Objectives/Goals: This work identifies ordinary and novel ingredients of a recipe for a learning community created by translational scientists to support their mutual interests in broadly engaged team science. The goal of creating this recipe is to share our findings, enabling the replication of this work at other CTSAs. Methods/Study Population: Inspired by O’Brien et al. (2025), our MICHR CoLab+ learning community (N = 8) is cooking up a “community recipe” for collaborative learning. Our multidisciplinary mix includes members from six MICHR teams. In our first guided session, we blended open dialog and reflection to identify key ingredients and tools, including metaphors for our team’s strengths, values, and goals. Using Teams, Miro, and generative AI, we captured early insights to refine over time. Results/Anticipated Results: By co-creating a community recipe, members of the MICHR CoLab+ deepen their understanding of shared strengths, values, and purpose. The recipe showcases what makes our community unique while fostering unity and belonging. Once complete, it will help us overcome barriers, accelerate discovery, and deliver real-world impact in translational science. Discussion/Significance of Impact: This work extends research helping translational scientists cross boundaries in universities. Building on work on learning communities for learning health systems (Ferguson, et al., 2024; Wenger, 1998), it shows how co-created “recipes” foster collaboration across disciplines, institutions, and cultures to strengthen translational science.

Objectives/Goals: Persistent gaps in inclusive research demand new approaches. We propose the Individualized and Differential Benefit (IDB) framework to develop tools that prioritize balance and elevate underrecognized voices within today’s research landscape. Methods/Study Population: The IDB framework ensures research benefits all, including individuals within underserved communities. To apply IDB, three criteria must be met: (1) inclusivity: work must apply to all and exclude none; (2) utility: work must benefit all stakeholders; and (3) differential benefit: work must offer greater advantages to individuals from any group who are most in need. This approach aims to elevate everyone while offering a tool to achieve equity. Results/Anticipated Results: Within the poster, we use two examples to illustrate IDB. In the first example, Health Outcome and Population Engineering, or HOPE, exemplifies how health outcomes are achieved with an individualized approach while population differences are modified. In the second example, Population Conscious Analysis, or PCA, is used to display a nuanced approach to analysis that yields an individual benefit while again yielding the opportunity for population-level differences. Discussion/Significance of Impact: IDB reframes public health research by prioritizing individual-level insights to drive precision, equity, and upstream interventions. It enables tailored strategies, community engagement, and scalable solutions that benefit all, but specifically those most in need.

Objectives/Goals: To investigate renin–angiotensin system (RAS) modulation as a therapeutic target for uterine fibroids using a xenograft model, translating insights from cardiometabolic–fibroid comorbidity into accessible treatment strategies for underserved populations. Methods/Study Population: We established a xenograft model by implanting matched human fibroid and myometrial tissue into NOG mice (n=6 per group) supplemented with estrogen pellets. This translational model mimics the clinical context of hypertensive patients with fibroids treated with RAS-modulating drugs. Mice were randomized to receive angiotensin II, losartan, enalapril, or vehicle control. Tumor volumes were measured weekly by caliper. At endpoint, xenografts are analyzed for proliferation (Ki67), fibrosis (α-SMA, collagen), and immune composition using flow cytometry and immunofluorescence. Results/Anticipated Results: Preliminary data from all 24 mice reveal variable fibroid xenograft growth across treatment groups, with no consistent dose–response pattern in gross tumor volumes. Ongoing molecular and cellular analyses may uncover treatment effects not reflected in volume measurements alone. Final analyses will integrate proliferative indices, fibrotic markers, and immune cell profiles to determine whether RAS modulation influences fibroid biology at the cellular level despite heterogeneous morphological responses. Discussion/Significance of Impact: Fibroids disproportionately affect Black women and are closely linked to cardiovascular disease. Repurposing FDA-approved antihypertensives offers an accessible, low-cost therapeutic avenue and exemplifies translational science by bridging mechanistic discovery with equitable treatment innovation.

Objectives/Goals: This work seeks to elucidate the upstream and downstream circuitry of the Keap1–Nrf2 axis in natural killer (NK) cells. We aim to define how the Keap1–Nrf2 axis sets a redox license to calibrate NK function and metabolic fitness, and in turn, to translate these findings into new strategies for metabolically enhancing NK and CAR-NK-based immunotherapy. Methods/Study Population: Keap1 and Nrf2 were investigated primarily in murine models, by using NK-specific knockouts of each gene (Ncr1Cre-Nrf2fl/fl and Ncr1Cre-Keap1fl/fl) in C57BL/6 mice. Flow cytometry was used to profile knockout versus wild-type differences in lymphocyte populations and subpopulations, including receptor repertoire, maturation, and effector function. In vitro and ex vivo experiments were performed by sorting cells from the spleens of knockout mice and their wild-type littermates. Human NK cells from healthy donors were analyzed with collaborators to validate the importance of the axis for human patients. Results/Anticipated Results: Transcriptomics analysis of NK cells during murine cytomegalovirus (MCMV) infection revealed reciprocal regulation of Nrf2 and Keap1, with Nrf2 transcripts downregulated and Keap1 upregulated at day 4 post-infection, highlighting the potential contribution of this pathway to NK cell responses during viral challenge. We demonstrate that both insufficient and excessive ROS buffering impair host survival in response to MCMV and acute myeloid leukemia (AML) and compromise control of tumor metastasis in melanoma models. Importantly, both knockout models also exhibited defects in NK cell expansion, maturation, and cytotoxicity. Ongoing studies map the metabolic, epigenetic and transcriptional programs downstream of this pathway in both murine and human NK cells. Discussion/Significance of Impact: As immunotherapies are developed and optimized, a rigorous understanding of the mechanisms through which immune cells kill pathogens is critical. Collectively, our work thus far identifies the Keap1–Nrf2 axis as a critical regulator of NK cell fitness that can be used to promote cell therapeutic development.

Objectives/Goals: The NIH Office of Data Science and Strategy created Data COUNTS (Collect Once, Use Numerous TimeS) to transform real-world data into actionable insights for translational research. The NEurOinformatics Network (NEON) applies this principle with neurology and national cohort use cases. Methods/Study Population: UNMC and Nebraska Medicine are deploying Data COUNTS to standardize data capture, ensure interoperability, and maximize reuse across research, clinical care, and operations. Teams integrate electronic health records, neuroimaging, biomarkers, wearable sensors, and social drivers of health into federated pipelines. HL7 FHIR and the OMOP Common Data Model maintain interoperability and reproducibility. Use cases include neurological disorders (multiple sclerosis, Alzheimer’s disease, and Parkinson’s disease), cancer outcomes through SEER, diabetes, and rural health disparities. Integration with the National Clinical Cohort Collaborative (N3C) leverages anchor variables and harmonized pipelines to connect local and national datasets for generalizable analytics and collaborative science. Results/Anticipated Results: By linking with CTSA, IDeA-CTR, SEER, N3C, and other national health data, this initiative strengthens Learning Health System infrastructure, accelerates precision medicine, and advances population health. Beginning at NM and UNMC, the model will extend to regional systems, especially in rural and underserved areas. Use cases in neurology, cancer, diabetes, and rural health demonstrate local impact, while SEER and N3C provide scalable pathways for harmonization and national integration. Discussion/Significance of Impact: Nebraska’s leading health system generates multimodal datasets that, connected with SEER and N3C through Data COUNTS principles, enable predictive modeling, explainable AI, and digital twins. These use cases operationalize a scalable framework for translational science.

Objectives/Goals: Triple-negative breast cancer (TNBC) is an aggressive subtype lacking targeted therapies. This study explores whether the Niclosamide derivative HJC0152 can act as an immune modulator to reprogram the immunosuppressive tumor microenvironment and enhance immunotherapy efficacy. Methods/Study Population: Niclosamide-derived HJC0152 was developed as a novel cancer therapeutic and immunomodulating agent. Human and murine TNBC cell lines, as well as primary mouse MDSC cells, were treated with HJC0152. PD-L1 expression in TNBC cells and key immunosuppressive markers in MDSCs (Arg1, Arg2, and iNOS) were evaluated. The immunosuppressive function of MDSCs was further assessed using a T cell co-culture assay. In vivo studies are planned to assess the therapeutic efficacy of HJC0152 in combination with anti-PD-1 therapy using syngeneic TNBC mouse models. Results/Anticipated Results: HJC0152 exhibits improved solubility compared to Niclosamide and displays potent anticancer activity both in vitro and in vivo. RNA-Seq analysis of human TNBC cells treated with HJC0152 revealed significant downregulation of PD-L1 gene expression, which was further validated at the protein level by Western blot analysis. In addition, HJC0152 suppressed Arg1 expression in mouse MDSCs, a key mediator of immunosuppressive myeloid cell function. Together, PD-L1 and Arg1 pathways represent critical mechanisms of tumor immune evasion, and our findings highlight HJC0152 as a promising immune modulatory agent capable of enhancing the efficacy of immune checkpoint blockade in TNBC. Discussion/Significance of Impact: This study presents a novel immunotherapy strategy for TNBC using HJC0152, which downregulates tumor-derived PD-L1 and inhibits Arg1 signaling in MDSCs. As the first investigation of HJC0152 combined with anti-PD-1 in TNBC models, it may support future clinical trials and offer a new therapeutic option for TNBC patients.

Objectives/Goals: Our objective was to use human-centered design methods to co-develop an intervention that enhances postpartum management of hypertensive disorders of pregnancy and reduces long-term cardiovascular (CV) disease risk. Methods/Study Population: Human-centered design is a problem-solving approach that centers the end-user including patients, clinicians, and community members. We conducted six focus groups with people who experienced HDP, doulas, lactation consultants, perinatal community health workers, midwives, Ob/Gyns, and family physicians (n=26). Design activities included patient journey mapping, group discussion, mad lib creation, rapid solution brainstorming, dot voting, impact/difficulty matrix analysis, and pitch concept development. Focus group sessions explored the patient journey and identified major unmet needs. These results informed the co-design of solutions to address key facilitators and barriers to positive care pathways. Results/Anticipated Results: Participants identified a need for a community-embedded care model that promotes continued engagement after delivery, timely blood pressure (BP) monitoring and management, chronic stress reduction, and a smooth transition to primary care. These findings informed the co-design of a multi-level intervention that includes 1) midwife-led dyadic mom–baby visits that facilitate evidence-based strategies: BP surveillance, medication management, and lifestyle education; 2) remote BP monitoring with bi-directional communication; 3) community-based doula care; 4) a co-located family support specialist that addresses social determinants of health and chronic stress; and 5) facilitated primary care transition. Discussion/Significance of Impact: Using human-centered design enabled the integration of lived experiences, clinical insights, and community perspectives. This participatory process ensures that actionable solutions are responsive to real-world needs and have greater potential to improve CV health.

Objectives/Goals: Outline researchers’ perspectives on an “Aim 0” approach of engaging with people connected to the research topic to leverage their perspective when developing research questions. Worked with researchers at varying levels of career stage to understand their thoughts and concerns of this work. Methods/Study Population: Used human-centered design (HCD) methods to conduct nine semi-structured interviews with researchers at varying levels of career stage (early to senior level researchers). Interviews lasted 30–60 minutes while participants answered questions using collage cards, journey maps, and card sorting HCD methods. Questions related to communication dynamics between investigators and their population, how communication could be more population-centered, a research project journey including decisions on who and when to engage populations, and barriers and facilitators of engaging populations earlier in the research process to incorporate their voice in the development of research questions. Results/Anticipated Results: An HCD analysis approach will be conducted to outline findings. Anticipated findings include outlined communication dynamics between researchers and their affected population. The approach of engaging with the population early in the research process to help determine research questions was thought of favorably; however, feasibility concerns, mostly around finding funding, were brought up often in the interviews. More anticipated findings include what career levels would be most beneficial for an “Aim 0” approach. Discussion/Significance of Impact: Engaging with the affected population earlier in the process will help streamline efforts of investigators to solve issues their populations are dealing with. This adds another step to the research process, and it helps to understand researcher’s perspectives of this added effort.

Objectives/Goals: The objective of this study is to assemble a community advisory board (CAB) of interdisciplinary stakeholders to contextualize study results and co-create translational materials that can be used by local governments, community leaders, and community-based organizations to prevent electric bicycle (ebike) injuries. Methods/Study Population: This study merges community-engaged research and data science approaches. We are assembling an interdisciplinary CAB who are key stakeholders in informing ebike safety outside of the scientific research community. The CAB will advise on a retrospective observational data science study currently in progress that uses a California wide dataset to examine how features of neighborhoods and intersections impact ebike injuries. The CAB will provide context for study results based on their professional experiences and co-design translational materials (e.g., policy brief) that convey research findings for effective translation into policies and practices to prevent ebike injuries. We will track data on CAB activities, perceptions of participation from members, dissemination and reach of translational materials. Results/Anticipated Results: We assembled a CAB of five individuals who are policymakers, local government officials, first responders, school board members, and non-profit workers. We will report data on the frequency of meetings, themes emerging from each meeting based on discussions with CAB members, and perceptions of the experience by CAB members. We will also report on the type and content of the translational products created, the process dissemination of these translational materials, and the reach of the translational materials. Discussion/Significance of Impact: The translational aspect of this project is significant for disseminating findings to communities outside of the research realm who can implement changes that improve ebike safety. Our goal is to shorten the time for research to be translated into policies and interventions by leveraging community-engaged research and data science approaches.

Objectives/Goals: Problems with cognition are common and distressing for people with systemic sclerosis (SSc). We are developing a patient-centered cognitive rehabilitation intervention using the multiphase optimization strategy (MOST) framework. Our study aim is to identify patient needs, preferences, and priorities to help address their cognitive challenges. Methods/Study Population: We conducted semi-structured interviews with 15 individuals with SSc to capture in-depth perspectives on desired intervention features, potential barriers and facilitators to implementation, and overall relevance of the intervention to daily life. Transcripts were analyzed using the Rigorous and Accelerated Data Reduction technique in combination with thematic content analysis. Codes with similar content were merged and organized into subthemes and themes to understand the data. Insights from these interviews are used to inform and refine a conceptual model to guide intervention development in the MOST preparation phase, ensuring that resulting components are acceptable, feasible, and closely aligned with patients’ specific needs, preferences, and priorities. Results/Anticipated Results: Participants were 55 years old on average (100% female, 80% White). Forty percent had diffuse cutaneous SSc, and 53% were within 7 years of diagnosis. Participants reported cognitive challenges affecting short-term memory, attention, and processing speed. They highlighted the need for flexible online sessions (having multiple time options to choose from), extra time for discussion, and peer support (a space for connection and group discussion). Barriers to participation included symptom fluctuation, fatigue, and limited time. Facilitators included peer support and user-friendly resources. These insights will refine a conceptual model to guide development of an intervention that is acceptable, feasible, and tailored to the cognitive and lifestyle needs of people with SSc. Discussion/Significance of Impact: We will incorporate feedback from this study to create a patient-centered cognitive rehabilitation intervention that is tailored to people with SSc. Involving people with SSc in intervention development provides the foundation to build a robust, relevant intervention for people with cognitive challenges.

Objectives/Goals: Since clinical trial participation is a key translational research initiative, the objectiveis to characterize research participants within a health system using electronic health record (EHR) data and evaluate how community-level factors influence research participation rates. Methods/Study Population: Retrospective, cross-sectional study of patients with ICD-10 research encounter codes (Z00.6) from 2016 to 2024 using Rush EHR data organized in the CAPriCORN Common Data Model. Research participation prevalence was the main outcome, calculated by geographic area (3-digit zip codes, Chicago community areas) and linked to Census data for neighborhood factor analysis. Variables include Area Deprivation Index (ADI), Social Vulnerability Index (SVI), education, occupation, and income. The Chi-square test compared demographics and t-tests compared mean SDOH variables between high- and low-participation community areas. Results/Anticipated Results: Of the 1,069,226 patients seen from 2016 to 2024, 11,937 patients had research visits. Of these patients, 45.4% lived in Chicago, 65 years or above is the largest age group (29%), 62% female, 58% White, 82% non-Hispanic, and 9% non-English preferred language. Overall, the research participation prevalence was 1.12%, higher outside of Chicago (1.21%) than within (1.03%). Within Chicago community areas, prevalence ranged from 0.34% to 2.8% (mean: 1.03%, SD: 0.44%). Higher-participating communities (above median) had significantly lower ADI (39.9 vs 61.9), lower SVI (0.51 vs 0.80), lower unemployment (8.0% vs 15.3%), higher bachelor’s degree attainment (43.6% vs 19.6%), higher income ($69,859 vs $36,376), and lower uninsured rates (7.4% vs 11.5%). Discussion/Significance of Impact: This work is the first local exploration of community factors and research participation using curated EHR data. Although limited by single-center data and required entry of EHR codes, it is a step into understanding factors that may influence research participation. Local estimates could be improved by utilizing data from all CAPriCORN sites.

Objectives/Goals: Community-engaged research (CER) emphasizes equitable partnerships between academic and community partners, yet many lack structured training opportunities to develop the necessary knowledge and skills to engage. Recognizing this gap, we launched a co-design process to develop a CER 101 course tailored for both community members and academics. Methods/Study Population: The CER101 design process is rooted in principles of co-design. CAB members, community partners, and academics meet as part of an iterative, interactive, facilitated process to share perspectives, exchange knowledge, and co-create course content. A 2-day workshop began with a process to align visions – define the audience of the course, identify barriers to engagement with the material, and define parameters of the final product. Activities included structured discussions, brainstorming sessions, and feedback cycles. Emphasis is placed on valuing first-hand experience alongside academic expertise to ensure accessibility and relevance. Results/Anticipated Results: The co-design process will produce a CER 101 training that reflects jointly developed foundational concepts, practical strategies for building equitable community–academic partnerships, and case studies highlighting effective CER practices. Participants are expected to demonstrate improved understanding of CER principles, enhanced ability to apply tools for collaboration, and greater appreciation for both lived and academic expertise. The training will strengthen mutual trust, expand shared capacity for CER, and inform iterative refinements to ensure sustained relevance and impact within the community of practice. Discussion/Significance of Impact: In addition to resulting in the development of a CER 101 course that includes foundational concepts and practical strategies for engaging in CER, the co-design process will lead to increased mutual understanding of CER principles, enhanced appreciation for diverse forms of expertise, and strengthen trust.

Objectives/Goals: To generate phenotype-specific insights that support tailored prevention strategies and advance precision cardiovascular risk management within Gulf health systems. Methods/Study Population: We analyzed collected primary care records and retained 14,616 complete cases. A composite CV risk index was constructed using a Framingham-based general CVD algorithm from standard clinical variables. “High risk” was defined as the top cohort quartile. Between-group differences were tested with the Kruskal–Wallis test followed by Dunn’s multiple comparisons with Benjamini–Hochberg adjustment. We then modeled (i) the binary high-risk outcome using multivariable logistic regression and (ii) the continuous index using linear regression. Adjusted models included body mass index (BMI), log(ESR + 1), log(CRP + 1), and statin use; variables embedded in the index were not re-adjusted to avoid over-adjustment. Results/Anticipated Results: The proportion above the high-index threshold differed by group: RA 29.4%, no autoimmune 25.7%, multiple-autoimmune 23.9%, SLE 19.8%, and Hashimoto’s 12.7%. The Kruskal–Wallis test was significant (H=68.6, df=4, p<10−13), with Dunn tests (BH-adjusted) showing RA > no autoimmune and SLE, and Hashimoto < all groups. In adjusted logistic regression, odds of high index were higher in RA (OR 1.20, 95% CI 1.06–1.37), lower in Hashimoto (OR 0.49, 95% CI 0.40–0.60), and not clearly different in SLE or multiple-autoimmune. Higher BMI (OR/unit 1.08), ESR (OR/log 1.06), and statin use (OR 4.97) were strongly associated. Discussion/Significance of Impact: In a large primary care cohort, RA showed modestly higher CV risk than non-autoimmune patients after adjustment, while Hashimoto’s showed lower burden. Findings support targeted control of risk factors, especially blood pressure, weight, and inflammation in autoimmune populations.

Objectives/Goals: The objective is to determine the relationship between prior progestin intrauterine device (IUD) use, duration of use, and adverse pregnancy outcomes and also to examine the return-time to fertility and whether demographic factors modify the effect of IUD use on pregnancy outcomes. Methods/Study Population: This is a prospective cohort study of 600 pregnant adults between 10 and 28 weeks of gestation who have not delivered a fetus. We will compare participants with and without prior progestin IUD use. Participants are recruited via flyers posted in university-associated OB-GYN clinics and via the health portal, MyChart. Participants complete a comprehensive contraception, medical, and pregnancy history and receive compensation. Pregnancies are followed until 6 weeks post-delivery, and outcomes are collected via the electronic health record. Pregnancy outcomes will be analyzed using multivariable logistic regression with adjustment for confounders, with sensitivity analyses stratified by duration of prior progestin IUD use. Results/Anticipated Results: We will characterize the prevalence and duration of progestin IUD use in the cohort and evaluate demographic factors associated with IUD use compared to other contraceptive methods. We will compare the rates of placental abnormalities, as well as miscarriage, preterm birth, hypertensive disorders of pregnancy, including preeclampsia and gestational hypertension, and other complications, between participants with prior progestin IUD use versus non-use. Among participants with prior IUD use, we will examine whether the risk of adverse pregnancy outcomes is associated with the length of use. We will also examine the time between the last contraceptive use and conception. Discussion/Significance of Impact: An association between prior progestin IUD use and pregnancy outcomes would be a novel finding and will have clinical implications for how contraceptive history may inform counseling and reproductive care. We hope that this study will rationalize the need for well-powered, larger studies to investigate the robustness of our study findings.

Objectives/Goals: To determine how loss of sympathetic nerves and β2-adrenergic receptor (Adrb2) signaling remodels the leukemic bone marrow niche, alters immune responses against leukemia, and influences acute myeloid leukemia (AML) progression. Methods/Study Population: AML was modeled using MLL-AF9-driven murine leukemia and human AML xenografts (MOLM-13 and patient-derived samples) in NOD scid gamma (NSG) mice. Sympathetic signaling was disrupted chemically with 6-hydroxydopamine or genetically using Adrb2 knockout mice. Leukemic burden, survival, and immune composition were analyzed by flow cytometry and imaging. Public RNA-seq and clinical datasets were used to assess ADRB2 expression and its prognostic value in AML patients. Results/Anticipated Results: AML induces sympathetic neuropathy in both mouse and human bone marrow. Denervation or Adrb2 loss accelerated AML and reduced survival in immune-competent mice, correlating with decreased CD8+ T cell activity. In contrast, denervation reduced leukemia burden in immunodeficient NSG mice, and β2-adrenergic agonist treatment enhanced AML proliferation in vitro. High ADRB2 expression in AML patients predicted worse survival, particularly in M5/MLL-rearranged subtypes. Discussion/Significance of Impact: Sympathetic signaling exerts dual roles in AML, supporting immune surveillance but directly acting on leukemic cells to promote AML progression. These findings identify β2-adrenergic signaling as a potential therapeutic target, especially in immunodeficient or chemotherapy-treated AML patients.

Objectives/Goals: Asynchronous imaging workflows delay follow-up for abnormal screening mammograms. Real-time artificial intelligence (AI) flags high-risk scans for synchronous, immediate review. We present pre-implementation qualitative and quantitative assessments to inform AI-enabled workflow redesign and improve outcomes. Methods/Study Population: We conducted a workflow study in the largest of 14 breast imaging centers in our health system. A multidisciplinary team of experts in breast imaging, implementation science, and AI/informatics conducted four clinical shadowing visits held on different weekdays (August 2023) and a time-motion study (February 2024) to identify opportunities for optimization in the current breast cancer screening imaging workflows. To determine the optimal time for potential AI workflow integration, we used statistical process control (SPC) charts to visualize patient volumes based on 2022 to 2023 scheduling data. Results/Anticipated Results: The selected site had an average daily patient volume of 67, including 29 screening mammography patients, 3 of whom with abnormal findings prompting a diagnostic workup. During shadowing, observers recorded time stamps for each breast imaging exam step, from check-in to check-out, to calculate exam duration and estimate waiting room capacity for diagnostic and screening mammography or ultrasound. We analyzed workflow to identify unnecessary tasks and radiologists’ disruptions that, if changed, could create the space for real-time reading without more difficult changes to workflow, for example, the number of radiologists and their deployment. SPC charts indicated that selected clinic days would be most promising for testing the new real-time workflows without major disruption to the system. Discussion/Significance of Impact: A detailed understanding of the workflow ensures future implementations achieve intended outcomes. This analysis helped identify modest opportunities in radiologist and staff downtime to support the new workflow, potentially improving satisfaction, reducing anxiety, and shortening time to diagnosis.

Objectives/Goals: To translate quantitative ultrasound (QUS) from imaging biophysics into clinical dermatology by evaluating its reproducibility, diagnostic differentiation, and potential as a noninvasive, point-of-care tool for nail disease assessment. Methods/Study Population: Diagnosing nail disease is difficult due to overlapping features and the invasiveness of biopsy. In a prospective study (recruitment ongoing), patients with onychomycosis, inflammatory nail disease (psoriasis or lichen planus), and controls underwent 15-MHz ultrasound of fingernails and toenails (n = 26 patients, 152 nails). Raw radio-frequency data were processed to derive three QUS parameters: Homodyned-K [α] (microstructural organization), effective scatterer diameter (ESD) (scatterer size reflecting tissue texture), and effective acoustic concentration (EAC) (scatterer density reflecting compactness). Metrics were analyzed with t-tests and mixed-effects models. Within-patient consistency and parameter reproducibility were evaluated to assess clinical translation feasibility. Results/Anticipated Results: Diseased fingernails (33 vs 54 controls) showed reduced α (−1.11, p < 0.001), reflecting loss of microstructural organization. Onychomycosis had the largest change (−1.25), while inflammatory nails were moderately reduced (−0.93). ESD showed opposite trends (+0.85, p = 0.02) indicating coarser scatterers in fungal disease, and EAC was uniquely elevated in lichen planus (+1.30, p < 0.01), consistent with fibrotic regions. In the toenail cohort (16 diseased vs 49 controls), onychomycosis showed parallel changes (α −0.9, p < 0.001; ESD +1.0, p = 0.02), supporting reproducibility across nail sites. Findings were confirmed by mixed-effects models. Together, these parameters quantify disease-specific microstructure and demonstrate technical and biological feasibility for clinical translation. Discussion/Significance of Impact: QUS provides interpretable biophysical markers that differentiate fungal, inflammatory, and fibrotic nail diseases. By bridging imaging physics with clinical diagnosis, QUS offers a scalable, noninvasive approach for point-of-care dermatologic imaging and advances the translation of QUS into practice.

Objectives/Goals: To evaluate the extent to which characterization of unstructured patient-generated health data (UPGD) (e.g., health journals) with human-guided machine learning (HGML) improves model explainability and integrates patient voices among renal cancer patients in a randomized control trial (RCT). Methods/Study Population: Data were collected in an RCT (NCT00505310) assessing the benefits of an emotional expression intervention on renal cancer patients’ quality-of-life outcomes. Over a 10-day period, patients completed four journal-type written responses to structured prompts about their health lifestyle behaviors or deepest thoughts and feelings linked to cancer. I will 1) evaluate the extent to which a standard ML model versus an HGML model aligns with principles of trustworthy health AI when characterizing UPGD, 2) evaluate the extent to which UPGD captures condition-specific data complementary to PROMs, and 3) extract journey maps from narratives about patient experiences navigating cancer care. Results/Anticipated Results: Results will illustrate a patient-centered value research approach that centers patient perspectives, contextualizes heterogeneous factors influencing patient outcomes, and mitigates biases in the health research machine learning pipeline. Discussion/Significance of Impact: This research contributes to the development of evidence-based and empirically validated strategies for health researchers to incorporate explainable HGML tools that facilitate the inclusion of patients’ experiences, perspectives, needs, and priorities throughout the research process.

Objectives/Goals: Caring for a loved one with heart failure (HF) is a chronic stressor that may elevate cardiovascular disease (CVD) risk. This study examined relationships between TNF-α, adiponectin, and clinical and demographic factors among HF family caregivers (FCGs). Methods/Study Population: This study is a secondary data analysis of baseline data from FCGs of individuals with HF enrolled in an intervention study (N=122, mean age 55 ± 11.7 years, range 25–80). The sample included 92% women and was racially diverse, with 59% identifying as African American, 39.3% as Caucasian, and 1.64% as other ethnicities. TNF-α and adiponectin levels were analyzed, and Spearman’s correlation was used to explore their associations with cardiovascular and metabolic indicators, including lipids and anthropometric measures. Results/Anticipated Results: TNF-α was inversely associated with total cholesterol (ρ = -0.241, p = 0.037) and adiponectin (ρ = -0.275, p = 0.017). Adiponectin was negatively associated with triglycerides (ρ = -0.208, p = 0.023), C-reactive protein (CRP; ρ = -0.283, p = 0.002), and metabolic syndrome (ρ = -0.181, p = 0.049). These findings suggest that TNF-α contributes to inflammation, while adiponectin may play a protective role in metabolic health. Discussion/Significance of Impact: Findings suggest that elevated TNF-α and reduced adiponectin may mark higher CVD risk in HF caregivers. Targeting inflammation and boosting adiponectin could reduce this risk. Longitudinal studies are needed to confirm these relationships and guide caregiver-focused interventions.

Objectives/Goals: The relationship between factors that strain a healthcare system and delays in antimicrobial dosing for patients with sepsis is unclear – including redosing after first administration. The objective of this study is to define elements of capacity strain that impact timely second antimicrobial administration using electronic health record data. Methods/Study Population: In our planned retrospective cohort study, patients are eligible for inclusion if they present to one of 19 Atrium Health hospitals’ emergency department (ED) between 2022 and 2024, meet 2 or more Systemic Inflammatory Response Syndrome criteria within 6 hours, and are subsequently admitted to an intensive care unit (ICU). Capacity strain is defined using multiple measures including ED and ICU census, ED acuity, patient turnover, and ED wait times. The primary outcome is time between first and second doses of antimicrobials. Secondary outcomes are hospital length of stay (LOS), ICU LOS, days free of mechanical ventilation, and 90-day hospital re-admission. Association between variables will be assessed using univariate and multivariable regression models. Fit statistics will compare model performance. Results/Anticipated Results: Based on prior literature, we anticipate finding high rates of antimicrobial dosing delays (>10% incidence). We hypothesize that higher census, higher ED acuity, higher turnover, and wait times will each be associated with increased risk for delay in antimicrobial administration. This association will hold true when exposures are assessed in the ED, as well as in the ICU. We also expect that a model containing a combination of our proposed exposures will outperform each individual measure. Discussion/Significance of Impact: Gaps remain in understanding how system pressures interact with sepsis care delivery and outcomes. Health system strain may contribute to poor adherence to evidence-based practices. Identifying key strain measures can help direct the design and implementation of targeted interventions to improve care delivery and resource allocation.