Objectives/Goals: To identify the genomic mechanisms underlying cross-species regulation of longevity among mammals and birds and to characterize the impact of those conserved pathways on human aging. More broadly, this study aims to develop a novel evolutionary approach to understand the genetics of complex traits. Methods/Study Population: High-quality genome sequences for 194 bird species and 295 mammal species with reliable longevity and body size data were obtained from publicly available resources. The data include coding sequence alignments of 16,863 mammalian and 14,565 avian one-to-one orthologous genes. Gene-wise relative evolutionary rates (RERConverge) and maximum likelihood phylogenetics (PAML) were computed to assess for evidence of purifying selection and positive selection for longevity. As part of ongoing analysis, human orthologs of selected results will be examined in the UK Biobank for validation. Results/Anticipated Results: Preliminarily, we have found a signal of concordant positive selection between two classes of vertebrates separated by hundreds of millions of years. Several genes show signals of positive selection in long-lived species of both birds and mammals. Ongoing work focuses on elucidating the relationship between relative evolutionary rates and positive selection, the overlap in selection signature between long-lived animals with large body sizes and those who are exceptionally long lived for their body size, and further elaborating on convergence between mammals and birds. Discussion/Significance of Impact: Currently, most scientific knowledge about aging is from experiments on short-lived model organisms. By systematically studying the genomes of long-lived mammals and birds, we aim to develop a new method for studying complex traits and uncover novel insights into the mechanisms of longevity.

Objectives/Goals: The bromodomain PHD finger transcription factor (BPTF) is an oncogenic driver of neuroblastoma. Our objective is to pioneer the discovery of the first class of chemical compounds that engage the PHD finger of BPTF and inhibit its biological function in cellulo, thereby establishing first-in-class chemical probes for this epigenetic reader. Methods/Study Population: Our previous work has identified a collection of small molecules that engage BPTF PHD in vitro. Following structure–activity relationships analysis, candidates will be used in a neuroblastoma cell model to validate BPTF PHD interaction in cellulo and predict therapeutic potential. Nanoluciferase bioluminescence resonance energy transfer (NanoBRET) will be used to confirm BPTF PHD engagement by compounds. Selective toxicity in neuroblastoma cells upon inhibitor treatment will be gauged by comparing cell growth and viability in the IMR-32 cell line against the HEK293 cell line. Treated HEK293 cells will be subjected to the assay for transposase-accessible chromatin (ATAC) and RNA sequencing methods to monitor changes in chromatin structure and transcriptional signatures against untreated cells. Results/Anticipated Results: We hypothesize that compounds with low micromolar potency for BPTF PHD in vitro will engage the target in cellulo and displace NanoLuciferase tagged protein from its HaloTagged® peptide binding partner. Additionally, we anticipate that our inhibitors will show cytotoxicity for IMR-32 cells with limited effects on HEK293 cells. We envision that inhibitor treatment in HEK293 cells will correlate with reduced chromatin exposure, suggesting that blocking the BPTF–histone interaction via PHD finger inhibition hinders the remodeling of transcriptionally silent heterochromatin into a transcriptionally active state. Finally, we expect that inhibitor treatment will result in diminished gene expression of oncogenic transcription factors, including N-Myc, a biomarker of neuroblastoma. Discussion/Significance of Impact: These first-in-class chemical probes for BPTF PHD will enable further investigation of BPTF and high-risk neuroblastoma progression, as well as its role in other diseases. In addition, these compounds will serve as a platform for the development of new anticancer agents that may improve outcomes for children that suffer from high-risk neuroblastoma.

Objectives/Goals: Despite persistent health disparities, rural individuals are underrepresented in clinical trials, due in part to access barriers. We investigated if targeted strategies enhanced recruitment, engagement, and retention of rural adolescents in the TEENS+ randomized clinical trial, a 4-month family-based behavioral weight loss intervention. Methods/Study Population: Adolescents (12–16 y) and parents with obesity were eligible for TEENS+. Treatment converted to virtual in COVID-19, allowing eligibility to expand to more rural areas. We leveraged Informatics, a practice-based research network, and direct marketing to identify potential rural participants. Targeted engagement strategies included: rural physician outreach, physician-endorsed letters, providing tablets and mobile hotspots, reimbursing travel, and offering in-person or remote assessment visits. Chi-square tests evaluated differences in screener completion and enrollment of rural families before (T0) and after (T1) changes were made. Noninferiority tests evaluated rural vs. nonrural retention and engagement (% attendance, % dietary self-monitoring) and engagement based on digital tool receipt. Results/Anticipated Results: N = 211 dyads enrolled (n = 54 in T1: 48% male; 41% Black). Screener completion by rural families significantly increased from T0 (9.8%) to T1 (15.1%; p = .043). Yet, there was no significant change in rural adolescent enrollment (T0 = 10%; T1 = 9%; p = .844). Sixteen adolescents (30%) received study tablets, and none needed mobile hotspots. Mean adolescent attendance was 75%±28% for group and 94%±18% for individual sessions, with no significant differences based on rural status or tablet use. Rural adolescent self-monitoring (via app) was 28%, compared with 50% for non-rural adolescents (p = .074). Retention was 94% at 4m and 89% at 8m for T1 participants, with no differences based on rural status. At the primary endpoint (12 m), retention was significantly higher for rural (100%) vs. non-rural (87%) participants; p = .013. Discussion/Significance of Impact: Rural adolescent screener hits increased yet enrollment was unchanged. However, rural attendance was comparable and retention exceeded, compared to nonrural participants. Strategies to yield equitable representation and engagement in clinical trials are essential for geographic generalizability and to reduce rural health disparities.

Objectives/Goals: This study aims to harmonize quality indicators (QIs) across University of Toronto-affiliated microbiology labs to establish universal benchmarks that enhance performance, patient safety, and health outcomes. Harmonized QIs will enable effective comparisons and enhance the consistency of care. Methods/Study Population: The study employed the Delphi method, a structured and iterative process to build consensus. An expert panel of clinical microbiology trainees, medical microbiologists, trainees, and site leads from five University of Toronto-affiliated microbiology labs was assembled. Initial insights were gathered through surveys and a comprehensive scoping review of the literature. The study involved two rounds of feedback, a SurveyMonkey-based survey, with a defined consensus of 75% agreement among participants. Followed by an implementation survey conducted through REDCap to assess how these QIs were adopted in practice and identify barriers to implementation. Results/Anticipated Results: The study achieved consensus on nine high-impact quality indicators, including blood culture volume and contamination rates, cerebrospinal fluid transport time, and turnaround times for Gram stain results. Blood culture contamination and positivity rates garnered the highest agreement, at 100% and 91%, respectively. While some indicators were widely accepted and implemented, others faced resistance due to feasibility concerns. The study also identified significant variability in the level of adoption across the participating laboratories, pointing to operational challenges and the need for further efforts to address these barriers. Discussion/Significance of Impact: This study highlights the importance of QI harmonization in improving lab services and patient safety. It reveals challenges in standardizing practices but promotes uniformity in QIs, laying the groundwork for better inter-lab collaboration, consistent outcomes, and improvements in microbiology.

Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patients using an automated pipeline. The dataset is divided into training (357 cases), calibration (63 cases), and testing (105 cases). Our probabilistic model is a convolutional neural network (CNN) with dropout regularization. It generates a prediction set containing high-probability correct predictions using conformal prediction techniques, which add an uncertainty layer to the CNN. Additionally, gradient-based localization mapping is employed to identify brain regions associated with low uncertainty cases. Results/Anticipated Results: The performance of the computational conformal model is evaluated using training and testing data with varying dropout rates from 0.1 to 0.5. The best results are achieved with a dropout rate of 0.5, yielding a fivefold cross-validated average precision of 72.19% and an F1-score of 70.66%. Additionally, the model provides probabilistic uncertainty quantification along with gradient-based localization maps that identify key brain regions, including the temporal lobe, putamen, caudate, and occipital lobe, relevant to obesity prediction. Comparisons with standard segmented brain atlases and existing literature highlight that our model’s uncertainty quantification mapping offers complementary evidence linking obesity to structural brain regions. Discussion/Significance of Impact: This research offers two significant advancements. First, it introduces a probabilistic model for predicting obesity from structural magnetic resonance imaging data, focusing on uncertainty quantification for reliable results. Second, it improves interpretability using localization maps to identify key brain regions linked to obesity.

Objectives/Goals: Pediatric inguinal hernia (PIH) is a common diagnosis managed by pediatric surgeons. Patients typically present in the outpatient setting for evaluation. However, a subset present emergently with hernia incarceration, which has a higher risks. Our research aims to identify and address disparities in access to care of PIHR. Methods/Study Population: This study has a sequential multi-methods approach. First, we will perform a retrospective cohort study of pediatric patients (ages 0–18) who have undergone PIHR at St Louis Children’s Hospital (SLCH) over the past 5 years (n =  3,421). We will assess the impact of demographic and system-based factors on patient outcomes. Second, we will conduct interviews with caregivers of patients who have undergone PIHR. Qualitative interviews will be coded to prospectively identify themes in barriers and facilitators to accessing care, understanding of disease, and effectiveness of risk factor education by the pediatric care team. Finally, we will analyze the readability index (RI) of patient provided materials to assess the impact of health literacy on access to care and PIHR outcomes. Results/Anticipated Results: We hypothesize that demographic factors and socioeconomic status (SES) impact outcomes in PIHR. We anticipate higher rates of incarceration and emergent presentation of children from low SES and racial/ethnic racial minorities in our dataset, with longer time from initial presentation to definitive repair. Moreover, we anticipate identifying significant barriers to accessing care for non-white patients and patients with low SES, with gaps in understanding about the risk factors, presentation, and management of PIH. Finally, we hypothesize that the RI of our patient provided materials do not match community literacy rates, and there will be opportunities to implement new patient provided materials to improve parental education and communication in the perinatal space. Discussion/Significance of Impact: More than 600 patients undergo PIHR annually at a large tertiary care pediatric hospital, with those who are born premature being at higher risk. Using a mixed-methods approach, we aim to implement targeted interventions to facilitate access to care and improve patient outcomes in PIHR.

Objectives/Goals: Increased follicle-stimulating hormone (FSH) is linked to declines in ovarian and metabolic function in older women. Obesity is both a manifestation and a driver of aging pathologies. In animal models, FSH and insulin resistance (IR) were reduced after 6 mos. of a nutritional therapeutic (GLYLO). Our goal was to translate preclinical evidence to humans. Methods/Study Population: An integrated, precision medicine approach identified a unique phenotype of aging-related debility relative to older females. A non-comparer pilot study was conducted to translate GLYLO preclinical findings to postmenopausal women with obesity (n = 85; >55 years; body mass index [BMI]  = 35.0±4.35; range: 30.3–42.8). Participants meeting the inclusion and exclusion criteria (n = 13) were enrolled and received two capsules of GLYLO (vitamins and natural products) daily for 6 mos. Assessments for FSH, estradiol (E2), IR (homeostatic model [HOMA-IR]), total cholesterol (TC), low- (LDL), high-density lipoproteins (HDL), safety biomarkers (e.g., red cell distribution width [RDW%], mean corpuscular volume [MCV]), and depression (Center for Epidemiologic Studies Depression Scale) were conducted prior to and after 6 mos. Results/Anticipated Results: Mixed-effect models with intent-to-treat analysis were applied to compare outcomes prior to (n = 13) and following (n = 7) the intervention. Significant reductions in FSH were observed (-13.1 [2.47] ∆/SD; p = 0.002)) following the 6-month intervention. Interestingly, BMI, E2 (p = 0.412), HOMA-IR (p = 0.885), TC (p = 0.363), and LDL (p = 0.145) were unchanged, while HDL decreased significantly (-9.7 [3.82] ∆/SD; p = 0.044). Other biomarkers, RDW% (-0.2 [0.05] ∆/SD; p = 0.009) and MCV (-2.3 [0.33] ∆/SD; p = 0004), were significantly reduced. All other safety parameters were not altered. Six participants reported mild to moderate adverse events (acid indigestion) and were lost to follow-up. Depression scores significantly increased (+4.0 [0.75] ∆/SD; p = 0.002)). Results were similar with and without intent to treat analysis. Discussion/Significance of Impact: Decreased FSH, but not IR, was observed following six months of GLYLO in postmenopausal women with obesity. Significant alterations in HDL, depression, RDW%, and MCV warrant further investigation. Findings are limited by the small sample size and loss to follow-up. Randomized, controlled trials are needed to confirm these results.

Objectives/Goals: Physical activity (PA) is a well-documented protective factor against many cardiovascular diseases. PA guidelines to reduce these risks and the impact of variability are unclear, and most studies only examine a 7-day activity window. This study aimed to examine factors related to variability in step counts in a 3-year study of adults aged ≥18 years. Methods/Study Population: Included were 6,525 participants from the Michigan Predictive Ability and Clinical Trajectories study, a prospective cohort of community-dwelling adults enrolled between 8/14/2018 and 12/19/2019 who received care at Michigan Medicine and were followed for 3 years. Data were collected from Apple Watches provided to participants via the HealthKit. This secondary analysis included those with ≥4 valid weeks of data (≥4 days with at least 8 hours of wear time). Season was defined as Spring (March 20–June 20), Summer (June 21–September 21), Fall (September 22–December 20), and Winter (December 21–March 19). GEE models against the outcome of variability, defined as weekly standard deviation of step counts, and the predictor of season were adjusted for age, sex, race/ethnicity, weekly average step count, diabetes, and body mass index. Results/Anticipated Results: The average (standard deviation (SD) step counts by season were 7101 (3434) in Spring, 7263 (3354) in Summer, 6863 (3236) in Fall, and 6555 (3211) in Winter. Compared to winter, there was statistically significantly higher variability in all other seasons (p Discussion/Significance of Impact: In this cohort of community-dwelling adults, we found significant differences in variability of physical activity by season, age, and BMI. Future work will examine how this variability impacts the risk of development of cardiovascular disease, incorporating the impact and recovery trajectories of COVID-19 and other acute respiratory infections.

Objectives/Goals: • To examine current barriers to research translation that collide with funder imperatives to share research results broadly. • To create community-driven guidance on broadening dissemination of research findings to communities to enhance health literacy and trustworthiness in the research process. Methods/Study Population: The Penn State Community Health Equity & Engagement in Research team, part of our Clinical and Translational Science Institute, completed six semi-structured focus groups in the Fall of 2023 (N = 46, including geographically diverse communities with and without research experience). Focus groups included presentation of evidence-based and novel approaches to lay results dissemination, including lay briefs and data walks, to elucidate experiences with and preferences for receiving research study results. Qualitative data were analyzed using MAXQDA software, with successful (>0.70) kappa coefficient achieved for interrater reliability. Codes and themes were developed inductively. Results/Anticipated Results: Focus group characteristics included N = 39 identifying as women, with a mean age of 56 years old and 10% identifying as Black/African American. Geographic breakdown included 49% rural, 44% suburban, and 7% urban. Thematic analyses demonstrated a perceived lack of trustworthiness and representation in the research process, with several cultural and geographic barriers to research accessibility and results dissemination. The majority of participants did not receive research results from prior studies and identified the importance of trusted community messengers to share findings broadly. Participants prefer receiving lay briefs, with health literate infographics, over published study manuscripts. Data walks featuring key findings presented by researchers within communities were highly desired. Discussion/Significance of Impact: Improving translation of study results in underrepresented communities is a catalyst for increasing engagement, demonstrating trustworthiness, and improving health literacy. The development of evidence-based lay dissemination methodologies can increase translation and bolster efforts to support informed, research-ready communities.

Objectives/Goals: We designed a forum to educate participants about bioethical issues in the application of big data (BD) and artificial intelligence (AI) in clinical and translational research (CTR) in underrepresented populations. We sought to determine changes in participants’ interests in ethics, bias, and trustworthiness of AI and BD. Methods/Study Population: 141 individuals registered for the forum, which was advertised to our partner institutions, minority-serving institutions, and community organizations. Registrants received email instructions to complete an AI Trustworthiness (AI-Trust) survey, a questionnaire with integrated qualitative and quantitative measures designed to better understand learners who engaged with the institution-specific AI/Data Science curriculum. Respondents completed the survey using personal devices via a link and QR code, with anonymized responses and enhanced privacy features. 82 people attended; 22 responded to the survey pre-forum and 22 post-forum. Pre- and post-forum responses were qualitatively compared to assess shifts in attitudes toward AI and BD and related interests in ethics, bias, and trustworthiness. Results/Anticipated Results: We found increased interests post- vs. pre-forum in the use of AI for CTR, AI bias and its effects on underrepresented populations, and ethical risk assessment and mitigation strategies for the use of BD to empower research participants. In contrast, trust in AI was lower post- vs. pre-forum. Moreover, respondents also indicated that the current application of AI in healthcare practice would result in increased racial, economic, and gender bias. In comparison, interest in ethical challenges, bioethical considerations, and trustworthiness regarding use of BD and AI in health research and practice did not differ pre- vs. post-forum. Discussion/Significance of Impact: Interest in the application of BD/AI in CTR increased post-forum, but AI distrust and bias expectations also increased, suggesting that learners become more skeptical and discerning as they become more knowledgeable about the complexity of the ethics of AI and BD use in healthcare, especially its application to underrepresented populations.

Objectives/Goals: This study aimed to enhance clinical trial data management through large language model information retrieval and generation techniques within the clinical trial reporting workflow. We focused on improving compliance with reporting, reducing human labor, and promoting standardized reporting structure and data quality oversight. Methods/Study Population: We used approved study protocols from UC Davis IRB-approved investigator-initiated studies compared to the same studies reported to ClinicalTrials.gov. Our baseline data extraction system employs commercial large language models (LLMs) and retrieval augmented generation (RAG) to isolate data sources within the secure extraction environment. We stratified protocol documents into easy, complex, and random categories based on study focus, document complexity, the extent of amendments or modifications, and completion metrics from ClinicalTrials.gov. We developed a pilot web-based architecture to capture variations in categorization, labeling, and reporting style and compared generated extraction data. We primarily focused on qualitative evaluation through a review of expert staff. Results/Anticipated Results: Our results revealed significant variations in reporting quality, with dependencies stemming from multiple authors and stages throughout the clinical trial protocol lifecycle. Based on these variations, we used prompt engineering to improve the pilot application’s output compliance with the protocol registration and results system (PRS) structured data format for various study types. We piloted the assisted workflow with prospective studies by partnering with study investigators and the clinical trial office staff to assist in review and clinical trial reporting creation. Initial studies reported by our system were approved and released to the public by PRS staff. We are refining content generation and workflows to different components of studies and evaluating their use in quality and training areas. Discussion/Significance of Impact: Our system fosters collaboration, efficient review, and compliance with clinical trial reporting standards. It supports the promise of AI-driven assistance in clinical trial management, design, and reporting. We focus on the multiple stakeholders, expertise, and data flows in the organizational management of clinical and translational science.

Objectives/Goals: Our research goal is to translate medical guidelines for adolescent-centered contraceptive counseling into improved clinical practice. Here, we describe the process of co-designing a training program for adolescent-serving primary care clinicians with teen, caregiver, and clinician advisory boards. Methods/Study Population: We recruited teens, caregivers, and clinicians residing in North Carolina to participate in three virtual advisory boards separated by role. Eligible teen advisors were assigned female at birth and 15–19 years old; eligible clinicians provided care for teen patients; and all groups were purposively sampled to reflect diverse identities and experiences. At each advisor meeting, we used human-centered design techniques to elicit participant priorities, generate training content and engagement strategies, and obtain feedback on the final training program. We conducted a focus group at our final meetings and used rapid qualitative analysis to understand our advisors’ experiences participating in program co-development. Results/Anticipated Results: We partnered with 20 advisors with diverse identities across geographic location, race and ethnicity, sexuality, and experiences with disability. During 15 meetings from January to May 2024 (five with each advisor group), we developed a 3-hour virtual, synchronous training for adolescent-serving primary care providers to improve their contraceptive counseling skills. The curriculum includes five interactive modules and a resource toolkit. Advisors described motivations to participate (e.g., chance to share their perspective, desire to make change), positive experiences with the advisory boards (e.g., opportunities to learn, to connect with others), and opportunities for improvement (e.g., better technology orientation). Discussion/Significance of Impact: We describe developing a successful longitudinal partnership with three community advisory boards and co-creating a training program that incorporates community-led priorities and perspectives, including youth. This approach can be adapted for other clinician training programs seeking to center community voices.

Objectives/Goals: The study is designed to co-develop a genetics education digital platform with community participants. Diverse populations, especially African American communities, are less likely to participate in genetic testing and clinical trials. To increase participation and community input, we want community participants across Louisiana. Methods/Study Population: A cross-sectional, mixed-methods study will be conducted to assess interest in learning about genetics through a digital education platform and to adapt the platform based on participant feedback. Specific Aim 1 will be achieved by recruiting a diverse cohort and collecting demographic data to identify participants’ characteristics. For Specific Aim 2, 35 community participants will be enrolled as a co-design team to complete 5-week educational modules. Feedback from focus groups will guide iterative platform refinements, ensuring the platform is culturally tailored and user-friendly. Results/Anticipated Results: For Specific Aim 1, we anticipate that the majority of participants will express interest in learning about genetics through the digital platform, with demographic data revealing a diverse participant pool, predominantly from African American and Hispanic communities. For Specific Aim 2, we expect that all 35 co-design team members will complete the 5-week modules. Feedback from the focus groups is anticipated to highlight the need for more user-friendly navigation, culturally tailored content, and enhanced visuals. These insights will guide the refinement of the digital platform for improved engagement and accessibility. Discussion/Significance of Impact: This study addresses a critical translational barrier – underrepresentation of African descended communities in genetic testing. By developing a culturally tailored, digital platform to engage these populations, the project aims to reduce health disparities, enhance genetic literacy, and foster inclusivity in genomic research.

Objectives/Goals: Rare disease patients often face lengthy delays in receiving accurate diagnoses or experience misdiagnoses due to a lack of available information. The NCATS Rare Disease Alert System (RDAS) is a public, comprehensive rare disease resource to collect and share accurate, up-to-date, and standardized data on rare diseases. Methods/Study Population: RDAS is composed of a frontend UI, Application Programming Interfaces, and backend Neo4j graph database. Each component of data collection, data annotation, data standardization, and data representation as steps were implemented during the process of each graph database creation. The UI allows users to search, browse, and subscribe to RDAS to receive the latest information and findings about their rare disease(s) of interest. The back-end data include four knowledge graphs built by integrating information from the NCATS Genetic and Rare Disease program, PubMed articles, clinical trials, and NIH grant funding. Ultimately, the integrative information pertinent to rare diseases from RDAS would advance rare diseases research. Results/Anticipated Results: Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting among mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research. Discussion/Significance of Impact: We developed an integrative knowledge graph with rare disease data and demonstrated its use as a source to identify and generate scientific evidence to support rare disease research. With the success of this study, we plan to implement advanced computation to analyze more funding related data and link to other types of data to perform further research.

Objectives/Goals: Early identification and profiling of planned studies is a critical administrative challenge in providing timely support for clinical trials. Here we describe the collaborative design and development of a clinical trial tracking dashboard to enhance support and quality improvement for investigator-initiated clinical trials at our institution. Methods/Study Population: Trial-CARE organized a workgroup with key stakeholders from WashU business units that manage grants, information technology, bioinformatics, data repository stewardship, and clinical trial support. The workgroup strategized next steps in a “proof-of-concept” effort to determine whether NIH investigator-initiated clinical trial metrics would be accessible via the WashU Data Warehouse. The WashU Data Warehouse is a data repository that pulls in pre-award and post-award data from sources such as the WashU Research-Grant Management System (RMS) and the NIH Reporter Tool. The proof-of-concept findings lead to implementing a plan for Phase 1 of the design, development, and piloting of a visual dashboard to track and offer targeted and timely support to NIH investigator-initiated clinical trials at WashU. Results/Anticipated Results: WashU currently has 217 grants submitted and 114 grants awarded for NIH clinical trials. In the proof of concept, we confirmed and successfully matched over 100 awarded NIH investigator-initiated clinical trials at WashU across RMS and the NIH Reporter Tool. We determined Phase I of the dashboard would track clinical trial data from these two sources via the WashU Data Warehouse. The pilot of Phase I of the dashboard will begin in February 2025. We also identified mission critical data elements not accessible via the WashU Data Warehouse (e.g., enrollment diversity, IND, and IDE). The plan to procure this data will require continued stakeholder support in Phase II of the dashboard to (1) expand data capture in RMS and (2) ingest additional data into the WashU Data Warehouse from RMS or new systems (e.g., ClinicalTrials.gov). Discussion/Significance of Impact: Early access to robust data about NIH investigator-initiated clinical trials via our newly created Phase I dashboard will better position our centralized service cores to support trial success, compliance, and quality improvement across the lifecycle of these clinical trials. In Phase II, we plan to expand the data available in the dashboard.

Objectives/Goals: To assess theory of mind and empathy in adolescents with Tourette syndrome (TS) and examine their association with social problems. This study aims to extend research in social cognition to an adolescent cohort with TS and identify a potential modifiable risk factor for social problems in TS that may serve as a novel intervention target. Methods/Study Population: We will enroll 50 adolescents with TS (ages 11–17) and 50 demographically matched controls along with one parent to complete a single in-person study visit. Adolescents with TS will be recruited through the Vanderbilt Center for TS and other Tic disorders. Controls will be recruited using university listservs and flyers posted in community and primary care settings. Adolescents will complete the NEPSY-II to assess theory of mind abilities and the Multifaceted Empathy Test – Juvenile to assess empathy with negative emotions. Parents will complete the Child Behavior Checklist to assess adolescent social problems. Results/Anticipated Results: Based on evidence of low self-other distinction in TS, we hypothesize TS adolescents will make more errors about the mental states of others (theory of mind) and report greater emotional reactions to faces (empathy) compared to controls. Further, greater social problems will be associated with greater disturbances in social cognition. To date, 15 adolescents with TS and 15 matched controls have completed the assessment (67% male; Mage  =  14.33 in both groups). Within this sample, adolescents with TS experienced more social problems than controls (Cohen’s d  =  .74, p  = .03). There were no between-group differences in theory of mind or empathy in this pilot sample. However, higher levels of both theory of mind and empathy were linked to experiencing greater social problems in the TS sample only (p’s < .05). Discussion/Significance of Impact: Preliminary findings suggest that while social cognition did not differ between groups, TS adolescents exhibiting high levels of theory of mind and empathy appear to struggle socially. This work could inform future interventions by highlighting the need to focus on social cognition and how these skills translate into social behaviors.

Objectives/Goals: Accurately stratifying patients with clinically isolated syndrome by risk of developing multiple sclerosis is of great clinical importance. Though numerous prediction models attempt to achieve this goal, no systematic review exists to independently evaluate these models. We aim to systematically identify and assess the risk of bias in all such models. Methods/Study Population: Studies developing or validating prediction models to assess risk of developing MS in patients with CIS who are not receiving an MS-indicated disease-modifying therapeutic will be identified via a systematic literature search. Studies will be evaluated for overall risk of bias using PROBAST (Prediction model Risk Of Bias Assessment Tool). Briefly, data sources, predictor, and outcome definition and assessment, applicability, and analysis will be assessed for each model in each identified study, and an overall risk of biased judgment will be assigned. Identified studies, predictors incorporated, results, and risk of bias assessment with accompanying rationale will be summarized in the final report. Results/Anticipated Results: Based on an initial exploratory search, we anticipate that most, if not all, identified prediction models will have high risk of bias. We anticipate that many studies will have limited applicability due to the use of outdated diagnostic criteria for definition of outcomes, or high risk of bias concerns originating from their analysis due to insufficient volume of included participants or poor model validation practices. We further anticipate that most, if not all, of the identified prediction models will have limited potential to be translated to use in a clinical setting. Discussion/Significance of Impact: Understanding how to identify patients with high-risk CIS may inform and improve clinician treatment decisions, patient outcomes, and future research study design. This work may also reveal flaws in current prediction models for CIS, opening new avenues of research and prompting development of improved prognostic models for patients with CIS.

Objectives/Goals: The goal of this study is to determine the function of the rete ovarii (RO), an uncharacterized secretory epithelial appendage to the ovary. I am testing the hypothesis that the RO is critical for the maintenance of the ovarian reserve and fertility, and progesterone signaling plays a role in the function of the RO. Methods/Study Population: For this project, I am utilizing a mouse model. To visualize the rete ovarii (RO) in vivo, I am using a Pax8rtTA; TRE-H2B-Gfp (PTG) reporter mouse, which expresses green fluorescent protein in the RO. To determine the role of the RO in fertility and maintenance of the ovarian reserve, I will surgically ablate the RO or perform a sham surgery on adult female PTG mice. Then, I will follow-up with quantification of the ovarian reserve and long-term fertility tracking studies. To determine how the RO responds to progesterone, the RO will be cultured ex vivo in the presence and absence of progesterone. I will perform a morphometric analysis of the RO, as well as collect secreted proteins from the media for proteomic analysis. Results/Anticipated Results: If the RO is important for ovarian homeostasis, I expect that in the absence of the RO, ovarian functions such as maintenance of the ovarian reserve and fertility will be impaired. Additionally, because the RO expresses progesterone receptors, I anticipate that the RO will be responsive to progesterone as shown in changes in the morphometric analysis and in changes in secreted proteins in the presence of progesterone. Discussion/Significance of Impact: A major gap in knowledge regarding female physiology and reproductive health is the role of the RO. We expect this work to reveal that progesterone signaling in the RO is important for regulating ovarian functions and to show that the RO is a critical modulator of female fertility and reproductive function.

Objectives/Goals: The growing burden of hypertension in the USA disproportionately impacts individuals with lower socioeconomic status and those in rural communities. This study aims to investigate specific social determinants of health associated with uncontrolled hypertension among rural, underserved patients residing in Alabama. Methods/Study Population: A retrospective review of electronic health records data from the UAB Selma Family Medicine clinic was conducted. The deidentified data were entered into the Population Health Assessment Engine (PHATE) to render social characteristics and community vital sign (CVS) scores, a social deprivation index, by census tracts. We provide descriptive statistics and compare the stated factors between patients with controlled and uncontrolled hypertension using logistic regression models. We are conducting phone surveys to gather individualized data on social determinants of health to assess the perceived social needs of patients with hypertension. The Protocol for Responding to & Assessing Patients’ Assets, Risks & Experience tool was used to develop the survey, and survey responses will be compared with the PHATE results. Results/Anticipated Results: Among 2129 patients, 375 (17.6%) had uncontrolled hypertension with a mean age of 58.7 ± 14.4 years. Women comprised 66.7% (250) and 65.1% (1142) of the uncontrolled and controlled hypertension populations, respectively. The prevalence of uncontrolled hypertension was 19.1% among Black patients and 8.2% in White patients. Patients with uncontrolled hypertension had a CVS of 82.37 ± 7.31 and 81.81 ± 8.40 patients with controlled hypertension. Patients with uncontrolled hypertension lived in areas where an average of 46 ± 11% were not employed, 36 ± 9% rented, and 28 ± 1% lived below the federal poverty line (FPL). Adjusting for age, race, and sex, a unit increase in the proportion of people below the FPL was associated with 43% higher odds of uncontrolled versus controlled hypertension (OR  =  1.43, 95% CI 0.44, 4.64). Discussion/Significance of Impact: Rural, underserved patients with hypertension experience multiple adverse social determinants, such as nonemployment rates, low income, and rental housing. Survey results will allow us to compare assessed and individualized social determinants that may be important to address when caring for this population.

Objectives/Goals: This project will enrich our understanding of basal breast carcinogenesis, highlighting the distinct biological differences in breast cancer risk between non-Hispanic Black (NHB) or non-Hispanic White (NHW) women, but also has the potential to provide real-world solutions thereby contributing to reducing health disparities in breast cancer outcomes Methods/Study Population: In the biological investigation, I am utilizing a unique cohort of normal breast tissues from Mayo Clinic patients who underwent breast reduction surgery that have self-identified as NHB or NHW premenopausal women. From these tissues, we are able to subculture human mammary epithelial cells, which we will use for our consequent experiments. For the qualitative study, I aim to recruit up to 40–50 eligible NHB women identified at increased risk for breast cancer and conduct semi-structured qualitative interviews informed by the National Institute on Minority Health and Health Disparities Research Framework. Results/Anticipated Results: Gene expression profiling on our cohort of 15 NHB and 40 NHW premenopausal women identified a gene expression signature in NHB women indicative of elevated Hedgehog signaling, a key factor in triple-negative breast cancer (TNBC) development and progression. I hypothesize that persistent activation of Hedgehog signaling within NHB women instigates the proliferation and transformation of basal stem cells within the breast, thus fueling TNBC development. Understanding the perceptions and experiences of NHB women that are identified as being at an increased risk for BC will aid in the identification of barriers and facilitators to their risk reduction care and in turn provide implementation strategies that could contribute to alleviating the racial disparity seen in TNBC morbidity, mortality, and preventative care. Discussion/Significance of Impact: The significance of this project lies in its dual approach, uncovering basal breast cancer mechanisms in a high-risk group using normal breast tissue and evaluating the perspectives from NHB women to enhance the practical relevance of our findings.