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: 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: The Wake Forest Clinical and Translational Science Institute (CTSI) has integrated academic goals of T0-T4 translation, scholarship, and education into our Academic Learning Health System (aLHS) framework. Our Translation Research Academy (TRA) provides rigorous training for outstanding and diverse K12 and early-career faculty to develop LHS core competencies. Methods/Study Population: The TRA Forum is the main vehicle for delivering an aLHS-oriented curriculum. Currently, the program includes six K12 scholars and 18 other early-career research faculty with facilitated access to CTSI resources. The TRA Forum is a 2-year seminar series that meets twice a month to discuss topics relevant to the aLHS, leadership, and career development. Inclusion of first- and second-year scholars facilitates peer mentorship, allowing Year 2 scholars to share insights with new scholars. Forum sessions are developed around adult learning theory: Each participant is asked to contribute their experience to discussions, and sessions focus on real-world examples. Results/Anticipated Results: Scholar and faculty commitment is very high. For the first 30 min., scholars present their work in small groups. This extends the range of disciplines exposed (64% of TRA graduates found this very helpful) and promotes translational traits of boundary crosser, team player, and systems thinker. Participants view the TRA as an opportunity to form internal peer networks, promote peer mentoring, and establish new collaborations. The remaining 60 minutes are used for education. Sessions include nominated topics and those providing a solid foundation in core aLHS competencies and characteristics of translational scientists. Educational sessions (97%) were rated as helpful or very helpful. Discussion/Significance of Impact: TRA scholars receive rigorous training in a highly supportive environment to produce aLHS researchers with skills to transcend boundaries, innovate systems, create new knowledge, and rigorously evaluate results.

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: 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: Objective: To examine whether familial longevity modifies the relationship between chronic kidney disease (CKD) and cardiovascular disease (CVD) in older adults. Goals include assessing the impact of familial longevity on 1) CVD prevalence, 2) CVD incidence, and 3) mortality among individuals with CKD. Methods/Study Population: An observational, longitudinal study. We examined 1,236 Ashkenazi Jewish adults (ages 65–94) from the LonGenity cohort. Estimated glomerular filtration rate (eGFR) was the independent variable, calculated using the CKD-EPI equation 2021. CVD prevalence, incidence, and mortality were our outcomes. CVD was defined as a composite of MI, PCI, CABG, or stroke. Exceptional longevity was defined as living past 95 years, grouping participants into OPEL (offspring of parents with exceptional longevity, n = 576) and OPUS (offspring of parents with usual survival, n = 604). Stratified logistic regression and Cox proportional hazards models assessed eGFR’s association with CVD, testing effect modification through eGFR × OPEL interaction terms. Median follow-up was 5.5 years. Results/Anticipated Results: A significant association was observed between eGFR and CVD prevalence in OPUS, but not in OPEL. No significant link was found between eGFR and CVD incidence or mortality in either group. Familial longevity did not modify the association between eGFR and CVD prevalence, nor the composite of CVD incidence and mortality, as the interaction term was nonsignificant. The LonGenity cohort’s health status may have influenced these results due to selection and survivor biases, limiting generalizability. Further research is needed to clarify familial longevity’s role in kidney function and cardiovascular outcomes. Discussion/Significance of Impact: Our findings suggest that kidney function, as measured by eGFR, may be associated differently with cardiovascular risk in those with and without familial longevity. The study highlights potential limits of familial longevity in modifying CVD risk associated with CKD, underscoring the need for tailored CVD prevention strategies.

Objectives/Goals: With qualitative interviews we aim to 1-Describe barriers and facilitators for post-transplant lymphoproliferative disease (PTLD) survivors’ access to late effects (LE) care. 2-Investigate clinicians’ perceptions of current and ideal PTLD LE care. Our long-term goal is to develop and pilot implementation strategies to standardize PTLD LE care. Methods/Study Population: Study population: We will recruit 20–25 PTLD survivors or their caregivers and 10–15 health care workers (HCW) from oncology, LE, and solid organ transplant (SOT) teams at St. Louis Children’s Hospital (SLCH). PTLD is a lymphoma-like cancer that occurs in solid organ transplant (SOT) recipients. PTLD survivors experience LE from cancer, yet many do not receive LE care. Research strategy: We will conduct qualitative semi-structured interviews based on the Consolidated Framework for Implementation Research (CFIR). A preliminary codebook will be based on CFIR and refined through transcript review. Team-based coding includes double coding and checking for intercoder reliability. We will generate coding reports to understand themes and identify barriers and facilitators of LE care. Results/Anticipated Results: We hypothesize survivors, caregivers, and HCWs will identify actionable factors to inform future studies to optimize LE care. We will examine the CFIR inner setting (resources, communication, and structural characteristics), outer setting (local attitudes and external pressures), innovation domain (adaptability, evidence base, and relative advantage), individuals domain (need, opportunity, and motivation), and implementation process domain. Our contribution will be novel. 1-This is the first assessment of barriers and facilitators for LE care in pediatric PTLD survivors. 2-We will consider input from HCWs across various disciplines delivering care to PTLD survivors. 3-We anticipate identifying unique contextual factors in PTLD survivors that will influence implementation of evidence-based LE care. Discussion/Significance of Impact: Pediatric cancer survivors experience LE. Coordinated care mitigates LE. PTLD survivors experience a high burden of LE, but less than 10% of PTLD survivors at SLCH follow in LE clinic. No studies have evaluated ideal delivery of LE care for PTLD survivors. Our findings will inform an implementation trial to improve delivery of LE care for PTLD survivors.

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: 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: 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: Large-scale tumor sequencing efforts have led to annotations of novel cancer hotspot mutations that may underlie driver or cooperative function. We have sought to define the molecular consequences of such hotspots associated with pediatric DICER1 syndrome cancers, with the ultimate goal of revealing novel targets that may inform new standards of care. Methods/Study Population: We have performed genomic analysis to identify tumor types (in TCGA and MSK-IMPACT patient data) for which mutations in the Dicer1 gene (encoding Dicer protein) emerge as the dominant signature of driver function. As Dicer is a critical RNA processing factor responsible for the generation of microRNAs, which are posttranscriptional gene regulatory molecules, we have modeled these mutations in human embryonic stem cells in order to study the direct effects on miRNAs and their target genes in an isogenic background. In addition to providing the required setting for unambiguous attribution of function to specific mutations, clonal human ES cells offer an opportunity for modeling of both developmental and cancer requirements associated with altered Dicer function. Results/Anticipated Results: Through generation of genomics and functional datasets from matched genotypes in Dicer mutated human ES cells, we have identified specific alterations in miRNAs and their effects on target genes. Unexpectedly, we found direct evidence for both loss of function and gain of function attributable to Dicer mutations. In addition, through integrated analysis of genomic data from tumor sequencing datasets and our human ES cell models, we have identified potential miRNA and target gene alterations that underlie tumorigenic potential, nominating gene candidates for targeted therapy in DICER1 syndrome. Direct mouse modeling of such candidate gene targets has revealed evidence for driver function of identified miRNA and their targets. Discussion/Significance of Impact: DICER1 syndrome cancers comprise a wide variety of rare pediatric tumor types. Presently, we still lack an effective standard of care. Furthermore, the previous lack of molecular profiling precluded targeted therapy opportunities. Our precise knock-in modeling of Dicer hotspots and deep profiling of relevant tumors now provide candidate targets.

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: Youth with IBD have preventive, psychosocial, and acute care needs beyond those of peers, yet receipt of services does not match those needs. Our objectives are to assess the feasibility of (1) an individualized care plan intervention to improve perceived and measured care quality and (2) a pragmatic trial design embedded in pediatric IBD practice. Methods/Study Population: This is a pilot rollout-design randomized trial (n = 60) at a regional academic medical center. Eligible patients are 13–19 years old with IBD for at least 3 months and scheduled for a follow-up visit during the trial. Research staff recruits from one cluster at a time until goal enrollment (14–16). Enrollees are randomized 1:1 to intervention (MyIBD now) or control (MyIBD after the trial). MyIBD combines a tabular summary of individualized acute, chronic, and preventive care needs with nurse facilitator support for patients to use the information. Surveys at baseline, 6 and 12 months measure care quality (Patient Assessment of Chronic Illness Care scale, vaccines, health services) and patient self-management skills (Partners in Health scale). Implementation outcomes are collected via chart review. Results/Anticipated Results: To date, 44 subjects have been randomized. Among subjects, the mean age is 16 years; 73% have Crohn’s disease, 77% have commercial insurance, 75% receive anti-TNF therapy, and 14% live in a rural area. Mean baseline perceived care quality (PACIC scale) is 76.9 (sd 16.3; out of 100); mean baseline perceived self-management skill (PIH scale) is 78.1 (sd 13.4; out of 96). On objective care quality measures, 59% have completed the HPV vaccine series, 32% have received an additional pneumonia vaccine; in the past year 68% have had a screening for mood disorders, 20% an emergency department visit for IBD, and 18% an IBD hospitalization. To date, the IBD clinical team has achieved 100% completion (intervention subjects receive MyIBD plus nurse facilitation) and 0% contamination (control subjects inappropriately receive MyIBD). Discussion/Significance of Impact: Study results to date support the feasibility of the pragmatic, embedded trial design and indicate opportunities for improvement in care quality as perceived by patients and as measured by common preventive and acute care quality indicators. An individualized care plan supported with nurse facilitation may improve pediatric IBD care quality.

Objectives/Goals: Digital recruitment can improve participant engagement in medical research, but its potential to introduce demographic and socioeconomic biases is unclear. This study investigates pathways participants took during a digital recruitment workflow in neurology, examining potential associations with socioeconomic and demographic factors. Methods/Study Population: As part of an ongoing study aiming to remotely capture speech from patients with neurologic disease, most participants seen in neurology on our campus are invited to complete a self-administered speech examination. We exported participant data from Epic (semi-automated identification and invitation), Qualtrics (eligibility screening), the participant tracking database (consent), and the recording platform (completion) for March to July 2024. Data visualization was performed using a Sankey diagram. Socioeconomic status was assessed using the housing-based socioeconomic status (HOUSES) index and area deprivation index (ADI) national rank. Kruskal–Wallis and Wilcoxon rank-sum tests were used to compare the median age, socioeconomic indices, and time taken to reach different steps of the study. Results/Anticipated Results: Of the 5846 invited participants, 57% were from urban areas, 23% from rural areas, and 20% from urban clusters. Most did not read/respond (2739) or declined (1749) the initial invitation via Epic. Of the 1358 interested participants, 415 completed the study. Participants from urban areas completed enrollment steps faster than those from rural areas and urban clusters, though the variance was large (42.6 ± 41.4 days vs. 50.6 ± 42.2 days and 50 ± 43.9 days, respectively; p  =  0.030). Female participants took longer to complete enrollment than males (48.7 ± 44 days vs. 40.5 ± 38.8 days; p  =  0.026). Participants who successfully finished the study had significantly lower ADI national ranks compared to other common pathways (40.6 ± 19; p  =  0.0021). No associations were found with the HOUSES indices. Discussion/Significance of Impact: Our findings support differences in participant engagement, with urban participants and males more likely to complete enrollment steps. Those who finished the study were less disadvantaged suggesting potential bias in digital recruitment. These findings can inform strategies to improve digital recruitment in neurology research.

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 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: The goal of this study is to investigate the peri-metastatic rims of peritoneal lesions to determine features that predict malignancy using both imaging processing techniques and machine learning. This information will subsequently be added to our existing knowledge of peritoneal lesions to improve classification accuracy as benign or malignant. Methods/Study Population: The study population consists of 521 imaged lesions from 163 subjects with cancers of GI-origin with biopsy results as well as the clinical subject information and follow up. All images were obtained during staging laparoscopy by the senior author (TS). On the images, the central lesion as well as the surrounding peri-metastatic rim will be segmented as regions of interest (ROIs). Image processing will be used to calculate a variety of metrics for these two regions. A general estimating equation approach will be used to determine significance of these metrics compared to the dependent outcome of malignancy determined on the pathology report as the ground truth. These ROIs and significant metrics will then be used to improve the accuracy of a machine learning model to classify these lesions as benign or malignant. Results/Anticipated Results: Our previous research showed that experts performed this task at only a 52% accuracy rate (classifying lesions as malignant or benign based on imaging). A previous machine-learning model on a much smaller dataset was able to achieve by contrast an area under the curve of 0.78. We anticipate that by including a larger dataset in addition to including the peri-metastatic rim, we will be able to improve the accuracy of the the model in this task while uncovering significant biomarkers as well that can be used in future studies. Discussion/Significance of Impact: Classifying peritoneal lesions determines the correct treatment for cancer patients whether chemo-radiation, definitive surgery or palliative surgery. This project aims to develop an improved model that can perform this task using nonlabeled laparoscopic imaging with a particular focus on the diagnostic value of the peri-metastatic rim.

Objectives/Goals: The University of Minnesota’s two-year TL1 program provides flexible and individualized education and training for a diverse cohort of scholars committed to pursuing impactful careers in clinical and translational science (CTS). The program aims to strengthen the nation’s biomedical workforce by developing scientists skilled in clinical and translational research. Methods/Study Population: The TL1 program recruits PhD candidates and postdoctoral fellows from a wide variety of graduate programs in colleges and departments across the University. To date, we have trained 26 predoctoral and 9 postdoctoral Scholars in 3 cohorts. Scholars represent dozens of disciplines and the full translational spectrum. These interdisciplinary cohorts are in a unique position to realize the fundamental characteristics of a translational scientist. Entrance/exit surveys and exit interviews provide program leadership with information for quality improvement and areas scholars believe contribute the most to their education and training in CTS. Results/Anticipated Results: Entrance/exit surveys indicated Scholar-perceived benefits of training in an interdisciplinary program, including growth in translational scientist characteristics (e.g., Boundary Crosser, Team Player). Exit interviews showed Scholars appreciated the cohort model bringing together trainees from many different research areas. They valued exposure to varied perspectives, talking through challenges and solutions with each other, and learning others shared similar issues. They valued the Scholar community they developed. Several felt siloed in their careers before the program and reported that TL1 participation connected them to others outside their own area of focus, expanded their knowledge about different research methods and revealed more pathways for translation. Discussion/Significance of Impact: Recruiting and training a diverse interdisciplinary cohort of pre- and postdoctoral TL1 Scholars promoted synergy in translational research, science skills and competencies, and transformed the perspectives of Scholars’ views on the importance of interdisciplinary collaboration to accelerate science.

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: The importance of interprofessional communication in healthcare is well documented. However, there has yet to be a theoretical framework for understanding this complex phenomenon. The purpose of this project was to develop a situation-specific theory about the culture of communication in the interprofessional healthcare setting. Methods/Study Population: We conducted individual virtual qualitative interviews with 16 healthcare professionals at different levels and from various disciplines. Participants were practicing clinicians at a tertiary care academic medical center. We transcribed audio-recordings of the interviews verbatim and aligned each transcript for accuracy, confidentiality, and representativeness. Data analysis was conducted using dimensional analysis, including open- and theoretical-coding utilizing an explanatory matrix for theory generation. Results/Anticipated Results: Our findings produced a situation-specific theory on the culture of interprofessional communication in the healthcare setting. Participants described the environments in which they practiced in terms of hierarchical system structures and through a conceptual perspective of resource investment. In other words, they viewed their time and their effort as valuable – and notably, scarce – resources, which they invest in patient care, interprofessional communication, and other tasks. There are two key pathways that characterize the culture of interprofessional communication: collaborative communication and transactional interactions, depending on whether an individual perceives the goals of another person, department, or level of the hierarchical institution as aligned with their own goals. Discussion/Significance of Impact: Positive cultures of interprofessional communication in the healthcare setting depend on perceived goal alignment among individuals, departments, and leadership. Future research can explore how perceptions of goal alignment are developed and empirically test this situation-specific theory in other healthcare system settings.