Mark W Burke, Ph.D - US grants

? DESCRIPTION (provided by applicant): Pediatric HIV infection remains a global health crisis with a worldwide infection rate of 2.1 million (UNAIDS, 2013). Children are much more susceptible to HIV-1 neurological impairments than adults, which is exacerbated by co-infections. A main and obvious obstacle in pediatric HIV research is sample access. The proposed studies will take advantage of ongoing pediatric SIV pathogenesis and vaccine studies to maximize the use of nonhuman primate resources by expanding on the original pediatric SIV-related immunology studies to include quantitative neuropathology studies. We hypothesize that HIV infection in infants will induce neurodegeneration and impaired neurogenesis. Neonatal rhesus macaques (Macacca mulatta) that infected with SIVmac251 will be used to test this hypothesis. After a 6 to 33 week survival time, the animals were sacrificed for quantitative histopathological analysis. AIM 1 will determine the extent of pediatric SIV-induced neurodegeneration in the hippocampus and fronto-limbic system and its relationship to viral loads. In AIM 2 we will determine the mechanism(s) of pediatric SIV-induced neuronal loss in the hippocampus and fronto-limbic system. The goal of this project is to assess the impact of early HIV infection on the brain towards the long-term goal of evaluating treatment paradigms designed to protect the integrity of the developing brain from combined viral and bacterial infections through an interdisciplinary approach.

Radiological imaging is a critical part of healthcare services which physicians rely heavily upon in the medical decision-making process. A major goal of modern radiology and imaging sciences is to exploit specialized biophysical modeling that simulates the biological process in the living tissue to generate sensitive imaging contrast for disease detection. In order to understand the relations between the simulated image contrast and the underlying pathophysiology, radiologic-pathologic image analysis has to be performed to validate the image correlations in tissue structure, pathology and disease characteristics. Given the complex microenvironments in the tissues, comparison of radiologic and pathologic images is particularly challenging. Many of the routine analyses in the laboratories largely depend on manual or semi-automatic counting and segmentation of cells and tissues in the “gold standard” pathological images using commercially available software that are designed for general purposes. Researchers often have to give up an ample amount of information that shows in the pathological images but not quantifiable using the existing methods. This project aims to close the gap by utilizing deep learning methodology to extract the important features in the radiological and pathological images for quantitative analysis of the correlations previously unattainable in the community. <br/><br/>To address the challenges that persist in comparing radiologic and pathologic images, the technical aims of the project are divided into three aspects: (1) deep learning algorithms for quantifying cell morphological phenotypes in the whole brain sections, (2) a graphical and interactive statistic toolbox to visualize the radiologic-pathologic image correlation analysis, (3) a website-as-a-service software package that implements computer-aided image analysis and database for radiologic-pathologic correlations in a user-friendly platform. The project outcome provides a novel deep learning methodology that can be used to standardize the benchmark evaluations in the development of radiological imaging biomarkers. The award enhances the graduate and undergraduate STEM education at the Howard University, with supports to a diverse and underrepresented cohort of the students in the biology and mathematics majors, through the use of cutting-edge artificial intelligence in the field of bioimaging research.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.