Objectives/Goals: This research aims to harness electronic health records (EHR) combined with machine learning (ML) to predict necrotizing enterocolitis (NEC) in preterm infants using data up to their first 14 days of life. We aim to provide interpretable results for clinical decisions that can reduce infant mortality rates and complications from NEC. Methods/Study Population: Through a retrospective cohort study using data from the University of Florida Integrated Data Repository and One Florida, we will develop machine learning models suitable for sequential data to predict NEC. Our inclusion criteria include very low birth weight (VLBW; < 1500g) infants born < 32 weeks gestation and EHR data availability from the first 14 days of life. We will include infants with NEC and infants without NEC to train our ML model. Exclusion criteria include infants diagnosed with spontaneous intestinal perforation and severe congenital anomalies/defects requiring surgery. Results/Anticipated Results: We anticipate that our model will provide an accurate and dynamic prediction for the risk of NEC in neonates using data up to the first 14 days of life. Our model will be interpretable to identify key risk factors and can integrate real-world clinical insights to increase early detection and improve patient outcomes. Discussion/Significance of Impact: The development of a model to predict NEC could be used in neonatal intensive care guidelines and protocols and could ultimately decrease mortality, reduce complications, improve the overall quality of care, and lower healthcare costs associated with NEC.

OBJECTIVES/GOALS: to investigate the potential impact of grandparental factors and multigenerational epigenetic inheritance on the development of ASD METHODS/STUDY POPULATION: Our study recruited participants from the CHARGE (Child Autism Risks from Genetics and the Environment) study, including grandparents, parents, and children. A questionnaire was used to gather information about the participants’ exposure to environmental factors. Saliva samples werecollected from 349 participants. Newborn dried blood spotsfrom probands and parents are still being collected from the California New born Registry. DNA was extracted from 349 saliva samples from 85 families and subjected to whole genome bisulfite sequencing (WGBS) to analyze DNA methylation. Sequence alignments and bioinformatic analyses will be performed using R packages called DMRichR and Comethyl. RESULTS/ANTICIPATED RESULTS: Sequence alignments and bioinformatic analyses are ongoing, utilizing DMRichR to identify individual genomic loci associated with ASD in each of the three generations and Comethyl to compare correlation patterns between methylation marks and selected variables, including grand parental exposures. New born blood spot collections of parents and probands are ongoing and will be used to identify potential ASD epigenomic signatures that are tissue and life-stage independent. DISCUSSION/SIGNIFICANCE: This research will provide new insights into the increased prevalence and underlying etiology of ASD that should pave the way for future research in the field. DNA Methylation signatures can help create molecular biomarkers which can be used together with behavioral clinical tests for diagnosis of ASD.

Educational tools for application of team science competencies in clinical research are needed. Our interdisciplinary group developed and evaluated acceptability of a virtual world game-based learning tool simulating a multisite clinical trial; performance hinges on effective intrateam communication. Initial implementation with clinical research trainees (n=40) indicates high satisfaction and perceived relevance to team science and research career goals. Game-based learning may play an important role in team science training.

Diversity, equity, and inclusion (DEI) are fundamentally important concepts for advancing clinical and translational science (CTS) education. CTS education spans a wide range of disciplines from cell biology to clinical and community/population research. This large scope both in terms of intellectual areas and target groups requires an understanding of existing educational approaches for DEI as we translate DEI from mere concepts into equitable actions within CTS education. In this  review, we provide the readers with the most common DEI educational approaches, including cultural humility, bias training, and improving mentoring to diversify the workforce. DEI educational materials can achieve maximal success and long-term impact when implemented as institutional-wide interventions, and the materials are not seen as an isolated or independent curriculum. Approaches, strategies, and programs to achieve this are many. However, many questions remain unanswered about what the best approach, strategies, and programs are to be implemented in institutional-wide education that will be embedded in CTS education.

Objectives/Goals: The Standards for Reporting Implementation Studies (StaRI) are the Enhancing the Quality and Transparency of Health Research (EQUATOR) Network 27-item checklist for Implementation Science. This study quantifies StaRI adherence among self-defined Implementation Science studies in published Learning Health Systems (LHS) research. Methods/Study Population: A medical librarian-designed a search strategy identified original Implementation Science research published in one of the top 20 Implementation Science journals between 2017 and 2021. Inclusion criteria included studies or protocols describing the implementation of any intervention in healthcare settings. Exclusion criteria included concept papers, non-implementation research, or editorials. Full-text documents were reviewed by two investigators to abstract and judge StaRI implementation and intervention adherence, partial adherence, or non-adherence. Results/Anticipated Results: A total of 330 documents were screened, 97 met inclusion criteria, and 47 were abstracted including 30 research studies and 17 protocols. Adherence to individual StaRI reporting items ranged from 13% to 100%. Most StaRI items were reported in >60% of manuscripts and protocols. The lowest adherence in research studies was noted around economic evaluation reporting for implementation (16%) or intervention (13%) strategies, harms (13%), contextual changes (30%), or fidelity of either the intervention (34%) or implementation (53%) approach. Subgroup analyses were infrequently contemplated or reported (43%). In protocols, the implications of the implementation strategy (41%) or intervention approach (47%) were not commonly reported. Discussion/Significance of Impact: When leveraging implementation science to report reproducible and sustainable practice change initiatives, LHS researchers will need to include assessments of economics, harms, context, and fidelity in order to attain higher levels of adherence to EQUATOR’s StaRI checklist.

Objectives/Goals: To design a flexible, comprehensive framework for Data Science Units to cultivate sustainable, long-term relationships with Clinical and Translational Science Research Units. Best practices for managing Data Science collaborations are presented to improve the quality and efficiency of research conducted throughout academic health centers. Methods/Study Population: Leaders of Data Science Units across six institutions formed a workgroup to develop guidance and best practices for Data Science Units to establish long-term, sustainable collaborations with Clinical and Translational Science Research Units. This guidance is based on tools and protocols developed and employed by the participating units, which range from larger groups with over 20 partnerships to a unit with three partnerships that is actively working to expand. Importantly, partnerships are highly variable, with some partnerships at one institution representing engagement with over 500 faculty, whereas some partnerships at another institution involve the lab of a single investigator. Results/Anticipated Results: We offer guidance in three domains: (1) Identifying the needs for a new partnership, including assessing required effort and data science expertise, setting partnership priorities, developing formal agreements, and identifying goals and metrics; (2) managing data science teams by implementing regular meetings, creating project intake and prioritization processes, and effort monitoring; and (3) evaluating the successes and failures/gaps of the collaboration by measuring the metrics mapped to the goals. For each domain, we provide specific suggestions on which parties should be involved and how frequently the processes should occur. This guidance is applicable both to larger collaborative partnerships and to smaller, single faculty or staff partnerships, whether they are new or well-established. Discussion/Significance of Impact: Effective collaboration between data scientists and clinical and translational investigators is key to advancing data-driven research. The guidance and resources are presented to support Data Science Units in successfully managing long-term collaborations through goal-development, evaluation, and adapting to evolving research needs.

Objectives/Goals: Develop a cervicovaginal mucus replacement to prevent bacterial vaginosis (BV) in pregnant women. Our therapeutic, specialized hydrogel in natural enhancement for vaginal health (SHINE-VH), is formulated through polymer chemistry, tested for efficacy and safety through microbiology, and translated through clinical and translational science. Methods/Study Population: We will develop SHINE-VH with optimized viscoelastic and mucoadhesive properties intended to mimic healthy vaginal mucus. SHINE-VH will be synthesized via robust photoiniferter methods and investigated through shear rheology, sugar binding, and permeability studies. To evaluate the biocompatibility and safety profile of SHINE-VH, we will utilize a series of in vitro models to test its impact on the viability and cytotoxicity of human vaginal epithelial cells. In addition, we will assess the capacity of SHINE-VH to fortify vaginal barrier integrity and modulate anti-inflammatory activities in a 2D epithelial barrier model exposed to BV-associated pathogens. Lastly, employing organ-on-a-chip technology, vaginal swabs from both healthy and suspected BV pregnant patients will be treated with SHINE-VH. Results/Anticipated Results: SHINE-VH will mimic the protective, hydrophilic gel network of natural mucins. The viscoelastic properties of our formulation determined by shear rheology will be tuned through concentration and polymer composition to mimic vaginal mucus. We will also show the facile movement of small molecule nutrients through the SHINE-VH network via sugar-binding and permeability tests. Additionally, we anticipate that the introduction of SHINE-VH, due to their xenobiotic nature as synthetic mucins, will modulate the microbiota by diminishing inflammation, thereby reinforcing the cervicovaginal mucus and cultivating a vaginal microbiome that is more resilient to the disruptive impacts of BV. Such modulation could lead to a marked difference between the SHINE-VH-treated and untreated groups. Discussion/Significance of Impact: BV affects ~30% of women globally and is associated with severe gynecologic and obstetric complications, representing a significant unmet need in women’s health. SHINE-VH offers a novel approach to BV management, aiming to strengthen vaginal mucosal integrity, potentially reducing BV prevalence, and improving women’s health outcomes.

Creating a sustainable residency research program is necessary to develop a sustainable research pipeline, as highlighted by the recent Society for Academic Emergency Medicine 2024 Consensus Conference. We sought to describe the implementation of a novel, immersive research program for first-year emergency medicine residents. We describe the curriculum development, rationale, implementation process, and lessons learned from the implementation of a year-long research curriculum for first-year residents. We further evaluated resident perception of confidence in research methodology, interest in research, and the importance of their research experience through a 32-item survey. In two cohorts, 25 first-year residents completed the program. All residents met their scholarly project requirements by the end of their first year. Two conference abstracts and one peer-reviewed publication were accepted for publication, and one is currently under review. Survey responses indicated that there was an increase in residents’ perceived confidence in research methodology, but this was limited by the small sample size. In summary, this novel resident research curriculum demonstrated a standardized, reproducible, and sustainable approach to provide residents with an immersive research program.

New technologies and disruptions related to Coronavirus disease-2019 have led to expansion of decentralized approaches to clinical trials. Remote tools and methods hold promise for increasing trial efficiency and reducing burdens and barriers by facilitating participation outside of traditional clinical settings and taking studies directly to participants. The Trial Innovation Network, established in 2016 by the National Center for Advancing Clinical and Translational Science to address critical roadblocks in clinical research and accelerate the translational research process, has consulted on over 400 research study proposals to date. Its recommendations for decentralized approaches have included eConsent, participant-informed study design, remote intervention, study task reminders, social media recruitment, and return of results for participants. Some clinical trial elements have worked well when decentralized, while others, including remote recruitment and patient monitoring, need further refinement and assessment to determine their value. Partially decentralized, or “hybrid” trials, offer a first step to optimizing remote methods. Decentralized processes demonstrate potential to improve urban-rural diversity, but their impact on inclusion of racially and ethnically marginalized populations requires further study. To optimize inclusive participation in decentralized clinical trials, efforts must be made to build trust among marginalized communities, and to ensure access to remote technology.

Diversity remains low among US colleges faculty, with only 3% identifying as Black or Hispanic. Moreover, underrepresented racial minority faculty often face unique challenges and are less likely than their white counterparts to earn higher academic rank, tenure, and funding, especially those who study health equity. We developed a novel program for health-equity focused pre-docs and junior faculty. The Disparities Researchers Equalizing Access for Minorities (DREAM) Scholars is a 24-month career development program led by the Center for Clinical and Translational Science (CCTS) that provides pilot and travel funding, career development seminars, mentoring, and writing retreats. We report the outcomes of the first Scholar cohort (N = 10), pre-docs n = 6; assistant professors, n = 4; seven were Black, one Hispanic, two White, one who identified as non-binary. At the end of the program, Scholars coauthored 34 manuscripts, 9 abstracts and 8 grants. Semi-structured interviews revealed seven major program strengths: funding, support and sense of community, accountability, exposure to translational science, network expansion, and exposure to multidisciplinary peers. Scholars provided feedback useful for subsequent cohorts. The DREAM program provided accountability and fostered a sense of community, expanded professional networks and enhanced scholarly productivity. The program serves as a model for implementation throughout the CCTSs.

Objectives/Goals: Delving into the intricate web of translational research collaborations, this study analyzed the evolving landscape of the Hispanic Alliance of Clinical and Translational Research from 2020 to 2024 using cutting-edge social network analysis (SNA). SNA is a powerful tool for visualizing, understanding, and harnessing the power of networks. Methods/Study Population: We conducted a systematic document review of all the Alliance IDeA-CTR Network Calls for Pilot Projects from 2020 to 2024 including key attributes of the investigators and collaborators (e.g., academic institution, highest degree, collaborator type). Scientific collaboration was defined as two or more researchers working together in a grant proposal for a pilot project application. Study data was recorded and tracked using an Excel spreadsheet. R-Statistical software was used to analyze and map the networks resulting from collaboration interactions comparing the 2020 Call and 2024 Call. Network statistics were performed including nodes, isolates, edges, components, density, diameter, average degree, and the size of the main component. Results/Anticipated Results: Within a vibrant network comprising over 150 investigators from local and national academic institutions, clinicians (49.3%), and basic researchers (25.4%) are predominant. Initial findings showcase a remarkable surge in interdisciplinary collaborations and affiliations over time. Preliminary findings demonstrated that the number of nodes/actors increased from 16 to 75 comparing 2020 to 2024 and the edges/relationships from 12 to 66. Notably, the number of translational research clusters surged from 4 to 18, with mentorship emerging as a critical conduit bridging diverse research clusters; 16 to 78 nodes in comparison from 2020 to 2024. More extensive collaborative clusters occurred across time with over 20 researchers collaborating. A mentor was the main actor connecting these research clusters. Discussion/Significance of Impact: This study unveils the intricacies and power of translational research dynamics, showing a palpable surge in collaboration diversity and depth. By harnessing data-driven insights, our approach catalyzes informed decision-making to amplify collaboration, diversity, and network efficacy, offering invaluable guidance for policy and practice.

Objectives/Goals: Osteosarcoma (OS) is the most common primary bone malignancy in humans and dogs. >40% of children and >90% of dogs succumb to metastatic disease. We hypothesize MYC overexpression in metastatic canine and human OS contributes to an immunosuppressive tumor environment by driving tumor-associated macrophage influx and T lymphocyte exclusion. Methods/Study Population: To characterize the role of oncogenic MYC signaling in the canine metastatic tumor immune microenvironment (TIME), 42 archived FFPE lung metastatic canine OS samples were evaluated for MYC copy number variation (CNV), mRNA, and protein expression via ddPCR, nanostring analysis, and immunohistochemistry (IHC). Seven samples also underwent GeoMX spatial profiling to more specifically evaluate T cell and macrophage transcriptional profiles based on MYC status. To determine the role of MYC target modulation as a potential therapeutic option, canine and human OS cell lines were treated with a novel MYC inhibitor (MYCi975) and assessed for effects on survival, proliferation, and cytokine profiles. Results/Anticipated Results: We demonstrate that copy number gains are not a key driver of MYC hyperactivity in canine metastatic OS. However, stratification based on MYC protein expression demonstrates that “MYC-high” tumors are associated with downregulation of cytotoxic effector T-cell associated transcripts and upregulation of tumor-associated macrophage (TAM) and extracellular matrix remodeling transcripts. We also report that MYCi975 treatment of canine and human OS cell lines results in significant inhibition of OS cell survival and proliferation at concentrations that are pharmacologically achievable in mice. Furthermore, we demonstrate MYC inhibition by MYCi975 is associated with reduced pro-inflammatory cytokine secretion in OS cell culture models. Discussion/Significance of Impact: While MYC overactivity in metastatic canine OS may not be genomically driven, other mechanisms that lead to increased MYC protein expression are associated with transcriptomic profiles supportive of local immunosuppression. Pharmacologic targeting of MYC may serve as a strategy to bolster immunotherapeutic options in metastatic OS treatment.

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.

NIH offers multiple mentored career development award mechanisms. By building on the UC Davis Clinical and Translational Science Center (CTSC) from its initial NIH funding in 2006, we created an institution-wide K scholar resource. We investigated subsequent NIH funding for K scholars and to what extent CTSC research resources were used. Using NIH RePORTER, we created a database of UC Davis investigators who obtained K01, K08, K23, K25, or K99, as well as institutional KL2 or K12 awards and tracked CTSC research resource use and subsequent funding success. Overall, 94 scholars completed K training between 2007 and 2020, of which 70 participated in one of four institutional, NIH-funded K programs. An additional 103 scholars completed a mentored clinical research training program. Of 94 K awardees, 61 (65%) later achieved NIH funding, with the majority receiving a subsequent individual K award. A higher proportion (73%) of funded scholars used CTSC resources compared to unfunded (48%). Biostatistics and Biomedical Informatics were most commonly used and 55% of scholars used one or more CTSC resource. We conclude that institutional commitment to create a K scholar platform and use of CTSC research resources is associated with high NIH funding rates for early career investigators.