Objectives/Goals: Glucagon-like peptide-1 (GLP1) use prior to bariatric surgery may represent a novel approach to treating obesity. The objectives of this study were to describe trends in pre-bariatric GLP1 use, investigate social and clinical factors associated with their use, and evaluate differences in clinical outcomes based on preoperative GLP1RA use. Methods/Study Population: Patients who underwent bariatric surgery at three Indiana hospitals from 2018 to 2023 were identified. Patients who utilized GLP1 in the year preceding surgery were compared to those who did not. Social determinants of health included insurance, income, and unemployment. Outcomes included rates of GLP1 use, 30-day postoperative readmissions, ED visits, and percent total weight lost (%TWL) at one year. Associations between preoperative GLP1 use and outcomes of interest were evaluated using multivariable logistic and linear regressions. Results/Anticipated Results: Of 2,169 patients who underwent surgery, 293 (13.5%) utilized GLP1 preoperatively. The rate of GLP1 utilization increased threefold from 2018 to 2023. Males were more likely to receive preoperative GLP1 (20.1% vs, 12.2%, p<0.001). There were no significant differences in social determinants of health or 30-day postoperative outcomes between patients who did and did not use GLP1RA preoperatively. Similarly, there were no significant differences in %TWL at one year postoperatively between groups (median 25.5% vs. 27.3%, coefficient: -0.78, 95%CI: -2.26–0.70). Discussion/Significance of Impact: Utilization of GLP1 in the year prior to bariatric surgery has significantly increased. Preoperative GLP1 use is not associated with worse 30-day outcomes or differences in %TWL at one year postoperatively. Further work is needed to evaluate whether GLP1 dosing and duration of treatment impact postoperative outcomes.

Objectives/Goals: Audience will understand the engage for equity CBPR conceptual framework used to develop our partnership survey. Audience will better understand our survey design and administration process. Audience will understand our initial experiences, community advisory board member responses, and how the process influenced our engagement efforts. Methods/Study Population: The objective of this assessment is to understand how our community partners perceive our engagement efforts. The questionnaire is based on the 12 constructs of the CBPR engage for equity model. We plan on annually assessing impact and refining engagement approaches in response to community partners’ perspectives. Community Advisory Board members and other partners will complete an online survey and participate in a subsequent discussion and planning session about the responses. We anticipate evaluating approximately 400 community partners who collaborate with the Wright Regional Clinical and Translational Science Awards between 2023 and 2030 with a RedCAP survey and discussion. The instrument uses validated questions from a range of engagement instruments to assess context, partnership structure, processes, outputs, and outcomes. Results/Anticipated Results: We hypothesize that both the survey and subsequent discussion about how respondents reacted to answering the questions will improve our ability to track and adapt our community engagement efforts. Discussion/Significance of Impact: Our annual survey is not project specific and using it longitudinally will inform our engagement process to research that aligns with community needs. We anticipate this will help us to guide our research agenda to align with community needs and define impact of our university–community partnership.

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

Objectives/Goals: Gait performance (GP) and global cognitive functions (GCF) are both critical for maintaining independence and quality of life in senior adults. Recent studies have explored the potential link between GP and GCF, encompassing executive functions. Methods/Study Population: PRISMA guidelines will govern this systematic review. This systematic review synthesizes published research from 2000 to 2024, including peer-reviewed articles, pilot studies, and randomized controlled trials, to examine the relationship between GP (how a person walks and stands) and GCP in older adults. The exclusion criteria will be based on studies focused on physical activities unrelated to balance, meta-analysis, and systematic reviews and those published in languages other than English or Spanish. Results/Anticipated Results: Our preliminary data indicate that gait, or walking speed, is significantly correlated with GCP in older adults, with slower walking associated with poorer global cognition. Specifically, gait speed during dual-task walking shows a strong correlation with working memory (p < .001) and processing speed (p < .05) in individuals aged over 60 years. Gender differences were observed, with women over 80 walking slower than men over 85 years, who walked faster, and women exhibited poorer global cognition than men. Discussion/Significance of Impact: Overall, a gait slowing between 0.68 and 1.1 m per second could predict a marker for the risk of developing dementia, indicating that monitoring gait speed in older adults may provide early warning signs, allowing for timely interventions. Enhancing GP and GCF can improve the quality of life and independence in older adults. Acknowledgments: Research supported by NIH: Award Number HCTRECD R25MD007607 from the NIMHD.

Objectives/Goals: Our goal for this project is to develop a metric that integrates the intersectional social and structural determinants of health and well-being into the existing policy development framework to impact the integration of such considerations on population mental health. Methods/Study Population: This project was developed from a case study module offered by the Translational Research Program at the University of Toronto. This course was designed to sharpen contextual inquiry skills and further develop a case through employing strategies, including outreach engagement with stakeholders, conducting informational interviews and formulating potential pathways forward based on the integration of insights from interdisciplinary perspectives. Results/Anticipated Results: The anticipated outcome would be improved mental health outcomes as measured by the Mental Health Commission of Canada’s Mental Health Indicators (Mental Health Commission of Canada, 2015) Discussion/Significance of Impact: Although there are established mental health indicators and policy development framework the two operate independently of each other. Our proposal bridge the gap between the sectors so that one may inform the other, and they can collectively formulate reflective and representative policies.

Objectives/Goals: To explore how generative AI and chatbot technologies can transform clinical research administration by improving operational efficiency, reducing administrative burden, and thereby enhancing overall productivity and accuracy in clinical research environments. Methods/Study Population: This explores AI’s application in enhancing clinical research administration. We specifically address AI’s role in QCT/MCA activities, charge master data cleaning, and generating IRB consent forms from award documents. AI algorithms optimize charge master data for accuracy and compliance. Generative AI models are employed to produce IRB consent forms efficiently, incorporating key grant documents. AI also conducts thematic analyses of historical CTSA aims to identify trends and recurring themes. Furthermore, AI-assisted tools enhance study design through innovative approaches to hypothesis generation, sample size calculation, and protocol development. Integrating these AI methods aims to significantly improve efficiency, accuracy, and overall quality in clinical research administration. Results/Anticipated Results: Incorporating AI into clinical research administration will yield improvements in efficiency and accuracy. AI-driven QCT/MCA steps are expected to reduce human error and enhance data integrity. Chargemaster data cleaning via AI prompts will likely result in optimized, error-free data, ensuring compliance with regulations. The use of genAI for creating IRB consent forms from grant documents should significantly streamline the IRB approval process, reducing preparation time and administrative burdens. Thematic analysis of CTSA aims by AI will provide deep insights into historical trends and recurring themes, aiding in strategic planning. AI-assisted study design tools are anticipated to optimize sample estimation, protocol development, and advance the quality of clinical research administration. Discussion/Significance of Impact: The significance lies in enhancing efficiency, accuracy, and quality in clinical research administration. By streamlining processes, reducing errors, and providing strategic insights, AI supports the CTSA mission to accelerate translational research, thus improving public health outcomes and scientific innovation.

Objectives/Goals: To develop a personalized computational framework integrating computational fluid dynamics (CFD) and topology optimization for designing intracranial aneurysm implants. The primary objective is to reduce intra-aneurysmal blood flow velocity and enhance thrombus formation for improved treatment outcomes. Methods/Study Population: Patient-specific aneurysm geometries were extracted from pre-treatment rotational angiograms. A CFD-driven topology optimization framework was employed to design implants that reduce intra-aneurysmal flow velocity. The fluid dynamics were modeled using Navier–Stokes equations and the structural integrity of the implants was ensured by linear elasticity equations. The solid isotropic material with penalization (SIMP) method was applied to optimize the implant’s porous architecture, balancing flow reduction with structural support. COMSOL Multiphysics software was used to implement the optimization. Results/Anticipated Results: The optimized implants demonstrated significant reductions in intra-aneurysmal blood flow velocity and improved hemodynamic conditions. Flow velocity within the aneurysm was reduced by 77%, and the fluid energy dissipation ratio showed a 78.9% improvement compared to pretreatment conditions. The optimized porous structures were tailored to the aneurysm’s specific geometry, providing personalized designs that improve flow stasis and thrombus formation. Further validation of the implants will be performed in vitro and in vivo to assess their effectiveness and biocompatibility. Discussion/Significance of Impact: This personalized implant design framework could lead to better treatment outcomes by reducing aneurysm recurrence and complications compared to current devices. It provides a pathway for improved occlusion rates and patient-specific solutions for intracranial aneurysms.

Objectives/Goals: Here, we utilize deep learning to automate the analysis of dual X-ray absorptiometry (DEXA) scans in the UK Biobank (UKB) imaging dataset to enable a large-scale assessment of lumbar spine disc degeneration, low back pain, and socioeconomic status. Methods/Study Population: Study Population: The UKB is a biomedical database that includes lateral spine DEXA imaging for 50,000 participants. Deep Learning Model Development: A computer vision model was developed that receives a DEXA scan as input and outputs a quadrilateral that corresponds to the corners of 5 lumbar vertebral bodies. The model is a deep, fully convolutional, encoder–decoder network using DeepLabV3. Statistical Analysis: To determine our preliminary model accuracy, we used the intersection over union (IoU) metric.We analyzed data using an ordinal regression model to determine the relationship between income/ neighborhood level multiple deprivation index (MDI) and low back pain (LBP), as well as a mixed effects model to estimate the relationship between income/MDI and disc height index (DHI). Results/Anticipated Results: Our model predicted vertebral body quadrilaterals in training and unseen test data (train IoU  =  0.96, test IoU  = .91) and was used to infer data for 10,440 participants. Confirming previous studies, there were significant relationships (p0.05) between income or MDI and DHI (Figure 2). Discussion/Significance of Impact: Low back pain is the world’s leading cause of disability, and socioeconomic factors play an important role. We found no relationship between disc height index and socioeconomic status. Thus, disc degeneration may not be a factor in this low back pain phenotype.

Objectives/Goals: The Research Education and Community Health (REACH) coalition proposes to develop the infrastructure for continuous and comprehensive collection of community health data to drive programs, education, and funding priorities across municipal agencies, institutions, and nonprofit organizations in Galveston County. Methods/Study Population: The workgroup through REACH will organize and adopt a comprehensive community health needs assessment that 1) accumulates existing, readily available data for shared use (e.g., Center for Health Care Data at the UT School of Public Health, the Texas Department of State Health Services Center for Health Statistics, and Epic Cosmos, a data aggregation tool, used by UTMB and other health systems to improve patient care); 2) utilizes data collected throughout the community (i.e. non-profits, municipal agencies, and law enforcement); and 3) applies qualitative data from focus groups and/or key informant interviews, so we can hear directly from community members about what their needs are. By doing so, we hope all can benefit from having access to current and relevant data to drive our programs, education, and funding. Results/Anticipated Results: This Community Health Needs Assessment is being coordinated by a diverse workgroup including community organizations, researchers, and policy makers who will benefit from access to current and relevant data. The Galveston Youth Risk Student Survey, completed every three years and most recently in 2024, revealed lingering health and mental health effects of the COVID-19 pandemic on County youth. This highlighted the need for community access to current, accurate, and ongoing data to drive programming, interventions, and education. The REACH Coalition, made up of 23 UTMB Centers and Institutes and 39 community organizations, is spearheading this effort as a part of its mission to facilitate collaborative research, service, and educational efforts. Discussion/Significance of Impact: Collected data will be used to establish and support ongoing, coordinated interventions in response to identified needs. Shared ownership of data and project implementation optimizes resources and reduces gaps and/or redundancy in community programming.

Objectives/Goals: Healthcare organizations must track electronic health record (EHR) activity at the user level, including logons, accessed records, and viewed or entered documentation. There is little standardization in EHR audit logs and nurse workload has not been explored using these data. In this project, we characterized nurse actions from EHR audit logs. Methods/Study Population: We performed an analysis of EHR audit log data collected from 8,149 nurses over 5 years at University of Utah Health. We preprocessed nursing-centric EHR audit logs from the Epic EHR by cleaning and preparing the data for analysis. We calculated basic statistics for the variables labeled user_id (nurse) and metric_id (action). We reviewed the actions used by nurses and categories the actions as navigation, view, and entry. To capture the clinical context of the actions, two nurses categorized each action. A third nurse resolved any discrepancies. Results/Anticipated Results: We found that of the 4,419 available metrics, nurses used 1,461 unique metrics during the timeframe. The actions most used by nurses were 1) report with patient data viewed, 2) inpatient system list, and 3) storyboard viewed. Most of the metrics were categorized as navigation. The number of nurses interacting with the EHR increased each year and on average, we found that 1000 unique metrics were used by each nurse user in a 24-hour period. The expected outcome is a set of actions that can be mapped to higher level nursing interventions and in the future contribute to models for nursing workload measurement. Discussion/Significance of Impact: We found great value in using EHR audit logs to provide insights into nursing actions. Information gleaned can benefit organizations that are crafting interventions to decrease workload. Ultimately, the goal is to ensure that nurses have an appropriate workload allowing for safe and high-quality patient care while maintaining their well-being.

Objectives/Goals: The operation of a clinical trials unit involves multifaceted tasks and stakeholders. A competent information system is critical to daily operations while ensuring smooth conduct of clinical research. We share 15 years of experience in the design and implementation of such a system at Mayo Clinic to inform other institutions with similar interests. Methods/Study Population: The Informatics team collaborated closely with nurse leaders and elicited input from additional stakeholders including nurse unit coordinators, lab managers, schedulers, investigators, study coordinators, and regulatory specialists throughout the phases of system design, development and continuous enhancements, and expansion. The stakeholders offered insights on the corresponding requirements throughout the study life cycle, from engaging with the study sponsor, operational review for protocol execution, development of study budgets, human subject protection and risk mitigation, data management and integration, to outcome monitoring, and regulatory reporting. The activities were then translated into functional components and implemented as a seamless and effective solution. Results/Anticipated Results: Patient safety, scientific rigor, operation automation, efficiency, and regulatory requirements were all considered in developing an integrated system, or the clinical research trials unit (CRTU) Tools. Our institution has leveraged the system for essential tasks from the study start-up, visit scheduling and execution, specimen collection and tracking, to individual protocol metrics and billing. We adopted a measure-as-we-go methodology so that data such as visit census, resource usage, and protocol deviation are tracked and collected during routine use of the system. Specifically, an issues/concerns/exceptions (ICE) tool is used for quality control and patient safety. Moreover, data quality greatly benefits from a task dictionary, standardizing the study activities that can be ordered and executed. Discussion/Significance of Impact: The implementation of a well-rounded clinical trials unit information system not only improves the operation efficiency and team productivity but also ensures scientific rigor and contributes to patient safety. We believe the experience can be informative to other institutions. More details will be shared in the poster.

Objectives/Goals: In alignment with the Clinical and Translational Science Awards (CTSA) UM1, continuous quality improvement (CQI) needs to be integrated into the elements and hub evaluation. As a first step to operationalizing this process, the University of Cincinnati hub (CCTST) evaluation team developed a systematic approach to prioritizing and sequencing tasks for aligning evaluation methods with CQI. Methods/Study Population: A scoring sheet was developed to provide a framework for prioritizing and developing the sequence and timeline for supporting elements in aligning their evaluation methods with CQI. The scoring sheet assessed: 1) impact – defined as the results from completing the task and implementing enhancements; 2) effort – defined as the amount of resources (time, personnel, and materials) needed to complete the tasks; 3) reach – defined as number of individuals (e.g., CTSA employees, members, researchers, trainees, and community members) impacted by the tasks or products of the tasks; 4) urgency – defined as a task that is time-sensitive due to a deadline and has a clear consequence if not completed on time. Each component was assessed using a 3-point scale (e.g., minimal, moderate, and high). Results/Anticipated Results: In Fall 2024, the CCTST evaluation team met with respective elements to collect data on their: 1) need from the evaluation team to support aligning their evaluation methods with CQI, 2) challenges and barriers to improving evaluation and aligning with CQI methods, 3) number of hours per month available to support improving evaluation methods, and 4) current resources to dedicated to conducting an evaluation. Next, the evaluation team will transcribe the data from the meetings and code the data into the scoring sheet for each element. The scoring sheet is anticipated to produce a score that will be used to develop the sequence, timeline, and initial tasks for supporting elements in improving evaluation methods and aligning with CQI over the first year of the UM1. Discussion/Significance of Impact: CTSA hubs’ evaluation teams operating at full capacity may encounter barriers to implementing CQI efforts. This systematic approach – assessing the impact, effort, reach, and urgency to sequence evaluation and CQI alignment – can support evaluation teams by ensuring a balanced workload and optimizing operations for quality improvement.

Objectives/Goals: Originally presented at TS24, we would like to discuss follow-up outcomes and preliminary findings“Building Empowerment Through Fitness” – objectives were designed to address the “whole women” in underserved segments of the population in impoverished and/or underprivileged communities with fewer opportunities and promote economic mobility. Methods/Study Population: A qualitative study design to understand unique cultural context and challenges faced by women residing in the Birmingham Housing Authority, regarding physical activity engagement, including sedentarism. The needs assessment approach examined barriers, support systems, and social networks. Semi-structured, pre, mid, and post program focus groups explored challenges and opportunities for health, such as diet and exercise, and are being analyzed by hand with a thematic analysis. The project framework used movement, home repair tools with a life-building/life repair curriculum to influence self-efficacy and program engagement. The study enrollment included 51 women over two cohorts in a 12-week project. Results/Anticipated Results: Preliminary are results showed the women did face heightened health risks due to sedentary lifestyles, educational gaps, and socioenvironmental barriers. We incorporated tool skills, healthy lifestyles, and social/emotional focus group to explore social/emotional perceptions, concerns, and needs. Although our results were on target, there were some unexpected outcomes related to family dynamics, literacy, housing stability, and peer support. Discussion/Significance of Impact: Women are more likely to be sedentary and have poor health. BeFit served as a solution to addressing problems related to economic mobility barriers, poor health, sedentariness, and low self-esteem/self-efficacy. Additionally, exercise/movement programs can be a powerful tool to address disparities and help prioritize health.

Objectives/Goals: The overall goal of this project is to determine bacterial transcriptional signatures from clinical sputum and assess their potential to monitor treatment response and predict the outcome of drug therapy in patients with tuberculosis (TB). Methods/Study Population: We are developing a novel transcript capture sequencing (TC-Seq) approach to sequence the mRNA of Mycobacterium tuberculosis (Mtb) and analyze transcriptomes from clinical samples containing minimal amounts of bacterial RNA. This protocol generates single-stranded biotinylated probes from Mtb DNA. Probes are hybridized to and allow enrichment of Mtb-specific mRNA within next-generation RNA sequencing libraries. We will apply TC-Seq to sputum samples collected throughout an 18-month Phase II clinical trial investigating response to TB treatment to compare the transcriptome of Mtb between patients whose treatment results in cure or relapse. Results/Anticipated Results: We have refined a technique to generate biotinylated probes starting from DNA of lab grown Mtb. This protocol achieves robust and unbiased sampling of the Mtb transcriptome from mixed samples containing both human and Mtb RNA. Preliminary sequencing of clinical sputum collected pretreatment has generated 1–4 million Mtb-specific reads, a sequencing depth that allows examination of the entire bacterial transcriptome. We will measure differential gene expression before and during treatment as well as between cure and relapse cases. These results will allow us to characterize bacterial response to treatment and identify bacterial markers that correlate with relapse. Discussion/Significance of Impact: Understanding Mtb activity during treatment will offer new ways to assess the efficacy of different treatment regimens. Crucially, identifying clear bacterial markers that demarcate a cure or relapse outcome will have a significant impact on determining patient eligibility for shorter drug therapy.

Objectives/Goals: Longitudinal studies capture dynamic host–environment interactions crucial to microbiome research. Commonly used mixed-effects models can struggle with small clinical samples, like many endometrial cancer studies. We will compare them to simpler fixed-effects models, aiming to develop a new fixed-effects differential abundance alternative. Methods/Study Population: Starting with simulated data for linear models with a single treatment effect, we will incrementally increase complexity to reflect real-world microbiome data. To observe conditions of misspecification, simulated data of repeated measures will be generated using different distributions, while varying parameters and inter-class correlation. In each condition, linear fixed- and mixed-effects models will be fitted 1000 times to empirically determine p-value, type 1 error rate, and power. We will increase the verisimilitude of our approach by using endometrial cancer data from our lab as a real background, while adding simulated signals. This data will be used to assess extant differential abundance models against our novel fixed-effects model. Results/Anticipated Results: Our initial findings indicate that fixed-effects models maintain control over type 1 error rates in small samples, while mixed-effects models do not perform as reliably. We are now exploring the effects of confounders on type 1 error rates and power in linear models, while our next step will evaluate generalized linear models in a differential abundance context. We expect that the fixed-effects models will continue to be as reliable as mixed-effects models, while being less computationally complex. After establishing our theoretical basis, our novel model should perform comparably to mixed-effects models on pseudo-simulated endometrial cancer data and better at small sample sizes. We will finally apply our model to real endometrial cancer data to identify microbial markers of disease predisposition. Discussion/Significance of Impact: By characterizing and improving these statistical tools, we seek to increase the robustness and power of microbiome study results (especially at small sample sizes), ultimately supporting better-informed translational research decisions. This work underscores the importance of statistical methods in advancing microbiome research.

Objectives/Goals: Delving into the intricate web of translational research collaborations, this study analyzed the evolving landscape of the Hispanic Alliance of Clinical and Translational Research from 2020 to 2024 using cutting-edge social network analysis (SNA). SNA is a powerful tool for visualizing, understanding, and harnessing the power of networks. Methods/Study Population: We conducted a systematic document review of all the Alliance IDeA-CTR Network Calls for Pilot Projects from 2020 to 2024 including key attributes of the investigators and collaborators (e.g., academic institution, highest degree, collaborator type). Scientific collaboration was defined as two or more researchers working together in a grant proposal for a pilot project application. Study data was recorded and tracked using an Excel spreadsheet. R-Statistical software was used to analyze and map the networks resulting from collaboration interactions comparing the 2020 Call and 2024 Call. Network statistics were performed including nodes, isolates, edges, components, density, diameter, average degree, and the size of the main component. Results/Anticipated Results: Within a vibrant network comprising over 150 investigators from local and national academic institutions, clinicians (49.3%), and basic researchers (25.4%) are predominant. Initial findings showcase a remarkable surge in interdisciplinary collaborations and affiliations over time. Preliminary findings demonstrated that the number of nodes/actors increased from 16 to 75 comparing 2020 to 2024 and the edges/relationships from 12 to 66. Notably, the number of translational research clusters surged from 4 to 18, with mentorship emerging as a critical conduit bridging diverse research clusters; 16 to 78 nodes in comparison from 2020 to 2024. More extensive collaborative clusters occurred across time with over 20 researchers collaborating. A mentor was the main actor connecting these research clusters. Discussion/Significance of Impact: This study unveils the intricacies and power of translational research dynamics, showing a palpable surge in collaboration diversity and depth. By harnessing data-driven insights, our approach catalyzes informed decision-making to amplify collaboration, diversity, and network efficacy, offering invaluable guidance for policy and practice.

Objectives/Goals: Translational researchers spend significant amounts of time finding available datasets and other research data resources for their purposes. Objectives of this program are develop and evaluate a multipronged approach to supporting researchers with existing data resources. Methods/Study Population: We established a dedicated service with expertise in data resources to increase awareness, understanding, and utilization of existing data resources. This program assists investigators and trainees discover appropriate data resources, formulate scientific problems in computable formats, advise on state-of-the-art data analytics, data management, build collaborations, mentor data users, and develop a service pipeline for streamlined data resource project management. This is accomplished through these essential functions: (1) Discover, catalog, document, and manage metadata resources, (2) train and present data resources to the research community, (3) provide individual consultations, and (4) explore and assess novel data resources. Results/Anticipated Results: In a phased approach, the data navigation program is performing outreach to the research community and integrating with existing data efforts on campus, presenting and demonstrating existing data resources, established a consultation service, and building core competencies into long-term usage and navigation of resources across campus. Evaluating the program monthly has shown an increase in various metrics for evaluating commitment and engagement including number of requests for access to data resource, consultations, publications and presentations, co-authorship, and proposals. Unawareness and inappropriate use of data resources leads to delays in performing research and potentially unnecessary duplications of efforts. Discussion/Significance of Impact: Our data navigation program has increased use of data resources in research. Next steps are to continue evaluation and further streamline informatics approaches to data discovery, abstraction, formulation, and analysis. Harmonized data resource programs are important translational science approach to foster the next generation of research.

Objectives/Goals: Trainees in clinical and translational science (CTS) must learn to effectively communicate their research ideas and findings to a range of audiences. As part of our science communication curriculum, we developed ORAL and WRITTEN science communication rubrics for our trainees to use across their courses and research activities. Methods/Study Population: The Tufts CTS Graduate Program is the training core of the Tufts CTSI and its associated pre- and post-doctoral T32 awards. Approximately 10 trainees with a range of backgrounds (e.g., physicians, medical students, master’s-level researchers, and basic science PhDs) matriculate each year. Faculty members and staff with expertise in science communication and pedagogy formed a committee to develop the rubrics. Because oral and written communication require different skills, we developed separate rubrics for each. We reviewed our current science communication curriculum, reviewed existing communication rubrics, and identified common mistakes students make. Following pilot testing by students and faculty pilot for one semester, we modified the rubrics based on informal feedback. Results/Anticipated Results: Both rubrics include a section to identify the target audience and specific items organized by theme. Oral rubric themes include presentation content, slides, verbal communication, nonverbal communication, and following instructions. Written rubric themes include overall, manuscript/proposal sections, and following instructions. The rubrics serve as feedback tools for faculty and students to evaluate work others produce and as self-evaluation tools. Feedback elements include a 4-point rating for each rubric item, open text feedback for each theme, and an open text holistic assessment. We now use the rubrics in our study design course, which features student presentations of planned research, and in our writing course. We anticipate collecting formal student feedback to further evaluate the rubrics. Discussion/Significance of Impact: Our rubrics can supplement existing science communication training and can be integrated into all CTS coursework and research activities. For future clinical and translational scientists to have the greatest impact, they must learn to effectively communicate findings to multiple audiences, ranging from experts in their field to the general public.

Objectives/Goals: To assess the feasibility, safety, and preliminary efficacy of implementing a supervised, outpatient aerobic and strength exercise regimen in newly diagnosed pediatric patients with acute lymphoblastic leukemia (ALL). We hypothesize that early implementation of exercise is feasible and may prevent well-known cardiometabolic late effects. Methods/Study Population: We will enroll 10–20 children (both males and females) with newly diagnosed ALL between the ages of 11–21 years to participate in a 12-month supervised, structured outpatient exercise regimen (STRONGER ALL). This regimen will consist of low- to moderate-intensity aerobic and strength exercises (either in person or coached virtually per patient preference) 3 times a week. This study will include 2 physical fitness assessments: 1) baseline and 2) end of study. Assessments will include resting energy expenditure, peak oxygen uptake, bone density, upper and lower extremity strength, flexibility, and questionnaires (feasibility and quality of life). Additionally, blood and urine specimens will undergo metabolomic analysis to identify biomarkers predictive of future cardiometabolic outcomes. Results/Anticipated Results: We expect that early implementation of STRONGER ALL in children undergoing chemotherapy will be feasible and preliminarily effective at mitigating risk factors for long-term cardiometabolic outcomes in survivors. Feasibility will be defined by recruitment capability (at least 50% of eligible patients agree to enroll), acceptance/compliance (at least 50% of participants complete the program with participation in at least 50% of sessions), data acquisition (collection and outcomes measures are appropriate), and practicability (program shows promise of being successful with pediatric ALL patients as measured by validated surveys administered to patients and caregivers). We anticipate that ALL patients participating in STRONGER ALL will have improved fitness and quality of life. Discussion/Significance of Impact: Evidence on the benefits of physical activity for ALL patients has not changed clinical practice. We aim to overcome the translational science barrier of patient- and system-level blockade in implementation of exercise in children with ALL. The evidence generated from this research may also be generalizable to other childhood cancer survivors.

Objectives/Goals: Both diabetes and obesity are major health issues in the USA. Though much focus is given on the impact of lifestyle modifications to control diabetes and obesity, more information needs to be established about the association of social determinants of health and them. This study explores these associations, focusing on the DMV area. Methods/Study Population: The data for this study were collected for 158 counties which cover two states Maryland and Virginia, and Washington DC from PolicyMap. PolicyMap is a geography information system (GIS) that aggregates different types of data from different sources for research purposes. County-level data on public health related to different SDoH like median age, and percentages proportions of different ethnicity (Hispanic or Latino), percentages of different race, and nativity (foreign born), gender (ratio of male to female), access to primary healthcare, social vulnerability index (SVI), and median household income was used in this study. Statistical methods like multiple regression, one way ANOVA, and Pearson’s correlation coefficients were used to determine which factors are associated with these two conditions. Results/Anticipated Results: For both diabetes prevalence and obesity, multiple linear regression model with backward elimination was used to select variables which associated with them. The backward elimination process selected the set of factors for which the adjusted R square was the highest. In both cases, median household income, median age of population, social vulnerability level, percentage of white population, and percentage of foreign-born population were found to be significant at 5% level of significant. Pearson’s correlation coefficients showed significant positive relationship between factors like obesity and diabetes, median age and access to health care, and negative relationship between obesity and foreign born. Income, healthcare access, and white population were found to be significantly different SVIs from ANOVA. Discussion/Significance of Impact: This research study found that some SDoH affect diabetes and obesity in the same direction. The association is positive for median age and negative for income, SVI, percentage of white population, and foreign born. The associations were found between actionable and non-actionable factors like percentage of white population with access to health care.