Objectives/Goals: Recruiting and enrolling participants in health research can be challenging for both the public and study teams. The University of Minnesota (UMN) developed a mobile-friendly, public-facing tool to increase research awareness and connect individuals with active studies that match their interests. Methods/Study Population: UMN developed StudyFinder, which integrates with the clinical trial management, electronic IRB, and ClinicalTrials.gov systems to provide accurate, current study listings with plain-language summaries. The tool pages include advanced Elasticsearch with filters, direct email links to study teams, nightly data refreshes, and an admin dashboard for easy content management and homepage updates. Results/Anticipated Results: Since launch, StudyFinder has received over 1.4 million page views. From Sept 2024–Aug 2025, the site had 78,000 visits, with 10,782 contact emails sent from potential participants to study teams. Analytics show continued year-over-year growth in traffic and engagement, demonstrating StudyFinder’s effectiveness in connecting the public to active research opportunities. Discussion/Significance of Impact: StudyFinder offers a scalable, shareable model for improving public access to research participation. Its open-source code and no-cost license enable other institutions to adopt and adapt the platform, advancing a national ecosystem web-based recruitment. StudyFinder is now available at eight hubs.

Objectives/Goals: Develop a reliable methodology to deliver and study asphalt emission exposure on lung physiology, enabling the assessment of acute volatile organic compound (VOC) effects with minimal confounding, and test filtration techniques for efficacy. Methods/Study Population: Sixteen healthy adults (18-50 years) visited the Mayo Clinic cardiopulmonary research lab in Scottsdale, Arizona. Subjects wore a mask fitted with a one-way breathing valve connected to a mixing chamber where they would inhale the VOCs that come from a cocktail of chemical compounds captured from heated asphalt. These VOCs were administered by study staff every 5 minutes for a total of 60 minutes of exposure. Pulmonary measures recorded before and after exposure included respiratory oscillometry, exhaled hydrogen peroxide and nitric oxide, diffusion capacity of the lungs for carbon monoxide and nitric oxide, lung ultrasound, basic spirometry, and transcranial Doppler ultrasound. Results/Anticipated Results: The VOCs delivered replicate urban environmental exposure on hot days, consistent with literature linking high VOC exposure to pulmonary disease and cancer. Preliminary findings show acute changes in alveolar-membrane conductance (DM; mean Δ=53.1 mL/min/mmHg) when unfiltered VOCs were administered. This response was accompanied by increased function gas exchange units (DM/Vc; mean Δ=1.027). These early findings confirm successful implementation of the exposure method and suggest reproducible physiologic responses to acute VOC exposure. Completion of this study is expected to reveal additional markers of pulmonary inflammation and validate a controlled, scalable platform for testing VOC exposure in the laboratory setting. Discussion/Significance of Impact: These methods are the first of their kind to provide a means to study the acute effects of exposure to asphalt in humans in a controlled, laboratory environment. This offers great significance in understanding the environmental exposures that are affecting the health of those that are exposed to asphalt daily.

Objectives/Goals: To discover novel phenogroups of atrial fibrillation (AF) from a real-world claims database using a multi-algorithm unsupervised machine learning (ML) approach and to reveal clinical factors associated with each phenogroup using supervised ML. We aim to establish clinically meaningful AF phenogroups to advance precision medicine. Methods/Study Population: We will leverage the Merative MarketScan databases (2016–2024) of real-world claims data with an estimated >600,000 AF patients. Four unsupervised ML clustering techniques will be employed to identify phenogroups: k-means, Gaussian mixture models, agglomerative, and HDBSCAN. Following cluster generation, the algorithms will be evaluated through internal validation via the Davies–Bouldin Index which calculates cluster compactness and separation, and the Adjusted Rand Index to determine stability between algorithms. Then, supervised ML via SuperLearner, which will include advanced algorithms (e.g., gradient boosting machines and support vector machines), will identify clinical factors associated with each phenogroup and will be evaluated using k-fold cross-validation and Shapley values. Results/Anticipated Results: We anticipate that the multi-algorithm approach will yield robust, distinct, and clinically meaningful AF phenogroups. SuperLearner will confirm the key clinical characteristics driving membership in each group (e.g., hypertension and heart failure). These data-driven classifications will provide a robust foundation for subsequent translational analyses. We will perform time-to-event analyses to assess the differential risks of stroke, coronary artery disease and myocardial infarction, heart failure, and rates of healthcare utilization across phenogroups. Next, we will employ the target trial framework to evaluate the comparative effectiveness of anticoagulant therapies on stroke incidence across phenogroups. Discussion/Significance of Impact: This work demonstrates a highly rigorous, multi-algorithm approach to characterize a large, real-world database of AF patients. By constructing novel AF phenogroups, we aim to move beyond the limitations of current AF classifications and advance precision medicine by providing the foundation for individualized therapeutic strategies.

Objectives/Goals: This study aims to explore transcriptional adaptation, where mutations in one gene trigger compensatory changes in related genes, and how this affects the variability in clinical manifestations of amyotrophic lateral sclerosis (ALS). Methods/Study Population: The presence of a premature termination codon triggers transcriptional activation. Therefore, we utilized CRISPR-Cas9 tool to generate a premature termination codon in CHCHD10 gene in multiple types of cells, including induced pluripotent stem cells derived from patient samples with known CHCHD10 mutations causative for amyotrophic lateral sclerosis. CRISPR-Cas9 tool was delivered via ribonucleoprotein electroporation, and transfect cell’s DNA were sequenced to validate gene editing. To confirm transcriptional adaptation, protein and gene expression changes will be assessed by immunoblotting and quantification of multiple target loci from whole-cell lysates of edited cells. Results/Anticipated Results: By employing CRISPR-Cas9-mediated editing, we hypothesize that transcriptional adaptation will enable the adaptive gene to functionally compensate for the loss of function in the gene of interest harboring a premature termination codon. Discussion/Significance of Impact: The significance of this research lies in its potential to identify novel genes capable of triggering transcriptional adaptation in humans. Understanding this mechanism could pave the way for new therapeutic strategies, particularly for CHCHD10-associated ALS.

Objectives/Goals: Ocular adverse events (OAEs) have been associated with GLP-1 use in clinical studies. This study aims to describe the proportion and distribution of ocular adverse events by treatment arm, including serious and non-serious events, in phase 2-4 clinical trials of GLP-1 drugs using publicly available ClincialTrials.gov data. Methods/Study Population: The ClinicalTrials.gov database was searched on 9/11/25 for interventional studies with GLP-1 as treatment (n= 1545). The search was filtered to terminated and completed studies with results (n= 349). Adverse event reports from Clinical Trials Transformation Initiative (CTTI) Aggregate Analysis of ClinicalTrials.gov (AACT) database were restricted to phase 2-4 drug studies (n= 240), of which 167 involved a GLP1-1 drug with 64 studies reporting ocular adverse events. Ocular adverse events were summarized as weighted and unweighted mean proportions by arm and seriousness, with chi-square, Fisher’s exact, and Wilcoxon tests assessing significance. Study characteristics were included in statistical models to further assess differences in ocular adverse event proportions. Results/Anticipated Results: Ocular adverse events ranked 10 among top organ systems affected in GLP-1 drug trials. Among 64 studies reporting ocular adverse events, serious ocular event proportions were higher in GLP-1 arms compared to placebo (weighted: p < 0.01; unweighted: p = 0.02). Most studies (n = 51) systemically assessed ocular events; 10 were non-systematic, and 3 did not classify reporting. The top 10 ocular events highlighted retinal detachment at significantly higher proportions in drug arms than placebo arms (weighted: 0.11% vs. 0.02%, p = 0.04). Cataract and diabetic retinopathy events were observed less frequently in drug arm than placebo arm (weighted: p = 0.02; p = 0.03). Discussion/Significance of Impact: Serious and non-serious ocular adverse events occurred more often in GLP-1 drug arms, driven by specific conditions such as retinal detachment. Using the AACT database enables large-scale evaluation of adverse event patterns, providing broader context than single-study analyses.

Objectives/Goals: Relevant publications serve as a comprehensive longitudinal data source that reflects the priorities, partnerships, and training a CTSA program supports. A content analysis of the publications was conducted to identify the specific areas where our CTSA program has the most impact and provides the most support. Methods/Study Population: A content and bibliometric analysis was conducted for the Frontiers CTSI. Articles were identified by searching for all current and previous grant numbers in Google Scholar and PubMed and identifying relevant publications through ERACommons. We utilized Covidence to facilitate the screening and coding process. Articles were included if they were published after the grant initiation (2011), attributed support to the grant, and were peer reviewed. Articles were then double coded for variables such as member institutions, CTSA collaboration, awardee type, disease area, and study type, etc. Bibliometric outcomes were retrieved from NIH iCite and Web of Science. Descriptives, t-test, and linear models were used to assess associations. Results/Anticipated Results: A total of 941 publications from 2011 to April 2024 were included for analysis. When examining collaboration between Frontiers and other CTSA hubs, 40% of publications had authorship representing multiple hubs, with 95% of the CTSA hubs represented. Most publications were directly supported by Frontiers: 44% from TL1 & KL2 scholars and 56% from past pilot awardees. The primary disease area supported was neurology, with 60% of publications describing T2: clinical research studies. The main output of interest, the NIH’s relative citation ratio (RCR), has an average of 1.64 over 13 years. Publications with collaborations between Frontiers and another CTSA hub were associated with higher RCRs. Discussion/Significance of Impact: This analysis provides us with a snapshot of frontiers CTSI’s activities and impact while also highlighting areas where our support can be strengthened. Next, we plan to incorporate altmetric data to create a more holistic view of our CTSA’s impact and collaborations.

Objectives/Goals: Trust among stakeholders is vital for effective implementation, boosting collaboration, and ensuring long-term commitment to a program. In the past decade, PRx has gained traction, but trust between farmers and healthcare clinics remains understudied. This study aims to identify facilitators and barriers to building trust for PRx implementation. Methods/Study Population: We partnered with 3 primary care clinics within a healthcare system in Atlanta, GA, conducting 7 one-hour focus group discussions (FGDs) with providers, residents, and staff (6-8 participants each), and 13 one-hour semi-structured interviews with healthcare administrators. Seventeen organic fruit and vegetable farmers within 2 hours of Atlanta were recruited via Georgia Organics and snowball sampling for 1-hour semi-structured interviews. FGDs and interview guides were based on the Exploration, Preparation, Implementation, and Sustainment framework’s preparation phase. FGDs were held in person; interviews were in person/Zoom. Sessions were recorded, then transcribed verbatim by a professional transcription company. The data were analyzed using a team-based rapid qualitative analytic approach. Results/Anticipated Results: Farmers and healthcare personnel stressed the importance of communication for building trust in a partnership for PRx implementation. They both value commitment but are concerned about mutual engagement and reliability in the PRx. Healthcare personnel worry about unmet patient needs and disappointment, and farmers worry about unsold produce and financial losses. Farmers feel that healthcare systems do not understand their operations due to differences in work culture, bureaucratic barriers, and staff turnover, which hinder trust. Healthcare workers want to understand farmers’ operations but lack the experience to initiate discussions and to integrate them into their workflow. Both reported that building trust involves having early conversations to establish alignment with their mission and workflows. Discussion/Significance of Impact: Findings highlight that building trust is crucial for farmers and healthcare personnel in PRx, starting with setting shared goals, recognizing sector differences, and promptly addressing barriers early in the partnership. Future efforts could involve stakeholders in co-design to foster mutual understanding and strengthen PRx implementation.

Objectives/Goals: Head and neck cancer (HNC) patients frequently experience cachexia (≥5% weight loss) that goes underrecognized in clinical practice. We assessed whether weights in electronic health records (EHR) were frequent and sustained enough to characterize weight-loss magnitude and timing, and to quantify cachexia underdiagnosis and diagnostic delay. Methods/Study Population: Patients were identified in the Emory Healthcare EHR (2016–2019) using ICD-10 codes for head and neck malignancies. Those with ≥2 codes within 90 days were eligible cases; the index date was the first code of the qualifying pair. Weights were extracted for all encounters six months before through one year after the index date; implausible values were removed. Feasibility analyses included all eligible patients while cachexia analyses were limited to those with ≥3 post-index weights. Baseline weight was the median pre-index value or the closest post-index value ≤14 days. Underdiagnosis was defined as ≥5% weight loss from baseline without a cachexia-related ICD code recorded 90 days prior to, or any time after, the index date: cachexia (R64), abnormal weight loss (R63.4), muscle wasting (M62.5x), or anorexia (R63.0). Results/Anticipated Results: EHR data demonstrated high feasibility for longitudinal analyses: among 561 HNC patients, the median number of weight measurements in the first year was 18 (IQR 8–27) with a median interval of 7 days (IQR 2–14). Among 499 patients analyzed for cachexia, the median max weight loss was 11.2% (IQR 4.9–18.2), occurring a median of 5 months after the index date (IQR 2.4–8.4). Overall, 373 patients (75%) lost ≥5% of baseline weight, yet cachexia-related ICD codes were present in only 113 (23%). Among uncoded patients, 274 met the ≥5% weight-based criterion, thus 55% of HNC patients were at risk for underdiagnosis. The median time to cachexia coding was 5.4 months after the index date (IQR 2.5–14.4). In 38% of HNC cachexia patients, coding occurred more than 6 months after ≥5% weight loss, indicating a meaningful diagnostic delay. Discussion/Significance of Impact: EHR data provide sufficient density and cadence to track cancer-related weight loss with precision and reveal that cachexia often goes unrecognized despite clear, measurable weight loss. These findings reveal a gap between data visibility and clinical action and support EHR-based monitoring to enable earlier recognition and timely care.

Objectives/Goals: This study investigates how VR-based Episodic Future Thinking (EFT) training enhances prospection and self-regulation mechanisms in children with ADHD by engaging neural processes of future-oriented planning within ecologically valid virtual contexts. Methods/Study Population: This randomized controlled trial included 80 typically developing children aged 4–10 years, randomly assigned to a VR intervention group (n=40) receiving 10 days of gamified Episodic Future Thinking (EFT) combined with conventional executive function training, or an active control group (n=40) receiving only conventional training. Intervention efficacy was evaluated using pre- and post-tests with five standardized measures: the Behavior Rating Inventory of Executive Function (BRIEF), grade-specific Executive Function Assessment Scales (Grades 1–3 or 4–5), Children’s Executive Functioning Inventory (CHEXI), Achenbach Child Behavior Checklist (CBCL), and Vanderbilt ADHD Diagnostic Rating Scale (VADRS). Results/Anticipated Results: Mixed-design ANOVA revealed that the VR-EFT intervention significantly enhanced cognitive and emotional regulation, while traditional training showed advantages in behavioral control and planning, reflecting the dual-system nature of executive functions. BRIEF and EFAS results were consistent, showing significant gains in inhibition, emotional control, planning, and working memory (η²=0.06–0.17). CHEXI indicated that the VR group improved in working memory and regulation, whereas the control group performed better in behavioral-level planning and inhibition. CBCL and Vanderbilt results showed reduced aggression, hyperactivity, and oppositional symptoms (η²=0.056–0.138), highlighting VR-EFT’s efficacy in enhancing self-regulation and reducing externalizing behaviors. Discussion/Significance of Impact: Findings suggest that VR-based Episodic Future Thinking (EFT) training effectively strengthens children’s cognitive–emotional executive functions and self-regulation. By integrating future-oriented cognition into intervention models, it provides a novel, evidence-based approach to enhancing executive development.

Objectives/Goals: Black women are adversely affected by HDP. Allostatic load is linked to poor pregnancy outcomes in Black women, but studies on its relation to HDP are limited and inconsistent, especially regarding racial disparities. The review aims to examine existing research on the association between allostatic load and HDP. Methods/Study Population: For this SCOPING review, a comprehensive search will be conducted of peer-reviewed literature in PubMed, Medline, Ovid Embase, Cochrane Library, and Google Scholar from database inception to October 1st, 2025. Relevant search terms will be used to identify studies that measure the exposure of allostatic load, a marker of chronic stress and the outcomes of hypertensive disorders of pregnancy, including preeclampsia, hypertension, and high blood pressure. Studies that discuss chronic stress as an exposure will also be included as a proxy measure of allostatic load. Inclusion criteria include a population of pregnant women and the availability of the study in English. This review will be conducted according to PRISMA guidelines. Results/Anticipated Results: This project is ongoing. Findings from this review will highlight the current landscape of literature exploring the association between allostatic load and HDP. Results will be organized into studies on allostatic load and HDP, chronic stress and HDP, and studies exploring racial differences in this association. Based on the findings, we hypothesize a correlation between allostatic load and HDP throughout the literature, with a particularly strong association observed among Black women. Discussion/Significance of Impact: The results of this review will offer insights into the relationship between allostatic load and HDP. This study will help establish a foundation for future research exploring this association and make the case for stress-reducing interventions as strategies to decrease racial disparities in maternal morbidity related to stress and HDP.

Objectives/Goals: This poster will provide key insights from a landscape analysis examining resources for community-engaged research (CEnR) across three University of Michigan (U-M) campuses. Highlights will include notable gaps and limitations and how the analysis will inform future U-M CTSA CE programming to support CEnR efforts at U-M. Methods/Study Population: A landscape analysis of CE resources available at U-M was conducted as part of a multipronged approach to inform CTSA CE services. Through a comprehensive review of U-M websites, the analysis identified 83 CEnR programs and services across Ann Arbor, Flint, and Dearborn campuses of U-M. Programs were mapped by audience (faculty, staff, students, and community partners), topic, and service type. Assessment of distribution, website clarity, and ease of navigation further illuminated opportunities to improve support and efficiency to better meet the needs of those engaged in CEnR at U-M. Results/Anticipated Results: The landscape analysis identified 83 CEnR-related programs and services at U-M, most concentrated in Ann Arbor, and with the highest concentration in a few units. Data analysis services, resources for sustaining and sharing work, and structured grant feedback were relatively rare. Availability of funding for community-focused initiatives was infrequent and generally under $10,000. Over 70% of resources were available to faculty or students, 18% were available to the community, and just under 13% were open to staff. Findings will be used by MICHR to identify opportunities for future CE initiatives. Discussion/Significance of Impact: The poster outlines a landscape analysis framework and process that can be replicated by other CTSAs to assess institutional CEnR resources. This approach helps identify gaps and develop supports that meet the evolving needs of research teams and the community partners they collaborate with.

Objectives/Goals: Describe key elements of CEARCH’s community-engaged research (CEnR) approach to program development. Provide three CEARCH case examples illustrating CEnR-based program development. Showcase a model for applying CEnR approaches in program design from ideation through evaluation. Methods/Study Population: CEARCH, the University of Minnesota Clinical and Translational Science Institute’s Community Engagement core, utilizes community-engaged research (CEnR) best practices aligned with its collaboratively established values to develop and implement programming. CEARCH builds programs through shared decision-making, power and resource sharing, conflict resolution, and authentic conversations that lead to a collaborative leadership approach. All programming is structured via a community–academic leadership model and engages community members through a governing council and workgroups. Results/Anticipated Results: Through a collaborative leadership approach, CEARCH’s process increases depth and relevance to the communities being served. As a result, programming is built on trusting relationships. Shared decision-making ensures that all relevant constituents are driving program needs, involved in its creation, and part of its leadership. CEARCH has co-designed community-engaged research training materials, hosted community and faculty co-led workshops, and facilitated community feedback sessions that feature community members as experts. In addition, community partners drove the development of a statewide CHW research training, the launching of a community research network, and co-facilitation of training workshops for community and academic partners focused on community-engaged research. Discussion/Significance of Impact: CEARCH uses CEnR practices to center community in program development and leadership. This approach deepens and broadens impact by valuing partners as knowledge holders, experts, and researchers and fosters authentic bi-directional collaboration.

Objectives/Goals: We aimed to evaluate the representativeness of the patient population that is accessible through PCORnet®, both at the network level and within individual studies that enroll patients using the network, and to develop concrete recommendations regarding the design and evaluation of future studies. Methods/Study Population: We evaluated potential study participants that could be recruited through PCORnet, defined as patients with a healthcare encounter and any diagnosis recorded at a participating network site in 2023, relative to US population data from the American Community Survey (ACS) with respect to demographic, geographic, and socioeconomic summary data. Individual studies, conducted using the network, rarely involve all PCORnet patients, but rather those who meet clinical eligibility criteria and pass through a series of operational filters, such as site selection. We evaluated all patients at sites that have participated in at least 10 studies, as well as individual studies published in the COVID-19 clinical domain, compared to corresponding, eligible patients across the entire network. Results/Anticipated Results: PCORnet included data from over 47 million people across the USA with everyday healthcare encounters in 2023. This population of potential study participants was generally representative of the US population, though less likely to be from rural communities (11.7% vs. 18.4%) and low socioeconomic status (SES) (18.9% vs 25%). Patients from sites that frequently participate in PCORnet studies (10+) were representative of PCORnet overall, with an opportunity to improve inclusion of Hispanic populations. Among individual studies in the COVID-19 clinical domain, retrospective studies were largely representative, but prospective studies demonstrated large gaps in representation. Discussion/Significance of Impact: PCORnet provides access to a representative population of patients receiving healthcare. Differences in SES and rurality may be explained by differences in healthcare access. Population-level data, available through PCORnet, can be used to improve study design and evaluation, particularly for prospective studies.

Objectives/Goals: By offering a summer program that provides foundational training in data science and health equity concepts, as well as their practical applications in clinical translational research (CTR), we seek to enhance awareness and self-efficacy of undergraduate students from diverse majors to career opportunities in clinical translational research. Methods/Study Population: Rapid is a 7-week summer training program for undergraduate students from diverse majors. The RAPID curriculum is comprised of 5 key components: 1) didactic training in data science skills and health equity principles, 2) application of skills in a research practicum, 3) exposure to examples of CTR, 4) clinical shadowing, and 5) professional development and career networking. To date, RAPID has had 2 student cohorts complete the program during the summers of 2024 and 2025 (N = 10). Each cohort was comprised of 5 students from various undergraduate majors (table 1). Admission to the program is based on GPA, letters of recommendation, and letter of interest. Results/Anticipated Results: For the summer 2025 cohort, we received 40 applications for 5 available positions in RAPID. We conducted pre- and post-program surveys to assess student confidence in 4 key research competencies. The scoring system for student confidence was as follows: 5 – extremely, 4 – very, 3 – moderately, 2 – slightly, and 1 – not confident. Student confidence in Data Science Research increased from 2.1 to 3.9, in Health Equity Research from 2.1 to 4.6, in Scientific Research from 3.1 to 3.9, and in Science Identity from 3.4 to 4.2. The curriculum components that ranked highest were the research practicum, mentoring, interaction with translational researchers, and networking opportunities, all of which received a rating of 4.4 out of 5 (4 – very and 5 – extremely satisfied). Overall, student satisfaction with RAPID was 4.6 out of 5. Discussion/Significance of Impact: Rapid seeks to inspire undergraduate students from diverse majors to pursue careers in CTR. We will track students longitudinally to gage career trajectories. Student feedback is promising, “The connections I gained through my peers, mentors, and guest speakers are priceless. I discovered a newfound sense of purpose within the field of data science.”

Table 1

Objectives/Goals: Little is known about how long COVID-19 vaccines protect patients with autoimmune diseases or about breakthrough infection risk factors in this population. We aimed to evaluate vaccine protection durability and identify predictors of breakthrough infection among vaccinated autoimmune patients in Qatar. Methods/Study Population: In a retrospective cohort study in Qatar, we included patients with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), or Hashimoto’s thyroiditis who received at least one COVID-19 vaccine dose. Follow-up started 14 days post-first dose and continued until breakthrough infection or April 30, 2023. Data on demographics, comorbidities, vaccination history, and prior infections were collected. Cox proportional hazards models with time since last dose as a time-varying exposure were used to estimate adjusted hazard ratios (aHRs) for breakthrough infection, adjusting for age, sex, autoimmune condition, and prior infection. Results/Anticipated Results: Among 6,039 vaccinated autoimmune patients, 1,350 had a breakthrough infection during follow-up. Breakthrough risk did not increase significantly within the first 6 months (aHR ~1.17 at 91–180 days vs 0–90 days), but was about twofold higher by 6–12 months (aHR 2.03) and remained elevated beyond one year (aHR 1.78). Male sex was protective, associated with ~30% lower risk than female sex (aHR 0.70), with an even larger advantage at >360 days (interaction p<0.05). Rheumatoid arthritis was associated with lower breakthrough risk than Hashimoto’s (aHR 0.69), and systemic lupus erythematosus had slightly lower risk (aHR 0.86). Prior SARS-CoV-2 infection also conferred additional protection (aHR 0.68). Discussion/Significance of Impact: COVID-19 vaccine protection in autoimmune patients wanes by six months. Male sex and prior infection conferred protection against breakthrough infection. RA and SLE did not show a higher risk than Hashimoto’s. These findings support booster doses around six months and highlight the need for tailored preventive strategies.

Objectives/Goals: This study evaluates whether BT2-mediated reduction of branched-chain amino acids enhances cognitive performance and alters cortical gene expression in transgenic Alzheimer’s disease mouse models, clarifying BCAA’s role in AD pathology and assessing BT2’s potential as a disease-modifying therapy. Methods/Study Population: Four groups of mice were used: wild-type vehicle, wild-type with 20 mg BT2, AD vehicle, and AD with 20 mg BT2(3,6-dichlorobenzobthiophene2-carboxylic acid). BT2, an inhibitor of branched-chain aminotransferase 2, was administered intraperitoneally to reduce BCAA accumulation implicated in mTOR overactivation and tau hyperphosphorylation. Cortical tissue samples were collected for RT-qPCR analysis of 17 genes linked to neurodegeneration. Y-Maze behavioral assays assessed spatial memory and cognition. ANOVA with post-hoc Tukey’s tests (α = 0.05) assessed intergroup significance between behavioral and transcriptional outcomes. Additionally, a Random Forest Regressor Model was utilized in order to identify gene expression patterns predictive of cognitive improvement following BT2 treatment. Results/Anticipated Results: BT2 (3,6-dichlorobenzo[b]thiophene-2-carboxylic acid) treatment restored Beclin-1, VPS-26, NRF2, and NF-κB expression to near wild-type levels, correlating with improved Y-Maze performance. These changes enhanced autophagy, reduced inflammation, and improved mitochondrial resilience. Partial recovery of BACE1, MFN2, and PSD-95 indicated moderate restoration of amyloid processing and synaptic plasticity. Enrichr analysis highlighted pathways in autophagy, mitochondrial biogenesis, and miRNA regulation, suggesting BT2 exerts neuroprotective effects by modulating metabolic and inflammatory homeostasis. The Random Forest Regressor Model’s Feature importance mapping further revealed that changes in Beclin-1, NRF2, and NF-κB expression most strongly correlated with behavioral recovery. Discussion/Significance of Impact: BT2 shows strong potential in mitigating Alzheimer’s pathology through branched-chain amino acid modulation. It normalizes NRF2, Beclin-1, and NF-κB, promoting neuroprotection and cognitive improvement. This supports BT2 as a promising metabolic therapy warranting further long-term studies.

Objectives/Goals: Cancer cachexia is a wasting condition common in pancreatic cancer, marked by loss of muscle, fat, and bone and driven by inflammation. Immune signals contribute to this damage. This study examines how muscle-resident macrophages may regulate muscle loss in pancreatic cancer cachexia. Methods/Study Population: To identify resident macrophages in cachectic muscle, Lyve1Cre-Rosa26;TdTomato mice were orthotopically transplanted with KPCML1 pancreatic cancer cells. Resident macrophages were analyzed via flow cytometry and immunohistochemistry. For muscle-specific macrophage ablation, lyve1-DTR mice (C57BL/6 background) received tumor cell injections followed by intramuscular diphtheria toxin (DTA) in the TA muscle. Quantitative RT-PCR assessed gene expression from whole muscle lysates and flow-sorted TdTomato+ cells. Results/Anticipated Results: We show that the population of muscle-resident macrophages increased as tumor burden increased. Further findings reveal that the ablation of the resident population of macrophages exacerbates muscle loss, suggesting that muscle-resident macrophages exude a protective phenotype on skeletal myofibers. This was confirmed with increased expression of atrophy biomarkers, MuRF1 and Atrogin-1, in muscles from DTA-injected mice. Additionally, preliminary data revealed an expansion of Toll-like receptor 7 (TLR7) expression in muscle-resident macrophages, suggesting that TLR7 signaling may contribute to their phenotypic regulation in cachexia. Discussion/Significance of Impact: Results suggest muscle-resident macrophages protect against cachexia-induced atrophy via anti-inflammatory activity. Future work will define their signals and targets. Given TLR7 upregulation, we will test if TLR7 signaling regulates their phenotype and contributes to muscle atrophy.

Objectives/Goals: From 2019 to 2025, the National Research Mentoring Network (NRMN) Coordination Center sought to facilitate the collective impact of the 11 NRMN Phase II research studies. Through the building and fostering of community and coordinating research, we sought to support the NRMN research teams in their collective efforts. Methods/Study Population: We worked with the 11 NRMN Phase II research studies, who in turn worked with a diverse group of researchers, from undergraduate to faculty, across the country. Results/Anticipated Results: We here present the two major products that arose from our work as a coordination center. The first, the NRMN data site, holds a measurement library and information for all the common measures used across the 11 NRMN Phase II research studies. Additionally, we summarize and present a book published on the lessons learned by our coordination center. Discussion/Significance of Impact: The NRMN data site serves as a powerful tool for those wishing to further explore the NRMN Phase II common data, one of the largest datasets available on biomedical research training and professional development. Our lessons learned book is an important reference for those wishing to replicate and run similar coordination centers in the future.