Objectives/Goals: Prompted by low survey response rates, the University of Cincinnati CTSA hub undertook a comprehensive redesign of our annual membership survey and methods. The objective is to describe our stepwise survey redesign process to increase participation and improve evaluation data quality. Methods/Study Population: As a first step, we updated the survey distribution list by removing all undeliverable and unsubscribed emails. Next, to provide a framework for revising survey items, we explicitly defined our evaluation questions: Who are our members; how often do they interact with the CCTST; how do they want to interact with the CCTST; what current resources or services are being used by our members; how do we reduce barriers to understanding what the CCTST offers & does? We prioritized survey items addressing ≥2 evaluation questions. Items were then categorized by membership role, constructs (e.g., awareness, improvement), and alignment with evaluation questions. Once the survey was finalized in the final form, the evaluation team presented the revised survey to the UC CTSA hub executive committee members for feedback. Results/Anticipated Results: Over 1382 (24.7%) email addresses were removed from the distribution list (n = 1185 were invalid; n = 158 had not accessed their CCTST account in >10 years; n = 8 unsubscribed from the distribution email; n = 31 no email listed for member). Using the evaluation questions, we reduced the survey from 72 to 43 questions. In addition, time to complete the survey ranged between 01:30 and 36:00 with an average of 05:39. After improvements were made to the survey and methods, we sent the survey to 4,426 valid email addresses. Of those that received the survey, 132 completed the survey (response rate = 3%). Discussion/Significance of Impact: Improvements aim to boost response rates, provide actionable insights, and guide CTSA hub governance and program development. Next, we will conduct qualitative focus groups with members in the spring to build on our quantitative results and provide in-depth insights regarding membership engagement.

Objectives/Goals: The Clinical and Translational Research (CTR) Pathway aims to increase the number of health science professionals across the WWAMI Region (Washington, Wyoming, Alaska, Montana, and Idaho) with significant awareness, interest, and experience in research. Methods/Study Population: Two cohorts of health sciences students have completed the formal coursework of the CTR Pathway, including sixty-three medical students from across the WWAMI region. Cohorts were organized into an Alaska in-person group, a Seattle in-person group, and a regional virtual group. Coursework includes a year-long series of three elective classes addressing key concepts of designing, conducting, and presenting results of a research project. Students engage in a dedicated full time 8-week summer research experience under the guidance of a research mentor. The culminating curriculum focuses on strong scientific communication by preparing students for poster and oral presentations at regional and national conferences. Results/Anticipated Results: The ultimate goal of the CTR pathway is to increase the number of health providers across the WWAMI region participating in CTR during their educational experience and subsequent professional careers. Many health sciences students hesitate to engage in research due to a perceived lack of necessary skills. The CTR pathway addresses this gap by equipping students with the research competencies needed to participate confidently in scientific inquiry. Feedback from the first two cohorts has been overwhelmingly positive, with many students highlighting how the CTR pathway enhanced their confidence and knowledge, empowering them to execute their proposed research projects and presentations successfully. The third cohort of students joined the CTR pathway in January 2026. Discussion/Significance of Impact: The CTR pathway positions early health sciences students to engage in research more deeply during their education and prepares them to seek additional training opportunities toward a career in research. Additionally, students located at regional sites and have a strong interest in practicing in rural areas.

Objectives/Goals: The Wake Forest Clinical and Translational Science Institute (WF CTSI) provides many services, but little is known about how utilization of services affects research outcomes. Our goal is to understand long-term outcomes of recurrent service use from existing resources with an eye towards creating new processes to track research outcomes. Methods/Study Population: The Dissemination, Implementation, and Continuous Quality Improvement team partnered with the Biostatistics, Epidemiology, and Research Design (BERD) program for this project. Initially, we aimed to track abstract and paper publications, and grant submissions (unfunded and funded) for all service requests coming through the BERD from 2017-2024. This was quickly deemed infeasible using prior CTSI data collection processes; however, detailed follow-up data did exist on funded CTSI pilots in the same time window. Consequently, we aimed to test associations between project attributes provided on the service request form and downstream research outcomes, including peer-reviewed publications and grant funding. We used chi-squared tests in the Python package “tableone” to test these associations. Results/Anticipated Results: Our sample only included WF CTSI pilot-funded projects; though, unexpectedly we found information on pilot projects funded by different sources including center/institutional pilots, federal funding, foundation grants, and internal/departmental funding. We found it most effective to link service requests with IRB numbers, because different principle investigators can submit requests on behalf of the same project. N=206 total projects were included in the analysis. Regarding peer-reviewed publications, associations were found with type of service, principal investigator rank, and IRB approval status. Regarding grant funding, associations were found with service type, principal investigator career stage, and previous principal investigator grant funding. Discussion/Significance of Impact: As part of a large academic learning health system, the WF CTSI provides support to various investigators and tracking long-term research outcomes is a priority. This investigation identified important predictors of successful publications and grants in pilot projects, though it also identified gaps in processes to track research outcomes.

Objectives/Goals: To address the disparity in type 2 diabetes within the Latino community, we utilized digital story telling narrative-based videos created by and for individuals with lived experiences. Methods/Study Population: We utilized digital story telling narrative-based videos created by and for individuals with lived experiences. The targeted population is the Latino community. Results/Anticipated Results: Our qualitative results were discordant with the quantitative findings. Rather than four distinct patterns based on self-efficacy and HbA1c levels, we found five common themes across the groups: (1) trauma shapes readiness for change; (2) caregiving responsibilities can hinder self-management; (3) persistence is key to long-term T2D control; (4) social support is essential; and (5) self-advocacy and a desire for knowledge drive success. These results highlight the multifaceted challenges Latino/Hispanic individuals face in managing type 2 diabetes, where overlapping social and cultural factors influence both self-efficacy and health outcomes. Discussion/Significance of Impact: Interventions for Latino/Hispanic communities should be well informed and family/community-centered, and that will strengthen the persistence, support network, and patient empowerment to translate confidence into sustained glycemic control.

Objectives/Goals: Pulmonary infections by Staphylococcus aureus (S. aureus) are difficult to eradicate in people with cystic fibrosis (CF, PwCF) and remain a significant disease burden. Our objective is to determine if a MEK1/2 inhibitor, ATR-002, can reduce S. aureus biofilm growth and synergize with antibiotics against multidrug-resistant strains of S. aureus. Methods/Study Population: To quantify the ability of ATR-002 to synergize with antibiotics, synergy checkerboard assays examined the effects of ATR-002 in combination with n=11 different antibiotics against the methicillin-resistant S. aureus (MRSA) strain USA300. We next quantified bacterial growth of n=6 antibiotic-resistant CF clinical isolates treated with a combinatory dose of 5 uM ATR-002 and antibiotic to confirm results from the checkerboard assay. Finally, S. aureus biofilms were established using USA300 or n=7 different CF clinical isolates in 96-well plates for 24 hours and then were treated with media, vehicle controls, or ATR-002 (5, 25, or 50 uM) for an additional 24 hours; bacterial biomass was quantified by crystal violet staining to assess the ability of ATR-002 to decrease biofilm growth. Results/Anticipated Results: Synergy checkerboard assays revealed that ATR-002 can synergize with the antibiotics gentamicin and amikacin, but had additive/indifferent effects with clindamycin, erythromycin, nafcillin, vancomycin, doxycycline, daptomycin, sulfamethoxazole-trimethoprim, linezolid, and puromycin. Additionally, two gentamicin/amikacin-resistant CF isolates were resensitized to gentamicin and amikacin with the addition of 5 uM ATR-002. However, CF isolates resistant to erythromycin, clindamycin, and nafcillin could not be resensitized to antibiotics with the addition of 5 uM ATR-002. Biofilms produced by MRSA, and n=3 out of 7 CF clinical isolates tested were significantly reduced by treatment of 50 uM ATR-002. Discussion/Significance of Impact: Our results demonstrate that ATR-002 can synergize with some antibiotics against MRSA, which is also observed in antibiotic-resistant CF S. aureus clinical isolates, and that ATR-002 can reduce established S. aureus biofilms. Future studies will explore the antibacterial effects of ATR-002 in additional clinically relevant models of infection.

Objectives/Goals: 1. Explore how to increase shared leadership of CE Studio by leveraging the expertise of community facilitators. 2. Understand processes implemented to enhance power sharing between community and academic partners participating in CE Studios. Methods/Study Population: In partnership with a community co-investigator, community members (N= 35) were recruited to serve as facilitators for the Northeast Ohio CTSC Community Engagement (CE) Studios starting in October 2024. A four-phase innovation method was conducted to onboard community facilitators and solicit feedback to increase power sharing during our CE Studios – Phase 1: Developing Facilitator Pipeline, Phase 2: Engaged Training and Development, and Phase 3: Enhancing Expert Experience. A selection of facilitators participated in virtual or in-person CE Studio (N= 4) to pilot Phase 4: Team Integration. Together, these innovations enhanced community expert recruitment and shared leadership of the CE Studio while strengthening the value of the CE Studio for investigators seeking community input. Results/Anticipated Results: We anticipate enhanced CE Studio outcomes by implementing community facilitator guidance and innovation throughout the CE Studio framework. Discussion/Significance of Impact: Empowering facilitators to provide their expertise throughout the full scope of the CE Studio framework through coaching, expert recruitment, training, investigator preparation, and reporting enhances the CE Studio experience. This power sharing increases trust and quality of the CE Studio experience and enhances academic partnerships.

Objectives/Goals: The Michigan Institute for Clinical & Health Research (MICHR) needs to maintain a balanced portfolio of initiatives with respect to the extent of innovation sought and the stage of implementation. We created a Translational Science (TS) Dashboard to visualize this balance of intent and execution. Methods/Study Population: In designing the dashboard, we considered innovation along a spectrum: making incremental changes to our current offerings; seeking out new offerings or audiences; and seeking both new offerings and new audiences. We also used MICHR’s TS Framework that guides both the identification of roadblocks along the translational spectrum and the development of potential solutions to those roadblocks. Categories in the TS Framework help us understand if innovation efforts are in the early stages of problem framing or later stages of testing, iterating, and/or disseminating. Using co-creation methods, we partnered with various MICHR program teams, whose initiatives represent a mix of innovation categories as well as framework implementation phases, to optimize the design and understand the utility of the dashboard. Results/Anticipated Results: We worked with various programmatic teams at MICHR to help them understand the elements of the dashboard and to co-create the dashboard interface. Our methods also helped teams understand the “right” amount of innovation for their programs, ensuring they considered both differentiation and resource constraints as well as alignment with MICHR’s overarching strategic goals. Programs had different resource considerations, tolerance for risk, and unmet customer needs that informed how to right-size their innovation portfolio and where efforts should map along the stages of roadblock identification and solution crafting. The perspectives and insights of MICHR’s executive leadership were key to informing how the dashboard could support data-informed decisions at the institute level. Discussion/Significance of Impact: The TS Dashboard can help both MICHR program and institute leaders understand how projects map along different phases of discovery, implementation, and innovation, ensuring that the portfolio of activities is appropriately balanced.

Objectives/Goals: To describe the pathway of support trial-care has put into place for washu and partner ctsa researchers navigating the investigator-initiated clinical trial life cycle. Methods/Study Population: Trial-care is a centralized research support service at washu supported by the center for clinical studies (ccs) and the institute of clinical and translational sciences (icts). our team consists of experienced investigators and research professionals with expertise in clinical trial management across all phases of the trial life cycle. for washu and partner ctsa researchers planning investigator-initiated clinical trials, we provide a free 60-minute consultation to analyze barriers and streamline processes. after the initial consultation, researchers may receive trial-care services (e.g., project management, quality and regulatory management, and/or data management). these services can be billed at an hourly rate or through direct sourcing of trial-care staff to the funded grant. Results/Anticipated Results: Trial-CARE’s support for investigator-initiated clinical trials accelerates all phases of the trial life cycle, such as pre-award planning and post-award start-up, implementation, and closeout. Since 2019, Trial-CARE has completed over 142 consultations across 20 Departments at WashU, SLU, and Mizzou. At the time of consult, 54.4% were in the pre-award planning phase and 45.6% were in the post-award start-up, implementation, or closeout phase. About 20% of consultations lead to the utility of Trial-CARE Services, and the top reasons researchers connect for consultation to receive Trial-CARE Services: (1) regulatory guidance (FDA Part 11, IND, IDE, IRB) = 55% (2) safety monitoring or site/data monitoring = 33% (3) data management = 23% (4) study budgeting = 19% (5) protocol development = 17%. Discussion/Significance of Impact: Trial-care uses a clinical trial dashboard to identify and track pre-award investigator-initiated clinical trials and is developing outreach and training programs to pro-actively engage these studies. the goal is to maximize clinical trial impact through quality (rigor and best practices), innovative capacity, and reproducibility.

Objectives/Goals: Apply the NCATS translational science principles as a framework to describe CTSA UM1 hub activities in terms of effective translational science approaches. Identify and characterize the distribution of principles across UM1-funded initiatives to support strategic management and reporting. Methods/Study Population: Hub-affiliated faculty and staff convened at an annual, in-person retreat for the Northwestern University Clinical and Translational Sciences (NUCATS) Institute. Module teams, including at least one co-lead from Workforce Development for Clinical Research Staff Professionals (C1), Community and Partner Engaged Research (C2), Resources and Services (D1), and Health Informatics (D3), completed a concept mapping exercise to identify which, if any, of the 7 translational science principles were exemplified by their initiatives. Teams worked collaboratively to map principles to each initiative. Data were aggregated at the module and hub levels. Results/Anticipated Results: Each module reported 5–10 active, UM1-funded initiatives. Every initiative was mapped to>2 translational science principles, with 2 initiatives mapped to all 7 principles. On average, 4.6 principles were identified per initiative. “Focus on Unmet Needs” was the most highly reported across all initiatives. “Boundary-Crossing Partnerships,” mapped to all Community and Partner Engaged Research (C2) projects, and “Creativity and Innovation” mapped to all resources and services (D1). 90% of Informatics (D3) initiatives were mapped to “Generalizable Solutions.” Across the hub, “Bold and Rigorous Approaches” was the least likely to be identified. Discussion/Significance of Impact: The translational science principles can be used to frame module-specific and hub-wide strengths and gaps. Concept mapping can be incorporated into project planning and reporting to increase shared understanding and alignment with the principles that support the translational science enterprise.

Objectives/Goals: Quantify environmental persistence and airborne stability of avian (aMPV) and human metapneumovirus (hMPV) and evaluate far-UVC light (at 222 nm) as a safe, non-chemical disinfection method to reduce viral transmission in agricultural and healthcare environments. Methods/Study Population: Subtypes A and B of aMPV and subtype B of hMPV isolates will be propagated in Vero and LLC-MK2 cells, respectively. Viral persistence will be quantified on fomites under varying temperature and humidity. Aerosol stability will be assessed using an environmental chamber and an atomizer system. Once baseline survival is established, samples and aerosols will be exposed to different far-UVC doses, intensities, and durations. The endpoints will be determined by observing cytopathic effects (CPE), virus titers will be calculated by the Karber method, and titers will be expressed as TCID50. Results/Anticipated Results: We anticipate quantifying surface and aerosol stability profiles of aMPV and hMPV across several porous and non-porous materials (fomites) and environmental conditions relevant to poultry and clinical settings. Far-UVC exposure is predicted to achieve rapid viral inactivation on non-porous surfaces and in aerosols without measurable cytotoxicity or material degradation. The findings will establish disinfection thresholds and demonstrate the practicality of far-UVC integration into real-world agricultural and healthcare biosecurity systems. Discussion/Significance of Impact: This work advances One Health biosecurity by establishing safe far-UVC disinfection protocols to control the spread of both aMPV and hMPV. Findings will inform infection control policies and reduce economic and health burdens in poultry operations and vulnerable human populations.

Objectives/Goals: To evaluate midstream outcomes of a pilot trial testing co-created implementation strategies that support rural Arkansas providers in using the announcement approach to deliver stronger, evidence-based HPV vaccine recommendations. Methods/Study Population: We conducted a matched-pair pilot trial in four primary care clinics serving rural populations in Arkansas. We co-created a bundle of implementation strategies with local experts using an evidence-based quality improvement process to support announcement approach HPV vaccine recommendations. We used a mixed methods approach to collect data on feasibility and acceptability of the bundled strategies and innovation, self-reported fidelity, and uptake of the HPV vaccine during the study period. This pilot will be used to support a subsequent full-scale hybrid-type effectiveness-implementation trial of the bundled strategies and evidence-based recommendations. Results/Anticipated Results: We anticipate that intervention clinics will demonstrate improvement in the primary study outcomes related to implementation of the locally-tailored strategies and innovation, e.g., feasibility and acceptability. We also anticipate a higher proportion of providers in intervention clinics will use the announcement approach for recommendations of the HPV vaccine to eligible patients. We also anticipate increases in HPV vaccine initiation among eligible patients especially in intervention clinics. This research was supported by the UAMS Translational Research Institute and the National Center for Advancing Translational Sciences (UM1TR004909/1K12TR004924). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Discussion/Significance of Impact: Our findings highlight how co-created implementation strategies can strengthen provider communication and boost HPV vaccine uptake in rural clinics, advancing equitable cancer prevention and informing scalable, evidence-based practices in underserved regions.

Objectives/Goals: Biological heterogeneity within psychosis spectrum disorders has proven to be a major obstacle in identifying reproducible pathophysiologic markers of disease. Here, we used normative modeling to assess cortical thickness deviations at the individual level. We then grouped patients into subgroups based on number of deviations. Methods/Study Population: T1 MPRAGE scans from 113 antipsychotic-naive first-episode psychosis patients were normatively modeled as well as 397 unrelated individuals from HCP Young Adult. The patients were subdivided by whether they had a greater than expected number of large deviations in cortical thickness across all regions by comparing norms established in the HCP dataset. Results/Anticipated Results: We identified four distinct subgroups of patients who more commonly share spatial deviation patterns compared to the overall group. The positive deviation subgroup, negative deviations subgroup, and both positive and negative deviations subgroups were larger in the first-episode psychosis sample than would be predicted from the healthy control data. Discussion/Significance of Impact: Our data provide further evidence for the existence of subtypes of psychosis spectrum disorder that differ in patterns of cortical thickness, specifically a subtype with increased cortical thickness. Normative modeling approaches might constitute an important step toward precision psychiatry.

Objectives/Goals: Diabetes mellitus (DM) increases cardiovascular (CV) mortality risk in chronic kidney disease (CKD). It is unknown if patients with CKD and DM have a greater risk of impaired CV functional capacity compared to those without DM. We investigated CV functional capacity alterations in CKD patients with and without DM using cardiopulmonary exercise testing (CPET). Methods/Study Population: We performed a cross-sectional study of data from the Cardiorespiratory Fitness in Individuals with CKD in Indiana (FIT-INDY) cohort. FIT-INDY is an ambulatory referral cohort consisting of patients within the Indiana University Health system. All individuals included in this analysis had been diagnosed with CKD and underwent CPET as part of their medical standard of care. CPET was used to assess peak oxygen uptake (VO₂Peak), the gold standard measure of CV functional capacity. Participants were stratified into two groups: CKD+DM (n=53) or CKD (n=63). Group comparisons were performed using a t-test, Mann–Whitney test, or Fisher’s exact test. Adjusted analyses were completed using multiple linear regression. Results/Anticipated Results: The CKD+DM group had a higher prevalence of males, higher body mass index, heart failure, use of beta-blockers, SGLT2 inhibitors, and GLP1-agonsits (all p’s < 0.05). Age, race, and estimated glomerular filtration rate were similar between groups (all p’s ≥ 0.05). CPET demonstrated greater CV functional impairment in the CKD+DM group for VO2Peak [CKD+DM = 11.5 ± 4.1 mL/kg/min, CKD = 14.4 ± 5.2 mL/kg/min], VO2AT [CKD+DM = 7.7 (6.2 - 9.4) mL/kg/min, CKD = 8.8 (7.1 -10.9) mL/kg/min], and peak HR [CKD+DM = 107 (91 - 123.2) bpm, CKD = 116 (99.2 - 131.2) bpm; all p’s < 0.05]. After adjusting for sex, left ventricular ejection fraction, and medications, significance remained for VO2Peak and VO2AT, but not peak HR. Echocardiography findings were similar between groups before and after adjusting (all p’s ≥ 0.05). Discussion/Significance of Impact: CPET revealed worse CV impairment in patients with coexisting CKD and diabetes that were not evident on resting echocardiographic assessment. These findings suggest that CPET could be utilized as a diagnostic tool to risk-stratify this patient population.

Objectives/Goals: To evaluate the extent to which characterization of unstructured patient-generated health data (UPGD) (e.g., health journals) with human-guided machine learning (HGML) improves model explainability and integrates patient voices among renal cancer patients in a randomized control trial (RCT). Methods/Study Population: Data were collected in an RCT (NCT00505310) assessing the benefits of an emotional expression intervention on renal cancer patients’ quality-of-life outcomes. Over a 10-day period, patients completed four journal-type written responses to structured prompts about their health lifestyle behaviors or deepest thoughts and feelings linked to cancer. I will 1) evaluate the extent to which a standard ML model versus an HGML model aligns with principles of trustworthy health AI when characterizing UPGD, 2) evaluate the extent to which UPGD captures condition-specific data complementary to PROMs, and 3) extract journey maps from narratives about patient experiences navigating cancer care. Results/Anticipated Results: Results will illustrate a patient-centered value research approach that centers patient perspectives, contextualizes heterogeneous factors influencing patient outcomes, and mitigates biases in the health research machine learning pipeline. Discussion/Significance of Impact: This research contributes to the development of evidence-based and empirically validated strategies for health researchers to incorporate explainable HGML tools that facilitate the inclusion of patients’ experiences, perspectives, needs, and priorities throughout the research process.

Objectives/Goals: To describe one method of strengthening community–academic research partnerships, the Community Scientist Program, and to provide an example of a successful collaboration this program enabled, the Kiosk Project. Methods/Study Population: In collaboration with its community and academic partners, NJ ACTS developed the Community Scientist program, a training and research partnership program that includes human research protections training and team building between community and academic partners for community-engaged research. This program has enabled rapid formation of subsequent research teams – and rapid project implementation. One example is the Kiosk Project. A community–academic team mobilized to take advantage of an internal pilot funding opportunity, designing and executing a project to address disparities in knowledge and participation in clinical trials. Results/Anticipated Results: Five community partners, including four Community Scientists, teamed up with academic researchers to user-test a short video to be placed in local medical settings. This video was designed to educate community members about clinical trials and encourage participation. Together, the team created recruitment and focus group materials. Within 2 months, community partners recruited 40 participants for two rounds for a total of four focus groups. The team reviewed the first focus groups’ comments to determine editing needs. Focus groups then viewed the revised video, concurring that the video successfully incorporated their feedback. Participants provided testimonials about their focus group experience, highlighting the value of their contributions for projects impacting their communities. Discussion/Significance of Impact: Methods for strengthening community–academic research partnerships include programs for developing community research champions and advancing shared research goals. Enriched research partnerships enable rapid recruitment and high levels of community engagement in advancing community health priorities.

Objectives/Goals: This is a scoping review of the use of generative AI (GenAI) for qualitative analysis in the health sciences. The primary objectives are to summarize the methods used for qualitative analysis based on the approach (e.g., inductive vs. deductive), metrics for examining GenAI’s performance by approach type, and best practices for improving performance. Methods/Study Population: We searched six databases (PubMed, EMBASE, CINAHL, Scopus, Web of Science, and PsycINFO) using a comprehensive search string tailored to each database. We identified 5,853 unique results, of which 223 were identified as potentially relevant after review of abstracts. We will conduct a full manuscript review for each potentially relevant result to confirm inclusion, including use of GenAI for qualitative analysis of physical or mental health text data where a full-length paper is available. Two study team members will review each manuscript to confirm inclusion prior to data extraction and coding. We will also enter each manuscript into Yale Clarity, a secure GenAI platform powered by multiple large language models (e.g., ChatGPT, Claude), to examine GenAI’s capacity to facilitate manuscript screening and coding. Results/Anticipated Results: We will code included articles for: the research topic area; type of qualitative data analyzed (interview, focus group, medical chart, text-based digital or social media content); type of study (original or secondary data, review); type of qualitative analysis (thematic analysis, content analysis, grounded theory); type of approach (inductive, deductive); steps in analytic process that generative AI was used (initial open coding and sense making, final coding based on established codebook, identification of overarching themes and narratives); GenAI platform(s) and large language model(s) used; types of GenAI prompts used to facilitate analyses; processes and measures/metrics to evaluate the accuracy and quality of GenAI’s results; outcomes and methods used to increase GenAI’s accuracy and/or quality. Discussion/Significance of Impact: Qualitative research is essential to understand patient perspectives and increase treatment effectiveness and accessibility for all. To actualize GenAI’s potential to facilitate rapid and large-scale qualitative analysis, we first need to understand its strengths, weaknesses, and best practices for maximizing GenAI’s accuracy and quality.

Objectives/Goals: To contextualize existing knowledge in translational science, women’s health research can benefit from artificial intelligence (AI) methods for obtaining insights out of massive scientific literature. This study explored the feasibility of making an AI model answer diverse questions based on a set of our institution-specific publications. Methods/Study Population: We developed an automated pipeline to retrieve PubMed publications on women’s health that acknowledged the Mayo Clinic Clinical and Translational Science Award of the past two cycles. The publications need to be indexed with MeSH terms (as a major topic) including “Women’s Health”, “Obstetrics”, “Gynecology”, “Female Urogenital Diseases and Pregnancy Complications”, and “Perinatal Care”, etc. In addition, we enforced the species to be “Human” and the demographics to include “Female.” The metadata of every article including title, abstract, publication year, journal, and author affiliation were saved into a single JSON file. The JSON file was then used as the input context along with a few test prompts submitted to the gemini-2.5-flash model. All the experiments were conducted in a Google Colab environment. Results/Anticipated Results: A total of 237 articles on women’s health were retrieved for the experiment. The AI model correctly summarized the top 5 journals but did not obtain the correct article counts until explicitly requested to heed accuracy. When asked to list three prominent departments and their focus areas, the model gave sensible answers aligned with our knowledge. When asked to identify 10 articles that best represent the T4 stage, the model returned appropriate answers that demonstrated an understanding of the implied emphasis on population health and widespread implementation. Lastly, the model provided well-organized advice with annotated experts and strategies, when asked to recommend collaborators for doing AI research in women’s health. Discussion/Significance of Impact: Based on a literature dataset, the general-purpose AI model delivered impressive results when asked to fulfill tasks that involved information extraction, summarization, reference to external knowledge, and strategy consultation. The ongoing work aims to enhance scalability and accessibility of the tool while adding more rigorous evaluations.

Objectives/Goals: Community-engaged research (CER) helps ensure that research findings reach communities. To support it, we administer a pilot program to foster partnerships, build research capacity, and generate data for larger initiatives. We sought to understand the determinants that influence community research pilot project implementation. Methods/Study Population: The community research award provides $30,000 and 24 months of support to a community–academic team. Awardees receive CTSA support through a capacity-building orientation, online written and video materials, and optional office hours. We conducted semi-structured interviews with the community and academic leads using an Integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS)-based guide to examine how the research project, recipients, context, and facilitation shaped implementation. Transcripts were analyzed using rapid qualitative analysis methods. Two coders independently reviewed each transcript and completed a summary template which were then analyzed by partner type and i-PARIHS construct. Results/Anticipated Results: A total of six community leads and seven academic leads who received funds between 2020 and 2024 were interviewed. Three community partners and three academic partners completed projects. Partner commitment and expertise were emphasized as project facilitators, while role ambiguity and turnover were barriers. Innovation was valued for adaptation to community context and increased capacity; however, some leads noted tensions between research and advocacy goals. Context barriers included limited funding, COVID-19, and shifting CBO, local, and national policies. CTSA-provided Facilitation was mentioned positively by nearly all community leads. Academic leads interacted with the CTSA team primarily when conflicts arose. Discussion/Significance of Impact: The i-PARIHS framework highlighted how strong partnerships, expertise, and adaptation to community context enabled projects, while role ambiguity, turnover, and contextual challenges impeded progress. Strengthening facilitation, clarifying roles and responsibilities, and planning for turnover may enhance future projects.

Objectives/Goals: This project aims to improve gait dysfunction treatment in Parkinson’s disease (PD) by developing adaptive deep brain stimulation (aDBS) that automatically modulates stimulation in response to specific movement states. We use chronic at-home cortical-pallidal recordings to classify walking vs. non-walking and turning vs. straight walking. Methods/Study Population: Local field potentials from globus pallidus (GP) and electrocorticography from premotor (PM) and primary motor (M1) cortices were recorded in four people with PD (2M/2F) implanted unilaterally (n=2) or bilaterally (n=2) with bidirectional neurostimulators (Summit RC+S, Medtronic, Inc). Concurrent at-home movement was captured with wearable ankle sensors (Rover, Sensoplex Inc). Neural-kinematic signals were segmented into 10-second walking/non-walking and 1-second turning/straight walking epochs. Logistic regression and linear discriminant analysis models classified movement states using power within various frequency bands. Random forests identified walking biomarkers compatible with the Summit RC+S device; real-time decoding performance was evaluated using in silico simulations. Results/Anticipated Results: Over 80 hours of at-home neural-kinematic data were analyzed across 6 hemispheres. M1 alpha (8-13Hz) and beta (13-30Hz) power was lower during walking compared to non-walking in all hemispheres. M1 and PM theta (4-8Hz), alpha, and beta power were lower during turning compared to straight walking in all but one hemisphere. Features from GP were most important for walking/non-walking classification, while cortical features predominated for turning/straight walking. Despite these shared features, the most important frequency ranges for classification varied widely across individuals. Within-subject walking/non-walking classifiers achieved strong performance (AUC:0.77-0.96), with simulations of real-time on-board decoding also demonstrating above-chance decoding in all cases (AUC:0.63-0.85). Discussion/Significance of Impact: These results support the hypothesis that cortical-basal ganglia oscillations are modulated by specific movement states. Furthermore, this work demonstrates one pipeline that can identify patient-specific movement biomarkers from long-term naturalistic neural-kinematic recordings, advancing the viability of aDBS for gait dysfunction in PD.