Objectives/Goals: Nearly 1 in 5 children with a suicide-related emergency department (ED) visit will have a suicide attempt within 18 months. Our objective was to determine demographic and clinical characteristics associated with time to ED revisit with self-injurious thoughts and behaviors (SITB) within 6 months. Methods/Study Population: We performed a retrospective cohort study of children aged 6 to 17 years with a primary mental health ED visit at 38 US children’s hospitals from January 2021 to December 2023. Data were obtained from the Pediatric Health Information System administrative database. We included the first index mental health ED visit for each child. Primary outcome was time to first ED revisit with a SITB diagnosis within 6 months of a patient’s index mental health ED visit, defined by ICD-10-CM diagnosis codes. We evaluated demographic and clinical characteristics associated with time to SITB revisit using a multivariable Cox Proportional Hazards Regression model. Results/Anticipated Results: We included 201,214 unique children, 8.7% had an ED revisit with SITB within 6 months. Among children with an SITB revisit, 74.6% had SITB diagnosed at the index visit. Multiple demographic characteristics were associated with longer time to revisit including male sex (HR 0.76, 95% CI 0.74-0.79) compared to female, private insurance (HR 0.83, 95% CI 0.80-0.86) compared to public, and Hispanic race and ethnicity (HR 0.81, 95% CI 0.77-0.85) compared to non-Hispanic White. Clinical characteristics associated with a shorter time to SITB revisit included index visit diagnoses for intentional self-harm (HR 1.32, 95% CI 1.27-1.38), suicidal ideation (Hazard Ratio (HR) 1.65, 95% CI 1.59-1.71), and mood disorder (HR 1.38, 95% CI 1.33-1.44). Discussion/Significance of Impact: Three in four children with a SITB ED revisit had SITB diagnosed at the index visit. Children with index visit suicidal ideation were at highest risk of SITB revisit. These findings highlight missed opportunities to identify and address pediatric SITB at the index ED visit and to prevent ED revisits.

Objectives/Goals: Rare pediatric cancer research has been hindered by limited sample size and disease-specific databases. The training and infrastructure required to create these datasets have been cost prohibitive. Automated electronic medical record (EMR) extraction and natural language processing (NLP) hold the potential to overcome these limitations. Methods/Study Population: In a mixed population of adults and children with thyroid cancer, we implemented an automated data extraction process for multiple data types, including structured demographic, laboratory, and billing data and unstructured free text from pathology reports. Data extraction and cleaning software transformed EMR data into a research-ready database. NLP using regular expressions extracted meaningful, structured data from free-text pathology reports. Results from the automated data extraction and NLP were compared to manual extraction, and results were quantified using accuracy, precision, recall, F1-score, and Cohen’s Kappa to account for random agreement. Additional iterations using machine learning and large language models will be performed to compare accuracy and identify an optimal strategy. Results/Anticipated Results: From 2016 to 2023, 2,899 patients with thyroid cancer were identified. In a 50-patient subset, manual extraction of pathology reports was compared to NLP-derived data. Histology type was correctly identified in 90% of cases (kappa = 0.85), and thyroid procedure identification accuracy was 89% (kappa 0.81). Focality was 98% accurate (kappa=0.96). Vascular, lymphatic, and perineural invasion, along with extrathyroidal extension and pathologic tumor stage, were 100% accurate. Pathologic nodal staging was 96% accurate (kappa=0.89). Kappa greater than 0.8 is considered almost perfect agreement. Discussion/Significance of Impact: Our results indicate that automated EMR extraction and NLP can be used to develop a thyroid cancer dataset that is highly detailed, accurate, and efficient to collect. After optimizing our system of data extraction, we will replicate this process in a multi-center study with the eventual goal to expand these algorithms across rare pediatric cancers.

Objectives/Goals: The study’s objective was to use real-world data to compare machine learning approaches for predicting colorectal cancer (CRC) screening status: (1) a Random Forest model optimized for overall accuracy and (2) Feasible Solution Algorithm (FSA) models focused on interpretability and identifying subgroups at risk of being unscreened. Methods/Study Population: The data included 22,549 adults aged 45–75 who visited a UK King’s Daughters Medical Center (UKKD) primary care site between 12/1/2023–11/30/2024, across five Appalachian counties. Patients with total colectomy, colon cancer, advanced illness, hospice care, dementia, and/or frailty were excluded. After complete-case analysis, 18,533 records remained and were split 80/20 for training/testing. Two machine learning methods, random forests and FSA, were applied to identify key predictors of CRC screening status. Within the FSA framework, logistic regression models were fitted using the training data, and model performance metrics were subsequently evaluated using the testing dataset. Results/Anticipated Results: Overall (n=22,549), 65.6% of patients completed CRC screening. The mean age was 59 years (SD = 8.2); most were White (97%), 40% had Medicare insurance, and 68% had multiple non-primary care provider (PCP) visits. The Random Forest model demonstrated the highest overall accuracy of 69% and had notably high sensitivity, correctly identifying approximately 9 of 10 screened patients. On the other hand, the FSA model offered greater specificity (65%) and interpretability, with the 2-predictor model identifying smokers and those with 0 non-PCP visits as the vulnerable subgroups who are least likely to be screened. Discussion/Significance of Impact: FSA identified smoking status, insurance type, number of non-PCP clinic visits, and age as key factors relating to CRC screening behavior. These findings can help clinicians and public health teams identify unscreened patients, guide targeted outreach, and advance translational strategies to improve population health.

Objectives/Goals: The need for accessible medical information has never been greater. Existing research and clinical documents are often too complex for the public to understand. This study aimed to evaluate the use of AI algorithms to convert research and clinical materials into language that is more easily understood while maintaining accuracy and informativeness. Methods/Study Population: We abstracted 21 half-page texts from informed consents, clinical documents, abstracts, and health-related websites. Each original text was then rewritten using 3 AI algorithms (resulting in 63 new, rewritten texts). Participants were given a set of 3 randomly selected surveys. Each survey contained the original text and an AI-rewritten text. Participants were then asked to answer 5 questions using a Likert scale 1-5 about the readability, clarity, and content of the two texts, their overall score of the new text, and which text they preferred. The study was IRB-approved. Participants were given a $10 gift card. Results/Anticipated Results: 68 participants (69.6% non-white, 65.2% female, mean age 58.3) completed the study task. 3 AI Algorithms were tested: * ConBART [Cripwell 2023]: context-aware method for text simplification * UL+Decoder [Flores 2023]: Flesh-Kincaid readability-driven method for text simplification * UBclear [House-made]: Iteratively improves readability with self-correction and retrieval-augmented generation. ConBART and UL+Decoder scored similarly across readability, clarity, and detail-fidelity metrics, but were preferred over original texts less than 50% of the time. UBclear scored higher in readability, clarity, detail, and overall score. Importantly, UBclear text was preferred over the original text 80% of the time, showing stronger alignment with human judgments of clarity, readability, and fidelity. Discussion/Significance of Impact: Our pilot study shows that AI can simplify existing research and medical care documents, potentially contributing to inclusivity and transparency in research and clinical care. “UBclear” was superior to the other 2 AI methodologies in readability, accessibility, and clarity while also being preferred over original texts and competing AI versions.

Objectives/Goals: Blood pressure (BP) control remains suboptimal among US patients with hypertension. Single-pill combination (SPC) therapies are commonly used to improve adherence; however, their effectiveness for achieving early and sustained intensive BP control is unclear. Methods/Study Population: We performed a post hoc analysis of SPRINT including 2,736 participants propensity matched in 1:2 ratio to compare effects of SPCs with equivalent multi-pill therapy. The estimated marginal odds of achieving optimal BP control were derived using generalized linear mixed models with repeated measures (LMMRM). The association between time-updated SPC use and BP change in short- (≤6 months) and long-term (>6 months) follow-up was assessed with LMMRM and SPC*time interaction term. Multivariable Cox models evaluated association of SPC use with CV events and serious adverse events (SAEs). Results/Anticipated Results: Among SPRINT participants (N=8623), 9.3% (N=803) were prescribed SPC at baseline with greater use in the intensive vs. usual care group (5.79 vs. 3.90 per 100 person-months; p-diff<0.001). Among matched pairs (SPC[n=912); multi-pill therapy[n=1824]), SPC use was associated with 22% increased likelihood of achieving target BP by 6 months [OR(95% CI): 1.22(1.05, 1.42)]. Participants receiving SPCs (vs multi-pills) experienced more rapid BP reduction in the first 6 months (-2.0 vs. -1.2 mmHg monthly change; p-diff<0.001). Over long-term follow-up, participants using SPCs achieved significantly lower SBP at each timepoint. The risk of the primary CV composite endpoint and SAEs was not significantly different between groups. Discussion/Significance of Impact: SPC therapy resulted in more rapid and sustained BP reduction and a greater likelihood of achieving BP control compared with multi-pill therapy without increase in SAEs. Future research is needed to identify optimal strategies for implementing SPC-based approaches at the population-level and optimize public health benefits of intensive BP control.

Objectives/Goals: We evaluated the associations of urine uranium with incident chronic kidney disease (estimated glomerular filtration rate <60 mL/min/1.73m2), incident albuminuria (albumin-to-creatinine ratio >30 mg/g), and baseline and follow-up plasma metabolite levels in participants without diabetes across the Great Plains and Southwest. Methods/Study Population: We evaluated participants with available urine uranium estimates, eGFR, uACR, and other variables of interest. In incident case analyses, participants were free of CKD (N=1,845) and albuminuria (1,693) at baseline (2001-2003). We used generalized estimating equations, clustering on family identifier, to estimate risk ratios (RRs) of incident CKD and albuminuria per doubling of urine uranium adjusted for sex, age, eGFR or uACR, study center, educational level, smoking status, BMI, and arsenic exposure. Nontargeted metabolomic profiling was analyzed by gas chromatography-mass spectrometry. In metabolome-wide association analyses, we used adjusted linear mixed models to estimate the association of urine uranium (N=1,223) with relative changes in metabolite concentrations over time. Results/Anticipated Results: Overall, the median (IQR) age was 35.9 (23.7, 46.8) years and 60% participants were female. The number of incident CKD cases was 28 (mean follow-up 5.6 years), and the number of albuminuria cases was 140. The adjusted RR (95% CI) per doubling of urine uranium was 0.90 (0.71, 1.15) for incident CKD and 1.00 (0.92, 1.08) for incident albuminuria. In metabolome-wide analyses, urine uranium was associated with a relative change in 8 unique metabolites and 31 unknown metabolomic features (p-value <0.05), with 5 unique metabolites (threonic acid, N-acetylornithine, isocitric acid, cellobiose, and 6-deoxyglucose) and 7 unknown metabolomic features reaching false discovery rate significance (BH-adjusted p-value <0.05). Discussion/Significance of Impact: Urine uranium was not associated with incident CKD nor with albuminuria. We identified 5 unique metabolites associated with urine uranium. Our findings are relevant for uranium-related kidney disease in people without diabetes, but require additional confirmation due to potential reverse causality and lack of drinking water uranium exposure.

Objectives/Goals: Randomized evaluation of interruptive EHR alerts in improving service delivery or patient health outcomes is usually carried out within the EHR system and in routine hospital operations. However, this study setting leads to increased risk of contamination, where factors outside the original design affect estimation and testing of treatment effect. Methods/Study Population: We focused on the effect of contamination on the design and analysis of individually randomized controlled trials (iRCTs) for the study of the effectiveness of EHR alerts. More specifically, we looked at post-randomization contamination due to clinician-induced correlations. To account for this kind of contamination, we extended the iRCT design to include clinicians’ random effects under the linear mixed effects model (LMEM) framework. We then proceeded to derive sample size and power formulas under different LMEM correlation structures. Using extensive simulation studies, we evaluated the derived formulas and assessed the effect of using a naive two-sample t-test that ignores post-randomization contamination. Results/Anticipated Results: We observed that the level of contamination on sample size, power, and type I error due to clinician-induced correlations was driven by three parameters: variances of the clinicians’ random intercepts and slopes, and the correlation between them. These parameters reflect the way and extent to which clinicians influence participant-level outcomes. For scenarios with common correlation structures from the literature, a test of the treatment effect based on a naive two-sample t-test led to inflated type I error. Using the naive test in the presence of contamination increased the risk of incorrectly concluding that the treatment effect is significant. We did observe valid naive tests with well controlled type I error but under extreme and unusual values of inter-cluster correlations. Discussion/Significance of Impact: Using iRCT as the baseline design for the interruptive EHR alert trials, a naive two-sample t-test may appear reasonable. However, our analytical and simulation findings indicate that a careful consideration of clinicians’ impact on patient-level outcomes in routine hospital operations is necessary for valid hypothesis tests and inference.

Goals: Asthma is a common condition affecting over 25 million Americans. Despite high prevalence, asthma is often undertreated, resulting in significant preventable morbidity and mortality. Our goal was to identify and characterize individuals with undertreated asthma to inform tailored interventions designed to improve care access and outcomes. Methods/Study Population: We used electronic health records (EHR) to identify patients with suspected undertreated asthma receiving care in a large, multi-site state-wide healthcare system serving >2 million people. Undertreated asthma was defined by 1) history of emergency department visit or hospitalization for asthma exacerbation or asthma-related symptoms and 2) no prior history of treatment with inhaled corticosteroids, the standard of care preventative treatment for patients with asthma. Asthma symptoms were defined as wheezing, cough, and dyspnea. Patients were required to have been treated with systemic corticosteroids and inhaled beta agonists (standard treatment for asthma exacerbation) and have history of eosinophilia to increase the specificity of asthma diagnosis. Results/Anticipated Results: We identified 3,318 people with suspected undertreated asthma evaluated between 2019 and 2024. These patients had high rates of healthcare utilization with a mean of 7.3 any-cause emergency department visits and 1.4 hospitalizations over the 5-year study period. Black and Hispanic patients were less likely to have a documented primary care physician or prior pulmonologist referral order when compared to white and non-Hispanic patients, respectively. Insurance coverage rates did not differ significantly between these racial/ethnic groups. Discussion/Significance of Impact: Individuals identified as having suspected undertreated asthma were found to have high healthcare utilization. Significant racial and ethnic disparities were observed, with associated gaps in primary and specialty care not explained by insurance status, highlighting potential opportunities for improving care and addressing disparities.

Objectives/Goals: Prenatal exposure to phthalates, parabens, and phenols (EDCs) may be risk factors for adverse birth outcomes, but the biological mechanisms driving these relationships are not well understood. This study aims to explore the associations between prenatal EDC exposure – individually and as a mixture – and the neonatal metabolome. Methods/Study Population: This study included 180 pregnant people from a Columbia Center for Children’s Environmental Health cohort in New York City (1998-2002). Eleven phthalates, 1 phthalate replacement, 5 phenols, and 3 parabens were quantified in umbilical cord plasma samples using gas chromatography-tandem mass spectrometry. Metabolomic features in cord plasma were characterized via liquid chromatography coupled with high-resolution mass spectrometry. Metabolome-wide association studies using linear regression and quantile g-computation were used to identify features associated with exposure to the individual and mixture of EDCs, respectively. Correlated metabolites were grouped into modules using weighted gene co-expression network analysis (WGCNA) to identify associations with EDC exposure at a systemic resolution. Results/Anticipated Results: We observed metabolome-wide associations between 18 individual EDCs and 64 unique features in the umbilical cord plasma (p-value <0.05). For example, 9 different phthalates were significantly negatively associated with methionine, creatinine, and 2-methylcitrate. The overall EDC mixture was negatively associated with 2-methylcitrate, arginine, and creatinine (p-value <0.05). In the WGCNA analyses, 13 individual EDCs and the overall EDC mixture were significantly associated with at least 1 of the modules. Pathway enrichment analyses revealed that these metabolites were related to energy, amino acid, and lipid metabolism. Discussion/Significance of Impact: Prenatal EDC exposure, individually and as a mixture, is associated with levels of metabolites measured in umbilical cord plasma at delivery. The identified EDC-responsive metabolites and associated pathways offer insight into the biological processes that could underpin the relationship between prenatal EDC exposure and adverse birth outcomes.

Objectives/Goals: The human coronavirus OC43 is a Betacoronavirus, like SARS-CoV-2, but causes limited disease. Current clinical OC43 strains replicate in airway epithelial cultures while the lab-adapted strains only replicate in immortalized cells. We will identify the host and viral factors causing this difference. Methods/Study Population: To observe the effects of specific mutations present in clinical isolates of OC43, we will construct infectious clones of clinical and lab-adapted strains. Through targeted mutagenesis, we will characterize structural differences present in the Spike protein of clinical strains and identify the tropism-determining genome regions. By manipulating expression levels of enzymes involved in sialic acid production including SIAE and CasD1 and using a carbohydrate microarray to further evaluate carbohydrate binding, we will evaluate whether clinical OC43 isolates use the same 9-O-acetylated sialic acid identified as a receptor for the lab-adapted OC43 strain (VR-1558). Results/Anticipated Results: In primary human lung epithelial cell cultures, clinical strains of OC43 persistently replicate through 30 days post-infection. In contrast, the lab-adapted strain did not replicate. Growth on standard immortalized cells demonstrated the reverse phenotype with extensive replication of VR-1558 but no replication by the clinical isolates. Using AlphaFold to predict the 3D structure of Spike from a clinical isolate of OC43, we identified differences at a region previously identified as the binding site for its 9-O-acetylated sialic acid receptor. Further testing will determine whether these structural differences in Spike are responsible for the observed differences in viral tropism. Discussion/Significance of Impact: This project will deepen knowledge of coronavirus biology and the drivers of respiratory cell tropism. It will also develop novel tools for the study of OC43 and particularly of clinically relevant circulating strains which are better models for developing broad-spectrum therapies.

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

Objectives/Goals: 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: Identify how graduates and talent acquisition specialists perceive the value and limitations of MS Clinical Research degrees in today’s clinical research workforce. Apply insights from this study to reimagine curriculum, mentorship, and industry partnerships that strengthen the value and recognition of MSCR programs. Methods/Study Population: This descriptive qualitative study investigated the perceived value of the Master of Science in Clinical Research (MSCR) degree amidst pharmaceutical industry shifts. Data were collected from 23 participants: 17 degree completers (DCs) and 6 talent acquisition specialists (TASs). Using purposive and snowball sampling, participants engaged in semi-structured focus groups, dyadic conversations, and individual interviews. Data were coded through directed qualitative content analysis, guided by Sheth et al.’s consumption values framework, with inductive coding to capture emergent themes. Results/Anticipated Results: Findings revealed that DCs emphasized professional identity formation, recognition barriers, curriculum impact, and beyond-the-classroom value; TASs stressed practical value, industry disruption, and bridging academia-workforce gaps. Both cohorts acknowledged MSCR’s symbolic and strategic value but highlighted persistent “experience paradoxes” where degrees alone could not overcome entry barriers. Discussion/Significance of Impact: Study shows MSCR builds core knowledge yet remains misaligned with industry needs due to skill gaps and credential ambiguity. Findings call for systemic professionalization, coordinated interventions across stakeholders, and research on identity, credential pathways, and employer perceptions.

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: 1.To develop the next generation of translational scientists in PR. 2. To equip pre-university/undergraduate students with the necessary skills, knowledge, and experiences for their academic and professional development from a holistic perspective of life. 3. To strengthen the technological-translational science (TS) infrastructure. Methods/Study Population: Develop the Center for Research Education and Science Communication-Counseling Opportunities (CRESCO) to provide support to PuS and UgS in TS skills, statistics, scientific writing, emerging technologies, mentoring-tutoring and counseling-orientation; organize/launched the ACADEMIA-TS (ACAD-TS) with the inaugural activity to provide training/student development in TS to PuS and UgS, including peer-mentoring with graduate students (GS); create the Small Project in Translational Science (SPTS) program to offer PuS and UgS hands-on experiences with participation in up-to-date research in TS under the mentorship of a principal investigator-mentor; offer the Title V Annual Symposium in TS; and acquire computers/laboratory supplies/equipment to health sciences undergraduate schools. Results/Anticipated Results: CRESCO established a training infrastructure by acquiring 535 licenses (Consensus, Scite.ai, Mind-Graph) to integrate AI into research and offered 122 mentoring sessions. There were 296 participants in four new workshops. In ACAD-TS’ inaugural activity (~200 attendees), 67 PuS expressed interest in the ACAD-TS; 12 PuS are part of it, and 38 PuS participated in a counseling workshop. The SPTS program integrated course enrolled 10 students (8 from other UPR campuses) and created five teams involving 26 students (8 PuS, 16 UgS, 2 GS) mentored by 5 Principal Investigators (PIs), representing 15 institutions. The 14th Annual Symposium had 136 attendees, with a 100% satisfaction rate (n=44). The technology/TS infrastructure was upgraded with 50 new computers and supplies/minor equipment for health science schools. Discussion/Significance of Impact: Established a comprehensive model to develop early-career translational scientists, including a course, SPTS, and counseling workshops. Integrating AI tools, focused mentorship, and enhanced infrastructure strengthened participants’ integral development, confirming this model boosts research capacity for the future TS workforce.

Objectives/Goals: Community engagement has long been an underutilized strategy when conducting dementia research, especially for the recruitment of high-risk communities such as refugees. We created CHAMP: Championing Healthy Aging in Multicultural Populations Health and Resource Fair, aimed to promote early cognitive screening in aging Black refugees. Methods/Study Population: On August 9, 2025, the Health and Aging from Multicultural Perspectives Lab (HAMPLAB), in partnership with Global Lexington and the Lexington Senior Center, hosted the inaugural CHAMP Health and Resource Fair. Twenty-two vendors participated in the fair, including the Neurology department, pharmaceutical practice department, Bluegrass Lions Diabetes Project, local fire department, and the Alzheimer’s Association. Over 200 Black refugees in Lexington Kentucky and 18 volunteers attended the event. Complimentary services provided at the health and resource fair include health screening (e.g., A1C and blood pressure measurements), fresh food produce, fire safety, and back-to-school supplies. Results/Anticipated Results: Thirty-eight percent of the 55 attendees screened for A1C exhibited elevated levels. Many of these individuals were not previously diagnosed with prediabetes or diabetes and were offered counseling and follow-up. Also, over 100 people were screened for high blood pressure, and 62 eligible middle-older age adults signed up for various dementia research participation. These results indicate the importance of community-based events in which high-risk individuals can be identified and presented with practical next steps. Discussion/Significance of Impact: The CHAMP Health and Resource Fair illustrates the dual benefit of community engagement: advancing public health awareness through immediate health feedback and strengthening recruitment for ongoing research initiatives.

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: A history of criminal incarceration in prison or jail is rarely considered in planning health services for individuals with mental illness. We estimate the impact of a history of incarceration on access to health care when needed among adults with mental illness. Methods/Study Population: Cross-sectional data from n=3,796 adults ages 32 to 42 who had been diagnosed with a mental illness were drawn from Wave 5 of the National Longitudinal Study of Adolescent to Adult Health database. Access to health care when needed was measured by answering the question “In the past 12 months, has there been any time when you thought you should get medical care, but you did not?” Regression-adjusted inverse probability of treatment weighting was used to adjust analyses of use of health care when needed for measured differences between individuals with prior incarceration (n=536) and those with no prior incarceration (n=3,260). Interaction terms were used to assess whether insurance, health and social status, chronic illness, and social support moderate the association between incarceration and unmet healthcare need. Results/Anticipated Results: After balancing group characteristics, individuals with a prior incarceration were 7 percentage-points (20%) more likely to have not received health care when needed during the past year (42% in those with prior incarceration versus 35% in those without prior incarceration; p<0.015). Weighted logistic regression models confirmed that incarceration history was significantly associated with unmet healthcare need (p<.05). Predicted probabilities ranged from 28-52% among those never incarcerated to 32-66% among those with incarceration histories. Marginal effects revealed that poor self-rated health, low self-rated social standing, and social support from family or friends amplified this association (p<.05). Discussion/Significance of Impact: A history of incarceration was associated with decreased access to healthcare when needed. Translational efforts to reduce disparities in healthcare access among adults with mental illness should include tailored models of care for individuals with incarceration histories.

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

Objectives/Goals: Accurate detection of peritoneal metastases during staging laparoscopy is critical for treatment planning, yet even skilled surgeons miss lesions. Thus, an optimized low-compute image-processing pipeline that maximizes recall while filtering non-lesion tissue can assist with this task. Methods/Study Population: The study population consists of 1414 images containing 5943 annotated lesions from 163 subjects undergoing staging laparoscopy for GI-origin cancer. All images were obtained during staging laparoscopy by the senior author (TS). From the original images, five binary masks were produced using red, green, blue, Canny, and Gabor filters. These were combined into a composite image, thresholded, and binarized after smoothing into a final prediction. Given the complex 16-dimensional space, a genetic fitness algorithm was used to optimize the final set of parameters to maximize recall while also penalizing coverage. Results/Anticipated Results: Preliminary data show that the algorithm has a mean recall of 0.90 ± 0.18 and a mean predicted positive rate of 0.48 ± 0.20. Thus, the algorithm is able to exclude 52% of the image area while capturing 90% of lesion pixels. We plan to further validate these results and maximize this pipeline to demonstrate that simple tools can yield useful results. Discussion/Significance of Impact: This project aims to develop a computationally simple image-processing pipeline that can achieve high recall while minimizing predicted positive coverage. This will be useful to highlight where lesions are most likely to exist and may be combined in the future with classification models.