Objectives/Goals: The clinical research professional (CRP) workforce suffers from high turnover. Stay interviews have led to increased satisfaction and reduced turnover in other industries. We describe a multi-institutional project to develop, disseminate, and evaluate a CRP-tailored Stay Interview tool reimagined as the Individual Retention Conversation (IRC) toolkit. Methods/Study Population: In August 2022, following on the heels of a series of un-meeting conversations focused on CRP workforce development, the CRP taskforce initiated a working group to tackle issues related to CRP workforce retention. As a first initiative, this multi-institutional working group set out to develop, disseminate, and evaluate a Stay Interview tool tailored for a CRP audience and reimagined as the IRC toolkit. A 2-phase pilot study was initiated across six academic medical centers (AMCs: ASU, Duke, MUSC, UAB, UPenn, VCU) to: 1) optimize the toolkit for the CRP audience and 2) evaluate the impact of the toolkit using a standardized CRP satisfaction survey. Quantitative and qualitative data were collected via surveys using the REDCap platform. Results/Anticipated Results: The optimization phase of the pilot included 69 participants (16 managers and 53 of their CRP team members) from 6 AMCs. Respondents identified most and least useful questions for stimulating meaningful conversations regarding job satisfaction and retention issues with additional feedback on the IRC experience and tools. CRPs and managers represented a variety of roles, with 77% patient facing. The majority were satisfied with the IRC experience (82%) and found the experience personally beneficial (76%). Managers were satisfied with the manager’s guide (90%). Quantitative and qualitative feedback was used to optimize the toolkit prior to launch of phase 2 in September 2024, which includes a longitudinal survey-based assessment of CRP job satisfaction and IRC-consequent work environment changes. Discussion/Significance of Impact: CRP retention is impacted by complex factors, many related to job satisfaction, supervisor /employee relationships, and beneficial work environments. Initial evaluation of the IRC suggests that this intervention fosters positive supervisor/employee relationships and beneficial work environment changes, which may lead to improved retention.

Objectives/Goals: How do we care for a patient whose mental health is deteriorated such that their decision-making capacity may be compromised? The high-potency opioid crisis in Ontario demands that we provide effective care for the affected population. We must also avoid patients having a subjective experience of coercion and must protect their human rights and dignity. Methods/Study Population: Ontario’s legislation governing mental health care will be explored: the Ontario Mental Health, Healthcare Consent, and Substitute Decisions Acts. We will identify best practices/learning across locales. Patients who have been involuntarily treated/confined will be welcomed to voice what the law should contain. International strategies for: coercion reduction practices, advanced care directives, less prohibitionist care culture, and supports for social determinants of health (SDH) may also help Ontarians. Patients, family members, law enforcement, judiciary, community agencies, and healthcare professionals will be invited to contribute via focus groups to drafted mental health care legislative improvements. Thus, we ensure law enforces patient-defined quality care and practical workflows. Results/Anticipated Results: We hope to emerge from our focus group consultation with draft legislative and procedural edits for Ontario’s mental healthcare laws to ensure that the laws protect human rights and that the laws reflect patient-defined needs. We must ensure controls are in place to de-risk power imbalances and limit the incidence of potential procedural misuse. We intend to design legislated procedures to ensure that people don’t get inappropriately involuntarily confined/treated. We will incorporate the perspectives and lived experiences of patients who have experienced involuntary treatment and/or involuntary medical confinement (locally in a focus group(s)) and internationally (in literature) to inform this legislative development. Discussion/Significance of Impact: We will learn from engaged stakeholders about how to shape Ontario’s legislation to support quality mental health care. We hope to identify and draft legislation improvements that voice what patients and their families’ value, drafts informed by evidence-based best practices and informed innovation. Via inclusion, we create a policy that serves.

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: The timing of neurosurgery is highly variable for post-hemorrhagic hydrocephalus (PHH) of prematurity. We sought to utilize microvascular imaging (MVI) in ultrasound (US) to identify biomarkers to discern the opportune time for intervention and to analyze the cerebrospinal fluid (CSF) characteristics as they pertain to neurosurgical outcome. Methods/Study Population: The inclusion criteria for the study are admission to the neonatal intensive care unit (NICU) with a diagnosis of Papile grade III or IV. Exclusion criteria are congenital hydrocephalus and hydrocephalus secondary to myelomeningocele/brain tumor/vascular malformation. We are a level IV tertiary referral center. Our current clinical care pathway utilizes brain US at admission and at weekly intervals. Patients who meet certain clinical and radiographic parameters undergo temporary or permanent CSF diversion. Results/Anticipated Results: NEL was implemented at our institution for PHH of prematurity in fall 2022. To date, we have had 20 patients who were diagnosed with grade III or IV IVH, of which 12 qualified for NEL. Our preliminary safety and feasibility results as well as the innovative bedside technique pioneered at our institution are currently in revision stages for publication. Preliminary results of the MVI data have yielded that hyperemia may confer venous congestion in the germinal matrix, which should then alert the neurosurgeon to delay any intervention to avoid progression of intraventricular blood. With regard to CSF characteristics, we anticipate that protein, cell count, hemoglobin, iron, and ferritin will decrease with NEL. Discussion/Significance of Impact: The timing of PHH of prematurity is highly variable. We expect that MVI will offer radiographic biomarkers to guide optimal timing of neurosurgical intervention. A better understanding of CSF characteristics could potentially educate the neurosurgeon with regard to optimal timing of permanent CSF diversion based on specific CSF parameters.

Objectives/Goals: To identify clinical trial teams that are at risk of not meeting their recruitment goals as early in the recruitment period as possible, this project aims to provide timely accrual information and projected forecasts for accruals by the end of the recruitment period across all trials at USC. Methods/Study Population: This project aggregates recruitment accrual data periodically from OnCore to create per-study accrual pages that contain an up-to-date accrual chart, metrics like expected and actual accrual per month, and projected recruitment based on an X-month moving average (3 months by default). Trials at risk are identified as early as possible by using these projections to classify risk. In this initial phase, we’ve classified trials as medium risk (80%–99% accrual) or high risk (less than 80% accrual). The dashboard is currently available for all clinical trials at USC and users are automatically restricted to the studies that they administer or work on depending on their role. Results/Anticipated Results: The dashboard will provide visibility across the institution for the current accrual for all clinical trials in a standard, user-friendly format and use the same metrics and definitions of risk for trial accruals not meeting their targets. This will allow the institution to identify trials that need intervention to get back on track using a single set of criteria across all research teams. Users in different roles, whether department heads, principal investigators, or study coordinators can view the current accrual for all the trials that they administer or work on in one central location. The dashboard will also help to identify quality issues in OnCore by performing data quality checks nightly. Discussion/Significance of Impact: By providing a central location for role-based access to timely clinical trial accrual for the institution, the dashboard helps to identify trials at risk of not meeting their recruitment targets as early as possible to provide corrective advice/measures.

Objectives/Goals: Our research group is focused on care of hospitalized persons with opioid use disorder (OUD) in the era of high-potency synthetic opioids (HPSO). In this work, we describe trends in patient-directed discharge (PDD) and inpatient treatment with medications for opioid use disorder (MOUD). We hypothesized that PDD is associated with MOUD dose and timing. Methods/Study Population: Patient data generated in the routine care of patients was automatically abstracted using a SQL query on Epic Clarity tables in the electronic health record (EHR). We included adult patients admitted to Johns Hopkins Hospital between July 1, 2019 and June 30, 2022, with an ICD-10-CM code for a list of opioid-related disorders (F11.X) consistent with OUD. Demographics, prior medication list, clinical care including hospital service, consultation services, COWS scores, length of time in emergency department, time of triage, time until receipt of methadone or buprenorphine, dosage and timing of MOUD, opioid medications other than methadone or buprenorphine, adjuvant medications; prior methadone or buprenorphine treatment and disposition. Query results were validated by manual abstraction of EHR. Results/Anticipated Results: The SQL identification of the cohort of patients with OUD was found to be accurate. Time of triage, discrete orders completed during hospitalization were well represented in the query. The query was able to identify individual opioid medications but unable to summarize total dose in Morphine Milligram Equivalents. The query did not extract accurate information from patient-controlled analgesia pumps due to the continuous nature of the medication rather than discrete doses reflected in the medication administration record. Finally, the query characterized prior treatment with methadone or buprenorphine as a binary variable – dosage and timing of that prior treatment could not be accurately represented. Finally, stimulant use is not reliably collected in the EHR and was unavailable. Discussion/Significance of Impact: Given the rise of HPSO, patients may not tolerate delay of MOUD. Improving the granularity of data collected will offer more insight into the inpatient treatment for OUD. Real-world data have promise but requires extensive technical expertise. Future work is needed to improve capture of derived variables such as total dosage of opioids in MME.

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: The never in mitosis kinase (NEK) family regulates vital processes, namely cell cycle progression, but their potential as therapeutic targets in TNBC has not been fully explored. Our studies aim to develop a toolkit to investigate the functional roles of NEKs in pathologies including carcinogenesis. Methods/Study Population: To assess differential NEK expression in normal and tumor tissues and correlation of gene expression with patient survival, we used Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan–Meier Plotter (KMPlot) pan-cancer analysis, respectively. Basal NEK protein levels were determined by immunoblot across a panel of cell lines, including breast cancer, osteosarcoma, hepatocellular carcinoma, and non-cancerous cells, to identify appropriate systems for evaluation of NEK function. Doxycycline-inducible cell lines were generated by transduction with lentiviral stocks of NEK shRNA and overexpression constructs and antibiotic selection. Expression was analyzed by qPCR and immunoblot. Results/Anticipated Results: Expression of NEK2, 4, 5, 6, 8, and 11 was higher in breast tumors compared to normal tissue by GEPIA analysis. Further examination using KMPlot showed a correlation between elevated NEK6 expression and decreased overall survival in patients with aggressive cancers. As an initial proof-of-concept study, we analyzed NEK6 protein expression in breast cancer cells. Levels of NEK6 were elevated in TNBC cells (MDA-MB-231) compared to hormone receptor positive (HR+) breast cancer cells (MCF7). Using complementary approaches to investigate the functional role of NEK6 in breast cancer, we depleted NEK6 expression using shRNAs in TNBC cells and expressed NEK6 in HR+ cells Discussion/Significance of Impact: Because kinase dysregulation promotes oncogenesis and metastasis, targeting kinases is a key strategy in therapeutic development. A NEK-specific molecular toolkit allows researchers to elucidate NEK functions and contributions to carcinogenesis, promoting advancement of novel therapies.

Objectives/Goals: We evaluated the implementation of a peer-facilitated research best practices training for Community Health Workers and Promotoras (CHW/Ps) at four new partner sites to increase the capacity and capability of a workforce increasingly involved in community-engaged research. Methods/Study Population: Staff were trained using a train-the-trainer model, and materials were disseminated to partners at three academic institutions and one community-based organization. Each site delivered the training virtually or in-person in English and/or Spanish. CHW/P learners at all sites completed online evaluation surveys about the impact of the training on their knowledge and skills for participating in research-related work, and two CHW/Ps from each site participated in follow-up interviews to gather feedback about their experiences. Staff completed fidelity monitoring, follow-up interviews, and three brief surveys regarding feasibility, acceptability, and appropriateness of implementing the training. Results/Anticipated Results: The four sites conducted six trainings with a total of 42 CHW/Ps. Two sites each conducted one in-person training in English while the other two sites each conducted two virtual trainings, one in English and one in Spanish. Staff noted facilitators to successful implementation, including providing a facilitator guide and course materials in both languages and tips sheets for navigating REDCap; using the train-the-trainer model; and compensating CHW/P learners for attendance. The primary barrier noted was not having a budget for in-person trainings (e.g., refreshments, printed materials). CHW/P learners reported positive experiences with few suggestions for improving the training. Discussion/Significance of Impact: Preliminary results suggest the research best practices training for CHW/Ps is feasible, acceptable, and appropriate for implementation by partners at academic institutions and community-based organizations, regardless of language (i.e., English or Spanish) or delivery (i.e., virtual or in-person).

Objectives/Goals: Endometrial cancer is one of the few cancers that has both a rising incidence and mortality rate. Molecular classification is becoming more important for the management of endometrial cancer but the ability to translate this into clinical practice remains constrained. Our goal is to use AI to predict the molecular subtype from histopathology slides. Methods/Study Population: We utilized the open source endometrial cancer datasets from The Cancer Genome Atlas (TCGA) (N = 387) and Cancer Proteomics Transcriptomic Tumor Analysis Consortium (CPTAC) (N = 135) to develop and train a vision transformer AI model. We used a proprietary cohort of patients (N = 548) for external validation. Whole slide images (WSI) and molecular subtype data were collected. Subtypes include POLE ultramutated (POLE), microsatellite instability (MSI-H), copy-number low (CNV-L), and copy-number high (CNV-H). WSI were preprocessed, and features were extracted. Modified STAMP protocol was used in training, utilizing a pretrained foundation transformer model (Virchow2). Cross-validation of the TCGA was used for initial training, followed by testing on the CPTAC dataset and validation on our proprietary cohort. Results/Anticipated Results: Fivefold cross-validation of the TCGA database (60% training, 20% testing, and 20% validation) developed a best overall model with a mean AUC of 0.74 (POLE 0.78, MSI-H 0.76, CNV-H 0.86, CNV-L 0.77). Overall precision 0.58, recall 0.55. CNV-H was the subtype with the most accurate prediction. CPTAC holdout testing revealed moderately high AUC (POLE 0.63, MSI-H 0.62, CNV-H 0.98, and CNV-L 0.76). Overall precision 0.54 and recall 0.58. Again, CNV-H was the most accurate prediction. Validation on our proprietary cohort revealed a drop in performance with overall mixed results by AUC (POLE 0.50, MSI-H 0.69, CNV-H 0.78, and CNV-L 0.61). Overall precision 0.57, recall 0.45. Again, CNV-H with the most accurate prediction but F1 score dropped from 0.77 in the CPTAC to 0.47 on validation. POLE was the least accurate prediction subtype. Discussion/Significance of Impact: The CNV-H subtype demonstrated robust performance, suggesting the model effectively captures the features associated with this subtype. CNV-L had moderate performance. MSI-H and POLE were notably lower. WSI-based AI models show translational potential for subtype prediction in the management of endometrial cancer but more work is necessary.

Objectives/Goals: Sexual minority populations report a disproportionately high prevalence of alcohol use, often attributed to coping with bi/homonegativity and systemic inequities across various social domains. This study aims to explore alcohol use patterns and associated neighborhood and individual factors among sexual minority populations (SMPs) using data from the NIH All of US dataset. Methods/Study Population: Alcohol use was assessed using the AUDIT-C (Alcohol Use Disorders Identification Test—Consumption) scale across a sample of 9,454 gay, 15,284 bisexual, 5,267 lesbian, and 349,748 straight participants. The AUDIT-C measured hazardous alcohol use, and logistic regression models were employed to examine its association with neighborhood-level factors (e.g., socioeconomic status, alcohol outlet density) and individual-level factors (e.g., age, race/ethnicity, income, and education) among SMPs. Interaction terms assessed how these relationships varied by sexual orientation. Sensitivity analyses were conducted to assess the robustness of the findings, including stratified analyses by gender identity and the exclusion of extreme outliers in alcohol use reporting. Results/Anticipated Results: Our analyses revealed that gay participants had the highest AUDIT-C scores (mean  =  3.60, SD  =  2.27), followed by bisexual (mean  =  3.35, SD  =  2.21), other SMPs (mean  =  3.18, SD  =  2.19), lesbian (mean  =  3.04, SD  =  2.08), and straight individuals (mean  =  3.05, SD  =  2.06). Alcohol use was positively associated with neighborhood disorder (β  =  0.12, 95% CI  =  0.07, 0.17), housing insecurity (β  =  0.14, 95% CI  =  0.03, 0.25), and male gender (β  =  0.98, 95% CI  =  0.96, 1.00). In contrast, neighborhood density (β  =  -0.11, 95% CI  =  -0.15, -0.07), food insecurity (β  =  -0.14, 95% CI  =  -0.20, -0.08), being Black, and identifying as bisexual were negatively associated with alcohol use. Sensitivity analyses determined no significant differences among specfic supgroups. Discussion/Significance of Impact: This study highlights important differences in alcohol use across SMPs and emphasizes the influence of neighborhood-level stressors (e.g., disorder and housing insecurity). These findings underscore the need for addressing social and environmental determinants of alcohol use in SMPs to mitigate the negative impacts of alcohol consumption.

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: 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: We assessed the feasibility of using a large language model (LLM) to create lay language descriptions of study protocols for recruitment, which has the potential to improve accessibility and transparency of clinical studies and enable participants to make informed decisions. Methods/Study Population: All studies from a clinical research recruitment platform were included, which features human-written lay descriptions and titles for study recruitment. Corresponding protocol summaries in the IRB system were extracted and translated into lay language using a LLM (gpt-35-turbo-0613). A subset was used to develop prompt variations through an iterative process. Prompt strategies evaluated include chain-of-thought and few-shot prompting techniques. LLM-generated and human-written descriptions were compared for readability using Flesch–Kincaid and Simple Measure of Gobbledygook (SMOG) reading grade levels and information completeness using Word Movers’ Distance (WMD). Results/Anticipated Results: A total of 55 study descriptions were included – 10 were used to develop prompts and 45 were used for evaluation. The final LLM instructions included multistep prompts. The LLM was first instructed to produce a two- to three-sentence long description without using scientific jargon and included two pairs of examples. The LLM was then asked to shorten the description and finally to provide an engaging title. LLM-generated and human-written summaries were similar in length (median (IQR) 328 (278.5–360.5) vs. 342 (203–532.5) characters, respectively). LLM-generated summaries had lower Flesch-Kincaid grade level (5.15 vs. 8.28, p Discussion/Significance of Impact: An LLM can be used to generate lay language summaries that are readable at a lower grade level while maintaining semantic similarity. This approach can be used to improve the drafting of summaries for recruitment, thereby improving accessibility to potential participants. Future work includes human evaluation and implementation into practice.

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: Acute myeloid leukemia (AML) is the second most common leukemia among pediatric populations. Approximately 15% of pediatric AML cases have KMT2A gene rearrangements (KMT2A-r), which confers a worse prognosis. Our goal is to better characterize the biologic landscape of KMT2A-r pediatric AML. Methods/Study Population: This study utilizes deidentified peripheral blood and/or bone marrow samples banked in the Children’s Mercy Tumor Bank Biorepository. We investigated four KMT2A-r pediatric AML patients and six patients with other AML subtypes using samples collected at diagnosis and remission that were stored in the “tumor bank.” In addition, we assessed 47 tumor bank samples from patients with other leukemia subtypes. We performed differential expression (DE) analysis on bulk RNA sequencing comparing KMT2A-r and all other AML subtypes, as well as single-cell RNA sequencing and proteomic analysis on the larger cohort. We then coalesced these data to better identify processes and pathways that are dysregulated in KMT2A-r AML, specifically aiming to find those that were contributing to leukemogenesis. Results/Anticipated Results: Transcriptomic analysis showed that HOXA10 and MEIS1, two genes associated with immature myeloid populations and KMT2A-r leukemias, were more highly transcribed in AMLs than other leukemias. In addition, our DE analysis showed significantly higher transcription of ITGA7, a gene shown to correlate with poorer prognosis in AML, in our KMT2A-r samples when compared to other AML subtypes. FAM46C, a tumor suppressor gene contributing to mRNA stabilization, was less highly expressed in KMT2A-r AML when compared to other AML subtypes. Of note, low expression of FAM46C is associated with poorer survival and treatment response in multiple myeloma, and our findings suggest it may also be relevant to AML. Proteomic analysis is currently in process. Discussion/Significance of Impact: Transcriptomic analysis identifies unique molecular features of pediatric KMT2A-r AML. We anticipate that our proteomic data will do the same and will also corroborate our RNA findings. Taken in combination, these results will provide a more complete picture of the specific mechanisms contributing to this aggressive leukemic subtype.

Objectives/Goals: To investigate interventional clinical trial participation overall and by race, gender, and age. Methods/Study Population: We used Epic Cosmos, an aggregated, de-identified EHR platform including over 270 million patients, to examine overall clinical trial participation and the race, gender, and age composition of participants versus non-participants. Patients ≥5 years old with known race and gender and at least one healthcare encounter between 2021 and 2024 were included. Interventional trial enrollment was identified by a “research flag” indicating current or past participation in an interventional study within an Epic system contributing data to Cosmos. Race was categorized as American Indian, Asian, Black, Native Hawaiian, or White. Age-adjusted relative representation (RR) ratios were used to compare participation, with RR >1 indicating over-representation and RR Results/Anticipated Results: Of 130,455,189 patients meeting eligibility criteria, 0.52% (673,425) of patients were active or inactive in an interventional clinical trial. Results are shown in the figure below. The poorest representation was from Asian and NH/PI persons. Representation was most similar to the patient population for whites and AI/AN persons. Black males participated less and women, more than predicted by patient composition. Older patients participated more frequently than younger (age, mean (SD), y, 53 (22) vs. 46 (23); p Discussion/Significance of Impact: This is the first study we know of describing interventional trial participation in the USA across millions and millions of patients. Further research is needed to clarify whether these differences are due to the nature of the studies themselves (e.g., OB/GYN trials including only women, etc.) versus disparities in recruitment or otherwise.

Objectives/Goals: To design and implement programming that better prepares graduate students for diverse roles in a variety of workforce environments, our study models the training landscape and programming needs of graduate students in behavioral, clinical, and biomedical graduate programs at a large Midwestern school of medicine and public health. Methods/Study Population: We conducted six focus groups (two graduate program manager focus groups and four graduate student focus groups), to assess the programming, career development, and training needs of graduate students. Using a grounded theory approach, we first engaged in open coding of a sample of transcripts. After developing a codebook, we continued with an iterative coding process interspersed with coder meetings to discuss emerging and changing codes. Using the framework of landscape analysis allowed our coding and modeling to go beyond graduate student needs and study the varying relationships and contexts that impact graduate students throughout their training, such as relationship to supervisor or institutional policies. Results/Anticipated Results: Preliminary results indicate that students wrestle with their status as both students and workers. Specifically, conflict arises between graduate and supervisor expectations around time spent in class, lab, and other career development activities based on these divergent roles. Students and program managers also note the disparities that arise from the university’s lack of standard, formalized policy on labor issues, such as paid leave. Data also suggest that students on training grants note the difference in access to career development resources compared to colleagues. In many cases, students themselves coordinate ad hoc programming to better suit their career and professional development needs, although this work is not a required aspect of their training. Discussion/Significance of Impact: We characterize current graduate training landscapes, which continue to shift as graduate student bodies diversify, unionize, and express interest in increasingly varied biomedical careers. Data from multiple perspectives facilitate creating, implementing, and evaluating supportive training programs that meet identified student needs.

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: 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.