Objectives/Goals: To generate actionable insights into the commercialization process in academic translational science by using comparative case studies to identify strategies, success factors, and challenges across CTSA hubs, informing best practices for innovators and institutions. Methods/Study Population: We will employ a comparative case study approach, engaging 11 CTSA hubs to document two or more product-specific commercialization case studies, including at least one successful and one unsuccessful example. To ensure the comprehensiveness and consistency of data collection and analysis across cases, a standardized framework developed by the TCSC WG is followed by all participating hubs for both quantitative data (e.g., milestones, funding) and qualitative insights (stakeholder interviews). Coding and downstream cross-case analysis is leveraged to identify unifying themes and discriminating factors in commercialization strategies and associations with success and failure. Results/Anticipated Results: We aim to identify patterns, barriers, and catalysts in academic commercialization, defining key resources along with effective practices and strategies. Findings will clarify the role of CTSA hubs and their collaboration with institutional partners such as Technology Transfer. Results will highlight best practices in IP management, funding, team dynamics, partnerships, and regulatory navigation. A comparative analysis across CTSA hubs will yield a case study repository, cross-case report, educational materials, and a peer-reviewed publication. Collectively, we expect findings to provide practical guidance to NCATS and academic institutions for accelerating biomedical innovation. Discussion/Significance of Impact: This study fills a critical gap by providing real-world, comparative insights into the commercialization process within academic medicine. Outcomes will support CTSA hubs, healthcare systems, innovators, and policymakers in improving translational efficiency and maximizing innovation impact.

Objectives/Goals: Telehealth introduces opportunities to improve access to Alzheimer’s disease and related dementias (ADRD) diagnostic services. We aim to investigate the effect of the 2020 Centers for Medicare and Medicaid Services (CMS) telehealth expansion (where virtual cognitive assessments were covered) on county-level prevalence of ADRD diagnoses. Methods/Study Population: A county-level difference-in-difference (DID) design was employed to investigate the change in ADRD prevalence among US 65+ fee-for-service beneficiaries from pre-expansion (2016-2019) to post-expansion (2021-2022) by comparing counties with and without broadband. 2016-2022 county-level ADRD prevalence estimates were defined by Chronic Conditions Data Warehouse methodology from the CMS Mapping Medicare Disparities Tool. Broadband presence was defined as counties with ≥1 high-speed internet provider and average download/upload speeds of 25/3 megabits-per-second. The parallel trends assumption was assessed using graphical and model-based approaches. Negative binomial regression models estimated the average effect of the CMS expansion on ADRD prevalence among counties with broadband. Results/Anticipated Results: Average county-level ADRD prevalence was 11.06% across the pre-expansion period and 7.15% post-expansion. Broadband was present in 70.35% of counties pre-expansion and 91.96% of counties post-expansion. Differences in ADRD prevalence by broadband were constant across the pre-expansion period. ADRD prevalence was 2.48% higher in counties with broadband versus counties without broadband post-expansion compared to pre-expansion (95% confidence interval (CI): 1.01, 1.04). Discussion/Significance of Impact: Improvements in ADRD detection may have occurred in counties with broadband following the CMS telehealth expansion. However, the effect is dwarfed by the substantial reduction in ADRD prevalence from pre- to post-expansion. This reduction is likely a consequence of the COVID-19 pandemic, which may have decreased overall diagnostic opportunities.

Objectives/Goals: Institutional clinical trial accrual metrics and associated monitoring practices are insufficiently characterized and lack consistency. We aim to assess current metrics and methodologies to inform the development of a tool and recommendations that institutions can use to strengthen monitoring capabilities and enhance trial performance. Methods/Study Population: We designed a survey to assess how CTSA institutions measure and monitor clinical trial accrual, including the types of data elements captured, collection methods, and institutional responses to low accrual. Existing survey instruments were reviewed to inform the development of an initial survey. Subject matter experts were consulted on survey questions, sampling strategies, and administration methods. The finalized survey will be distributed to representatives from CTSA institutions. Findings will inform the development of a self-evaluation tool and recommendations to identify gaps and support institutions in strengthening accrual monitoring practices. Results/Anticipated Results: We will disseminate the final survey to CTSA institutions in 2026. We anticipate variability in accrual metrics and monitoring practices within and across institutions nationally. Discussion/Significance of Impact: Further work is needed to establish consistent definitions, collection methods, and reporting frameworks to improve accrual evaluation and address trial pipeline challenges. Without standardization, harmonizing trial data across sites is difficult, limiting opportunities for shared learning.

Objectives/Goals: The objective of this study was to identify subgroups of young adults with type 1 diabetes (T1D) defined by HbA1c measured 12 months after participating in a randomized controlled trial for a behavioral intervention. Methods/Study Population: This study is a secondary, exploratory analysis of 76 young adults with T1D with complete data who enrolled in the DiaBetter Together RCT. A regression tree model is used to identify subgroups of participants according to HbA1c measured 12-month post-enrollment. Predictors include baseline participant characteristics, questionnaire scores, and clinical characteristics. Model fit is informed by restricted sum of squares, pruned using cost complexity assessment, and number of observations per node. Results/Anticipated Results: The final regression tree predicting 12m HbA1c included two branches and five leaves defined by baseline HbA1c, Diabetes Distress: Friend/Family (DDFF), and Diabetes Strengths: Getting help with diabetes management (DSGMH). Participants with baseline HbA1c <6.87% had the lowest mean 12m HbA1c (M=6.5, SD=0.7). Two unique subgroups were identified among patients with baseline HbA1c between 6.87 and 9.91 defined by DSGMH. The mean 12m HbA1c was 8.13 (SD=1.1) and 10.0 (SD=2.6) for DSGMH ≥6.06 and <6.06, respectively. Among patients with baseline HbA1c>9.91, two unique subgroups were defined by DDFF. The mean 12m HbA1c was 9.99 (SD=1.5) if DDFF <3.55. The highest 12m HbA1c was observed among participants with baseline HbA1c ≥9.91% and DDFF≥3.55 (M=13.0, SD=0.7). Discussion/Significance of Impact: Baseline HbA1c, DDFF, and DSGMH were important predictors of subgroups of patients defined by 12m HbA1c. Psychosocial variables were important predictors beyond demographic and clinical variables. Clinicians and researchers may wish to target interpersonal aspects of diabetes distress and strengths in work with young adults with T1D.

Objectives/Goals: Limited systematic analysis of real-world evidence (RWE) on pharmacogenomic (PGx) testing utility, which can help provider decision-making and avoid serious adverse effects, exists. This study explores clinical-behavioral outcomes and implementation considerations, for PGx testing in real-world clinical settings for PGx biomarker CYP2D6. Methods/Study Population: Our systematic review investigated drug–gene pairs with strong evidence (Level A, Final classification by the Clinical Pharmacogenomics Implementation Consortium [CPIC]) for CYP2D6. Search strings were deployed across Google Scholar, PubMed, Scopus, and Semantic Scholar. Papers were included only if they presented, measured, and/or described real-world clinical or patient and/or provider behavioral outcomes based on EHR or claims data assessment following PGx testing for the CYP2D6 biomarker, included PGx biomarker(s)–drug pairs with CPIC Level A, Final Evidence designations, and published in English. Study quality and bias risks were assessed using the Newcastle–Ottawa scale or A comprehenSive tool to Support rEporting and critical appraiSal…of reSearch outcomes (ASSESS) tools, where applicable. Results/Anticipated Results: Of the 218 articles identified, 25 met our inclusion criteria and explored 9 CPIC Level A, Final CYP2D6–drug pairs. Overarching qualitative themes were 1) variation in CYP2D6 biomarker testing and interpretation and 2) PGx test implementation and data considerations. CYP2D6–drug pairs were reported across four therapeutic areas (analgesia [n = 21], psychiatry [n = 17], oncology [n = 7], and gastroenterology [n = 6]) with the two most researched drugs being codeine (n = 21) and tramadol (n = 18). Six (6) articles reported PGx clinical outcomes, considered to be a “measurable change in symptoms, overall health, ability to function, quality of life, or survival outcomes” in relation to PGx testing. Discussion/Significance of Impact: Our findings indicate a need to address both inconsistent phenotype categorization and racial, ethnic, and genetic ancestry classifications and to improve EHR interoperability with PGx test results. Future RWE-PGx studies should associate dosage and regimen changes to both discrete provider choices and patient clinical-behavioral outcomes.

Objectives/Goals: We developed and implemented a leadership training course tailored for clinical research teams, focusing on emotional intelligence, strategic thinking, communication, and project management to enhance knowledge and maximize team performance. Methods/Study Population: An informal, multi-institution survey and literature review revealed a lack of leadership training tailored to clinical research staff professionals (CRPs). In response, we developed RISE (Relate | Inspire | Strategize | Elevate), a 9-week immersive program led by subject matter experts. Sessions focused on four key themes: 1) Leadership Fundamentals, 2) Interpersonal Effectiveness, 3) Trust Building, and 4) Building a Community of Practice. Designed for experienced CRPs, cohorts were limited to 30 participants to foster engagement, peer learning, and long-term professional growth. Results/Anticipated Results: The inaugural RISE cohort included 28 experienced CRPs with expertise in areas such as regulatory knowledge, data management, study coordination, and award administration. Pre-/post-assessments showed a 16% (mean; range 8-20%) knowledge gain in leadership domains including team management, communication, vision setting, motivation, resilience, and decision-making. The strongest areas of understanding (≥95%) were empathy, near-peer mentoring, and translational scientist traits. Participants rated the course ~90% for helpfulness, relevance, and actionability, with a net promoter score of 93%. Valued elements included DISC assessments, networking, and elective readings. Discussion/Significance of Impact: CRPs are vital to clinical research leadership. RISE delivers tailored training in leadership best practices for this group. In response to strong demand, the curriculum is being refined and efforts are underway to expand access across the region.

Objectives/Goals: Six CTSA hubs collected Translational Science Benefits (TSBM) Model data and implemented standardized coding to enhance rigor and reproducibility, assess the real-world impact of CTSA-supported research, and analyze cross-institutional impact trends. Methods/Study Population: Six CTSA hubs surveyed supported investigators who self-reported the TSBM benefits resulting from hub-supported project(s). De-identified data was shared with the University of California (UCI) CTSA. UCI coordinated with hubs to code their own data using a TSBM coding tool to assess which applicable criteria were met. UCI collected feedback on usability and facilitated discussions to enhance reliability across coders. UCI provided final datasets to hubs for site-specific analysis of benefits and conducted cross-site analyses of TSBM benefits. A benefit was demonstrated when 2 necessary criteria were met, and 80% of the possible criteria were met. Results/Anticipated Results: TSBM data were coded by 6 CTSA hubs from 86 pilot research projects. Most pilots were funded for 1 year (80%) with the mean award over $41,000. Investigators for projects were surveyed up to 7.5 years post-award, with the median timepoint around 3 years. Across hubs, 274 potential benefits were reported and 34 demonstrated benefits were confirmed from coding. Pilots more commonly had demonstrated clinical and medical benefits (16.3% of projects) than community (5.8%) or economic (3.5%). No projects had demonstrated policy benefits. Over 24% of projects had one or more demonstrated benefit of any kind. Demonstrated benefits were reported in 27.8% of projects surveyed within 2.5 years, 18.2% of projects from 2.5 to 5 years, and 50% of projects over 5 years post award. Discussion/Significance of Impact: A standardized, reliable coding process for TSBM data creates rigor for assessing real-world impact data that hubs can confidently connect to their support of CTSA funded-projects. This shared methodology across hubs allows for cross-institutional impact analysis and dissemination.

Objectives/Goals: A key barrier to clinical trial (CT) access is knowing which trials are enrolling. Frontiers has developed a Trial Finder mobile app that displays streamlined, up-to-date trial availability that can be integrated to other institutions. We’re gathering usability feedback and exploring interest from other institutions in adopting the tool. Methods/Study Population: The Frontiers Trial Finder app is a unique tool that extracts trial data and status directly from a Clinical Trial Management System (CTMS). It includes helpful links to ClinicalTrials.gov and provides ways to connect with the study team. The app updates automatically every night, storing the extracted data in a separate stream that can be accessed through the app or online. Its interface is custom-designed in-house and fully customizable, allowing the integration of additional resources such as FAQs, links to partner affiliates, and more. During onboarding, we identified two key efforts: first, organizing the trial directory by defining parent groups and subgroups; and second, encouraging research personnel to keep CTMS trial information current, as it now serves both operational and marketing purposes. Results/Anticipated Results: To organize the trial directory, we used the institution’s divisions as primary categories and disease types as subcategories. Since the CTMS previously didn’t capture ”disease type,” we added this field to the CT activation process. For studies already underway, we generated a report of currently enrolling trials. CTMS and CTSA staff collaborated to manually assign a disease type to each study. Going forward, adding a disease type to the directory will be a standard part of CT activation. Another challenge was that study teams were not accustomed to updating the CTMS when a study remained active but was no longer enrolling – largely because the CTMS had not been used for marketing. To support this shift, we launched a communication campaign to ensure all study teams understood and adopted the new expectations. Discussion/Significance of Impact: Depending on the state of an institution’s CTMS, onboarding this tool may require institutional messaging effort. However, the long-term advantages of automated data exchange significantly improve the efficiency of connecting volunteers to trials. Clinicians and volunteers alike can benefit from reliable, up-to-date trial availability.

Objectives/Goals: The Dissemination and Implementation (D&I) Launchpad at the University of Wisconsin – Madison (UW) supports a growing community of implementation scientists with the goal of strengthening the field. We conducted a survey to identify those doing the work, assess experiences, priorities, and needs to strengthen D&I at UW. Methods/Study Population: In September 2025, the UW ICTR D&I Launchpad administered a landscape survey via REDCap to UW individuals who had signed up for the D&I listserv, attended a D&I event, or received a D&I grant. The survey was also promoted at events for broader participation. The survey assessed D&I identity, skills, training experiences and needs, barriers, support preferences, and network. Of 851 invited and 13 requests, 229 responded (26.5% response rate). Respondents were 53% faculty, 39% staff, and 8% students/postdocs from over 10 UW schools, colleges, and partners. Results/Anticipated Results: Most respondents (67%) identified as contributing to D&I science; 33% said “No” or “Not sure,” citing lack of training, awareness, or other identity. Skill levels varied: 41% beginner, 39% intermediate, 12% advanced, and 4% expert. Top support needs for D&I research were funding, training, grant advice, and consultations. 47% reported receiving D&I training, most frequently through UW programs, coursework, and national programs. Requested training topics included implementation frameworks, strategy design, and trial design. 59% reported challenges, including funding, IRB, and lack of collaborators. Barriers to participating in training included time, cost, and relevance. Most individuals expressed interest in future D&I training, events, and growing the D&I community at UW. Discussion/Significance of Impact: The survey revealed strong interest in D&I across UW, with many contributing to the field but lacking formal training or support. Findings will guide the D&I Launchpad’s efforts to expand training, resources, and the D&I community network.

Objectives/Goals: To improve communication, efficiency, engagement of clinical research workforce by developing a centralized, customizable, and easily accessible curriculum selector tool for role-based onboarding, continuing education, and professional development. Methods/Study Population: We designed aREDCap-based curriculum selector to address training communication gaps within a large academic Learning Health System. The tool catalogs required optional and professional development trainings for varied clinical research roles (e.g., coordinators, regulatory staff, data managers, and PIs). Each role-specific curriculum includes interactive computer-based modules, live courses, job aids, and links to external resources. Research managers can assign curricula, and automated notifications share assignments with staff and stakeholders. Industry standards, input from subject matter experts, and pilot testing guided design, usability, and implementation planning. Results/Anticipated Results: The tool improved awareness and accessibility of training resources, streamlining onboarding and professional development. Early feedback from pilot users indicated improved satisfaction with clarity and customization of assigned training. Managers valued centralized tracking and assignment transparency. We anticipate measurable gains in training completion rates, time efficiency, and staff retention. Future enhancements include expanding to responsible conduct of research and investigator education modules to support enterprise-wide research training coordination. Discussion/Significance of Impact: A centralized, customizable training selector enhances workforce readiness, standardizes education, and reduces inefficiencies in research staff onboarding and development. Broader adoption may strengthen institutional capacity for high-quality, compliant clinical research.

Objectives/Goals: To define how stress alters dopamine system function in PTSD with comorbid psychosis and determine whether orexin receptor antagonism with suvorexant can normalize dopamine neuron activity by targeting the insular cortex–orbitofrontal cortex (IC–OFC) circuit. Methods/Study Population: We used in vivo electrophysiology to record ventral tegmental area dopamine neuron population activity in rats following restraint stress. Chemogenetic and pharmacological manipulations targeted the IC–OFC pathway to assess its role in stress-induced dopamine dysregulation. Suvorexant, a dual orexin receptor antagonist, was administered systemically or directly into the OFC. Fiber photometry recordings measured IC-evoked glutamatergic transmission within the OFC to determine how suvorexant modulates circuit-specific activity contributing to altered dopamine function. Results/Anticipated Results: Foot shock stress produced robust increases in dopamine neuron population activity. Intra-OFC suvorexant infusion reversed these effects, restoring dopamine system function to baseline levels. Chemogenetic inhibition of the IC–OFC circuit produced a similar normalization, confirming this pathway’s role in stress-induced dopamine dysregulation. Fiber photometry revealed that suvorexant attenuated IC-evoked glutamatergic signaling in the OFC, indicating that orexin receptor antagonism modulates excitatory input within this cortical circuit to restore balanced dopamine activity. Discussion/Significance of Impact: These findings identify the IC–OFC circuit as a key mediator of stress-induced dopamine dysfunction and reveal orexin receptor antagonism as a promising strategy for treating PTSD with comorbid psychosis. Targeting cortical orexin signaling may enable circuit-specific precision therapies.

Objectives/Goals: We aim to develop and optimize extracellular vesicle (EV)-mediated delivery of Cas9/single-guide RNA (sgRNA) ribonucleoprotein (RNP) complex to target ACSL4, a key driver of dysregulated fatty acid metabolism in androgen receptor (AR)-independent prostate cancer, and to evaluate its therapeutic potential in vitro and in vivo. Methods/Study Population: We examined ACSL4 gene editing using lentiviral delivery and EV-mediated delivery of Cas9/sgRNA constructs. In HEK-293T cells, we optimized EV treatment frequency and concentration. In PC3 cells, we analyzed gene editing efficiency in detached cell population in comparison to attached cell population. Editing was determined by next generation sequencing. To examine EV-mediated gene editing in vivo, we employed a xenograft mouse model produced by subcutaneous injection of PC3 cells. EVs were administered by intratumoral injection three times a week for three weeks, and tumor growth, histology, and gene editing were assessed. Future studies will include patient-derived xenograft models to evaluate translational relevance. Results/Anticipated Results: Lentiviral delivery achieved up to 36% ACSL4 gene editing. EV-mediated delivery showed dose-dependent gene editing in HEK-293T cells, which contained up to 13% editing. Gene editing was enriched in detached PC3 cells, reaching a maximum editing efficiency of 47%, potentially contributing to cell death. Attached PC3 cells showed 1-2% editing and reduced proliferation after EV treatment. In the ongoing xenograft mouse model experiments, EV treatment is expected to decrease tumor growth, reduce proliferation, and induce apoptosis, confirmed by histology and sequencing. Further optimization of EV dosing and delivery aims to further enhance editing efficiency and therapeutic impact. Discussion/Significance of Impact: EV-mediated delivery of Cas9/sgRNA RNP to target ACSL4, a key metabolic driver of aggressive disease, represents a novel therapeutic strategy for treatment-resistant prostate cancer. Continued work on this nonviral platform will enable targeted, tumor-specific editing while minimizing safety concerns associated with viral delivery.

Objectives/Goals: EEG neurofeedback is a promising neuromodulation approach for improving mental health outcomes but lacks standardized, neurophysiologically-grounded treatment protocols. This study uses high-density EEG (hdEEG) analyses to identify neural network patterns that inform development of evidence-based pediatric neurofeedback protocols. Methods/Study Population: This translational neuroscience project leverages resting-state hdEEG data and behavioral assessments from 600 six-year-old children in the Beginnings study (Arkansas Children’s Research Institute; Andres, NCT00616395). Analyses will identify functional and effective connectivity patterns across major brain networks – Default Mode (DMN), Salience (SN), and Central Executive (CEN) – using graph theoretical metrics, phase-locking values, and multivariate analyses. Identified network patterns will be linked with neuropsychological measures, including the Child Behavior Checklist (CBCL), to inform development of standardized neurofeedback protocol recommendations grounded in developmental neuroscience. Results/Anticipated Results: We anticipate identifying distinct patterns of functional connectivity that predict variation in children’s emotional and behavioral functioning. Findings are expected to highlight network patterns – particularly within emotion regulation and executive control circuits – that can guide neurofeedback protocol design. Anticipated products include neurophysiologically informed recommendations for standardized frequency bands, target networks, and protocol parameters to support future clinical trial development. This project represents a key T1 translational step toward establishing a neuroscience-based framework for EEG neurofeedback treatment protocols. Discussion/Significance of Impact: By integrating neurophysiological and behavioral data to guide protocol development, this work addresses a critical translational gap in the EEG neurofeedback literature. Findings will establish a foundation for standardized, evidence-based neurofeedback interventions that could improve accessibility and efficacy in clinical settings.

Objectives/Goals: Adolescent suicide is a major and growing public health concern, as suicide-related hospital visits continue to rise. Additionally, brain regions implicated in suicide risk are still undergoing change during adolescence. Here, we explore the longitudinal impact of suicide risk factors on brain functional connectivity during adolescence. Methods/Study Population: Data from the Adolescent Brain and Cognitive Development (ABCD) Study were used for this analysis. The ABCD Study is an ongoing, multi-site, longitudinal study that follows kids starting at 9 years old until they’re 18 years old. The study sample is meant to be representative of the current US population, based on various demographic criteria obtained from the US census. For this study, baseline and Year 2 functional MRI imaging data and parent and youth reports of depression, non-suicidal self-injury (NSSI), and suicidal thoughts and behaviors (STB) from the ABCD Study, were used for this analysis. Clinical and imaging data were added into a statistical model (linear mixed-effects model) to describe how the relationship between suicide risk and brain development changes over time. Results/Anticipated Results: A total of 9653 subjects (51% Female), between the ages of 8.9 – 13.8 years, across two timepoints: Baseline (N=6493) and Year 2 (N=3,160), were included in the analysis. Of this sample, 861 (8.9%) endorsed depression, 1428 (14.8%) NSSI, 2146 (22.2%) STB, and 3012 (31.2%) any of the three suicide risk factors at any timepoint. Results showed a significant interaction between the association of network connectivity and STB group status with age in the approach (B = -15.0, 95%CI [-26.4, -3.8]), avoidance (B = -8.5, 95%CI [-16.3, -0.9]), regulation (B = -14.4, 95%CI [-26.2, -3.1]), approach-avoidance (B = -14.4, 95%CI [-28.3, -0.5]), and approach-regulation (B = -15.6, 95%CI [-28.8, -2.7]) network. Discussion/Significance of Impact: Findings suggest, in early adolescence, presence of STBs is associated with changes in communication in regions involved in reward-(approach), emotion/threat-processing (avoidance), and their cognitive regulation (regulation). The causal relationship is unclear, but it’s an important step to understanding the neural mechanisms of suicide.

Objectives/Goals: This study aims to evaluate and establish a reliable strategy for identifying at-risk diabetic foot ulcer cases in hospitalized patients utilizing machine learning, enhancing the accuracy of care delivery and facilitating future research on DFU outcomes. “At-risk” is defined as risk for further serious complications, most notably, amputation. Methods/Study Population: Data analyzed for this work started with a previous computable phenotyping study that compared multiple computable phenotype strategies. The dataset created for subsequent ML analysis includes a random sample of 351 hospital admissions during the years 2022–2024. These hospitalizations were manually curated by SME’s, scored and annotated for their inclusion as hospitalized DFU patients, resulting in a “gold standard” dataset for “at-risk” patients. We analyzed six clinical variables hypothesized to be predictive of “at-risk” status (imaging [XRAY, MRI], specialty consultations, and clinical findings [infection, ulcer]). All possible combinations (n=63) were evaluated using precision, recall, F1-score, and accuracy metrics. The dataset contained 42 positive outcomes (12.0% prevalence). Results/Anticipated Results: The findings show, while INFECTION, comprised of two SNOMED codes, is the strongest single predictor, adding ULCER and XRAY provides optimal balance. This combination achieved an F1-score of 72.1% with 93.2% accuracy, 70.5% precision, and 73.8% recall, correctly identifying 31 of 42 positive cases. Adding additional variables beyond this three-feature model yielded diminishing returns with no meaningful improvement in predictive performance. Discussion/Significance of Impact: This research will then be used to proactively identify potential DFU patients for outreach and intervention. A parsimonious three-variable model (XRAY + infection + ulcer) provides optimal predictive accuracy for identifying patients, offering a clinically practical decision support tool with strong discriminative performance.

Objectives/Goals: The research industry continues to struggle with workforce instability: an aging workforce, staff shortages/burnout, retention/turbulence issues, and attracting qualified staff. We describe an intra-institutional approach at Virginia Commonwealth University to prepare undergraduates for entry-level positions across the research landscape. Methods/Study Population: To address research workforce expansion and job readiness within the institution, the VCU Student Pathways Collaborative was established as an inter-professional collaboration among key research business units. This collaborative effort resulted in the development of the Minor in Research Ecosystems, an academic intervention specifically designed to cultivate qualified entry-level personnel. Consisting of 18 credits, the minor is comprised of four core courses focusing on the competencies in job descriptions for various research ecosystem positions, one elective within the student’s chosen research domain, and culminating in an internship experience. The minor is designed to be accessible for all majors fostering diversity, inclusivity, and specialized content knowledge and skill sets. Results/Anticipated Results: The minor, launched in fall 2025, is designed to prepare students for careers that sustain and accelerate the research enterprise. A core goal is bringing awareness to the vast and varied career paths that research has to offer – paths many students reported they simply did not know existed. In the near future, the minor is expected to support institutional workforce needs, leading to a sustainable, qualified, and skilled workforce by specifically preparing students for the diverse research professional roles that they previously did not know existed. Additionally, early outreach to undergraduate advisors, faculty, and student organizations has revealed significant demand for pathways into research professional careers, a demand that is driving high enrollment projections. Discussion/Significance of Impact: The undergraduate Minor in Research Ecosystems is designed to meet the growing demand for a workforce capable of navigating the complex and interdisciplinary research landscape. By equipping students with critical knowledge and practical skills, the program ensures graduates are prepared to immediately serve as effective research team members.

Objectives/Goals: A participatory mixed methods study was conducted to examine Arab Americans’ attitudes toward clinical dementia research and identify the cultural and contextual factors that influence their participation. Methods/Study Population: A community council partnered with the study team to conduct this study. Arab American adults (N = 51) completed a modified version of the Research Attitudes Questionnaire (RAQ-7) assessing views of clinical dementia research. Mean item scores and frequencies were analyzed. A subsample of participants (N = 11) participated in focus groups to further explore the factors that influence their attitudes toward research and barriers and facilitators to participation. Qualitative data were thematically analyzed to contextualize survey findings. Results/Anticipated Results: Participants reported highly positive attitudes toward dementia research (M=3.96–4.29/5), with >80% agreeing that clinical dementia research is safe and important. Most endorsed a collective responsibility to volunteer for dementia research (90%) and had confidence in the protection of their information (88%). Yet 98% had never participated in dementia research (2% unsure). Focus groups revealed that their lack of participation is largely explained by structural and systemic barriers, including the absence of Arabic-language studies, transportation, misalignment with community priorities, and the broader political context shaping their health experiences and trust in institutions. Discussion/Significance of Impact: This study demonstrates that despite having positive attitudes toward clinical dementia research, Arab Americans do not enroll in studies. Findings underscore the need for transformative models of research, such as community engaged approaches, that may better address barriers to participation.

Objectives/Goals: Tibial bone stress injuries are running injuries that have a high risk of recurrence. Tibial load contributes to the development of bone stress injuries; however, this variable is clinically inaccessible. The objective of this study is to establish a relationship between tibial load during running and clinical tests to support safe return to running. Methods/Study Population: Thirty-two healthy recreational runners (13 F, 19 M, age 23.6 y, height 1.77 m, mass 73.71 kg, weekly volume 16.91 miles) ran at a self-selected pace on an instrumented treadmill for 5 minutes, while a 7-camera motion capture system collected whole body kinematics. Inverse dynamics were used to calculate ankle joint reaction forces and moments which were then used in a musculoskeletal model to calculate tibial axial force. Single leg heel-raise endurance, hand-held dynamometry ankle strength, and Y-balance tests were also assessed. Linear mixed models will be used to determine relationships between tibial force during running and strength and balance measures. Results/Anticipated Results: Data analysis is ongoing. Based on preliminary data, it appears that peak tibial bone force during running may demonstrate a relationship with lower extremity strength and balance. Clustering participants into “high” and “low” tibial force production during running may help identify specific ranges of strength and balance assessment outcomes that are appropriate for clinical goals. Discussion/Significance of Impact: The results of this study will attempt to establish relationships between tibial bone loads during running and strength and balance assessments that are clinically feasible. Results have potential to directly impact clinical practice by providing objective measures that may be used as surrogates for tibial bone force in a clinical setting.

Objectives/Goals: TMEM106B forms age-dependent amyloid fibrils that are elevated in dementia patients and correlated with increased TDP-43 pathology. Cerebrovascular disease (CVD), a major dementia co-pathology, may contribute to TDP-43 dysfunction. Understanding the relationships among CVD, TMEM106B, and TDP-43 is crucial for developing targeted therapies. Methods/Study Population: We compared autopsy brains across TDP-43 pathologies and CVD to investigate potential differences in TMEM106B levels. Autopsy brain samples (N=58) were selected from the Biggs Brain Bank to include frontotemporal lobar degeneration with TDP pathology (FTLD-TDP), limbic-predominant age-related TDP-43 encephalopathy (LATE), TDP-43 negative Alzheimer’s diseases neuropathologic change (ADNC), and controls. Immunofluorescence staining using anti-TMEM106B (239-250aa) was performed on formalin-fixed posterior hippocampal sections to assess whether CVD burden had an impact on TMEM staining patterns. Quantitative digital pathology analyses were conducted on whole slide images (WSI) to quantify TMEM106B fibril accumulation. Results/Anticipated Results: Age was the strongest predictor of TMEM106B fibril accumulation (p < 0.001), and females showed higher fibril burden than males (p = 0.035). TDP-43 pathology was associated with increased TMEM106B accumulation (p = 0.03). Greater co-existing cardiovascular disease (CVD) pathology burden showed a trend toward reduced TMEM106B fibril accumulation, suggesting an attenuating, and possibly dose-dependent effect of CVD pathology on the observed TDP-43 and TMEM106B association (p = 0.058). Across ADNC (Braak tangle stage, Thal amyloid plaque phase, CERAD neuritic plaque density score), increasing CVD pathology burden did not modify TMEM106B fibril levels with increasing Braak stage (p = 0.64), Thal phase (p = 0.39), or CERAD score (p = 0.56), and TDP-43 did not increase TMEM106B in the presence of these AD lesions. Discussion/Significance of Impact: Age, sex, and TDP-43 are determinants of TMEM106B fibril. Increasing CVD pathology appears to blunt the TDP-43 and TMEM106B association, possibly due to hypoxia-driven lysosomal dysfunction. Additional co-pathologies do not appear to drive TMEM106B fibril accumulation. Future work will assess if lower TMEM106B may reflect compensatory change.