✓ Developing an Efficient Translational Pipeline for Biospecimen Collection

PI: Tessa Andermann, MD, MPH (Assistant Professor, Infectious Diseases/Medicine, School of Medicine)
Impact Statement: "We propose the development of translational research infrastructure to improve access to clinical laboratory samples and to further translational science at UNC. Our pilot project will first target the urgent need for access to clinical isolates of multidrug-resistant pathogens in order to rapidly identify infectious outbreaks in North Carolina."

✓ Using Ultrasound to Evaluate Muscular Characteristics in Older Adults and their Correlations with Function

PI: John Batsis, MD (Associate Professor, Medicine-Geriatric Medicine, School of Medicine)
Impact Statement: "The information gained in this study will allow the investigators to tailor the intervention to specific individuals – getting the right intervention to the right patient. The risks of the study are minimal to individuals in relation to the potential benefits they may receive. Outcomes of this research could include novel diagnostic and more precise diagnoses, new and more rational use of therapies, and improved understanding of why some people remain healthy despite exposures and risk factors for disease."

✓ Integrated Digital Health platform to assess and respond to mental health crises in children and adolescents

PIs: Aysenil Belger, PhD (Professor, Psychiatry, School of Medicine) and Alper Bozkurt, PhD (Professor, Electrical & Computer Engineering, College of Engineering, NC State)
Impact Statement: "The proposed DHP will overcome the limitations in real-time monitoring and management of mental health in adolescents. by supporting adolescents in managing their stress reactivity and associated daily mental health. Low-powered operating environment, and delivery of timely notifications will personalize recommendations and interventions."

✓ Feasibility and Validity of Smart Ring Technology to Reduce Barriers for Assessment of Physical and Mental Health and Well-Being

PIs: Malia Blue, PhD (Assistant Professor, Exercise & Sports Science, College of Arts & Sciences) and Shelby Baez, PhD (Assistant Professor, Exercise & Sports Science, College of Arts & Sciences)
Impact Statement: "This project will identify a feasible and valid smart ring device that will eliminate barriers for collecting pertinent health data associated with in-lab data collection. The smart ring and customizable online dashboard will lead to a sustained, powerful influence on future research studies that measure and intervene on mental and physical health."

✓ Development of an organotypic co-culture model for testing of inhaled therapies for respiratory disease

PI: Elizabeth Corteselli, PhD (Assistant Professor, Pediatrics, School of Medicine)
Impact Statement: "This project will provide a novel platform for preclinical testing of aerosolized drug delivery for pulmonary diseases that incorporates realistic dosing and pharmacokinetics, the 3D complexity of the airways, and diverse donors. Use of this model will aid in drug discovery for fibrotic pulmonary diseases."

✓ Developing Methods for Gene Expression Analysis of Antiplatelet Drug Exposure

PI: Kevin Friede, MD (Assistant Professor, Cardiology/Medicine, School of Medicine)
Impact Statement: "Gene expression data available in biorepositories is underexploited due to complexity of data analysis and limited techniques for external validation. This proposal aims to demonstrate that these barriers can be overcome, allowing for significant expansion of research devoted to 'omics in large datasets."

✓ Illuminating the Prognostic Potential: Predicting the Drug Effects on Cancer Tumors with Deep Neural Network

PIs: Zhishan Guo, PhD (Associate Professor, Computer Science, NC State) and Ning Sui, PhD (Assistant Professor, Molecular and Structure Biochemistry, NC State)
Impact statement: "Our machine learning breakthrough promises to transform cancer care, ensuring fair and personalized drug efficacy predictions, expediting clinical trials, and shaping the future of equitable, personalized medicine with vast potential for daily clinical application across diverse cancer patient populations."

✓ Bronchoalveolar Lavage Fluid-Derived Organoids as Translational Models for Pediatric Respiratory Disease

PI: James Hagood, MD (Professor, Pediatrics, School of Medicine)
Impact statement: "Few reports establish organoid models for pediatric lung disorders. Bronchoalveolar lavage fluid-derived organoids offer a novel, less invasive approach to creating disease-specific models. This could revolutionize personalized medicine, drug screening, and treatment development."

✓ Affect-based impulsivity in borderline personality disorder (BPD): developing a neurocomputational phenotype

PI: Michael Hallquist, PhD (Associate Professor, Psychology & Neuroscience, College of Arts & Sciences)
Impact statement: "Current treatments for BPD show moderate, but unstable effects and require long-term treatment. Extending on insights from addiction and anxiety research, we will interrogate similar neurocomputational circuits in BPD. The work has significant potential to inform treatment targets in BPD."

✓ New strategies to circumvent humoral immunity to adeno-associated virus (AAV) in gene therapy

PI: Zongchao Han, PhD, MD (Associate Professor, Ophthalmology, School of Medicine)
Impact statement: Adeno-associated virus (AAV) is a promising gene therapy platform, but current strategies fall short clinically. This team's advanced AAV delivery system overcomes these limitations, enabling effective treatment for retinal diseases like age-related macular degeneration (AMD), and paving the way for meaningful therapeutic outcomes in various other conditions."

✓ Treating peanut allergy using modifiable nanoparticles

PI: Michael Kulis, PhD (Associate Professor, Pediatric Immunology, School of Medicine)
Impact statement: This research aims to develop a targeted therapy for peanut allergy that induces long-term systemic tolerance by deleting PN-specific B cells and promoting T-regulatory cells. If successful, it could surpass current treatments and significantly improve the quality of life for affected individuals."

✓ Large Language Models to Facilitate Developing AI Approaches Identifying Abnormalities Within Medical Images

PI: Weili Lin, PhD (Professor, Radiology & Biomedical Engineering, Director, Biomedical Research Imaging Center, School of Medicine)
Impact Statement: "The proposed approaches are clinically impactful and highly translational by mitigating one of the major impediments for developing AI approaches in medical imaging – the need to manually annotate abnormalities in images. The proposed approaches are applicable to medical disciplines beyond Radiology, e.g., Dermatology and Pathology, where images are also widely employed in daily clinical practices and can easily integrate EHR information to further improve the robustness of AI algorithms."

✓ Promoting research participation in communities through community outreach and engagement

PI: Laura Milko, PhD (Assistant Professor, Genetics, School of Medicine)
Impact Statement: Engagement of disadvantaged communities in translational research on health innovations requires careful study of the effectiveness of methods used to overcome barriers to participation. This study will provide generalized information about best practices for engagement of diverse communities to encourage broad participation in translational science."

✓ Exploring the Utility of Linked Patient Portal Message Data for Translational Science

PIs: Joshua Niznik, PharmD, PhD (Assistant Professor, Medicine, School of Medicine) and Anna Kahkoska, MD, PhD (Assistant Professor, Nutrition, Gillings School of Global Public Health)
Impact statement: "The proposed study will leverage a novel data source with great potential to advance the science of clinical research using real-world data by developing methodological tools to repurpose patient portal messaging data for research and generating novel insights into portal user activity."

✓ Measuring patient data density: Developing and applying metrics to accurately define cohorts and identify bias

PIs: Emily Pfaff, PhD, MS (Assistant Professor, Medicine, School of Medicine) and Michelle Hernandez, MD (Professor, Pediatrics, School of Medicine)
Impact statement: "The team proposes to create quantitative metrics to measure Electronic Health Record (EHR) data density. These metrics will offer researchers the tools to identify and remediate demographic and social biases introduced when selecting cohorts and engaging in data-driven clinical research."