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: We aim to explore the associations of race/ethnicity and socioeconomic status (SES): 1) with grip strength, walking speed, and comorbidity index cross-sectionally and 2) with the change in comorbidity index and mortality risk over four years of follow-up in cancer survivors. Both aims will examine the potential mediating role of cytomegalovirus (CMV) infection. Methods/Study Population: This study includes 1,602 cancer survivors (mean age = 72 years, 10% Black, 54% female) from the Health and Retirement Study (HRS), a nationally representative U.S. sample followed for health outcomes until 2020. HRS measured CMV immunoglobulin G (IgG) antibody levels (from blood samples), grip strength, and walking speed in 2016. We will apply linear regression to examine the associations of race/ethnicity and SES with grip strength, walking speed, and comorbidity index cross-sectionally and with the change in comorbidity index over four years of follow-up. We will apply Cox proportional hazard regression to examine the associations of race/ethnicity and SES with mortality over four years of follow-up. In all models, we will investigate the potential mediating role of CMV infection in these associations. Results/Anticipated Results: We expect that CMV infection mediates the associations of race/ethnicity and SES with age-related health outcomes, including muscle weakness (measured by grip strength), decreased functional performance (measured by walking speed), comorbidity index, and mortality in elderly cancer survivors. Discussion/Significance of Impact: If our hypothesis is confirmed, the findings may inform physicians to closely monitor CMV infection among cancer survivors from socially disadvantaged groups and apply treatment if needed. Several oral medications for CMV exist, and CMV vaccines are currently undergoing testing in clinical trials. This will make the treatment for CMV more accessible.

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: Personalized cancer therapy based on genomic testing is advancing patient care. Genomic alterations in fibroblast growth factor receptor (FGFR) predict response to FGFR inhibitors; however, the role of RNA expression and protein activation is not known. We propose to examine the phospho-proteomic signature in FGFR-altered cancers to identify new candidates for FGFR-targeted therapies. Methods/Study Population: In our preliminary study, we have curated a cohort of FGFR2 mutants (13 FGFR2-fusions and 4 FGFR2 point mutations) with known clinical outcomes to FGFR inhibitors and 8 FGFR2 wild-type (WT) cholangiocarcinoma tumor samples to investigate the phospho-proteomic fingerprint using a clinical grade reverse phase protein array (RPPA). RPPAs are high throughput quantitative antibody-based proteomics assays that can quantify hundreds of proteins in thousands of patient tissues providing a high degree of sensitivity through laser tumor microdissection (LCM). We have selected proteins in the FGFR signaling pathway including FGFR2, AKT, ERK1.2, STAT1/3, FRS2, and PLCg to define the range of phospho-proteomic signal between FGFR2 WT and mutant cancers. All samples will undergo evaluation with RNASeq for gene expression. Results/Anticipated Results: Our initial analysis defined the range of RNA expression of FGFR2 and pFGFR2 protein signal (Y653/654 and Y769) between FGFR2 WT and FGFR2 mutant samples. On average, the FGFR2 mutant cohort displayed higher FGFR2 RNA expression compared to the FGFR2 WT cohort. There is no apparent correlation between RNA expression and clinical response to FGFR-targeted therapy. However, in this small cohort, there is no significant difference in FGFR2 phosphorylation between FGFR2 WT and mutant cancers. RPPA analysis of FGFR downstream signaling proteins reveals a wide range of phosphorylation, but no significant difference between FGFR2 WT and mutant cancers. Discussion/Significance of Impact: These findings illustrate the complexities of FGFR signaling between FGFR2 WT and mutant cancers. These data suggest that tumors with genomically WT FGFR may display increased pFGFR2 and downstream signaling phospho-proteins. We propose a larger study of cholangiocarcinoma to evaluate evidence of FGFR pathway activation in WT tumors.

Objectives/Goals: Port-site metastasis (PSM), defined as the spread of malignancies to the abdominal wall at the site of surgical ports, poses a significant challenge in cancer management. The objective of this summary overview is to describe the prevalence of and risk factors associated with PSM in various gynecological cancers after laparoscopic surgery. Methods/Study Population: Study design: Systematic Review and Meta analysis Search strategy: All international databases, without language limitations, from January 1990 to December 2023. Inclusion/exclusion criteria: Cohort, case–control, or cross-sectional observational studies reporting the frequency of, or risk factors for PSM in young and middle-aged women will be included (using the PRISMA checklist). Data extraction: two reviewers independently extracting pertinent data (using the STROBE checklist). Quality assessment and risk of bias: The quality of each study will be assessed according to the Quality Assessment Tool for Observational Studies. Results/Anticipated Results: This meta-analysis of 36 studies evaluated the proportion of successes across various populations, with a pooled proportion of 0.02 (95% CI: 0.01–0.02) based on a random-effects model. Significant heterogeneity was identified (I²  =  88.12%), reflecting notable variability between studies. Despite this, the overall effect was statistically significant (p  =  0.00). A subgroup analysis will be conducted to explore potential sources of heterogeneity, considering factors such as cancer stage, diagnostic methods, surgical approach (conventional or robotic), and study type (retrospective/prospective). Discussion/Significance of Impact: By identifying the prevalence of, and the risk factors for, PSM, this study will better inform personalized treatment approaches, surveillance strategies, and surgical decision-making to improve patient-related outcomes and long-term survival in women with gynecological malignancies.

Objectives/Goals: The goal of this study is to resolve the complexity of the adipose precursor cells and identify potential therapeutic targets/mechanism to treat obesity, diabetes, cancer cachexia, and related metabolic conditions in both white and brown adipose tissues. Methods/Study Population: White and brown adipose precursor cells were isolated from neonatal P0 mice and expanded in culture for single-cell RNA sequencing analysis. Unsupervised machine learning was used to unbiasedly cluster and categorize sequenced cells. Differentially expressed genes were used to identify populations via pathway analysis. Populations were then validated with published datasets via integration, reference mapping, and module scoring to ensure our dataset is reflective of known literature. Then, white and brown datasets were combined and unbiasedly clustered. Finally, signaling inferences using CellChat was used to identify significant signals being sent to and received from each cluster based on ligand–receptor pairs. Results/Anticipated Results: ScRNAseq revealed 7 subclusters in both white and brown adipose tissues. Differential expression and trajectory inferences revealed that white and brown precursors develop into two distinct fates: committed adipogenic precursors (CAPs), where these cells will be mature lipid-laden adipocytes; or fibro-adipogenic precursors-like (FAPLs), where these cells preferably stay in a fibroblast-like, antiadipogenic phenotype. Integrating white and brown cells with subsequent reclustering reveals that white and brown FAPLs are highly similar to one another by being clustered together. Cell signaling inferences and pathway analysis reveal that white and brown FAPLs may participate in the regulation of adipogenesis and angiogenesis of the adipose tissue. Discussion/Significance of Impact: Our results demonstrated that brown and white precursor cells share a common regulatory subpopulation with similar gene expression profiles, highlighting a more interconnected regulatory landscape in adipose tissue than previously understood. These findings reveal novel mechanisms of systemic metabolism and provide new therapeutic targets.

Objectives/Goals: This scoping review examines how socioeconomic status (SES) and sociodemographic status (SDS) disparities are considered in transition interventions for congenital heart disease (CHD) patients. By identifying gaps, it aims to guide future research and interventions to address inequities in transitional care. Methods/Study Population: A systematic search of the literature was performed using PubMed, Scopus, and Web of Science. Literature was searched from January 1990 to October 2024 and revealed 823 articles. Upon initial screening, 71 duplicates, 76 non-SES focused articles, and an additional 128 irrelevant articles were excluded. A total of 548 full-text articles were reviewed. Articles that did not focus on transition interventions for CHD patients were excluded. Studies were analyzed for factors affecting care transitions with special attention to SDS and SES factors. SDS factors were defined as age, gender, race/ethnicity, and geographic location, while SES factors were defined as income level, education, employment status, and access to care. Results/Anticipated Results: Out of 548 articles reviewed, only 18 addressed SES factors, and 10 examined SDS factors in the transition from pediatric to adult care. The most common interventions were patient education (33%), care coordination (29%), and family support (21%), but they lacked tailoring to SES/SDS factors. Patients from low-income households were 50% more likely to experience care discontinuities and 40% less likely to participate in transition programs. Health literacy interventions were generic, overlooking socioeconomic differences. Tailored transition programs are needed to address low health literacy and financial barriers, potentially improving outcomes for disadvantaged patients in rural and underserved areas. Discussion/Significance of Impact: This review exposes the limited focus on SES and SDS disparities in CHD transition interventions. Disadvantaged patients face barriers like limited access to care and low health literacy. Developing tailored programs to address these gaps is crucial for enhancing transitions and improving long-term outcomes for vulnerable CHD patients.

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: 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: 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: This study illuminates the efforts of a Clinical and Translational Science Award (CTSA) Hub to share stories of its aspirations, challenges, successes, opportunities, and impact when pursuing its complex goals, and how storytelling contributes to the narrative of the translational science work (via storytelling strategies, products, and benefits). Methods/Study Population: We utilized an environmental scan of a CTSA Hub (University of Minnesota Clinical and Translational Science Institute (CTSI)), including case study vignettes of its storytelling practices and products. We triangulated data from diverse data sources: grant applications, reports, and publications; public stories/news related to CTSI activities and impact; scientific publications; organizational/policy documents; and interviews with CTSI stakeholders featured in published sources. Results/Anticipated Results: TS storytelling uses and strategies include communicating the essence of research translation, promoting program utilization, engaging community, reporting to stakeholders, and evaluating for accountability, learning, and improvement. Storytelling challenges include complexity of translation; balancing the scientific rigor with an engaging narrative; identifying appropriate stories that resonate with diverse stakeholders and are at an appropriate level of maturity; and building capacity using storytelling. Facilitators include supportive infrastructure to integrate stories; leadership endorsement of storytelling as a valuable strategy; capable cross-functional teams of communicators, administrators, and researchers to facilitate the integration of data into storytelling. Discussion/Significance of Impact: The environmental scan provides evidence and lessons learned on leveraging storytelling as a useful tool for communicating CTS goals, actions, and findings, engaging stakeholders, building a narrative around scientific discoveries, evaluating and improving programs, and addressing health disparities in translational science.

Objectives/Goals: Patients with multiple chronic conditions (MCCs) face care coordination challenges and poorer health outcomes. Outpatient telehealth may be an effective way to enhance MCC patient care given the need for multiple visits and specialists. This study seeks to describe telehealth utilization between 2013 and 2023 in Arkansas. Methods/Study Population: We utilized the Arkansas All-Payer Claims Database (APCD) to identify patients diagnosed with high-prevalence MCCs comprising diabetes with comorbid hypertension, hyperlipidemia, or asthma. We then measured telehealth utilization defined as any claim associated with a telehealth modifier code, a place of service code defining the service as occurring in the patient’s home, or remote patient monitoring. Finally, we created payer-specific (e.g., commercial or Medicaid) yearly measures of the number of any telehealth claims among MCC patients divided by the number of MCC patients for that year. Linear regression was used to measure the difference in utilization during the COVID-19 pandemic (i.e., 2020–2023) versus prior to the pandemic (i.e., 2013–2019). Results/Anticipated Results: Overall, the COVID-19 pandemic era was associated with an increase of telehealth utilization among commercial patients by 1.01 telehealth claims per MCC patient (95% CI: 0.39 to 1.62, p Discussion/Significance of Impact: Variations in telehealth uptake among MCC patients suggest heterogeneity in its suitability and necessity. We will later evaluate whether telehealth use is associated with different levels of inpatient and emergency department utilization. We expect the findings to provide clarity on the suitability of telehealth use by MCC disease status.

Objectives/Goals: Present a framework for hosting Community Grand Rounds, where community and academic partners showcase completed community-engaged research (CEnR) projects. This highlights innovative dissemination methods, engages diverse audiences, elicits community responses, and advances the translational science of CEnR. Methods/Study Population: Our approach involves planning and outreach to collaborate with promotion of academic and community engaged grantees to develop community dissemination events that translate the science of CE into accessible, relatable, culturally relevant formats for diverse audiences. These events incorporate interactive presentations that encourage active participation and feedback from attendees. Following each event, an evaluation is completed to assess community impact. Key strategies for hosting, facilitating, and utilizing diverse marketing to ensure that events are tailored to culturally diverse community groups, including regional implementation when practical. This collaborative approach meets a critical need and strengthens the bond between researchers and the communities they aim to serve. Results/Anticipated Results: These events create a feedback loop between the community and academic researchers. It was not just about telling people what was found. We created opportunities for community members and academics to build trust, give us feedback, ask questions, and discuss how findings could be practically applied. By presenting the findings in an accessible way within the community, community members are more informed and empowered to make decisions or advocate for changes in their own lives based on the research. Academics also benefited from community feedback, which provided new insights to help refine future research questions and methods. The goal is for shared conversation and understanding between community members and academics to inspire real-world applications and policy change directly informed by the research. Discussion/Significance of Impact: Community Grand Rounds are one dissemination strategy to leverage community–academic collaboration to present tailored research, fostering engagement, understanding, and action between researchers and community members. This approach effectively enhances the translational science of CEnR by involving and benefiting the community.

Objectives/Goals: This qualitative study evaluated facilitators and barriers to clinical research participation among lower-income, predominately racial/ethnic minority communities to inform a Co-Learning series. The Co-Learning series of community members, stakeholders, and researchers developed strategies to improve diversity in research. Methods/Study Population: Community focus group (FG) participants were recruited from urban communities experiencing poverty, whereas stakeholder FGs included diverse community leaders and researchers. The Social Ecological Model was used as the guiding theoretical framework throughout the qualitative design. Directed content analysis using three independent coders reaching 100% consensus identified prominent factors for increasing diversity in research. The co-learning series was comprised of community members, stakeholders, and researchers who discussed the prominent FG factors over seven in-person meetings to develop a toolkit to increase diversity in research. Results/Anticipated Results: Community (n = 51) and stakeholder (n = 50) FG participants were 54.0±16.0 years of age, 81.2% female, 39.6% Black/African American, 51.5% Hispanic, and 60% had Discussion/Significance of Impact: Due to the lack of inclusivity in clinical research, findings are typically not applicable to health disparity populations limiting the benefit of research to all. This study provides practical community-driven strategies to increase diversity in clinical research, highlighting the role of trust, relationships, and cultural relevancy.

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: 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: Men and women with opioid use disorder (OUD) show differences in their initiation, use patterns, and outcomes that may have biological underpinnings. Here we present data directly comparing adult male and female rats across heroin-induced behaviors in order to provide insight into the nuances of sex differences in OUD. Methods/Study Population: We first used a 6-hour intermittent access heroin self administration paradigm to quantify six distinct drug-taking and drug-seeking behaviors. Based on the sum of the z-scores for each behavior, we classified rats as having high- or low-severity phenotypes. In a separate group of rats, we adapted this classification system to a 10-minute continuous access self-administration paradigm to better represent the timeframe of use common in people. Finally, we examined locomotor sensitization following daily heroin injections in two groups of rats. The first were given 2 mg/kg/day i.p. heroin for 10 days and the second were given 0.55 mg/kg/day i.v. heroin for 20 days. Results/Anticipated Results: In the 6-hour intermittent access, both sexes showed variability across individuals, but a greater proportion of females were classified as having a high-severity phenotype compared to males. This difference in severity distributions was also found in the 1-hour continuous access experiment. Consistent with the literature, in our sensitization experiments, we found that males had a lower baseline level of locomotion compared to females. Across sex and route of administration, rats treated with heroin initially decreased locomotion, but returned to baseline over the course of treatment. Females given i.v. infusions showed a rapid escalation of locomotion past baseline that was not seen in males or following i.p. injections. Discussion/Significance of Impact: Our results indicate a consistent pattern of females having a greater behavioral response to heroin compared to males. This suggests a sex-based effect on OUD that may interact with gender-based influences. As such, future research needs to consider sex in the development of treatments for OUD and other substance use disorders.

Objectives/Goals: Some children with asthma benefit from care from asthma specialists: allergist/immunologists or pulmonologists. However, a limited supply of specialists poses access problems for some children. Our objective was to describe the state of specialist care for pediatric asthma in Maine. Methods/Study Population: Using Maine all-payer claims data for April 2018–April 2021, we identified children with asthma meeting criteria for subspecialist referral based on NIH Expert Panel Report criteria. We determined which of these children had encounters with an allergist-immunologist or pediatric pulmonologist during the study period and performed logistic regressions to determine what demographic traits were associated with receipt of care. Results/Anticipated Results: Among children meeting criteria for specialists referral, 33% had an encounter with an asthma specialist. Specialist encounters were associated with younger age (p<0.001) and male sex (p = 0.003). Encounters were negatively associated with Medicaid insurance (versus commercial, p<0.001) and living in more rural areas (RUCA codes 4–10, p<0.001), lower childhood opportunity index (high or less, <0.001), and longer travel time to pediatric asthma specialists in Maine (<0.001). Discussion/Significance of Impact: Specialist care for children with complex or severe asthma is an evidence-based practice that is not equally accessible to all children in Maine. This may restrict their access to specific asthma therapies such as allergic desensitization and monoclonal antibodies.

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: 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