor, Department of Chemistry, University of Texas at El Paso
Seungpyo Hong, PhD, Professor and Milton J. Henrichs Chair in Pharmaceutical Sciences, Carbone Cancer Center and Biomedical Engineering, University of Wisconsin-Madison
One type of therapeutics can prevent the initial stages of the human host infection by a specific and strong binding of SARS-CoV-2 spike (S) protein to the human membrane protein angiotensin-converting enzyme 2 (ACE2). The receptor binding domain (RBD) of the S protein is a promising target for therapeutics that could block the SARS-CoV-2 – ACE2 binding. The main objective of this Pilot project is to establish, in collaboration with Lela Vukovic and Seungpyo Hong, a library of computationally designed and experimentally tested peptides with a high binding affinity to different strains of the S protein, occurring geographically and temporally. Our preliminary simulations have shown that mutated ACE2-based peptides can have strong binding to the S protein variants. We propose to develop and use a systematic optimization approach, combining molecular dynamics simulations and Monte Carlo methods, to identify peptides with the strongest binding to mutated S proteins that could inhibit their binding to ACE2. The binding strengths will be tested experimentally using a biolayer interferometry and a surface plasmon resonance.
"Virtual Coach: Health Matters Google Classroom to Support Frontline Staff to Protect People with Intellectual and Developmental Disabilities During and After the COVID-19 Pandemic"
Beth Marks, PhD, Research Associate Professor, Department of Disability and Human Development, UIC College of Applied Health Sciences
Jasmina Sisirak, PhD, Research Assistant Professor, Department of Disability and Human Development, UIC College of Applied Health Sciences
Matthew Janicki, PhD, Associate Research Professor, Department of Disability and Human Development, UIC College of Applied Health Sciences
Today’s COVID-19 pandemic creates an urgent need to see how access to testing, provision of life-saving equipment, and treatment impact people with intellectual and developmental disabilities (IDD). Many people with IDD have health conditions that put them at higher risk, especially those living in congregate care settings, where COVID-19 is rapidly spreading. In our project we want to use a survey to learn about how community-based organizations that provide services to people with IDD are preparing and responding to COVID-19 Pandemic. We also recognize that health promotion is important now than ever and want to create a Virtual Coach: Health Matters Google Classroom to see if this online, any-time, anywhere health promotion training program is effective for frontline staff and people with IDD to follow public health guidelines and stay safe and healthy. The Virtual Coach Health Matters Classroom will use lessons from our Administration on Community Living approved, evidence-based Health Matters TM Program and Health Matters Curriculum for people with IDD and their frontline supports to understand, consider, and communicate health behaviors during and after the pandemic. We will maintain the Virtual Coach Classroom (open-sourced) at www.HealthMattersProgram.org.
"Racial, Ethnic and gender disparities Among a COVID-19 hyperTensive population (REACT)"
Heather Prendergast, MD, Professor, Department of Emergency Medicine, UIC College of Medicine
Dawood Darbar, MD, PhD, Chief, Division of Cardiology, Professor of Medicine & Pharmacology, Department of Medicine, UIC College of Medicine
Pavitra Kotini-Shah, MD, Assistant professor, Department of Emergency Medicine, UIC College of Medicine
Amer Ardati, MD, Assistant Professor, Department of Medicine, Division of Cardiology, UIC College of Medicine
The COVID-19 pandemic has disproportionately affected individuals with underlying health conditions, the elderly, and minorities. Nearly 60% of the COVID-19 deaths have been observed in patients with high blood pressure. In Chicago, approximately 75% of the deaths have occurred within minority populations with 45% seen in African Americans and 29% in the Latinx population. Two-thirds of these deaths have been observed in men. Coronaviruses are known to use a specific receptor (angiotensin-converting enzyme 2ACE2) to enter into targeted cells within the body, and certain blood pressure medications could potentially alter this association. Apart from studying the characteristics and the clinical outcomes in COVID-19 patients with high blood pressure, we will examine the role of genetic variations within the ACE/ACE2 receptors and the severity of illness and outcomes in COVID-19 patients across race-ethnicity and gender in patients admitted from the UI Health Emergency Department, which serves a high proportion of patients with uncontrolled high blood pressure. The R.E.A.C.T study will help us better understand factors that contribute to adverse outcomes following COVID-19 and contribute to efforts for individualized treatment among a predominately minority population.
“Artificial Intelligence Modeling for COVID-19 Disease (AIMCOVID) to define COVID-19 clinical disease severity stages, improve clinical management decisions and develop a clinical outcome predictionmodel”
Andrew Trotter, MD, Assistant Professor, Department of Medicine, UIC College of Medicine
Christian Ascoli, MD, Instructor of Medicine, Department of Medicine, Division of Pulmonary, Critical Care, Sleep and Allergy, UIC College of Medicine
Scott Borgetti, MD, Assistant Professor of Clinical Medicine, Medical Director of Outpatient Parenteral Antibiotic Therapy, UI Health
Andrew Boyd, MD, Associate Professor, Department of Biomedical and Health Information Sciences, College of Applied Health Sciences, Acting Associate Vice Chancellor for Research in Computing and Data Initiatives, Associate Chief Health Information Officer for Innovation and Research, UIC
Houshang Darabi, PhD, Associate Professor, Department of Mechanical and Industrial Engineering, UIC College of Engineering
William Galanter, MD, PhD, Clinical Associate Professor, Department of Pharmacy Systems, Outcomes and Policy, UIC College of Pharmacy
Bhrandon Harris, MD, Assistant Professor, Clinical Family Medicine, Director of Primary Care Clinical Informatics, UI Health
Ravishankar Iyer, PhD, Professor, Illinois Carle College of Medicine, University of Illinois at Urbana-Champaign
Min Joo, PhD, Associate Professor, Department of Medicine, Division of Division of Pulmonary, Critical Care, Sleep, and Allergy, UIC College of Medicine
Karl Kochendorfer, MD, Chief Health Information Officer, UI Health
Martha Menchaca, MD, PhD, Assistant Professor, Department of Radiology, UIC College of Medicine
Richard Novak, MD, Professor of Medicine and Head, Department of Medicine, Division of Infectious Diseases, UIC College of Medicine
Natalie Parde, PhD, Assistant Professor, Department of Computer Science, UIC College of Engineering
Artificial intelligence (AI) and machine learning (ML) are powerful tools to defineCOVID-19 disease severity, improve clinical management decisions and develop a model to predict disease and treatment outcomes. AIM-COVID will create a comprehensive clinical database of patients with suspected or confirmed COVID-19 at UI Health from the electronic medical record and supplemented using bioinformatics tools such as natural language processing(NLP)and incorporation of radiology data. This database will be used to develop dynamic AI and ML models to define stages of severity of COVID-19 disease, predict outcomes of COVID-19 infection and improve treatment decisions. Updated data through time and clinical expert input will serve to develop, test and refine the models. By developing AI and ML COVID-19 models, AIM-COVID has wide ranging applications including informing evidence-based clinical management decisions and guidelines, predicting and improving how health resources are used and defining COVID-19disease severity in future clinical research studies. AIM-COVID will harness multidisciplinary expertise across the University of Illinois System in clinical medicine including infectious disease, public health, primary care, hospital-based medicine, pulmonary and critical care medicine and radiology, in addition to informatics, computer engineering science, systems engineering, natural language processing, ML and AI.