Objectives/Goals: Unhealthy lifestyle habits may increase medical students’ risk for metabolic dysfunction-associated fatty liver disease (MAFLD) and non-alcoholic fatty pancreatic disease (NAFPD). This study aims to investigate how these lifestyle factors affect liver and pancreas health in preclinical medical students using diagnostic ultrasound imaging. Methods/Study Population: Using diagnostic ultrasound imaging, we propose a research study to evaluate the anatomical changes of the liver and pancreas associated with lifestyle among medical students in UCC. Forty-two (42) medical students from the Central University of the Caribbean who are in their preclinical years will be recruited to perform an abdominal ultrasound. To measure the diameter of the right liver lobe, we will employ the craniocaudal measurement method established by Riestra. et al. (2018). The parameter established by Rumack et al. (2011) will be utilized to assess liver texture and categories by Lee JS et al. (2009) to pancreas fat infiltration grades. Results/Anticipated Results: This study expects to reveal a significant correlation between the lifestyles of preclinical medical students and the health of their liver and pancreas, particularly in size and texture. We anticipate identifying specific lifestyle factors – such as dietary habits and physical activity levels – that contribute to the prevalence of hepatic and pancreatic steatosis. Additionally, we expect to highlight the need for targeted interventions to promote healthier lifestyles among medical students to mitigate risks associated with MAFLD and NAFPD. Discussion/Significance of Impact: This study is significant for monitoring changes in liver and pancreas health, preventing complications, and improving health quality while reducing future costs. It may guide the creation of tailored wellness programs for medical students, enhancing their well-being and contributing to better healthcare practices and educational strategies.

Objectives/Goals: People experiencing homelessness (PEH) face excess cervical cancer burden and unique barriers to screening. As part of a broader study addressing cervical cancer disparities in homeless populations in Indiana, our goal was to engage unhoused women in a human-centered design process to inform a homeless shelter-based self-sampling intervention. Methods/Study Population: An established community-academic partnership enabled meaningful engagement of homeless communities in Indiana and informed the need to understand and address cervical cancer disparities in this population. Rapid assessment surveys (n = 202) and in-depth interviews (n = 30) were conducted with PEH at two major shelters in Indianapolis and Lafayette to understand cervical cancer screening coverage, knowledge, attitudes, and practices; barriers and facilitators; and acceptability of human papillomavirus (HPV) self-sampling for onsite shelter-based screening. A human-centered design session with (n = 12) unhoused women further explored motivators and concerns regarding self-sampling and informed key messages and informational materials to encourage uptake of screening. Results/Anticipated Results: At least 37% were overdue for screening (last screened >5 years ago; 50% were last screened >3 years ago), far greater than national (22%) or state (24%) averages. Despite common misconceptions regarding indifference toward preventive healthcare among homeless populations, most (87%) wanted to be screened and believed it is important for their health. Competing priorities for daily survival, transportation, cost, provider mistrust, stigma, and related trauma were common barriers to screening. Enthusiasm for HPV self-sampling centered on convenience, privacy, and comfort in taking one’s own sample at the shelter. Notable concerns included lack of confidence regarding ability to self-sample correctly, unhygienic conditions in shelter restrooms, preference to be seen by a doctor, and the need for education. Discussion/Significance of Impact: The unique challenges of PEH require human-centered strategies to improve cervical cancer screening access. Willingness to be screened and acceptability of HPV self-sampling is high. Identified concerns and preferences will guide implementation of HPV self-sampling delivered by trusted community health workers in homeless shelters in Indiana.

Objectives/Goals: The Fanconi anemia (FA) pathway is responsible for faithful DNA damage repair. FA mutations are common in multiple types of cancer, including HPV-negative head and neck cancers. We sought to identify synthetic lethal genes within this pathway to target treatment of FA-mutant tumors through siRNA- and chemical-compound-based screens. Methods/Study Population: First, we completed siRNA-based and chemical compound-based screening assays to identify gene targets that reduce patient derived Fanconi pathway mutant cell (FA-D2) viability compared to Fanconi pathway proficient cells (FA-D2 + FANCD2). Five aurora kinase (AURK) inhibitors from the compound screen were chosen for further evaluation. Cell lines were treated with AURK inhibitors or siRNA-based AURK knockdown to assess viability, proliferation, DNA repair, and cell cycle progression differences. Patient mutational, mRNA expression, and outcome data were accessed through The Cancer Genome Atlas (TCGA) portal and the Caris CODEai portal. We stratified patients by tumor AURKA and AURKB mRNA levels and assessed differences in patient survival, tumor grade, and DNA repair proficiency. Results/Anticipated Results: In both screens, AURKA came up as a target to selectively reduce the growth of FA-D2 cells compared to FA-D2 + FANCD2 cells. All five AURK inhibitors identified showed selective growth inhibition (~50–75%) in FA-D2 cells at low nanomolar doses. We narrowed our selection to hesperadin, an AURKB-specific inhibitor, which showed the highest selectivity. siRNA knockdown of AURKA and AURKB decreased cell viability by 50% and 20%, respectively. Patients with FA-mutated tumors from the TCGA pan-cancer dataset had high AURKA (twofold) and AURKB (threefold) mRNA expression. AURKA and AURKB tumor mRNA expression was significantly associated with poor patient survival. Homologous recombination deficiency scores were increased ~5-fold (p Discussion/Significance of Impact: We hypothesize that in FA-deficient backgrounds, loss of AURKA or AURKB leads to heightened genomic instability due to cell cycle dysregulation and accumulated DNA damage. Our findings warrant investigation of the therapeutic potential for AURK inhibitors, specifically hesperadin, in FA-mutant head and neck cancers.

Objectives/Goals: Failure to achieve recruitment goals results in termination of ~20% of clinical trials and delays >85% of trial timelines. We aim to develop an electronic heath record (EHR)-based recruitment tool to ease identification of participants. We sought to determine whether criteria listed on clinicaltrials.gov could support selection of tool variables. Methods/Study Population: To inform the variables to include in the EHR-based recruitment tool, we data scraped clinicaltrials.gov to identify key inclusion and exclusion criteria common across a variety of diabetes clinical trials. We included actively recruiting or recently active phase 2 and 3 clinical trials of adults aged >18 years of age in the USA. We classified identified variables as clinically relevant or not and compared clinically relevant terms with inclusion and exclusion criteria (~20 variables) that were individually identified by three diabetes clinical trialists and two clinical research coordinators (CRCs). Results/Anticipated Results: We reviewed 203 clinical trials listed on clinicaltrials.gov. We identified 115 terms, 91 of which were clinically relevant. Three of 3 clinical trialists, 1 of 2 CRCs, and all trials listed age as a key variable. Consistent with data scraping, all trialists and CRCs identified glucose-lowering medications and kidney function as important criteria. Gender, ethnicity, and race were less commonly noted on clinicaltrials.gov and listed by 2 of 3 trialists and one CRC. Cardiovascular conditions (e.g., history of myocardial infarction), thyroid function tests, and contraceptive requirements were common criteria on clinicaltrials.gov, but only one trialist and one CRC identified these variables. Active infections (e.g., HIV) and c-peptide were not highlighted by trialists or CRCs but common on clinicaltrials.gov. Discussion/Significance of Impact: An EHR-based recruitment tool may facilitate identification of trial participants, but identifying key variables to include is essential. We found that data scraping for variables on clinicaltrials.gov mostly aligned with expert opinion, suggesting that automating variable selection via extraction from clinicaltrials.gov may be acceptable.

Objectives/Goals: Translational researchers often struggle to navigate a complex constellation of institutional resources spanning the IRB to bioinformatics units. We had two aims 1) Systematically map all institution-wide research support units and 2) leverage this database within a generative AI virtual concierge tailored to local investigator queries and needs. Methods/Study Population: This study leveraged mixed methods approach. First, we conducted needs assessments of local study teams to identify barriers to translation, revealing that research resources are often unknown to study teams. Second, we identified all investigators, institutional units, and offices offering such resources that we call research support units (RSUs). RSUs were surveyed, collecting contact information (leadership, website, physical location), services provided, type of research supported, and performance metrics. Third, the resource database was integrated into a large language model (LLM, e.g., ChatGPT4o) using a retrieval augmented generation (RAG) system within an R Shiny application called virtual concierge. Queries and responses are recorded for quality improvement. Results/Anticipated Results: Needs assessment focus groups consisted of clinical and basic science investigators, study team members (e.g., clinical research assistants), core directors, and administrators (n = 26). Six sessions were conducted in Spring 2024. A major resultant theme was difficulty finding RSUs “by trial and error” and lacking a “clear defined pathway” for accessing RSUs. This prompted a survey-based environmental scan to identify institutional research resources. There were 122 diverse RSUs ranging from the IRB, to grant writing, to single cell sequencing. Each research unit offered a median of 6 service types, totaling 410 service types overall. The resultant Virtual Concierge meaningfully responds to investigator resource queries with appropriate contact and access information. Usability testing is underway. Discussion/Significance of Impact: Linking researchers with translational resources requires mutual understanding, timely communication, and coordination across teams. We systematically filled these information gaps between investigators and institutional resources. Our Virtual Concierge AI bot can help researchers navigate resources through the translational process.

Objectives/Goals: Decentralized clinical trials (DCTs) shift participation outside traditional sites. This poster explores innovative administrative strategies for incorporating decentralized elements, improving flexibility, access, and efficiency in clinical research. Methods/Study Population: Modern administrative approaches are crucial for unlocking DCT potential by streamlining logistics, enhancing participant experience, and ensuring regulatory compliance. Key innovations include expedited IRB Review: using innovative template language; innovative consent strategies – eConsent and remote/virtual and contactless enrollment; telehealth communications – Twilio (voice calls and text messages)/Zoom (virtual video visits); automated participant compensation and rideshare services – Greenphire ClinCard®; flexible data collection – automated recruitment/intake forms and research surveys – REDCap; mobile research unit/self-collection lab kits via mail. Implementing decentralization into your study unlocks the potential to rapidly recruit more diverse populations. Results/Anticipated Results: The integration of decentralized elements into clinical trials holds significant potential for reducing participant burden, thereby improving recruitment, retention, and overall trial efficiency. However, the success of decentralized clinical trials relies heavily on the implementation of modern administrative approaches that streamline operations and maintain regulatory compliance. Together, these administrative advancements not only enhance the operational flexibility of decentralized trials but also support the inclusion of diverse populations by reducing geographical and logistical barriers. Discussion/Significance of Impact: As decentralized clinical trials continue to evolve, the adoption and further innovation of modern administrative solutions will be essential in improving the overall efficiency and inclusivity of clinical research.

Objectives/Goals: Collaborations between Academic Medical Centers (AMCs) and Historically Black Colleges & Universities (HBCUs) are critical to addressing health disparities and building research capacity. Herein, we examine the Duke-NCCU Collaborative Translational Research pilot funding program [2018–2023] to identify opportunities, challenges, and lessons learned from querying key stakeholders. Methods/Study Population: The Duke-NCCU collaborative pilot funding program was launched to support new inter-institutional collaborations that aim to accelerate research discoveries into testing in clinical or population settings. Eight one-year, $50,000 collaborative grants were awarded. Each funded team was assigned a CTSI Project Leader (PL) for project management support. To evaluate the program, we developed surveys targeting principal investigators (PI) and PLs. Questions covered collaboration motivation, goals, outcomes, operational processes, project management support, institutional differences, and challenges. Qualitative analysis will be employed to evaluate the responses and identify common themes. Results/Anticipated Results: The PI survey examines aspects of inter-institutional collaborations, focusing on common themes, such as authorship, definition of success, and institutional culture. The PL survey prompts feedback on managing inter-institutional teams, expectations, and challenges. Select questions were shared between both surveys to capture both perspectives. Surveys were reviewed by members of the Duke CTSI evaluation and team science teams. The PI survey will be disseminated to 16 investigators, while the PL survey will reach 5 project leaders. Built on Qualtrics, each survey takes 20–30 minutes to complete. To encourage participation, incentives will be offered as two $100 gift card drawings. Respondents can choose to complete the survey on Qualtrics or through a recorded and transcribed Zoom session. Discussion/Significance of Impact: AMC-HBCU inter-institutional collaborations drive innovation, workforce development, and equitable dissemination of outcomes. This study exemplifies collaboration, offering insights into translational research collaborations critical to advance equitable healthcare and improving population health.

Objectives/Goals: This Weill Cornell Clinical and Translational Science Collaborative (CTSC) project evaluates whether large language models (LLMs) can generate accurate summaries of translational science benefits using the Translational Science Benefits Model (TSBM) framework, aiming to identify optimal LLMs and prompting strategies via expert review. Methods/Study Population: We are using prompt engineering to train multiple LLMs to generate one-page impact profiles based on the TSBM framework. LLMs will be selected via benchmarks, focusing on models excelling in information extraction. Leading LLMs (e.g., Llama 3.2, ChatGPT 4.0, Gemini 1.5 Pro, and Claude) and other high-performing models will be considered. Initial work has utilized Gemini 1.5 Pro. Models use data from CTSC-supported projects in WebCAMP, our local instantiation of a translational research activity tracking system used by >20 CTSA hubs, and manuscripts from the Overton database cited in policy documents. Human experts will evaluate the quality and accuracy of LLM-generated profiles. Results/Anticipated Results: Preliminary results using Gemini 1.5 Pro indicate that LLMs can generate coherent and informative impact profiles encompassing diverse areas within the TSBM. Face validity appears satisfactory, suggesting the outputs align with expectations. We anticipate that further exploration with other LLMs and expert validation will reveal strengths and weaknesses of the LLM approach, including the potential for naccuracies (“hallucinations”), informing further refinement of models and prompting strategies. Analysis of manuscripts cited in policy will provide valuable insights into communicating policy-relevant benefits effectively, and benchmark comparisons will identify optimal LLMs for this use case. Discussion/Significance of Impact: This project demonstrates LLMs’ potential for streamlining and enhancing impact reporting in translational science, enabling broader dissemination of research outcomes and promoting better understanding among stakeholders. Future work will integrate LLM-based reporting into research infrastructure.

Objectives/Goals: We reorganized the University of Illinois Chicago, Center for Clinical and Translational Science education programs in part to extend assessment beyond funding success. We evaluated the impact of early career grant coaching on applicants’ perceived preparedness for grant submission, confidence in securing grants, and self-reported benefits. Methods/Study Population: Modeled after the National Research Mentoring Network, groups of ~5 investigators who wanted to submit a career development grant proposal met weekly for ~12 weeks with a senior faculty coach to refine aims pages, biosketches, and career development plans using a real-time peer review process. To evaluate impact, we assessed perceived preparedness (“How prepared do you feel to submit your grant proposal?”) and funding confidence (“How confident do you feel in your proposal’s funding chances?”) on entrance and exit surveys using a visual analog scale (max 100 points). Exit surveys also included an open-ended question: “What did you learn or accomplish during the coaching group?” We used paired t-tests to evaluate pre-to-post participation changes and reviewed free responses for additional context. Results/Anticipated Results: From June 2023 to September 2024, 21 people (12 postdocs/1 research assistant professor/7 tenure-track assistant professors/1 other; 13 female) were enrolled in 4 sessions. Seven people did not finish the sessions and were lost to follow-up. For the 14 participants who completed both the entry and exit survey, perceived preparedness for grant submission increased from 30.1 ± 23.5 to 67.1 ± 24.6 (p  =  0.001). Funding confidence in securing funding also increased from 40.8 ± 19.1 to 64.0 ± 20.7 (p  =  0.003). Participants valued the structure and accountability of the groups and reported other benefits, such as improved writing skills. Opportunities for feedback were also appreciated, with one participant stating, “I learned to face my fear of constructive criticism.” Discussion/Significance of Impact: Coaching improved perceived preparedness for grant submission and confidence in securing funding for participants’ current submissions and provided potentially durable benefits such as receiving and positively responding to constructive feedback. The impact of such programs likely exceeds the short-term financial return on investment.

Objectives/Goals: Academic research centers struggle to recruit and retain a diverse, competent clinical and translational science (CTS) workforce. The clinical research professional career pathway is particularly underrecognized among undergraduates, despite offering multiple career opportunities. Methods/Study Population: To address these challenges, two undergraduate outreach programs were developed. First, an undergraduate certificate program in CTS (UC-CTS) was designed to equip students with the skills and knowledge needed to enter the workforce immediately after graduation. Second, a “CTS Roadshow” was launched to build awareness of CTS careers within a variety of undergraduate programs and majors. In this recruitment initiative, principal investigators (PIs) and CRPs visit classrooms to share information about CTS career pathways and offer insights into the roles, responsibilities, and professional opportunities available in the field. Results/Anticipated Results: The UC-CTS program launched in Fall 2024 as a 12-credit certificate that includes two paid internships: one in patient care and one in research. Currently, the program has 5 students enrolled with an anticipated increase in enrollment to 20 students by Spring 2025. Similarly, the CTS Roadshow has reached an increasing number of students each year. Since its inception in Spring 2023, the CTS Roadshow has introduced over 750 undergraduates across 21 courses to CTS careers. The CTS Roadshow has engaged students from a variety of majors, including biology, psychology, medical sciences, legal, finance, accounting, and pre-health programs, increasing awareness of and interest in CTS careers. Discussion/Significance of Impact: Both programs aim to expose students to CTS careers early in their education, better preparing them for full-time CTS roles after graduation. A rise in qualified applicants pursuing CTS careers locally and regionally is expected, improving job satisfaction and retention through enhanced preparation for the career field.

Objectives/Goals: This study aims to address mobility disability in the rural South by collecting advanced clinical measures in underserved communities, uncovering relationships between socioeconomic status, mobility, and physical health, providing data to aid clinicians in diagnosis and treatment, and improving healthcare delivery in disadvantaged areas. Methods/Study Population: We will recruit 50 participants aged 65+ years from a diverse range of areas in the rural South. Data collection will occur at community health fairs, employing a community-centered approach. Assessments include mobility measures using portable inertial sensors, physical health assessments: body composition (TANITA BC-568), muscle size analysis (ultrasound), central and peripheral blood pressure. Demographic information: We will analyze relationships between ADI, mobility, and physical health measures. This approach allows for comprehensive health evaluation in participants’ own communities, facilitating trust building and immediate dissemination of health information. The study design enables investigation of socioeconomic impacts on health and mobility in this underserved population. Results/Anticipated Results: Preliminary data from our ongoing community health fairs (n = 172) show promising feasibility for data collection in rural settings. Importantly, initial analyses reveal a significant correlation between higher area deprivation index (ADI) scores and reduced mobility performance, supporting our hypothesis that socioeconomic factors influence physical function. We anticipate further findings, including more detailed relationships between ADI and specific mobility parameters (e.g., gait speed and balance), associations between ADI and poorer physical health measures (e.g., increased arterial stiffness and decreased muscle mass), and interrelationships between mobility impairments and cardiovascular health markers Discussion/Significance of Impact: Building vital relationships with rural communities while uncovering critical links between mobility and physical health. By bridging urban science and rural needs, we are addressing health disparities and informing targeted healthcare strategies. Our findings will improve clinical decision-making and healthcare delivery in underserved areas.

Objectives/Goals: To identify clinician and researcher barriers, facilitators and learning preferences for patient partner engagement in research. In addition, to describe the strategies, our Patient Partners Program has planned for building clinician and researcher capacity to engage patient partners in clinical research. Methods/Study Population: Our program to promote authentic patient–researcher partnerships to advance clinical and translational research is grounded in participatory approaches to maximize meaningful engagement. We utilized small group listening sessions with health care providers involved in clinical research at the University of Michigan, Michigan Medicine healthcare system. Insights from these sessions are informing the development of learning models and curriculum content. We used purposive sampling to recruit individuals (n = 12) with a wide array of patient engagement experiences across diverse clinical departments. The study materials and interview guide were co-created with a patient research partner who also participated in co-facilitating the listening sessions. Results/Anticipated Results: The interview guide included questions about the benefits, challenges, and supports to engagement and capacity building training programs for researchers. The listening sessions were recorded, transcribed, and analyzed for common themes. Preliminary findings have identified the following themes related to barriers: (1) identifying and onboarding patient partners, (2) communication challenges, and (3) institutional and structural challenges (e.g., time constraints and difficulty offering compensation) and facilitators: (1) institutional and administrative support, (2) flexibility, and (3) respect, trust, and partnership. Improved knowledge about how and when to engage patient partners was identified as a key component to build researcher capacity in patient partnered research. Discussion/Significance of Impact: Integrating patient partners into study teams accelerates innovation and translational science, increases the relevance of research findings, improves health outcomes and patient empowerment, and elevates the value of the patient perspective allowing researchers to gain a new point of view from an individual with lived experience.

Objectives/Goals: Recognizing a critical need for sustained education and community beyond formalized training periods at Columbia University’s NCATS-CTSA, we created the TRANSFORM (TRaining And Nurturing Scholars FOr Research that is Multidisciplinary) Evolution program to preserve the networks cultivated during the KL2 program. Methods/Study Population: We will provide an overview of the genesis, expansion, key components, and programming for the TRANSFORM Evolution program. The program is designed for current and alumni of the KL2 program and late junior faculty that receive our CTSA’s Irving Scholar Award. TRANSFORM Evolution is a faculty and alumni network offering a platform to support the next generation of clinical and translational researchers while fostering a lasting community of collaboration and educational activities throughout a scholar’s career lifespan. Results/Anticipated Results: Salient components include the opportunities for social interaction, such as social/happy hours and member led education/career development sessions pertaining to topics that support thriving in an academic career. The program operates with financial resources and the support of a program manager. Evolution adopts a holistic, long-term approach, focusing on the entire professional lifespan by encouraging the development of enduring opportunities for our alumni. The program is intentionally structured to meet the evolving needs of its participants, through the beginning to more established phases in their professional careers. This continuity underscores the program’s capacity to adapt and remain relevant, informing and supporting sustained career progression and scholarly productivity. Discussion/Significance of Impact: The program has been an instrumental adjuvant in facilitating the transition to each career stage. By cultivating a community rooted in a common foundation – the KL2 and Irving Scholars programs, the program has created a robust support system that is crucial for the career development of clinician-scientists.

Objectives/Goals: The Competency-Based All-Level Training (COBALT) curriculum standardizes learning for clinical research coordinators (CRCs) across multiple institutions within Washington, Wyoming, Alaska, Montana, and Idaho (WWAMI), with flexible, topic-specific training at all experience levels, in both academic and industry-sponsored research. Methods/Study Population: The COBALT curriculum was developed in collaboration with subject matter experts, managers of CRCs, and other clinical research professionals from institutions across the WWAMI region. A thorough assessment of existing training programs within the region helped identify gaps and unmet needs. The curriculum was designed within a user-friendly learning management system that can be accessed across institutions. It has been piloted with diverse groups and committees of CRCs, professionals, and institutional leaders in clinical and translational science. Ongoing evaluation is being conducted qualitatively using built-in assessment surveys, demonstrations, and interviews. Results/Anticipated Results: The anticipated outcome is the creation of a highly standardized and efficient training program for clinical research professionals across multiple institutions in the WWAMI region. This program will be carefully tailored to each professional’s level of experience, ensuring personalized skill development. By aligning training with diverse expertise levels, the initiative aims to significantly elevate the quality, consistency, and overall performance of clinical research efforts across the regional research community. Discussion/Significance of Impact: The COBALT curriculum provides a vetted learning curriculum for CRCs, with a tailorable training plan that helps them collaborate with their supervisors to tailor training to their career goals. This ensures that the curriculum is not only comprehensive but also adaptable to individual needs.

Objectives/Goals: Research supports the use of music to improve the care and well-being of adults living with dementia; however, the practice and implementation of music in elder care communities is not regulated. The goal of this qualitative study was to survey elder care communities in Northeast Kansas to determine the use of music with people living with dementia. Methods/Study Population: We interviewed staff (n = 10) at five elder care communities in the Kansas City Metro area and observed musical activities and artifacts in shared living spaces within each community. Interview questions included details of the frequency and purpose of using music, who determined which music to use, and any effects, positive or negative, the interviewee believed to be associated with the use of music. Musical events, visiting musicians or music therapists leading group sing-alongs were observed at two communities, and music-related activities led by staff were observed at two others. Results/Anticipated Results: Music was used in some way at each of the five communities. Each location had recorded music available to residents in the shared living spaces, and most had a piano in the main lounge area. During the sing-along and music-related activities, residents were observed singing along to songs from memory, engaging with one another and the group leader and smiling. Staff employed by each community varied in their level of musical training and experience, from none to a full-time music therapist in residence. Staff interviewed said they believed music was helpful to aid memory recall, reduce anxiety, and to engage interest. Interestingly, a music therapist at one site also described how music during mealtimes created too much of a distraction for residents and interfered with dietary care. Discussion/Significance of Impact: It is clear from both the staff interviews and direct observations of musical activities that music is important to consider for people living with dementia in care communities. Guidelines for implementation and minimum standards would be helpful to ensure all care community residents can experience benefits highlighted by staff in this study.

Objectives/Goals: In Fall 2024, we designed a collaborative scholar retreat model to create dialogue among our training programs. The purpose of the retreat was to foster collaboration and provide unique networking opportunity for our KL2, T32, and TL1 scholars to share their research across the translational spectrum and learn more about Clinical and Translational Science Institute (CTSI) resources and tools. Methods/Study Population: The CTSI Fall Scholar Retreat brought together a diverse group of 25 scholars who attended in-person a full-day program. The program included presentations on CTSI resources and Team Science on How to Become a Better Team Member in cross-disciplinary and cross-functional groups. The KL2 Scholars presented motivational talks on their career and professional development journeys. Mentoring roundtable included discussions on subthemes like characteristics of a good mentor/mentee, organizing your mentoring team, different mentor roles, and fears of approaching new mentor/mentee. TL1 and T32 scholars also presented posters describing their ongoing research project from the planning stages to initial observations to completed studies. Results/Anticipated Results: To measure the effectiveness and impact of the CTSI Fall Scholar Retreat, we conducted an evaluation using REDCap survey and received an 88% response rate. On the Likert scale of 1–5 (1 = not at all valuable, 2 = not very valuable, 3 = neutral, 4 = very valuable, and 5 = extremely valuable), 92% of the scholars found the sessions to be valuable. Net Promoter Score of 9.6 (scale of 1–10) was measured to collect the scholar feedback and most of them are likely to recommend the Scholar Retreat to other scholars. Discussion/Significance of Impact: The in-person retreat proved to be a unique platform to interact, collaborate, learn, and grow for all scholars at different levels of their career and research. Inclusion of HRSA-funded T32 post-doctoral program provided cross-level collaboration and helped promote a culture of continuous learning in clinical and translational science.

Objectives/Goals: The objective of this project was to develop electronic health record-based algorithms, or computational phenotypes, for carbapenem-resistant Enterobacterales (CRE) and extended-spectrum beta-lactamase-producing Enterobacterales (ESBL) infections based on public health surveillance case definitions. Methods/Study Population: We analyzed electronic health records (EHR) from a retrospective cohort of patients within the University of Colorado Health System. We detected CRE and ESBL infections using a set of string-matching algorithms for bacterial organism, specimen source, and antibiotic susceptibility test results. Infections were limited to the first case of each organism per patient in a 30-day period. We conducted manual chart review on a subset of cases and non-cases for validation purposes. We then performed a descriptive analysis to examine demographic characteristics of patients with CRE and ESBL infections including sex, age, infection type, and organism. All analyses were conducted using R statistical software. Results/Anticipated Results: There were 634 CRE infections from 2013 to 2023, with the majority from urine (n = 448, 70.7%) or blood specimens (n = 56, 8.8%). Over half of CRE case-patients were female (n =  362, 57.1%). The mean age was 65.0 years. Most isolates were identified as Enterobacter cloacae complex species (n = 353, 55.6%). There was a total of 4,363 ESBL infections from 2019 to 2023, and most were urine (n = 3,550, 81.4%) or blood specimens (n = 457, 10.5%). Most ESBL case-patients were female (n = 3,030, 69.4%) with a mean age of 61.2 years. The majority of isolates were identified as Escherichia coli (n = 2,932, 67.2%), followed by Klebsiella pneumoniae (n = 368, 8.4%). Validation results are pending, including sensitivity and specificity of the algorithms. Discussion/Significance of Impact: Demographic characteristics of CRE and ESBL case-patients were similar to findings from traditional public health surveillance. The algorithms will supplement traditional surveillance methods, inform future epidemiological research using EHR data, as well as contribute to the development of standardized EHR-based definitions for CRE and ESBL.

Objectives/Goals: Our goal was to determine whether a consensus exists around 1) what the main barriers to innovation in Health artificial intelligence (AI) are 2) where there are gaps in education and training in Health AI and 3) where in their workflows organizations should implement AI to see the most immediate impact on productivity. Methods/Study Population: We employed a three-round Delphi method survey to stakeholders with health and/or engineering expertise. The first round was open-ended to generate responses to the three research questions. The second round asked participants to rank the responses and provide feedback as to their reasoning. The third round provided aggregated results and feedback and asked participants to re-rank the responses. Participants were attendees at a conference that brought people with health and/or engineering backgrounds together to discuss innovation in Health AI. 55 people in total participated across the three rounds. Results/Anticipated Results: Consensus emerged on all three questions: lack of trust was seen as the single greatest barrier to innovation, experience with implementation as the greatest gap in training, and automating health documentation as the point of most immediate impact. Consensus also emerged as to which of the 10–15 responses to each question were top priorities, which were somewhat significant, and which were not that important. Some of the rankings (such as implementation) seemed to reflect hot topics of discussion at the conference, but others (such as documentation) only emerged as significant in the surveys. Discussion/Significance of Impact: We successfully employed the Delphi method to discover what stakeholders think about three important questions in Health AI. Interestingly, although we polled experts from both health and engineering backgrounds, their answers converged on all three questions.

Objectives/Goals: This project provides a framework for collaborative design of a health technology intervention. We aim to provide guidance for those seeking to incorporate community-informed insights into their intervention development, particularly within the context of a large randomized controlled trial. Methods/Study Population: As part of the patient engagement arm of the Championing Hypertension Remote Monitoring for Equity and Dissemination study, we designed patient-facing intervention components for a home hypertension monitoring system. In the design process, we convened patient consultants via regular meetings, one-on-one interviews, and focus groups, and then subsequently analyzed meeting recordings, audio transcripts, and stakeholder notes. All design sessions were offered in either English or Spanish, and sixteen participants were involved. An inductive thematic analysis is being performed utilizing two coders, and Dedoose has been used to organize and refine the data. Results/Anticipated Results: The randomized trial will begin late 2024, with overall design findings incorporated into the intervention. A more detailed qualitative analysis will map these design findings to overarching theory from the Technology Acceptance Model. These qualitative themes will provide a model of patient involvement in study design in a large randomized controlled trial, potentially guiding future improvements in technology-based healthcare delivery interventions. Discussion/Significance of Impact: As technology becomes integral to healthcare, involving patients in intervention design and rollout can improve efficacy and utilization. By offering a framework for patient involvement in intervention design, we aim to create a replicable model to enhance the translation of research into practice.

Objectives/Goals: In recent years, there has been growing interest in the development of air pollution prediction models, particularly in low- and middle-income countries that are disproportionately impacted by the effects of air pollution. Recent methodological advancements, particularly in machine learning, provide novel opportunities for modeling efforts. Methods/Study Population: We estimate daily ground-level fine particulate matter (PM2.5) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2023 using a multistage approach. Spatial and temporal predictor variables include data from the moderate resolution imaging spectroradiometer (MODIS), Copernicus Atmosphere Monitoring Service (CAMS), and additional meteorological and land use variables. We employed machine-learning-based approaches (random forest and gradient boosting algorithms) to downscale satellite measurements and incorporate local sources, then utilized a generalized additive model (GAM) to geographically weight predictions from the initial models. Model performance was evaluated using 10-fold cross-validation. Results/Anticipated Results: On average, the random forest, gradient boosting, and GAM models explained 75, 82, and 83% of variations measured in PM2.5 concentrations. PM2.5 levels were generally higher in densely populated urban centers and lower in suburban and rural areas. Important predictors of ground-level PM2.5 included wind (both u and v components), 2-meter mean air temperature, elevation, and the normalized difference vegetation index (NDVI). Discussion/Significance of Impact: Using novel machine learning-based approaches, we developed robust models with fine-scale spatial (1-km2) and temporal (daily) variations of PM2.5 in Mexico City from 2005 to 2023. The predicted PM2.5 concentrations can further advance public health research on air pollution in Mexico City and beyond.