Objectives/Goals: Primary care physicians (PCPs) have limited awareness of assistive technology (AT) devices that can improve the daily functioning of older adults. This study aimed to assess the quality of the My Assistive Technology Guide (MATG), an informative web app, among PCPs and to describe their experiences using it. Methods/Study Population: In this pilot project, our team – comprising an established researcher, an undergraduate faculty member, and a graduate student – enrolled ten PCPs. In Phase I, the PCPs received training on how to use the MATG and were encouraged to utilize it for 30 days. At the end of this usage period, we implemented a concurrent parallel mixed-method design to collect both quantitative and qualitative data. Quantitative data were gathered using the User Mobile Application Scale (uMARS), while qualitative data was obtained through interviews. Data analysis involved descriptive statistics and thematic content analysis. Results/Anticipated Results: The mean score for the subjective quality of the MATG was high, 4.1 ± 1.1. The information domain received the highest rating, with a mean score of 4.6 ± 0.51, while the engagement domain received the lowest rating, at 3.3 ± 1.5. Overall, subjective quality was rated moderately high (mean 3.9, interquartile range 1.2), with perceived impact rated the highest at 4.8 ± 0.4. PCPs reported increased awareness, knowledge, attitude, intention, and behaviors to learn about AT and to inform and recommend AT devices to older adults. In addition, PCPs provided suggestions to improve the MGAT and its integration into their medical practice. Discussion/Significance of Impact: The results demonstrated the high quality and utility of the MATG, indicating that it could serve as a valuable resource for PCPs in addressing functional disabilities among older adults. Future research should evaluate the effectiveness of the MATG in enhancing older adults’ function in daily living activities.

Objectives/Goals: To develop an informatics framework that will allow study of environmental effects on stillbirth at large scale (i.e., US-level) and leverage recent advances in machine learning and artificial intelligence to produce reproducible results that can be compared across multiple institutional settings. Methods/Study Population: Experimental exposure data are often available in “absolute time,” where a clinical event can be anchored using a timeline transformation. We associate each stillbirth event with a set of {ti…ti+1}L shapelets [1] associated with a location, L, and time intervals for the entire dataset. These shapeless are aggregated using a state-of-the-art shapelet classifier [2]. An autoencoder is used to reduce the dimensionality of the stillbirth classification and to cluster stillbirth events according to their corresponding exposure patterns. The stillbirth cluster can be analyzed for other nonexposure (i.e., genetic, SDoH, and demographics) factors, which may be enriched and/or depleted. Results/Anticipated Results: The framework we are developing leverages a shapelet-based approach to produce clusters of stillbirth events according to their corresponding exposure patterns. These clusters can be analyzed for depletion or enrichment of nonenvironmental factors. This analysis will inform how to formulate (or not) class models of exposure that can be more informative and have better predictive power than overall population models. Moreover, the finding of depletion and enrichment of physiological properties of the individuals may lead to novel physiological hypotheses to better understand the injury mechanisms that the environmental exposure profile produces. Discussion/Significance of Impact: Nearly 20,000 babies are stillborn in the USA each year [3]. Environmental exposures, usually studied as time averages over certain periods of time, have produced mixed results for stillbirth risk [4]. However, temporal profiles matter [1], and we argue that they can be assessed using shapelet technology.

Objectives/Goals: To strengthen postdocs’ skills in developing and reviewing competitive proposals, advancing translational research by leveraging core facilities at Indiana University, Purdue University, and the University of Notre Dame, fostering collaboration, and preparing participants of the program for future funding success. Methods/Study Population: The Postdoc Challenge is a state-wide program for postdoctoral scholars across Indiana to gain hands-on experience in developing and reviewing competitive grant proposals. Participants compete for a $5,000 award to use any of the Indiana Clinical and Translational Science Institute (CTSI)-approved core facilities. Through workshops and following NIH guidelines for the review process, participants refine their proposal-writing skills and serve as reviewers for peers’ proposals. The program is designed to support translational research, utilizing the core facilities at Indiana University, Purdue University, and the University of Notre Dame. Participants are trained in grant proposal writing, reviewing using NIH criteria, and engaging in peer discussions, developing critical skills necessary for future funding success. Results/Anticipated Results: Since its launch in 2014, the Indiana CTSI Postdoc Challenge has funded 54 postdoctoral researchers, with 276 applications reviewed. The program has strengthened participants’ grant proposal writing skills and peer review capabilities. Career outcomes include several postdocs securing assistant professor positions and other prestigious roles, such as an Associate Principal Investigator at the National Biodefense Analysis and Countermeasures Center, and a Toxicologist at the CDC. Participants also report enhanced collaboration, greater access to core facilities, and significant career advancement, including support for international postdocs seeking permanent residency. Discussion/Significance of Impact: The Postdoc Challenge has significantly improved postdoctoral researchers’ grant proposal writing and peer review skills, helping them secure faculty and research positions. By fostering collaboration across Indiana’s top universities and utilizing core facilities, the program accelerates translational research and career success.

Objectives/Goals: Engage the research professional (RP) workforce in assessing job satisfaction, motivators, barriers, and levels of support throughout the research enterprise. The goal of this collaboration is to foster a positive culture, inform manager training, ensure RP retention, and enhance career mobility pathways. Methods/Study Population: Methods include a HR data compilation/analysis and focus groups for new RP’s (Results/Anticipated Results: This initiative will articulate the current culture and climate of the research enterprise and identify key strategic areas for growth. The UMN Clinical and Translational Science Institute (CTSI) will build off the design of an internal survey at Vanderbilt (2018) to encompass organization-specific challenges, the post-pandemic research landscape, and the UMass Diversity Engagement Survey. This process will also generate specific insights including RP sentiment statements, trends in how RP’s describe their day-to-day work, assessment of barriers, analysis of retention benchmarks, and defining employee hopes/motivators. The CTSI will also identify salient RP growth opportunities, leadership competencies, and areas of non-monetary compensation to improve satisfaction and career mobility. Discussion/Significance of Impact: Targeted interventions will be developed to address RP satisfaction barriers and leverage opportunities for KPI improvement. Results will be disseminated to managers, administrators, and the CTSI network. Resource development will include RP personas, job description/hiring templates, and strategic guides for key operational challenges.

Objectives/Goals: To determine the heterogeneity in CD4:CD8 ratio in a well-characterized cohort of PWH and to investigate the predictors of intestinal CD4:CD8 ratio reconstitution (CD4:CD8>1) and its impact on systemic inflammation. Methods/Study Population: We enrolled 52 PWH on ART and with peripheral HIV-RNA Results/Anticipated Results: PWH had a lower CD4:CD8 ratio both in the peripheral blood [p1. This subset of PWH was more likely female (62% vs. 38%, p = 0.0158), diagnosed with HIV for a longer time [p = 0.0347] have longer duration of most recent viral suppression [p = 0.0365] higher CD4+ T cells at enrollment [p =  0.0262] and higher CD4+ T cell nadir. Multiple logistic regression showed that duration of HIV infection [OR 1.13 (95% C.I. 1.02–1.3)] and CD4 =  T cell nadir[OR 1.01 (95% C.I. 1.001–1.016)] were associated with colonic CD4:CD8 >1. Colonic CD4:CD8 ratio partially correlated with the peripheral blood CD4:CD8 ratio (r = 0.274, p = 0.068) and with the pro-inflammatory cytokines IL-20 (r =  -0.413, p = 0.036) and SLAMF-1 (r =  -0.329, p = 0.074). Discussion/Significance of Impact: In PWH, CD4:CD8 ratio

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: 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: Access to accurate public health information is an essential component to ensuring health equity. We launched our social media channels on Instagram, Facebook, and TikTok to highlight, engage with, and bring culturally tailored and language appropriate health and research information to our target communities. Methods/Study Population: Monitoring engagement patterns with our content on each platform influenced the development of a range of innovative campaigns in both English and Spanish that were informed by our core values of inclusivity, trust-building, ongoing bidirectional communication, and co-creation. These three platforms were chosen to ensure reach and engagement with the different demographics within our target populations. The campaigns included those that provided relevant and accurate health information, highlighted the diversity of our team, uplifted our community partners, and gave voice to our community members. This content included health-related infographics, mini-documentary reels, video essays, interviews, and photos. Results/Anticipated Results: We assessed effectiveness, reach, and engagement based on the robustness of the analytics for each platform. Facebook content, the majority of which is in Spanish, appealed more to older, Latino community members. TikTok content appealed more to younger (under 35), primarily English-speaking community members, while Instagram appealed more to organizational partners and community health workers. A 2023 trendline analysis of average monthly Instagram content reach and interactions indicated a moderate-to-strong relationship between our tailored content and audience engagement. Storytelling techniques consistently outperformed other content types across platforms, and community partner collaboration drastically enhanced our visibility, reach, and further validated our approach. Discussion/Significance of Impact: Social media has become increasingly central to bidirectional information dissemination. Implementing tailored strategies and leveraging storytelling techniques is an effective means of engaging diverse audiences, enhancing public health communication, and building and maintaining trust by providing accurate, accessible information.

Objectives/Goals: Our study team won a Public Health Answers to Speed Tractable Results (PHASTR) contract to conduct a target trial emulation to answer “Does metformin show a reduction of severe outcomes of COVID-19 or of Long COVID in the N3C Data Enclave?” We quickly delivered an answer due to productive technical and collaboration support in the N3C. Methods/Study Population: Our analytic plan was updated based on helpful feedback from the PHASTR program. We performed a trial emulation analysis using the N3C data, comparing adult new users of metformin to controls prescribed fluvoxamine, fluticasone, ivermectin, or montelukast. The composite outcome was Long COVID or Death (LC/D) within 180 days of COVID infection. We used entropy balancing to estimate the average treatment effect with a weighted log-linear model. Productivity was enhanced by reusing code workbooks and validated codesets from related N3C projects. The team of 4 (physician, informaticist, data programmer, and statistician) and key unpaid advisors spent 10 weeks developing and analyzing the data. Results/Anticipated Results: Totally, 9,660 patients were identified for analysis. After weighting, there were 248 in the metformin and control groups. In the metformin group, 4.0% developed LC/D vs. 8.5% in the control group, with an adjusted risk ratio (aRR) of 0.47 (95% CI 0.25 to 0.89). Results were consistent across subgroups and sensitivity analyses. The PHASTR contract structure helped produce high-quality results quickly by not only providing funding but also requiring a compressed timeline for a small team to focus on the study. The most time was spent on contract execution, enclave provisioning, and too many last-minute download requests. A project final report was submitted in March and a full manuscript was submitted in September. Discussion/Significance of Impact: The analysis was productive because the environment made reuse easy and supported rich collaborations among clinicians, informaticists, epidemiologists, statisticians, and data developers. Advice from PHASTR advisors (Axel) and N3C diabetes domain team members was also key to a faster completion.

Objectives/Goals: This study aims to gather user feedback from clinical research professionals on the usefulness and relevance of regulatory resources found on a new regulatory web portal, OpenRegSource, to enhance its usability, thereby advancing this project from the initial to the full implementation phase of the implementation science framework. Methods/Study Population: The Regulatory Knowledge and Support core of the Southern California Clinical and Translational Science Institute developed a regulatory web portal called OpenRegSource to help researchers gain basic regulatory information prior to professional and/or paid consultation. Before publicly launching, a virtual focus group (FG) composed of 21 members of the local clinical research workforce was given two weeks to explore the web portal and answer three surveys. Two other research professionals also gave feedback outside of the focus group. The user feedback data was analyzed and discussed by the web portal project team. Updates were then made accordingly. Once the portal was launched, a plan was implemented to collect usage metrics and additional feedback for continuous improvement. Results/Anticipated Results: Of the 21 FG participants, 20 completed the feedback survey specifically for their experience with the web portal. 65% (13/20) said the number of resources was just right. 90% (18/20) found the resources to be very relevant to their respective topics. 85% (17/20) found the resources very useful and somewhat useful to their daily work activities. 75% (15/20) found the organization of the portal to be good or very good. 85% (17/20) found it very easy and somewhat easy to navigate the web portal. 90% (18/20) found the portal to be effective in providing its audience with a basic understanding of regulatory requirements. 95% (19/20) found the portal useful for novice research professionals. 85% (17/20) found the web portal useful overall. Participants were also able to provide commentary feedback for specific pages. Discussion/Significance of Impact: Obtaining stakeholder input during the development of a resource or tool is essential to ensure the final product meets user needs and is effectively utilized. In this case, involving the feedback of clinical researchers will help improve OpenRegSource to better facilitate the advancement of their work.

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: 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.

Objectives/Goals: The goal of the RC2 Systems Marketing Analysis for Research Translation (SMART) special innovation program is to develop and test a structured approach for working with research teams and communities to accelerate the translation of clinical and community innovations to address health inequities by integrating social marketing with community-based system dynamics. Methods/Study Population: The SMART program is a consultancy service for CTS teams focused on selecting and tailoring implementation strategies for advancing equity. We use social marketing for understanding the alignment of practice innovation feature sets with community priorities for advancing health equity; and community-based system dynamics to understand and refine the dynamics of scaling up and sustaining the implementation of innovations with sufficient reach to address regional health inequities. The program is implemented as community-engaged group model-building workshops with research teams, with follow-up marketing analyses and computer simulation of implementation strategies of innovations and development of implementation roadmaps. We use developmental program evaluation to revise the SMART program. Results/Anticipated Results: Anticipated results from piloting the SMART innovation program with four research teams include (1) design matrices pre and post-workshop for each innovation; (2) system dynamics simulation models and analyses of implementation and scale-up of innovations; (3) analysis of the SMART program for highest impact, with priors for estimating subsequent SMART program performance; and (4) a revised SMART program based on results from the developmental program evaluation. Discussion/Significance of Impact: This work highlights the feasibility and benefits of combining methods that have been cited in implementation science for understanding the complexity of implementation to accelerate the translation of innovations into clinical and community settings for advancing health equity.

Objectives/Goals: Using secure systems for sharing documents with external collaborators is essential for all researchers. These documents may include protected health information (PHI) or sensitive materials like protocols, study reports, DSMB reports, publications, presentations, abstracts, and statistical analysis plans (SAPs). Methods/Study Population: We surveyed the ACTS Biostatistics, Epidemiology, and Research Design Special Interest Group (BERD-SIG) to gather information about the systems they are currently using or have used in the past for document sharing with external collaborators. The survey focused on the security of these systems, particularly in relation to sharing documents containing PHI. In addition, the survey included questions about various system features of interest. These features included version control, simultaneous editing by multiple users, and access rights management, such as the ability to assign different permissions (e.g., read-only, write, and download) to different individuals. We also invited participants to provide feedback on any additional positive or negative aspects of the systems they use. Results/Anticipated Results: We received 28 completed survey responses. Respondents had an option for choosing more than one system. The top current systems reported were Microsoft Teams (OneDrive, SharePoint) (n = 16), Box (n = 11), Google Docs/Drive (n = 10), and Dropbox (n = 6). Among other systems listed individually were Filelocker, REDCap, Slack, Website, Significant Media Shuttle, and Zulip. Notably, 15 responses indicated the respondents were unsure if their system is secure for sharing documents containing PHI. Respondents also offered feedback on both the positive and negative aspects of these systems. For example, a key advantage of Box was its password-controlled access. However, its incompatibility with office tools and the challenges for external collaborators attempting to access the system were noted as drawbacks. Discussion/Significance of Impact: Utilizing secure institutional document-sharing systems and understanding their features significantly affects the effectiveness and security of collaborations among researchers, particularly with external partners. This knowledge is especially crucial when sharing documents containing sensitive patient and study data.

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: To assess the effect on the immune response to COVID-19 in children exposed to pesticides. The hypothesis is that increased pesticide exposure results in different immunological response to COVID-19. The goal of the proposal is to improve scientific knowledge on factors affecting COVID-19 and identify a modifiable factor to reduce these disparities. Methods/Study Population: A cross-sectional analysis of children (aged 5–17 years) with asthma to assess pesticide exposure and immune markers of SARS-CoV-2. SARS-CoV-2 infection or vaccination was determined with blood exposome RNA analyses assessed from blood samples taken at baseline. Immunological response was measured using neutralizing, phagocytizing, and NK-activating antibody responses biomarkers using plasma antibody isotyping, effector functions, T-cell activation-induced marker (AIM), and recall cytokine secretion assays on lysed, whole blood. Pesticide exposure was assessed as concentration of four urinary metabolites in a spot urine sample adjusted for creatinine. Unadjusted regression models were created to assess the effect of 3-phenoxy benzoic acid, a common pyrethroid pesticide, on immune markers. Results/Anticipated Results: Children’s (N = 30) average age was 10 years (interquartile range: 8–11) A majority of children were male (63%) and Non-Hispanic Black (73%). The majority of children had markers of SARS-CoV-2 infection (77%). Of the 4 pesticide metabolites assessed, only 3-PBA was commonly found (77% of samples > LOQ). Higher urinary concentrations of 3-PBA are associated with a significant (p Discussion/Significance of Impact: Significant associations in cytokine and inflammatory marker may indicate a Th2-skewed response, and dysregulated cytokine responses can lead to severe disease. A suggested increase in T-cell activation markers (e.g., CD4, CD8) may indicate potential exhaustion if excessively activated.

Objectives/Goals: Second-generation antipsychotics (SGA) are used to treat mental disorders in youth but are linked metabolic syndrome (MetS). Most data on prescribing practices and risk factors are from short-term studies (6–12 months). We aim to characterize prescribing and identify clinical and genetic predictors of MetS using electronic health records (EHR). Methods/Study Population: EHR data were extracted from Cincinnati Children’s Hospital Medical Center (CCHMC) for patients aged ≤21 years prescribed SGAs from 7/1/2009 and 7/1/2024, identifying prescribing prevalence. Next steps are to create an SGA-MetS case–control dataset 8 weeks after an SGA prescription. A case will be defined by meeting 3 of 5 criteria: 1) BMI ≥95th percentile for age/sex; 2) fasting glucose ≥100 mg/dL or use of anti-diabetics; 3) triglycerides ≥110 mg/dL; 4) HDL-C ≤40 mg/dL; 5) systolic/diastolic BP ≥90th percentile for age/sex or use of antihypertensives. The prevalence of SGA-MetS will be calculated by dividing SGA-MetS cases by total SGA users. Logistic regression will identify clinical predictors of MetS, and we will evaluate the association of polygenic risk scores (PRS) of BMI and type 2 diabetes with SGA-MetS risk. Results/Anticipated Results: Our preliminary analysis identified 30,076 patients who were prescribed SGAs (mean age 12 years, SD = 4; 58.8% female; n =  17685). Most self-identified as non-Hispanic (95%, n = 28,595) and of White race (76%; n =  22,935), with 18.5% self-identifying as Black or African American (n = 5,579). The most commonly prescribed SGAs were risperidone (n = 12,382, 41.1%), aripiprazole (n = 9,847, 32.7%), and quetiapine (n = 5,263, 17.5%), with much lower prescribing rates of other SGA known of their low risk of MetS (e.g., ziprasidone 5.5%, lurasidone 1.4%, paliperidone (n = 316, 1.1%), or others cariprazine (n = 72), asenapine (n = 43), brexipiprazole (n = 39), iloperidone (n = 24), and clozapine (n = 20). Discussion/Significance of Impact: Our analyses found that risperidone, quetiapine, and aripiprazole were the most prescribed SGA, with risperidone/quetiapine linked to a higher risk of MetS. We will present ongoing work identifying risk factors for SGA-MetS and examining the association with PRS. Our work has the potential to identify high-risk patients for personalized treatment.

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: We aim to predict and rank conserved, immunogenic targets within key malaria proteins using computational tools. These tools incorporate parasite protein diversity and regional HLA allele frequencies to prioritize antigens for further validation and inclusion in a malaria vaccine targeting circulating strains. Methods/Study Population: We identified 42 conserved malaria proteins with nonredundant functions for P. falciparum invasion and transmission as vaccine targets. Protein sequence datasets were constructed from samples collected in highly endemic areas. We predicted targets most likely to be presented to CD4+ and CD8+ T cells. We designed and used heuristic-based and AI-weighting models that integrated predicted binding affinities to HLA alleles, HLA allele frequency data, and sequence conservation to score and rank targets. We validated our model by comparing predicted epitope distributions with published in vitro and in vivo immunogenicity data available in the Immune Epitope Database and Tools repository. Results/Anticipated Results: We successfully predicted and ranked targets within the vaccine candidate proteins, identifying conserved and HLA-nonspecific targets that correspond to positive immunogenicity data, validating our approach. We are currently analyzing model performance by comparing predictions to over 5,800 experimentally validated P. falciparum targets from clinical trials and immune assays. We will evaluate each models’ accuracy and ability to prioritize targets and compare their performances as measured quantitatively by precision and area under the curve metrics. We expect the AI-based model to significantly outperform the heuristic approach, improving the identification of effective vaccine targets. Discussion/Significance of Impact: By incorporating parasite diversity and regional HLA allele frequencies, our approach addresses the challenge of directing the human immune response against genetically diverse P. falciparum strains in highly endemic areas. This strategy could significantly enhance malaria vaccine efficacy and can be adapted for use against other pathogens.

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.