Objectives/Goals: To advance the clinical utility of a risk calculator for identifying young children’s mental health risk, we: 1) replicate an established mental health risk algorithm in two toddler-preschool samples; 2) determine added predictive value of child and parenting assets, advancing a strengths-based approach; and 3) discuss next steps for implementation. Methods/Study Population: Data were from two studies: A population-based Cohort 1 (N=2,763) and an independent risk-enriched Cohort 2 (N=323). In Cohort 1, children (48% girls) were 51.9% Black, 21.4% White, and 2.7% Other Race; 23.8% were Hispanic. In Cohort 2, children (44% girls) were 49.2% White, 38.1% Black, and 8.4% Other Race; 11.1% were Hispanic. Risks and assets were assessed in toddlerhood/early-preschool, and psychopathology was measured later in preschool. Epidemiologic risk prediction methods were applied to: 1) replicate the published risk model that includes demographics, irritability, and adverse childhood experiences (ACEs); 2) examine added predictive utility of child and parenting assets. Predictive utility was based on area under the curve (AUC) and/or the integrated discrimination improvement (IDI). Results/Anticipated Results: The previously published risk algorithm that includes demographics, child irritability, and child ACEs was replicated in both cohorts (AUC=.70 for both; IDI=.07 in Cohort 1 and .06 in Cohort 2). Via the IDI, there was added predictive utility of child assets (i.e., social competence) in both cohorts (IDI=0.008 in Cohort 1 and 0.02 in Cohort 2), as well as added predictive utility of parenting assets (i.e., parenting involvement/self-efficacy) in Cohort 1 (IDI=0.004). Preliminary evidence for barriers/facilitators regarding early childhood mental health screening in pediatric primary care and preschool settings will also be discussed, as part of a roadmap for future implementation of the calculator in routine care. Discussion/Significance of Impact: Improving early mental health risk algorithms through a strengths-based lens is essential for evidence-based and equitable decision-making. We have laid the groundwork for future implementation of a mental health decision tool in routine care of young children, from pediatric primary care to preschool.

Objectives/Goals: The plasma membrane (PM) is the nexus for cell signaling, where lipid composition governs receptor organization and T cell function. We aim to define PM dynamics in T cell states and develop PM-based strategies to preserve anti-tumor activity. Methods/Study Population: As such, we induced a dysfunctional T cell state, known as T cell exhaustion, through chronic stimulation or TGF-β1, hypoxia, and tumor cell co-culture and measured membrane biophysical properties through ratiometric confocal imaging of molecular probe Di-4-ANEPPDHQ. Complementary biochemical data from shotgun lipidomic analysis, immunofluorescence staining, and biochemical fluorescent assays paint a more complex picture of membrane fluidity changes during T cell exhaustion. Results/Anticipated Results: In both cases, we observed decreased PM rigidity compared to acute or normoxic T cell activation, specifically in T cells with upregulated PD1, LAG-3, and Tim-3. Dysfunctional T cells also exhibit changes in specific lipid species. This includes increased free cholesterol and decreased esterified cholesterol among other species. Discussion/Significance of Impact: Adoptive cell therapy is limited in solid tumors due to a hypoxic, suppressive TME that drives T cell exhaustion. Our data suggest exhausted T cells have fluid plasma membranes that impair synapse formation. Future work will validate this in patient-derived xenograft/TCR-T models that will pave the way for clinical translation.

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.

Objectives/Goals: Autoantibodies targeting beta-2-glycoprotein I (anti-β2GPI) are found in many patients with antiphospholipid syndrome (APS), where they trigger the release of pathogenic neutrophil extracellular traps (NETs). Here, we investigated the relevance of the cell signaling protein CD14 in this process to assess its potential as a therapeutic target. Methods/Study Population: Peripheral blood was collected from healthy donors or primary APS patients (all meeting the most recent ACR/EULAR Criteria). Isolated neutrophils were subjected to bulk RNAseq analysis. Neutrophil expression of CD14 and activation markers was validated by immunoblotting and flow cytometry. For mechanistic studies, affinity-purified anti-β2GPI IgG was added to isolated primary human neutrophils or whole blood in the presence or absence of the CD14 blocking antibody atibuclimab. CD14 and activation marker expression were assessed by flow cytometry, and NETs were visualized by immunofluorescence microscopy and quantified via SYTOX green assay. Ongoing work will involve CD14 blockade in mice subjected to the electrolytic inferior vena cava model of thrombosis, accompanied by APS or control IgG injection. Results/Anticipated Results: Neutrophils from APS patients overexpressed CD14 when compared to healthy controls, and patient neutrophil surface CD14 showed a strong positive association with the activation markers CD11b, CD18, and CD66b. Further, healthy neutrophils activated with patient-derived anti-β2GPI increased surface expression of CD14. Given the association between CD14 and APS-associated neutrophil activity, we asked whether CD14 blockade with atibuclimab could mitigate the effects of anti-β2GPI on neutrophils. Indeed, atibuclimab treatment attenuated anti-β2GPI-induced expression of CD11b, CD18, and CD66b, and NET formation. In vivo, we anticipate that CD14 blockade will reduce APS IgG-enhanced thrombi size and plasma levels of NET biomarkers and inflammatory cytokines. Discussion/Significance of Impact: Neutrophil CD14 appears to be required for neutrophil activation and NET formation triggered by anti-β2GPI in patients with APS. This work identified neutrophil surface CD14 and its associated downstream pathways as novel therapeutic targets with the potential to restrain not only thrombosis but also the upstream inflammation that underlies APS.

Objectives/Goals: Kidney transplant rejection is a leading cause of graft failure. Diagnosis is dependent on allograft biopsy that 1: focuses on immune infiltrate and 2: does not inform treatment or recovery potential. Using single-cell transcriptomic technologies, we assess rejection’s impact on kidney cells and begin to define outcome-affecting interactions. Methods/Study Population: Single-cell RNA sequencing was performed on 25 kidney biopsies from 15 separate patients who had undergone acute cellular rejection. Cell typing was performed using canonical genes, and differential gene expression profiling was performed in R with Seurat. The proportion of cell types present at different time points and grades of rejection was determined. We focused first on renal parenchymal cell types and their gene expression changes with rejection grade and treatment of rejection. The spatial localization of these cell states was determined with spatial transcriptomics using the Xenium Prime 5K pan-human tissue panel on 11 kidney biopsy sections. After annotation, spatial-informed niches were identified to investigate cellular changes associated with injury. Results/Anticipated Results: From the single-cell data, we were able to identify several renal parenchymal cell states. Focusing on the proximal tubule (PT) cells as the most numerous cell type in the renal cortex, we identified healthy, injured, and damaged cell states based on expression of tubular injury markers, genes associated with epithelial to mesenchymal transition, and expression of interferon-response genes. The proportion of PT cells in an injured state increased with rejection severity and decreased with rejection treatment. Utilizing the spatial transcriptomic data, we were able to identify both healthy and injured PT states and define their localization with immune cell types and histologic injury patterns. We found varied transcriptomic niches and that niches with injured PT cells were enriched with immune cells. Discussion/Significance of Impact: PT cells exhibit patterns of injury in response to rejection and when injured cells exist in spatial niches containing immune cells that are contributing or responding to injury. Understanding these interactions will further define the injury process caused by rejection and guide development of personalized rejection treatment.

Objectives/Goals: To evaluate the feasibility of developing salivary DNA methylation-based biomarkers for objective diagnosis and stratification of Temporomandibular Disorders (TMD), addressing the critical need for quantitative diagnostic tools to complement current subjective assessments and clinical examinations. Methods/Study Population: This cross-sectional pilot study enrolled 13 TMD patients and 4 pain-free controls from the University of Minnesota TMD Orofacial Pain Clinic. Participants underwent comprehensive DC/TMD diagnostic evaluation and completed validated instruments measuring pain intensity (VAS), psychological status (PHQ-9, PCS), and functional impairment (GCPS). Saliva samples were analyzed using Illumina EPIC BeadChip arrays interrogating ~850,000 CpG sites. Bioinformatic analyses included principal component analysis, identification of differentially methylated sites, and development of composite Methylation Profile Scores for patient stratification Results/Anticipated Results: Principal component analysis showed clear clustering separation between TMD patients and controls. Methylation profile scores demonstrated clear stratification between controls, high-impact, and low-impact TMD subgroups. High-impact TMD participants (n=6) showed significantly elevated clinical severity scores compared to low-impact TMD (n=7): pain intensity (52.0±11.3 vs 25.0±13.8), depression (20.5±7.3 vs 8.4±4.9), and catastrophizing (12.6±8.1 vs 6.4±6.5). Promoter-level analysis identified candidate genes associated with TMD severity phenotypes, supporting the potential for epigenetic biomarkers to enable objective patient stratification and guide precision treatment selection. Discussion/Significance of Impact: Epigenetic biomarkers capturing TMD heterogeneity could transform clinical practice by enabling early-risk stratification, preventing progression to high-impact chronic pain through personalized interventions, and reducing the substantial healthcare burden of this prevalent orofacial pain condition.

Objectives/Goals: Attention deficit hyperactivity disorder (ADHD) affects 5.9% of youth worldwide and is associated with mental health disorders. In adults, HeartMath biofeedback improves ADHD, emotional regulation, and anxiety. Breathing practices promote similar outcomes. Our goal was to examine the synergistic effect of these therapies in children with ADHD. Methods/Study Population: A 10-week, 2-arm randomized controlled clinical trial was conducted in a rural health care clinic to examine the safety, feasibility, preliminary efficacy, and self-reported child and parent perceptions of a biofeedback plus breathing practices intervention (Healing Minds) in a pediatric population (n=12; M=5; F=7; 11.6 ±3.6 years). Children were randomized to waiting list placebo (P) or treatment (T) intervention [Weekly biofeedback (HeartMath) and breathing exercises (Qi gong, belly, box, and alternate nostril) by trained medical staff]. Symptoms and severity of ADHD (Vanderbilt; Conners), Screen for Child Anxiety-Related Disorders, Child & Adolescent Trauma Screen (CATS), CATS-Caregiver, Child & Youth Resilience Measure, and perceptions by interview were assessed at baseline, 5 and 11 weeks. Results/Anticipated Results: Twelve children were enrolled (6=T; 6=P) into the clinical trial. Nine completed the study [T (N=4); P (N=5)]. One moderate, unrelated adverse event was reported. Treatment attendance rates varied [90% (2), 70% (1), 60% (1), and 30% (1)]. Children self-reported reduced panic attacks, improved sleep, and feelings of self-control. Parents perceived a calmer home with fewer meltdowns, less overwhelming events, and expressed satisfaction with biofeedback, Qi gong, and belly breathing, but not box and alternate nostril breathing. Questionnaires were perceived as burdensome, and placebo waiting list delays were unacceptable to parents. School-related scheduling challenges and missing assessment data limited study implementation. Small sample sizes limited statistical power and the evaluation of outcomes. Discussion/Significance of Impact: Healing Minds shows promise as a beneficial adjunct to clinical care in rural children with ADHD. The program was shown to be safe and feasible. Children and parents self-reported beneficial effects. Future studies should include a patient-centered study design, focused breathing techniques, fewer assessments, and improved school coordination.

Objectives/Goals: To evaluate how integrating genomic risk scores with environmental pollutants, endocrine disruptor exposure, and lifestyle factors enhances predictive accuracy for breast and ovarian cancer, improving early-risk stratification and advancing equitable precision prevention among diverse female populations. Methods/Study Population: A secondary data analysis was conducted using publicly available datasets from the UK Biobank and NIH All of Us cohorts (n = 5,128 women aged 25–70). Polygenic risk scores (PRS) for breast and ovarian cancer were combined with environmental exposures (PM 2.5, endocrine disruptors) and lifestyle factors (BMI, alcohol intake, physical activity). Eligible participants had complete genomic, environmental, and behavioral data. Multivariate logistic regression and random-forest algorithms evaluated predictive performance and interaction effects. Prior frameworks (Garcia-Closas et al., JNCI, 2014; Dudbridge et al., Nat Genet, 2018) guided variable selection, data harmonization, and fivefold cross-validation to ensure generalizability across cohorts. Results/Anticipated Results: Models integrating genomic, environmental, and behavioral variables achieved an AUC of 0.87 for breast cancer and 0.83 for ovarian cancer, outperforming PRS-only models (AUC = 0.74, p < 0.001). Fine particulate matter (PM 2.5) exposure and elevated BMI showed significant interactions with BRCA1/2 and CHEK2 variants, amplifying risk by ~1.5–2.3×. Counties with lower environmental quality had 10.8 more breast cancer cases per 100,000 (Gearhart-Serna et al., Sci Rep, 2023). Integrating pollution and lifestyle metrics improved early-risk classification, calibration, and predictive equity across populations, reducing false negatives by 18% and highlighting actionable, low-cost prevention targets for global women’s health. Discussion/Significance of Impact: Integrating genomic, environmental, and behavioral data enhances precision-medicine accuracy for cancer risk assessment. This approach supports equitable prevention by accounting for modifiable exposures and social determinants, reframing cancer prevention through precision public health, and informing population-level screening policy.

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: In CTSA funding calls issued since 2021, the NCATS has differentiated the terms “translational research” and “translational science” which has resulted in confusion between the terms. We explore why this distinction has become challenging for CTSA hubs to implement as a requirement of pilot and UM1 awards and reflect on ways to reduce confusion. Methods/Study Population: We conducted a historically informed critical analysis of data collected from three sources. First, we looked at the scholarly literature on definitions of translational science and translational research since 2006. Second, we analyzed NCATS funding calls from 2006-2024 to identify if and how translational science and translational research were defined and utilized. Lastly, we used our professional experience over the last five years working at a CTSA hub and developing trainings to differentiate translational research and translational science for pilot awards. Results/Anticipated Results: Results suggest three reasons why challenges are commonplace. First, we demonstrate that NCATS has not consistently communicated this distinction, resulting in different interpretations of the concept of translational science. Second, we argue that the systems-level conceptualization of translational science is uncommon in biomedical science training, potentially contributing to gaps in understanding. Third, we show that the academic performance measures, funding, and infrastructures for biomedical research in the United States are not designed to support translational science projects, systems, and careers. Most notably, there are no NIH R01 mechanisms dedicated to translational science that provide standard NIH career pathways. Discussion/Significance of Impact: We propose education and peer-to-peer brainstorming sessions to improve the ability of CTSA investigators to understand the scope of translational science and translational research. However, to truly support long-term work in translational science, NCATS would need to develop a clear career path that includes R01s in translational science.

Objectives/Goals: Effective collaboration is needed to respond to the scientific challenges of our times. A related challenge is how to support individual and team learning for such collaborations. Our project goal is to describe and evaluate team training approaches and lessons learned at one CTSA since the inception of#_msocom_1 its Team Science Core in 2017. Methods/Study Population: The Institute of Translational Science (ITHS) Team Science Core has offered team science-focused training and education for nearly eight years. During this time, our approach has evolved to meet changing needs and contexts. Initially, most of our offerings were in person, including an annual multi-day in-person workshop for interdisciplinary research teams (3 or more individuals from 2 or more disciplines). During and since the COVID-19 pandemic, the majority of our offerings have shifted to online trainings which has helped expand regional access. Throughout this period, qualitative and quantitative data have been collected about and from participants. We have carried out mixed methods analyses to describe and evaluate these team training approaches and lessons learned. Results/Anticipated Results: Since 2017, the ITHS Team Science Core has offered ~250 trainings for more than 6000 individual participants (and >500 interdisciplinary research teams). Participants included students (undergraduate and graduate), trainees, faculty, clinicians, and research professionals from the WWAMI (Washington, Wyoming, Alaska, Montana, Idaho) Region and beyond. Feedback from participants in workshops, seminars, classes, and other activities has been overwhelmingly positive; 80–95% of respondents agreed or strongly agreed with the statement that “participation will improve my team’s collaboration.” Qualitative responses (short answers and interviews) illustrate ways that individuals and teams applied their learning to improve communication, clarify roles, and increase confidence in using team tools. Discussion/Significance of Impact: Clear benefits of participating in team science education and training were reported. A shift to virtual formats increased accessibility and regional representation. Qualitative interviews and short answer responses offered an enriched understanding of how team training influenced collaboration and implementation of team science practices.

Objectives/Goals: Current techniques for immunosuppressant therapeutic drug monitoring (TDM) are not patient-centric which is prohibitive for those in remote areas. We conducted stakeholder engagement meetings to identify context, attitudes, design requirements, and potential unintended consequences for developing an at-home test for immunosuppressant TDM. Methods/Study Population: We conducted two virtual focus group discussions with 10 organ transplant recipients, some of whom were located in rural and remote locations, including members of the community engagement committee of the Center for Innovations in Cancer & Transplant and the Transplant Recipient Advisory Council. We also conducted a virtual focus group discussion with transplant providers and had individual conversations with one provider and one patient who were unable to attend the focus groups. Results/Anticipated Results: Patients had strong interest in at-home testing to reduce financial and time burdens. Providers wanted faster test turnaround times than available with current centralized labs, especially for managing patients in remote settings. Both patients and providers agreed that at-home tests could enhance patient engagement and empowerment in healthcare, and they preferred a device that would provide highly quantitative and accurate results available on the same day. Finally, both patients and providers also expressed concerns about potential increased work created for caregivers and healthcare workers. Discussion/Significance of Impact: We will incorporate these findings into our design engineering and continue iterative stakeholder engagement to co-design a device that can transform TDM and transplant care.

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: This study investigates racial differences in white matter hyperintensity (WMH) burden across the Alzheimer’s disease spectrum, adjusting for demographic and clinical variables, to clarify mechanisms driving disparities and improve equity in diagnosis and prevention. Methods/Study Population: We analyzed cross-sectional MRI and clinical data from 1,649 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI): 682 cognitively normal (CN), 584 with mild cognitive impairment (MCI), and 383 with AD. Of these, 124 participants (7.5%) identified as African American: CN (n=70), MCI (n=39), and AD (n=15). Linear mixed effects models were used to evaluate the effect of race, cognitive status, time since first MRI, age, sex, education, intracranial volume (ICV), MRI acquisition type, and APOE ε4 status on total WMH volume. Results/Anticipated Results: Older age, greater time since MRI, and larger ICV were significantly associated with increased WMH burden across cognitive groups. Use of FLAIR imaging was negatively associated with WMH volume, reflecting differences in image acquisition sensitivity. Higher educational attainment was linked to lower WMH burden, suggesting a possible neuroprotective effect. The most pronounced associations were observed within the AD subgroup. After adjusting for all covariates, race was not a statistically significant independent predictor of WMH burden. Discussion/Significance of Impact: Although race did not independently predict WMH burden, structural and demographic factors – such as age, education, and MRI technique – significantly influenced WMH volume. Findings underscore the interplay of biological, social, and technical variables in brain aging and the need for greater African American inclusion.

Objectives/Goals: This study assesses mercury exposure in dogs and cats from Heredia, Costa Rica, near artisanal gold mining areas. As companion animals share environments with humans, they may serve as sentinel species for mercury contamination in mining-affected regions. Methods/Study Population: Noninvasive hair samples are being obtained by trained veterinary professionals from each animal through opportunistic collection or with owner’s consent, ensuring minimal stress. One hundred and fourteen samples were collected from Costa Rica, and matched samples are being collected from the Colorado sites. Mercury concentrations will be measured using inductively coupled plasma mass spectrometry (ICP-MS). Results/Anticipated Results: Results will be reported in parts per billion and assessed for statistical significance between groups. The results of this feasibility study will help determine whether companion animals living near ASGM sites exhibit higher mercury accumulation compared to those in less contaminated or historically impacted regions. Discussion/Significance of Impact: Findings may inform future public health surveillance, environmental monitoring, and policy decisions related to mining practices and community exposure risk.

Objectives/Goals: We aim to understand how estradiol (E2) modulates proliferation and migration in distinct breast cancer subtypes within a collagen-based 3D platform that serves to better recapitulate human tissue architecture. This work seeks to bridge in vitro hormone biology with translational modeling of hormone-driven tumor behaviors. Methods/Study Population: MDA-MB-231 (triple-negative) and MCF-7 (hormone receptor-positive) breast cancer cells, tagged with red fluorescent protein, were exposed to varying E2 concentrations (0 to 10-4 M) in both 2D and 3D culture. In 2D, 2,000 cells/well were seeded in 96-well plates. Wells were imaged on Day 1 using confocal fluorescent microscopy, treated with E2, and reimaged on Day 5 to assess post-treatment counts. Normalized proliferation was calculated as Day 5/Day 1 cell count. In 3D, per well, 60,000 cells were embedded in a 12 µL base layer of 0.3% type I collagen, overlaid with 40 µL plain collagen, and topped with 150 µL E2-containing media. On Day 5, microscopy and a machine learning algorithm assessed cell counts and migration, defining migratory cells as those >1 standard deviation from the mean z-position of the tumor body. Results/Anticipated Results: In 2D, MCF-7 cells demonstrated significant dose-dependent proliferation to E2. Normalized cell counts increased progressively from 0 M to 10-10 M, at which point proliferation peaked, followed by a significant decline at higher concentrations. In contrast, MDA-MB-231 cells showed stable proliferation across the 0 to 10-6 M range, with a marked reduction in count at 10-4 M. In 3D, MDA-MB-231 cell proliferation remained stable across low and intermediate E2 doses, with a marked reduction at 10-4 M. MCF-7 cells, however, showed no significant differences in normalized proliferation across any E2 concentration in 3D. Quantitative migration analysis revealed that the percentage of migratory cells remained statistically unchanged across all E2 concentrations for both MCF-7 and MDA-MB-231 cells in the 3D matrix. Discussion/Significance of Impact: Dimensionality influences hormone responsiveness in breast cancer, highlighting limitations of 2D assays in modeling clinical behaviors and predicting outcomes. Advancing biomimetic 3D platforms like these may ultimately enable patient-specific screening of endocrine response and guide personalized therapy for hormone-sensitive cancers.

Objectives/Goals: Methicillin-resistant Staphylococcus aureus is a nefarious bacterial pathogen and is classified as a serious threat. MRSA is resistant to most B-lactam antibiotics (penicillins, cephalosporins, etc.). The goal of this study is to identify an antibiotic adjuvant capable of resurrecting B-lactam antibiotics for treatment of MRSA infections. Methods/Study Population: A fluorescence-reporter assay was used to screen a compound library. Minimum-inhibitory concentrations were assessed for the compounds against various MSSA and MRSA strains. A common resistance mechanism to B-lactams by MRSA is by the function of the bla operon. One gene in this operon encodes for a B-lactam sensor/signal transducer protein BlaR, the primary target of this study. Inhibition of BlaR by compound 1 (best potentiator of oxacillin) was studied by nano-differential scanning fluorimetry (nanoDSF), surface plasmon resonance (SPR), scanning electron microscopy (SEM), and in vivo assays. Results/Anticipated Results: We identified 80 compound hits from a 1,974-compound NCI library. Twenty-four compounds showed potentiating ability (2- to 4,096-fold decrease in MIC for oxacillin). Seven compounds exhibited melting temperature shifts by nanoDSF of BlaR, indicating binding. SPR determined compound 1 has a binding affinity of 31 mM to BlaR-SD. SEM images showed disruption in the S. aureuscell wall on exposure to compound 1and oxacillin. S. aureusN315 showed 3-log reduction in bacterial count treated with a mixture of compound 1 and oxacillin. Discussion/Significance of Impact: Compound 1 targets BlaR-SD, which restores S. aureussusceptibility to treatment by oxacillin. There are currently few antibiotics available in the clinic capable of treating MRSA infections. The combination hold promise of a treatment option for MRSA.

Objectives/Goals: We advance multimodal modeling, including digital twins (DTs), within an AI-enabled Learning Health System (LHS) to turn healthcare data into insights for precision care and translational science. DTs are dynamic patient models integrating EHRs, neuroimaging, biomarkers, wearables, and other real-world data (RWD) tools. Methods/Study Population: Multiple sclerosis (MS) provides a compelling use case of heterogeneous manifestations including fatigue, mobility deficits, cognitive deficits, and sleep disturbance, requiring multimodal integration to capture complexity. DTs integrate EHR, neuroimaging, biomarkers, wearable, and other RWD via ETL pipelines using standards like FHIR, OMOP, and DICOM. Modeling uses ensemble learning, LSTM/DBNs, dimensionality reduction, and bias auditing. Explainable AI (XAI) provides transparency through counterfactual “what if” analyses, such as testing whether risk changes if a patient improves gait speed, adjusts therapy, increases sleep duration, or lowers biomarker levels. Continuous monitoring detects model drift, ensuring reliability per FDA V3: verification, validation, vigilance. Results/Anticipated Results: A robust extract–transform–load (ETL) pipeline maps these inputs into standards such as Fast Healthcare Interoperability Resources (FHIR), the Observational Medical Outcomes Partnership (OMOP) common data model, and the Digital Imaging and Communications in Medicine (DICOM) format, ensuring interoperability and reuse across sites. Development emphasizes robustness through parameter optimization, ensemble learning, prevention of data leakage, preprocessing, dimensionality reduction, and regularization. Key AI applications include MRI lesion and atrophy segmentation with nnU-Net, temporal deep learning for longitudinal forecasting with deep belief networks (DBNs) and long short-term memory (LSTM) models, ensemble modeling to improve generalizability, and bias auditing to promote fairness. Discussion/Significance of Impact: This initiative advances digital health, neuroinformatics, and precision medicine for MS and other complex diseases. Aligned with NIH ODSS Data COUNTS and FDA V3, the framework supports discovery, regulatory science, and generalization to other variable conditions.

Objectives/Goals: Early identification of heart failure with reduced ejection fraction (rEF) by emergency medical service (EMS) providers is challenging, particularly in rural settings. We developed a rEF decision aid that addresses barriers to adoption using demographics, measurements, and computerized findings from ubiquitous EMS 12-lead defibrillators. Methods/Study Population: We identified all patients at Tufts Medical Center between Jan 1, 2012 and Apr 16, 2024 who had a 12-lead ECG taken within 30 days of an echocardiogram. Using the echocardiogram left ventricular ejection fraction (LVEF), each ECG-echo pair was classified as reduced EF (rEF – LVEF < 40) or non-rEF (LVEF >= 40%). The rEF is the outcome in a step-wise logistic regression model (LRM) to predict rEF. Variables of interest in the model were selected based on literature review in clinical assessment for heart failure with reduced ejection fraction (HFrEF) and what can be provided by a 12-lead ECG. We also included left ventricular damage score (e.g., Selvester Score) due to previous myocardial infarction (MI). Prior MI is a known risk factor in HFrEF. We used R 4.5 to perform the analysis and logistic regression modeling. Results/Anticipated Results: 2,289 patients were identified with a 12-lead ECG taken within 30-days of an echocardiogram. Our final regression model included demographic and ECG features: age, sex, myocardial scarring score, computerized interpretation of conduction defects (bundle branch block, early repolarization, and intraventricular conduction defect), and acute myocardial infarction findings. The rEF prediction model had an area under the curve of 0.684 and good calibration along the identity line up to predicted probability of 0.3. Discussion/Significance of Impact: A LRM to predict rEF using an ECG is computationally efficient and address important barriers of adoption in the EMS setting. It does not require additional EMS training or equipment. LRM transparency facilitates communication between paramedics and medical control clinicians to make shared decisions regarding treatment and transport decisions.

Objectives/Goals: Broad and shallow data from large national cohorts provide generalizable real-world insights, while deep and narrow data from smaller cohorts capture detailed multimodal measures. This project integrates both approaches to study cognitive decline in Parkinson’s disease, which affects up to 50% of patients. Methods/Study Population: This longitudinal study includes validated digital biomarkers of motor, non-motor, sleep, and driving activity in 150 participants, plus novel social connectedness measures in 150 PD – partner dyads. PD and cognitive status are assessed annually; 4 weeks of continuous real-world behavior via actigraphy, social connectedness, and driving sensors are collected biannually. Anchor variables from pilot PD, PPMI, and LongROAD enable harmonization and cross-cohort modeling. Model development focuses on generative time series models that can capture the joint distribution of multimodal time series data for predicting values for future time steps. Together combined with genetics, plasma, and neuroimaging, they enable digital twins to predict decline and identify modifiable risks. Results/Anticipated Results: This project is designed to make impactful contributions to scientific knowledge, technical capability, clinical practice, and healthcare equity. Addressing key gaps in understanding PD cognitive decline, developing clinically validated digital biomarkers, and advancing data modeling for public health and precision medicine, including the creation of Digital Twin models. By integrating real-world data and social context into PD research, we will enhance clinical trials, improve patient outcomes, and provide clinicians with valuable tools, leading to a deeper understanding of PD and ADRDs. The data generated will be mined and shared with colleagues for years, driving new research and insights. Discussion/Significance of Impact: This project enhances monitoring and prediction of cognitive decline in PD, developing clinically valid, interpretable tools for real-world health. This generalizable platform unifies digital, imaging, and biomarker data as a model for translational research, advancing trial readiness across neurological and chronic diseases.