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OBJECTIVES/GOALS: Target: Computationally identify the markers of ulcer severity and risk of amputation from datasets that include demographics data, clinical, laboratory data, and medical history over 6000 patients. METHODS/STUDY POPULATION: In this study we will use tables of demographics such as age, gender, and ethnicity/race. Inspired by previous research we’ll include wound age (duration in days), wound size, number of concurrent wounds of any etiology, evidence of bioburden/infection, Wagner grade, being non ambulatory, renal dialysis, renal transplant, peripheral vascular disease, and patient hospitalization. Another table will include laboratory vital signs to include physiological variables such as height, weight, body mass index, pulse rate, blood pressure, respiratory rate, and temperature. We’ll include also social data like smoking status, socio-economic status, housing condition. RESULTS/ANTICIPATED RESULTS: Our project aligns with previous efforts to identify high risk Diabetic Foot Ulcer individuals but also takes a different perspective by collecting and marking clinical data from a subset of patients (e.g., severity, Hispanic versus non-Hispanic) and computationally process these data to provide a tool that can identify DFU severity and high-risk patients. We will obtain samples from Hispanics and non-Hispanics because these two groups are likely to have significant differences in the progression of ulcer severity. The rationale is that by comparing these two groups, we will assess and study the factors that are differentially present. It is our expectation that the proposed project will provide an easy-to-use tool for DFU progression and risk of amputation and contribute to identify high-risk individuals. DISCUSSION/SIGNIFICANCE: Diabetes prevalence estimates in Bexar County, TX exceeds national estimates (15.5% vs. 11.3%) and diagnosed cases are higher among Hispanic adults (13.4%) compared to their non-Hispanic white counterparts (9.5%). Late identification of severe foot ulcers minimizes the likelihood of reducing amputation risk. 

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients.