Objectives/Goals: Lung transplant is a life-saving surgery for patients with advanced lung diseases yet long-term survival remains poor. The clinical features and lung injury patterns of lung transplant recipients who die early versus those who survive longer term remain undefined. Here, we use cell-free DNA and rejection parameters to help elucidate this further. Methods/Study Population: Lung transplant candidacy prioritizes patients who have a high mortality risk within 2 years and will likely survive beyond 5 years. We stratified patients who died within 2 years of transplant as early death (n = 50) and those who survived past 5 years as long-term survivors (n = 53). Lung transplant recipients had serial blood collected as part of two prospective cohort studies. Cell-free DNA (cfDNA) was quantified using relative (% donor-derived cfDNA {%ddcfDNA}) and absolute (nuclear-derived {n-cfDNA}, mitochondrial-derived {mt-cfDNA}) measurements. As part of routine posttransplant clinical care, all patients underwent pulmonary function testing (PFT), surveillance bronchoscopy with bronchoalveolar lavage (BAL), transbronchial biopsy (TBBx), and donor-specific antibody testing (DSA). Results/Anticipated Results: Over the first 2 years after transplant, the number of episodes of antibody-mediated rejection (p) Discussion/Significance of Impact: Clinically, early-death patients perform worse on routine surveillance PFTs and experience a worse degree of CLAD. These patients also have higher levels of cfDNA as quantified by n-cfDNA and mt-cfDNA. These results provide preliminary evidence that early-death patients have worse allograft rejection, dysfunction, and molecular injury.

Objectives/Goals: • To determine the impact of liraglutide on inflammation and organ injury during sepsis. • To investigate the protective effects of liraglutide on microvascular dysfunction in a clinically relevant model of sepsis. • To provide evidence for the potential therapeutic use of GLP-1 receptor agonists in endothelial dysfunction in sepsis. Methods/Study Population: Sepsis was induced in mice (N = 34) by intraperitoneal injection of cecal contents (1.8 mg/g body weight) and 24-hour hyperoxia (FiO2 90–95%). Mice received saline or liraglutide (0.1 mg/kg) at 6 and 18 hours post-injection and fluids and antibiotics at 12 hours. At 24 hours, mice were euthanized for plasma, bronchoalveolar lavage (BAL), and tissue collection. Plasma inflammatory markers, organ injury markers, and BAL components were measured. In vitro, primary human lung microvascular endothelial cells (HLMVECs) were treated with saline or liraglutide for 24 hours before exposure to saline or LPS (100 ng/mL). HLMVEC barrier dysfunction was evaluated using express permeability testing (XPerT) and electric cell-substrate impedance sensing (ECIS) to measure transendothelial electrical resistance (TER). Results/Anticipated Results: In murine sepsis, illness severity scores and lung injury were improved in mice pretreated with liraglutide (N = 10). Plasma blood urea nitrogen (BUN; P = 0.0036), alanine transaminase (ALT; P = 0.0311) and vascular inflammatory markers MCP-1 (P = 0.0172), ICAM-1 (P = 0.0356), and Pecam-1 (P = 0.0493) in plasma were reduced in mice treated with liraglutide. In HLMVECs, liraglutide (1.5 nM) significantly reduced LPS-induced barrier dysfunction measured by XPerT assay (P = 0.0030) and ECIS (P = 0.0075). Discussion/Significance of Impact: Liraglutide reduces illness severity, vascular inflammation, and organ injury in a two-hit model of sepsis. Liraglutide has direct effects in the microvascular endothelium, limiting LPS-mediated barrier dysfunction. These findings support a protective role for GLP-1 receptor agonism in sepsis, mediated through the microvasculature.

Objectives/Goals: Primary sclerosing cholangitis (PSC) manifests with an inflammatory milieu that leads to fibrotic scarring of the liver. Human PSC liver bile ducts are enriched with neutrophils; however, their infiltration and functional role is unexplored. Our goal is to investigate the mechanism and impact of peribiliary neutrophil infiltration observed in PSC. Methods/Study Population: Primary cholangiocytes (bile duct cells) isolated from WT and mouse models of PSC (3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC)-fed mice and Mdr2-/- mice) were analyzed by RNA-sequencing. Immunofluorescence (IF) was performed on liver tissues from PSC patients and mouse models of PSC for markers of bile ducts and neutrophils (KRT19 and MPO). Intrahepatic leukocytes (IHL) isolated from mice livers were evaluated for neutrophil abundance and activation state. Anti-Ly6G antibody-mediated neutrophil depletion in Mdr2-/- mice was analyzed by IF, histology, and cytometry by time-of-flight (CyTOF). Cholangiocytes stimulated with TNFα (to induce an inflammatory phenotype) were analyzed for neutrophil chemoattractants with genetic and pharmacological interventions. Results/Anticipated Results: RNA-seq analysis of primary cholangiocytes from PSC mouse models revealed enrichment in inflammatory and neutrophil degranulation pathways. Flow cytometry and RT-PCR analysis revealed an increase in the neutrophil population in PSC mice with activated phenotype. Peripheral depletion of neutrophils in Mdr2-/- mice alleviated liver injury and inflammation, along with a reduction in peribiliary neutrophil infiltration and attenuated bridging fibrosis. CyTOF analysis showed a significant reduction in CD8+ T cells upon neutrophil depletion, implying neutrophils sustain CD8+ T cells in PSC liver. Mechanistically in cholangiocytes, TNFα mediates expression of neutrophil chemoattractants, CXCL1 and CXCL8, through the cyclic GMP-AMP synthase stimulator of interferon genes (cGAS-STING) pathway. Discussion/Significance of Impact: Our findings suggest that activation of the STING pathway in cholangiocytes in cholestatic liver disease triggers an immune response resulting in peri-portal neutrophil infiltration via CXC chemokines. The sustained presence of these activated neutrophils engages the adaptive immune system to perpetuate the inflammation and fibrosis seen in PSC.

Objectives/Goals: This poster discusses key methodological and theoretical issues in translation and implementation for improving HPV vaccine recommendations in clinics serving rural communities. Methods/Study Population: Leveraging implementation science, the study of how to improve the uptake of evidence-based practices, this pilot study uses a mixed-methods effectiveness-implementation design to engage local experts in identifying a bundle of locally-tailored implementation strategies to facilitate uptake of evidence-based HPV vaccine recommendations. In partnership with the University of Arkansas for Medical Sciences Rural Research Network, we will follow an evidence-based quality improvement process to develop locally tailored implementation strategies, which we will then evaluate for acceptability, feasibility, and effectiveness. Results/Anticipated Results: This study aims to generate actionable insights into the design and implementation of tailored, evidence-based communication strategies that can be scaled to improve HPV vaccine uptake in rural communities. Findings from this pilot study will be used to support a future full-scale Hybrid-Type 3 effectiveness-implementation trial to evaluate the bundle of tailored implementation strategies. Discussion/Significance of Impact: By addressing the rural-specific determinants of evidence-based HPV vaccine recommendations, this research will contribute to a deeper understanding of how to support high-quality, evidence-based provider recommendations in rural, underserved communities, and will mitigate rural disparities in HPV-related cancers.

Objectives/Goals: Hearing loss (HL) can result from environmental and genetic factors. Some genetic variants may be more prevalent in populations living in geographic or cultural isolation. This study explores the genetic variants associated with HL in Puerto Rico and correlates these with auditory and balance disorders to uncover novel variants. Methods/Study Population: After obtaining individual informed consent and assent for a minor when applicable, we will collect clinical audiological data and biological samples (n  =  600) from families across Puerto Rico with a history of severe to profound HL. Genomic DNA will be extracted, and exome and mitochondrial genome sequencing will be conducted to identify causal variants in genes associated with HL. The study will assess the prevalence of both novel and reported variants in genes associated with HL and investigate founder variants in the Puerto Rican population. Involvement of genes so far not associated with HL will also be considered when a genetic diagnosis cannot be established. Auditory phenotypes will be correlated with genetic findings, allowing for a comprehensive analysis of genetic contributions to HL in this population. Results/Anticipated Results: This research will advance understanding of the genetic causes of HL in Puerto Rico, leading to more accurate diagnoses, personalized treatment options, and the discovery of novel genes associated with HL. It will also serve as an evidence-based reference to analyze the adequacy of current neonatal hearing screening protocols in PR. Recruitment and sample collection have begun, and we expect our findings to uncover population-specific variants. These results will provide a foundation for further genetic studies aiming at identifying the causes of HL in Puerto Ricans regardless of age of onset. Discussion/Significance of Impact: This study will enhance our understanding of hereditary HL and serve as a basis for developing population-specific diagnostic tools and interventions, particularly in the Puerto Rican population. The research will support future genetic studies and address health disparities in HL in the island.

Objectives/Goals: Given the challenges that early career research scientists face, especially preparing for promotion and tenure, the decision on whether to join a research team can be fraught. We developed a novel training to support informed decision-making regarding new scientific teaming opportunities. Methods/Study Population: A team science workshop entitled “Should I join this research team” was designed for early career investigators from varied disciplinary backgrounds. Learning objectives for attendees included 1) describing the role of team science in translational research, 2) determining if teaming opportunities are a good fit, and 3) crafting thoughtful responses to requests. The training was initially delivered to 38 attendees (11 K scholars) during a virtual national meeting. We adapted this training for in-person delivery to K and T scholars at our CTSA regional partners. Instructional methods shared across virtual and in-person modalities included self-reflection, think and share activities, and scenario application. In-person delivery also included short video clips and small group discussions. Results/Anticipated Results: Multiple Likert-scale items were completed by workshop participants before and after completing the workshop to evaluate attendees’ confidence in their perceived abilities to explain strengths and limitations of team science, identify characteristics of effective science teams, evaluate a team invitation, assess costs and benefits, negotiate collaborative team invitations, etc. Preliminary data from the virtual workshop suggests that 54.6% of scholars were either not at all or only slightly confident in evaluating a teaming invitation. After the workshop, 45.5% reported being very confident, and 9.1% reported extreme confidence in evaluating a team invitation. Evaluation of the in-person training, along with a comparison of virtual and in-person learning outcomes will also be presented. Discussion/Significance of Impact: Our multimodal training is designed to equip early career investigators with the tools needed to evaluate and respond effectively to research team invitations. We believe this novel training will result in informed teaming decisions for early career research scientists.

Objectives/Goals: Aims are to identify the gaps and discrepancies between cancer care teams at Princess Margaret (PM) and primary care providers (PCPs). To ensure the transition from hospital care at PM into the community integrates the expressed needs of PCPs and cancer specialists. To ensure PCPs have the necessary resources to provide high-quality care to patients. Methods/Study Population: Phase 1 is the preparation phase, which consists of searching the literature and conducting contextual inquiry with experts in relevant fields, such as cancer survivorship and primary care. This phase is crucial to the planning of this project as the information gathered will be used to define the problem space and outline the scope of the project. Next (phases 2 and 3) we aim to create and distribute surveys to PCPs to gather data on current protocols and resources. We plan to distribute this survey by emailing PCPs and accessing PCP networks. Upon completion of the survey, we will review the data and assess which areas need further investigation. Then, we will create an interview guide keeping in mind the areas that need to be supplemented and aiming to validate the need. Results/Anticipated Results: A resource that presents guidelines for PCPs to assist in them taking on follow-up care responsibilities for low-risk cancer survivorship patients. These guidelines may include information such as communication pathways between PCPs and the PM Cancer Care team, expected follow-up care measures, and timeframes for follow-up care. The development of this guideline will assist in alleviating the burden on the PM Cancer Centre system as it will facilitate low-risk patients transitioning back to family care. Discussion/Significance of Impact: There is an increasing demand for oncology services post-cancer treatment at the PM Cancer Centre and the current cancer model follow-up care is not sustainable by oncologists alone. There is a need to explore innovative personalized pathways to follow-up care based on an individual’s needs and integrate family doctors.

Objectives/Goals: The informed consent (IC) process is similar between clinical trials and expanded access (EA), which allows clinical use of investigational products outside studies. Physicians face unique barriers to IC in clinical environments. This project assesses IC documentation, identifies potential barriers, and evaluates efforts to improve compliance. Methods/Study Population: This is a continuous quality improvement project. To assess the compliance of IC processes for EA patients, informed consent documents signed by EA patients in 2023 were collected and reviewed against institutional standards. Five components of each form were evaluated, and the number and type of noncompliant documentation were tracked. Five physicians who provided EA treatments in 2023 were interviewed and the transcripts were analyzed to identify barriers to physician’s and teams’ IC processes. Efforts made to address these barriers and improve the compliance of informed consent documentation are being tracked and trends in compliance are being evaluated. Results/Anticipated Results: Sixty seven (67) signed informed consent documents for EA treatments were systematically reviewed and 34% were found to be compliant in all key aspects assessed. Analyses of interview notes, transcripts, and memos identified barriers to informed consent processes for expanded access treatments, including the infrequent or irregular occurrence of EA treatments making it difficult for care teams to develop and maintain their understanding of IC process and resources. Efforts made to improve compliance by pre-populating available information into informed consent documentation and removing unnecessary boxes in these forms may have driven improvement in compliance with further efforts underway. Discussion/Significance of Impact: This project evaluated the compliance of IC documentation for EA treatments and identified drivers affecting physicians’ IC processes for these patients. Different strategies to improve the compliance of IC documentation were evaluated and potential best practices for EA support were identified.

Objectives/Goals: Sexual minority populations (SMPs), including lesbian, gay, and bisexual groups, disproportionately encounter discriminatory experiences due to bi/homonegativity and systemic inequities across various social domains. We aim to understand how the neighborhood-level stressors and resilience sources differed across specific groups in SMPs. Methods/Study Population: Utilizing the NIH All of Us’ cloud-based platform, we selected cohorts self-identifying as gay (n  =  9,454), bisexual (n  =  15,284), lesbian (n = 5267), or straight (n  =  349,748). We explored multiple key measures of neighborhood-level stressors (e.g., neighborhood disorder, neighborhood cohesion, and environment index) and resilience sources (e.g., neighbor cohesion, social support), and other factors (e.g., food insecurity, housing insecurity, and housing instability) by their sexual orientations using analysis of variance or Chi-square analyses. Results/Anticipated Results: Our sample comprised 60.8% females and 37.5% males identifying as non-binary or transgender, with an average age of 55.6 years (SD  =  17.1). The racial composition was 56.0% White, 19.4% Black, 18.7% Hispanic, and 5.9% others (e.g., Asian, multiracial). Compared to straight individuals, SMPs reported high neighborhood stressors (e.g., disorder, worse environment) but lower neighborhood-level resilience sources (e.g., social support, cohesion). In addition, bisexual groups reported highest prevalence of housing insecurity (6.7% vs. 2.3%), housing instability (36.0% vs. 19.6%), and food insecurity (26.57% vs. 12.21%). Discussion/Significance of Impact: SMPs, particularly bisexual individuals, face greater neighborhood stressors and fewer resilience sources than their straight counterparts. These findings call for targeted interventions to address these disparities and promote health equity, using large-scale datasets to inform community-based solutions.

Objectives/Goals: The University of Minnesota (UMN) CTSI and Medical School sought to increase the diversity of translational research-intensive faculty by recruiting highly promising new-to-UMN tenure track faculty in the Medical School. Increased resources and career development will increase recruitment and address barriers to their promotion and success. Methods/Study Population: In 2019, the Medical School Dean committed to fund 15 Early Career Research Awards (ECRA) Scholars to recruit outstanding new-to-UMN, tenure track faculty. Supplementing usual departmental recruitment packages, ECRA Scholars receive: 75% salary and fringe benefit support for 3 years; an additional $500,000 of research funds; and augmented mentoring and coaching with required participation in the relevant CTSI career development program. Department Chairs propose meritorious candidates for Review Committee consideration based on the Chair nomination letter, scientific plan, mentoring plan, CV, and additional letters of recommendation. To foster community building, there is an annual mini-retreat for the ECRA Scholars and other underrepresented CTSI Scholars with an external visiting professor. Results/Anticipated Results: Fifteen tenure-track faculty have been recruited as ECRA Scholars since 2019 into 9 different departments. One additional Scholar has been accepted and three have completed the program. Three ECRA Scholars were K awardees at the time of recruitment. The CTSI career development programs utilized were the K Accelerator (10), KL2 Scholar (2), and K-R01 (5) Programs, with 3 involved in two programs. The Scholar degrees include 10 PhDs, 3 MD/PhDs, and 2 MDs, with additional MPH/MS (5), MSW (1), and DPT (1) degrees. ECRA Scholars have been awarded multiple NIH R21, Foundation, and internal grants currently under review include Ks and R01s with 5 additional K, 2 new R01, and 1 revised R01 submissions planned for the 2024–2025 academic year. Discussion/Significance of Impact: The ECRA program has successfully augmented recruitment of outstanding underrepresented research-oriented early-stage faculty to the University of Minnesota School of Medicine, contributing to many Departments. The CTSI has provided career development, networking, and a broader community of Scholars, with increased diversity in CTSI programs.

Objectives/Goals: This study demonstrates the utility of the CBID biodesign process for identifying and prioritizing high-impact neurosurgical needs. The research emphasizes the process’s role in developing innovative medical technologies that align with the healthcare ecosystem’s demands and stakeholder priorities. Methods/Study Population: The CBID Spiral Innovation Model, integrating clinical, technical, business, and strategic considerations across clinical challenges in neurosurgery was employed over a 15-week period at a tertiary care center. The process involved three phases: (1) needs identification through 8 weeks of clinical immersion, (2) 7–8 weeks of stakeholder engagement via informational interviews, surveys, and conferences, and (3) iterative refinement based on evidence generation and market value. Stakeholders included over 70 clinicians (neurosurgeons, neurocritical care specialists, neurologists, etc.) across 15 institutions as well as more than 10 payers and hospital administrators. Data collection encompassed direct observation, structured interviews, and comprehensive literature review. Results/Anticipated Results: The initial list of 300+ identified neurosurgical needs was reduced to 271 after clinician and market input. High-level market and clinical evidence assessments further reduced this to 74 needs. Finally, through iterative evaluation of evidence generation, market opportunity, and stakeholder feedback, five critical unmet needs in stroke, traumatic brain injury, hydrocephalus, and epilepsy were identified for technological innovation. These needs met the criteria for clinical importance, economic viability, and market accessibility. The findings highlight the effectiveness of the biodesign process in creating a roadmap for innovation that is both clinically relevant and commercially viable. Discussion/Significance of Impact: This study underscores the effectiveness of structured need-finding and prioritization within neurosurgery. Integrating stakeholder perspectives and rigorous analysis, it provides a replicable framework for medical innovation to accelerate the development of impactful solutions across medicine.

Objectives/Goals: Discovery Day aims to bring diverse and underrepresented groups of potential biomedical research participants into research spaces to increase transparency, knowledge of the research process, trust in research, and interest in STEM fields. Methods/Study Population: Discovery Days are one-day events held on Saturdays at a large hospital in the Midwest. Attendees are recruited through flyers, social media, and other media. Recruitment targets communities that are predominantly underrepresented in research, such as Black/African American and Hispanic/Latino. Events included lunch, presentations, interactive lab demonstrations, Q&A sessions, and a tour of the BioBank research facility. Families completed surveys assessing demographics, trust in research, understanding of research, and interest in STEM careers. Descriptive statistics were used to summarize findings. Results/Anticipated Results: At a Discovery Day held in May 2024, 58 individuals attended. Each family (N  =  30) completed a 15-item survey. Most (70%) participants identified with diverse racial and ethnic backgrounds, with the largest group identifying as Black/African American. Five diverse neighborhoods were represented, as expected for our recruitment strategy. Following Discovery Day, 73.3% of participants reported their trust in research increased, and 93.1% of participants indicated their understanding of research increased. 37.5% reported interest in learning about STEM jobs or internships, and 100% of participants would recommend Discovery Day to friends and family. Discussion/Significance of Impact: We hope that by increasing transparency and trust around the research process, community members that may benefit from research (e.g., genetic research on chronic diseases) may be more likely to participate. Additional findings and future goals for Discovery Day will be discussed.

Objectives/Goals: Community engagement in pediatric emergency medicine research is completed mostly when an exemption from informed consent (EFIC) is involved. A campaign was designed to engage the community surrounding an academic pediatric emergency department in an informal discussion on any pediatric acute care and research topics they felt were important. Methods/Study Population: A flyer inviting members of the community to a virtual session was circulated through social media and word of mouth. Five members of the community attended the first session, including one with healthcare expertise and another with clinical research experience. The participants were not asked any personal characteristic questions and were allowed to self-identify during the discussion, to maintain the informal nature of the session. Results/Anticipated Results: All the participants identified as women, and mothers to children ranging in age from 11 weeks to 14 years. The participants highlighted community engagement as pivotal for advancing children’s health. They stressed the inclusion of groups traditionally underrepresented in healthcare systems, including patients and families who rarely utilize acute services and whose children have no chronic medical conditions. Critical issues in emergency and urgent care for children were extensively discussed, with a focus on when acute medical treatment is necessary and determining appropriate healthcare settings – emergency departments, urgent care centers, or primary care offices. The participants unanimously supported research leading to practical solutions for improving children’s health outcomes. Discussion/Significance of Impact: A group of community caregivers can lead to an established collaborative effort to enhance children’s healthcare outcomes through community engagement, informed decision-making, and practical application of research findings to families and caregivers. A standing community meeting is planned based on the feedback from the first session.

Objectives/Goals: Understanding the interconnections among over 20,000 human diseases spanning organ systems could inform more precise diagnosis and treatment of diseases. Here, we examine whether the ability of large language models (LLMs) to learn universal representations of concepts can be leveraged to discover complex relationships across human diseases. Methods/Study Population: To address the challenge of computationally representing thousands diseases spanning multiple organ systems, we used internal representations of concepts by LLMs to encode diseases based on their descriptions from standard disease ontologies (ICD10 and Phecodes). To do this, we leveraged application programming interfaces (APIs) of three LLMs-GPT3.5, Mistral and Voyage to encode disease relationships. We then performed unsupervised clustering of the diseases using their encodings (embeddings) from each LLM to determine whether the resulting clusters reflect disease relationships. To enable deeper exploration of disease relationships, we developed interactive plots that provide a system level view of the relationships between thousands of diseases and their association with specific organ systems. Results/Anticipated Results: We found that unsupervised analysis of disease relationships using the LLM encodings reveal high similarities among diseases based on organ systems they affect. All the LLMs clustered diseases into groups largely defined by the organ systems they affect without being trained to specifically classify diseases into their corresponding organ system classification. An exception to this was tumors in which we observed that most tumors cluster together as a group irrespective of the organs they affect. Interestingly, we found that tumors affecting anatomically related organs show higher similarity to each other than to those affecting distantly related organs. In addition to anatomical relationships between diseases, we found that the LLM embeddings capture genetic relationships between diseases. Discussion/Significance of Impact: Overall, we found that the LLM-derived encodings uphold biologically and clinically significant relationships across organ systems and disease types. These results suggest that LLM encodings could provide a universal framework for representing diseases as computable phenotypes and enable the discovery of complex disease relationships.

Objectives/Goals: We aim to develop an intuitive interface to understand the possible relationships between data from different RNA-Seq technologies. It can help novice users and educators to understand, analyze, explain, and visualize such datasets from diverse platforms, all without the need for additional software installations or strong programming expertise. Methods/Study Population: An online interactive interface is developed, integrating robust algorithms for three distinct types of analyses: DESeq2 for bulk RNA-seq, CIBERSORTx for deconvolution, and Seurat for single-cell analysis, with plans to include more algorithms. It allows a demo mode for training using the sample datasets and option for tailored analysis using user’s partially processed data. The interface provides capability to process bulk RNA-seq data from raw counts or a differential gene list. Further, deconvolution analysis for bulk RNA-seq data can be done using raw counts and single-cell data analysis can be performed using processed sequence reads, organized into three key files: barcodes, matrix, and features. Users also have an option to download the results as a zipped file, for samples from human and mouse studies. Results/Anticipated Results: Users with an active internet connection can access the interface from any major web browser. They can adjust parameters – such as genome type, cutoff thresholds, and batch effect correction – according to their specific needs. Bulk RNA-seq results are presented in the form of volcano plots, heat-maps, clusters, gene expression across samples, DEGs, and enrichment plots from KEGG and GO analyses. Deconvolution analysis can be performed using either the “LM22” signature matrix (for human leukocyte cell types) or Derm22 (for skin-specific cell types). The single-cell workflow provides results including quality control metrics, UMAP clustering, gene expression plots/tables, and cluster annotation using CellTypist. Comprehensive details on methods and tutorials are available in the GitHub repository. Discussion/Significance of Impact: Although multiple workflows are available to process bulk and single cell RNA-Seq data along with deconvolution methods to bridge the gap between the two, this is the first online interface to provide the capability to explore and analyze data from all three approaches in one place, without requiring strong computational expertise or resources.

Objectives/Goals: Diamond Blackfan anemia (DBA) is caused by loss of ribosomal proteins leading to death of red blood cell progenitors. We identified a novel heterozygous variant (c.167+769C>T) in RPL30 in a patient with DBA. We hypothesized that this variant, in a gene not previously studied in DBA, would demonstrate DBA phenotype and reveal early drivers of disease. Methods/Study Population: To study the role of our novel variant, we developed an induced pluripotent stem cell (iPSC) model, including wild type (WT) and CRISPR-edited RPL30 mutant clones. We differentiated the iPSC into hematopoietic stem cells, identified cell populations with flow cytometry, and applied single-cell RNA sequencing. We identified erythroid clusters for differential gene expression analysis, using R Studio DESeq followed by Gene Ontology (GO) enrichment analysis. We are differentiating cells into red blood cells for further comparison with flow cytometry, bulk RNA sequencing, protein analysis, and hemoglobin staining. Our approach has relied on multidisciplinary expertise in clinical hematology and genetics, basic science study of ribosomes, computational biology, stem cell, and hematopoietic biology. Results/Anticipated Results: Compared to WT hematopoietic stem cells, RPL30mutant cells had significantly decreased expression of RPL30. Analysis of top differentially expressed genes revealed downregulation of HSPA1A which encodes heat shock protein 70 (HSP70), chaperone of a critical red blood cell transcription factor. Loss of HSP70 protein has been implicated in RPL-mutated red blood cells previously as a potential modulator of severe DBA phenotype. Upon GO enrichment analysis of downregulated genes, biologic process terms GO:0042254 ribosome biogenesis, GO:1903708 positive regulation of hemopoiesis, and GO:0045646 regulation of erythrocyte differentiation were all highlighted as driver terms. We expect further differentiation to reveal early death of RPL30mutant cells with associated downregulated HSP70. Discussion/Significance of Impact: Our results support our hypothesis that the RPL30 variant downregulates erythropoiesis, with a potential early role of HSP70 protein. Upon completion of our study, we will demonstrate the role of RPL30in DBA pathogenesis as well as provide understanding of its drivers, which is critical for improved management of this disease.

Objectives/Goals: Substantial evidence supports the use of community engagement in CTS. Yet, there is a lack of empirical basis for recommending a particular level of community engagement over others. We aimed to identify associations between level of community involvement and study process outcomes, focusing on procedures to promote enrollment and inclusion. Methods/Study Population: Using manifest content analysis, we analyzed community engagement (CEn) strategies of studies indexed in ClinicalTrials.gov, focusing on studies 1) associated with 20 medical schools located in 8 southern states in the Black Belt, 2) conducted in 2015–2019, and 3) on 7 topics: cancer, depression, anxiety, hypertension, substance use disorder, cardiovascular disease, and HIV/AIDS. Data source was the ClinicalTrials.gov entry and publication for each study. We categorized each study on level of community involvement as described by the study protocol CTSA Consortium Community Engagement Key Function Committee Task Force on the Principles of Community Engagement continuum. Outcomes included recruitment and representativeness. Other codes included funder type, study phase, study status, and time to enrollment. Results/Anticipated Results: Of 890 studies that met inclusion criteria, only 493 had published findings. 286 studies (58%) met enrollment targets. Only 9 studies described any level of CEn (1 outreach, 3 consult, 1 involvement, 3 collaboration, and 1 shared leadership). Time to enrollment for these 9 studies (mean 28.78 mos.) was shorter than for studies without CEn (mean 37.43 months) (n.s.). CEn studies reached significantly higher enrollment (CEn mean  = 2395.11, non-CEn mean  = 463.93), p Discussion/Significance of Impact: Results demonstrate the substantial effect of CEn on enrollment and inclusion in clinical studies. However, the infinitesimal number of studies that reported CEn did not allow comparisons of level of engagement on the outcomes. Findings highlight ethical questions surrounding the lack of publishing incomplete studies.

Objectives/Goals: Idiopathic inflammatory myopathies (IIMs) are autoimmune diseases influenced by genetic and environmental factors. This study aims to explore infection patterns preceding IIM onset by applying temporal data mining and machine learning to deidentified patient records and corroborate results from molecular analysis. Methods/Study Population: The dataset used in this work was extracted from TriNetX with a focus on patients who have IIM. Risks for developing the outcomes were assessed using case–control cohorts. For each participant, information was extracted about diagnosis code, date of infection, and study visit in which the infection was reported. This data were then temporally encoded and used to generate sequence files for each of the outcomes. Unsupervised temporal machine learning was then preformed on these files to detect frequent subsequences of infections. Python library scikit-learn was used to perform the unsupervised machine learning with k-means clustering. Results/Anticipated Results: The results of this study identify infections associated with the onset of IIM by analyzing temporal infection patterns. Frequent sequences of infections uncovered, with specific patterns linked to different cohorts, offer insights into the etiology of IIM. Common and cohort-specific infection sequences will help validate existing research and provide new avenues for exploring the disease mechanisms. The findings will highlight significant infection patterns, which will inform our understanding of IIM onset across various patient populations. Discussion/Significance of Impact: The results will provide key insights into pre-symptomatic infection sequences related to IIM onset, enhancing understanding of its etiology and pathogenesis. These findings may aid in developing more precise screening methods for early detection and confirm previous results from analyzing immune signatures of infections in IIM.

Objectives/Goals: High-performing translational teams (TTs) effectively draw knowledge from empirical data to develop health solutions. However, some TTs lack rigorous data approaches, resulting in inefficiency. The ICTR data science initiative integrates team-oriented data science for more innovative and reproducible translational research. Methods/Study Population: To help TTs better leverage data science, the Institute for Clinical and Translational Research (ICTR) at the University of Wisconsin-Madison orchestrated a strategic initiative involving four main actions. • Assess needs. Determine how TTs are using data science and identify essential tools for success. • Establish partnerships. Develop strategic relationships to centralize resources and engage data scientists. Provide team science training to ensure effective integration. • Develop educational pathways. Design and implement workshops to demystify novel data science tools and upskill translational scientists. • Facilitate culture change. Identify ways that all ICTR services can help identify needs, foster educational pathways, and encourage partnerships to help TTs better leverage data science. Results/Anticipated Results: Initial assessments indicated that fewer than 25% of TTs receiving pilot awards used data science tools, and only 10% had a data scientist on their team. Data from collaboration planning sessions indicated that few TTs used data science, but all were interested in learning more. To address this deficiency, ICTR partnered with the Data Science Institute and the Section of Applied Clinical Informatics. This expertise informed resource development (e.g., a data science primer, websites) and generated workshops. Educational opportunities include tailored workshops to help TTs better curate data and create more efficient workflows, graduate course modules to improve rigor and reproducibility, and seminars illustrating translational applications of AI, visualizations, and large data integration. Discussion/Significance of Impact: The ICTR Data Science Initiative was designed to empower TTs to more effectively integrate data to power translation. As data science approaches and expertise are embedded within teams, we anticipate continued increases in interest and usage of data science tools, collaborative publications, and data rich applications for extramural funding.

Objectives/Goals: REDCap is a popular electronic data capture tool. However, training users in how to best utilize REDCap can be a challenge for many institutions. The Clinical and Translational Science Institute (CTSI) strives to setup a self-service training program that takes the day to day roles of users into account. Methods/Study Population: Our new training curriculum is a collaboration with our Workforce Development team and our REDCap Support Team. The REDCap team functions as the subject matter experts and generate a training outline based on a certain feature or topic. The Workforce Development team transform that outline into an LMS style course that’s available online. In order to organize the courses for maximum relevance to user, we engaged with various REDCap training and regulatory experts around the globe. Based on their input, we organized the various training courses into a role-based schema. The training courses are freely available online and contain an optional test and completion certification in order to comply with regulatory standards like 21 CRF part 11 or GDPR. Results/Anticipated Results: We released the first 17 training courses in July 2024 with another 20 courses planned in the near future. Responses to the courses have been overwhelmingly positive from users and the greater REDCap community. Our collection of training courses won the best website award at the yearly REDCap conference in 2024. To date we have had 137 people go through a training course with the optional test and completion certificate. While the majority have been from the USA, a significant portion hails from other countries. We believed these people only represent a small subset of users due to the optional nature of the test and accreditation section. Discussion/Significance of Impact: Our new role-based training curriculum is crucial in giving REDCap users the training tools they need for their particular role. The certification option fills a niche for professionals to demonstrate their REDCap proficiency to further their careers. Overall, this user training should increase the utilization of REDCap in all research endeavors.