Objectives/Goals: Community gardening can foster healthy behaviors among low-income communities. This project aimed to develop a community garden. The primary objectives of this project are (1) assessing the need for and perspective on a community garden at the childcare center, (2) installing the garden, and (3) engaging children in gardening education. Methods/Study Population: This project took place at a childcare center in Harrisburg, PA. Most (74.6%) residents identified as Black or Hispanic/Latino. Every child at the center was eligible for free or reduced lunch. A listening session was held with directly impacted community members to discuss the need for a community garden. Four caregivers, 1 early childhood educator and a master gardener (n = 6) attended the listening session, in which they shared their personal strengths and challenges in growing food. Attendees provided suggestions on what foods they wanted to grow. Children enrolled in the center’s summer program (n = 50) were then invited to participate in weekly gardening activities for 9 weeks. Activities were targeted to preschoolers (3- to 5-year-olds). Older children enrolled in the summer program were welcome to participate. Results/Anticipated Results: Feedback from the listening session was positive. Attendees provided ideas on what to grow and shared interest in expanding the garden to the broader community. Project staff installed four garden beds and planted a variety of herbs (basil, mint, and lavender), fruits (strawberry and melon), and vegetables (tomato, squash, pepper, and onion). Roughly 20–50 children were engaged in the garden each week. Eight weeks into the project, one member from the broader community noticed the garden’s growth and expressed gratitude to the staff, stating “I saw you when you first started planting. This is great what you are doing for the kids.” Children and the center’s staff responded positively to the activities. The staff expressed verbal gratitude for the project and were enthusiastic about maintaining the garden. Discussion/Significance of Impact: Developing a community garden was feasible in this sample and shows potential to (1) increase children’s food literacy and vegetable acceptance and (2) bridge the gap from farm to early childcare education. The project’s success paves way for future gardening initiatives that address food access issues within other diverse low-income populations.

Objectives/Goals: The study is designed to co-develop a genetics education digital platform with community participants. Diverse populations, especially African American communities, are less likely to participate in genetic testing and clinical trials. To increase participation and community input, we want community participants across Louisiana. Methods/Study Population: A cross-sectional, mixed-methods study will be conducted to assess interest in learning about genetics through a digital education platform and to adapt the platform based on participant feedback. Specific Aim 1 will be achieved by recruiting a diverse cohort and collecting demographic data to identify participants’ characteristics. For Specific Aim 2, 35 community participants will be enrolled as a co-design team to complete 5-week educational modules. Feedback from focus groups will guide iterative platform refinements, ensuring the platform is culturally tailored and user-friendly. Results/Anticipated Results: For Specific Aim 1, we anticipate that the majority of participants will express interest in learning about genetics through the digital platform, with demographic data revealing a diverse participant pool, predominantly from African American and Hispanic communities. For Specific Aim 2, we expect that all 35 co-design team members will complete the 5-week modules. Feedback from the focus groups is anticipated to highlight the need for more user-friendly navigation, culturally tailored content, and enhanced visuals. These insights will guide the refinement of the digital platform for improved engagement and accessibility. Discussion/Significance of Impact: This study addresses a critical translational barrier – underrepresentation of African descended communities in genetic testing. By developing a culturally tailored, digital platform to engage these populations, the project aims to reduce health disparities, enhance genetic literacy, and foster inclusivity in genomic research.

Objectives/Goals: As the aging population increases, maintaining cognitive and physical health becomes crucial. Executive functions (EF), including reaction time, sustained attention, and spatial memory, are essential for daily life and independence in older adults but tend to decline with age, especially in Alzheimer’s disease (AD) patients. Methods/Study Population: Physical balance (PB) impairments further exacerbate this decline, affecting the quality of life and independence. Balance training (BT) emerges as a potential non-pharmacological intervention to enhance EF. This study aims to 1) assess the association between PB impairments and EF deterioration in older adults with AD; 2) evaluate the impact of BT on EF enhancement in this population; and 3) explore changes in brain-derived neurotrophic factor (BDNF) biomarkers before and after physical activity related to BT. This pilot study employs a cross-sectional design with older Hispanic adults (>65 years) in Puerto Rico, comparing a control group without BT intervention to an experimental group with BT intervention. Both groups will be assessed for EF and BDNF biomarkers pre- and post-physical activity. Results/Anticipated Results: Expected outcomes include identifying significant correlations between PB impairment and diminished EF, demonstrating measurable EF improvements following BT, and evidencing sustained BDNF release post-BT despite PB impairments. By understanding the biological mechanisms linking BT and cognitive improvements, particularly the role of BDNF, this research could inform future strategies to mitigate cognitive decline in AD patients through targeted physical interventions. Discussion/Significance of Impact: The findings of this study could provide valuable insights into the development of new preventive interventions, enhancing the quality of life and independence for older adults.

Objectives/Goals: There has been a significant increase in the engagement of researchers with persons with lived and living experience, driven by a growing recognition of the invaluable insights and expertise these individuals bring to the research process. However, there remains a need for research teams to learn how to engage with patients and community partners. Methods/Study Population: The EMPOWER project is a collaborative of members with lived/living expertise of substance use during pregnancy. EMPOWER meets three times per month in support of their goals of patient-centered research, changing the narrative through conversations, and building the capacity of persons with lived experience to co-lead research. Through over 4 years of active collaboration and co-learning, EMPOWER has identified and created methods that support meaningful patient engagement and capacity building. Results/Anticipated Results: EMPOWER has identified the following methods that support meaningful engagement and capacity building: Mind-Mapping Exercises, Personal and Collective Journey Mapping, 8-month research training curriculum, Digital Storytelling, Learning Labs, and World Cafe Meetings. These methods have led to a collaborative of 20 individuals with lived experience of substance use to identify research gaps, prioritize research questions, design data collection tools, analyze research data, disseminate research results, and consult with healthcare teams in efforts to improve clinical care. To-date EMPOWER have given over 10 invited presentations and is currently writing manuscripts. Discussion/Significance of Impact: Meaningful patient and community engagement is critical to developing and implementing healthcare interventions. Unfortunately, engagement efforts are often lack in building capacity of patients and community, which impedes their ability to fully engage in all phases of research.

Objectives/Goals: To determine the acceptability of a mobile/school-based diabetes and prevention clinic to overcome transportation barriers and improve attendance at endocrinology appointments for youth with type 1 diabetes, type 2 diabetes, MODY diabetes, and obesity living in socially vulnerable communities Methods/Study Population: This study utilized a 3-phase implementation process focused on feasibility, demand, and acceptability. Phase 1: identified high-need areas using patient volume and disease control metrics. Collaborated with school districts to define needs, enrolled them through a memorandum of understanding, and subsequently recruited and consented existing patients attending these schools. Phase 2 piloted the program to refine logistics and workflows. Semi-structured interviews and surveys were conducted with caregivers to assess program acceptability and satisfaction. Phase 3 is ongoing and will track clinical outcomes including glycemic control, appointment attendance, hospital admissions, emergency department visits, and self-management metrics. Results/Anticipated Results: Demand for the program was high with 91% (10/11) of approached districts agreeing to participate. District enrollment was staggered, starting with 1 district in August 2023, expanding to 5 in September, 7 in October, and reaching 8 districts by May 2024. From August 2023 to July 2024, the school-based mobile clinic conducted 355 individual clinical encounters addressing diabetes and obesity. The encounters included 180 for type 1 diabetes, 69 for type 2 diabetes, 8 for MODY diabetes, and 98 for obesity. Interviews and surveys with 36 caregivers are ongoing to evaluate program satisfaction. Discussion/Significance of Impact: The data suggest that this innovative healthcare delivery model is feasible. District-level enrollment demonstrates a demand for the program. Results from the interviews and surveys will further characterize the program’s acceptability among caregivers and lay the groundwork for future efficacy testing.

Objectives/Goals: Pregnancy increases vulnerability to stress and mental health symptoms, particularly among Hispanic women in Puerto Rico (PR), a population with unique socioenvironmental adversities, such as poverty and natural disasters. This study examined the relationships between life adversities and psychological distress in this at-risk population. Methods/Study Population: Participants (n = 50) in this cross-sectional study were recruited from an obstetrician’s office in Southern PR. All participants provided written consent and completed the Adverse Life Experiences Scale (ALES, Cronbach’s alpha = 0.71) to identify lifelong adversities faced and the overall duration (chronicity). Measures of psychological distress included the Perinatal Anxiety Screening Scale (PASS, Cronbach’s alpha = 0.96), the Edinburg Postnatal Depression Scale (EDPS, Cronbach’s alpha = 0.87), the Perceived Stress Scale (PSS, Cronbach’s alpha = 0.79), and the Brief Resilience Scale (BRS, Cronbach’s alpha = 0.86). Descriptive and Spearman’s rho correlation analyses were conducted. Results/Anticipated Results: The mean age of the participants was 27.90 years (SD = 6.05), with most in the first trimester (66.0%). On average, participants reported 4.32 (SD = 3.1) out of 23 lifetime adversities. The most common adversities were natural disasters (60.0%), loss of a beloved (58.0%), and financial difficulties (38.0%). Nearly half (44.0%) experienced five or more adversities. A significant number of participants met the clinical threshold for anxiety (38.0%, PASS), depression risk (22.0%, EPDS), moderate-to-severe perceived stress (52.0%, PSS), and low resilience (24.0%, BRS). The overall duration of adversities was significantly associated with anxiety (rs = 0.50, p = 0.001) and stress (rs = 0.50, p = 0.007). Discussion/Significance of Impact: Hispanic pregnant women in PR face high levels of adversity and distress, which can negatively affect both maternal health and fetal development, influencing long-term child outcomes. Early identification and targeted interventions addressing adversities, can improve maternal mental health and child health-development outcomes.

Objectives/Goals: Here, we utilize deep learning to automate the analysis of dual X-ray absorptiometry (DEXA) scans in the UK Biobank (UKB) imaging dataset to enable a large-scale assessment of lumbar spine disc degeneration, low back pain, and socioeconomic status. Methods/Study Population: Study Population: The UKB is a biomedical database that includes lateral spine DEXA imaging for 50,000 participants. Deep Learning Model Development: A computer vision model was developed that receives a DEXA scan as input and outputs a quadrilateral that corresponds to the corners of 5 lumbar vertebral bodies. The model is a deep, fully convolutional, encoder–decoder network using DeepLabV3. Statistical Analysis: To determine our preliminary model accuracy, we used the intersection over union (IoU) metric.We analyzed data using an ordinal regression model to determine the relationship between income/ neighborhood level multiple deprivation index (MDI) and low back pain (LBP), as well as a mixed effects model to estimate the relationship between income/MDI and disc height index (DHI). Results/Anticipated Results: Our model predicted vertebral body quadrilaterals in training and unseen test data (train IoU  =  0.96, test IoU  = .91) and was used to infer data for 10,440 participants. Confirming previous studies, there were significant relationships (p0.05) between income or MDI and DHI (Figure 2). Discussion/Significance of Impact: Low back pain is the world’s leading cause of disability, and socioeconomic factors play an important role. We found no relationship between disc height index and socioeconomic status. Thus, disc degeneration may not be a factor in this low back pain phenotype.

Objectives/Goals: To develop an informatics framework that will allow study of environmental effects on stillbirth at large scale (i.e., US-level) and leverage recent advances in machine learning and artificial intelligence to produce reproducible results that can be compared across multiple institutional settings. Methods/Study Population: Experimental exposure data are often available in “absolute time,” where a clinical event can be anchored using a timeline transformation. We associate each stillbirth event with a set of {ti…ti+1}L shapelets [1] associated with a location, L, and time intervals for the entire dataset. These shapeless are aggregated using a state-of-the-art shapelet classifier [2]. An autoencoder is used to reduce the dimensionality of the stillbirth classification and to cluster stillbirth events according to their corresponding exposure patterns. The stillbirth cluster can be analyzed for other nonexposure (i.e., genetic, SDoH, and demographics) factors, which may be enriched and/or depleted. Results/Anticipated Results: The framework we are developing leverages a shapelet-based approach to produce clusters of stillbirth events according to their corresponding exposure patterns. These clusters can be analyzed for depletion or enrichment of nonenvironmental factors. This analysis will inform how to formulate (or not) class models of exposure that can be more informative and have better predictive power than overall population models. Moreover, the finding of depletion and enrichment of physiological properties of the individuals may lead to novel physiological hypotheses to better understand the injury mechanisms that the environmental exposure profile produces. Discussion/Significance of Impact: Nearly 20,000 babies are stillborn in the USA each year [3]. Environmental exposures, usually studied as time averages over certain periods of time, have produced mixed results for stillbirth risk [4]. However, temporal profiles matter [1], and we argue that they can be assessed using shapelet technology.

Objectives/Goals: This study tests the REDCap Clinical Data Interoperability Service (CDIS) for streamlined data extraction from electronic health records (EHRs) for research. Managed by Clinical and Translational Science Institute, IU Health, and Eskenazi Health, CDIS offers real-time data syncing, automated workflows, and HIPAA-compliant data security. Methods/Study Population: The REDCap CDIS uses the Fast Health Interoperability Resource (FHIR) Application Programming Interface (API) to extract data from EHRs. It includes the Clinical Data Pull (CDP), which automatically pulls EHR data into user-defined REDCap fields, and the Clinical Data Mart (CDM), which collects longitudinal patient data. Three use cases were selected to assess the CDIS’s effectiveness in extracting data from the IUH Cerner and Eskenazi Epic EHR systems. The technical team set up clinical data mapping and adjudication processes, simplifying complex manual data extraction. Results/Anticipated Results: The CDIS successfully achieved real-time data synchronization during pilot testing with each EHR system. We extracted demographics, drugs, procedures, labs, and conditions. The mapping interface supports many-to-one data point mapping for the study data dictionary, and the adjudication process ensures data quality before integration into the REDCap database. The CDIS also improved data security and HIPAA compliance. An implementation intake process was developed for Indiana University investigators, allowing them to use the service for affordable clinical data extraction from EHR systems. Discussion/Significance of Impact: The implementation and testing of the REDCap CDIS demonstratesits effectiveness in streamlining EHR data extraction for research. The CDIS facilitates real-time data synchronization, automated workflows, and enhanced data security, offering a cost-effective solution through collaborative oversight with research teams.

Objectives/Goals: Rare disease patients often face lengthy delays in receiving accurate diagnoses or experience misdiagnoses due to a lack of available information. The NCATS Rare Disease Alert System (RDAS) is a public, comprehensive rare disease resource to collect and share accurate, up-to-date, and standardized data on rare diseases. Methods/Study Population: RDAS is composed of a frontend UI, Application Programming Interfaces, and backend Neo4j graph database. Each component of data collection, data annotation, data standardization, and data representation as steps were implemented during the process of each graph database creation. The UI allows users to search, browse, and subscribe to RDAS to receive the latest information and findings about their rare disease(s) of interest. The back-end data include four knowledge graphs built by integrating information from the NCATS Genetic and Rare Disease program, PubMed articles, clinical trials, and NIH grant funding. Ultimately, the integrative information pertinent to rare diseases from RDAS would advance rare diseases research. Results/Anticipated Results: Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting among mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research. Discussion/Significance of Impact: We developed an integrative knowledge graph with rare disease data and demonstrated its use as a source to identify and generate scientific evidence to support rare disease research. With the success of this study, we plan to implement advanced computation to analyze more funding related data and link to other types of data to perform further research.

Objectives/Goals: As a priority area in translational science, rural health research can benefit from informatics methods for conducting thematic and environment scans. This study demonstrates an efficient approach to gaining insights about the rural health research literature by automated bibliometrics analysis. Methods/Study Population: We developed an automated pipeline to retrieve the 1972–2023 PubMed publications indexed with the MeSH terms “Rural Health” and “United States”. The article metadata in XML format were downloaded and parsed, including title, year, journal, author institutions, and MeSH terms. Each institution address was augmented by Google Maps API to obtain the county and latitude/longitude coordinates. Summary statistics were computed for the publication years, journals, author departments, and locations. A topic network was generated from the frequent co-occurring MeSH terms. The institutions were linked to Rural-Urban Continuum Codes and labeled on a map to visualize their geographic distribution. Results/Anticipated Results: A total of 4564 articles on rural health were analyzed. Two salient peaks of publications were revealed, one around 1978 and the other around 1993. The top author departments include Family Medicine, Nursing, Pediatrics, and Epidemiology. The five leading institutions reside in Chapel Hill, Minneapolis, Iowa City, Seattle, and Atlanta. The geographic distribution shows few institutions that reside in deep rural areas are well published on rural health, although the most scholarly productive institutions do seem adjacent to some moderately rural pockets. The frequently identified topics pertain to age group, study design, and specific concepts such as health services accessibility. Discussion/Significance of Impact: The two publication peaks in history were likely linked to certain policy milestone or seminal publication. Primary care and epidemiology departments have been most active in rural health research. Of concern, the geographic distribution of authors suggests under-investment in rural institutions.

Objectives/Goals: We aim to identify how IDH mutant (IDHm) gliomas use exosomes to modulate the local and systemic immune system. We will do so by characterizing differential miRNA expression between IDHm and IDH wild type (IDHwt) exosomes and identifying the specific immune cell population targeted by exosomes in vivo. Methods/Study Population: Exosome RNA will be isolated from cultured patient glioma samples and perform small RNA sequencing to investigate differential expression of miRNA between IDHwt and IDHm exosomes. We will then utilize miRNA target databases in conjunction with bioinformatic pathway analysis to generate potential target regulatory pathways. To identify the in vivo effect of tumor exosomes, we will generate a novel glioma mouse model that has been genetically engineered to release labeled exosomes using the RCAS retroviral system. We will collect peripheral blood and tumor tissue for flow cytometric immune profiling and single-cell RNA sequencing. The transcriptomic data will be analyzed to identify subsets of immune populations that have taken up the labeled exosomes and assess the resulting expression changes in those cells. Results/Anticipated Results: From the small RNA sequencing and bioinformatics analysis, we expect to find several unique miRNA expressed in IDHm exosomes that induce immunosuppressive pathways in local and systemic immune cell populations when compared to IDHwt exosomes. Furthermore, using our novel murine model, we expect to be able to track endogenously released exosomes in the local tumor microenvironment and in the circulating blood. We hypothesize that IDHm exosomes specifically target precursor myeloid cells within the local and peripheral circulating immune populations and induce the expansion of monocytes, M2 macrophages, and mono-MDSCs. Discussion/Significance of Impact: Immunosuppression in IDHm glioma has hindered the development of adequate therapies to treat this fatal disease. Our study will illuminate the mechanism by which tumor exosomes can suppress immune surveillance. These results will help identify new therapeutic targets to sensitize the immune system against glioma cells.

Objectives/Goals: We aim to predict and rank conserved, immunogenic targets within key malaria proteins using computational tools. These tools incorporate parasite protein diversity and regional HLA allele frequencies to prioritize antigens for further validation and inclusion in a malaria vaccine targeting circulating strains. Methods/Study Population: We identified 42 conserved malaria proteins with nonredundant functions for P. falciparum invasion and transmission as vaccine targets. Protein sequence datasets were constructed from samples collected in highly endemic areas. We predicted targets most likely to be presented to CD4+ and CD8+ T cells. We designed and used heuristic-based and AI-weighting models that integrated predicted binding affinities to HLA alleles, HLA allele frequency data, and sequence conservation to score and rank targets. We validated our model by comparing predicted epitope distributions with published in vitro and in vivo immunogenicity data available in the Immune Epitope Database and Tools repository. Results/Anticipated Results: We successfully predicted and ranked targets within the vaccine candidate proteins, identifying conserved and HLA-nonspecific targets that correspond to positive immunogenicity data, validating our approach. We are currently analyzing model performance by comparing predictions to over 5,800 experimentally validated P. falciparum targets from clinical trials and immune assays. We will evaluate each models’ accuracy and ability to prioritize targets and compare their performances as measured quantitatively by precision and area under the curve metrics. We expect the AI-based model to significantly outperform the heuristic approach, improving the identification of effective vaccine targets. Discussion/Significance of Impact: By incorporating parasite diversity and regional HLA allele frequencies, our approach addresses the challenge of directing the human immune response against genetically diverse P. falciparum strains in highly endemic areas. This strategy could significantly enhance malaria vaccine efficacy and can be adapted for use against other pathogens.

Objectives/Goals: This study aimed to investigate the role of artificial intelligence (AI) in translational science, including personalization of interventions and drug development. Methods/Study Population: A comprehensive literature search was conducted via PubMed, the Cumulative Index for Nursing and Allied Health Literature (CINAHL), Cochrane Library, Medline, and Web of Science. The risk of bias in the eligible studies was assessed using the risk of bias in nonrandomized studies. Data were systematically extracted and analyzed. Results/Anticipated Results: The literature search yielded 2129 records, from which 20 studies that met the eligibility criteria were included. Meta-analysis demonstrated the high specificity of AI-based diagnostics, reassuring the reliability of AI. Furthermore, AI applications significantly improved biomarker identification through machine learning algorithms, enhancing prognostic accuracy and treatment personalization. Moreover, AI showed enhanced diagnostic precision with high sensitivity and specificity in cancer detection, further validating its role in healthcare. AI-driven risk stratification was used in chemotherapy decisions. Discussion/Significance of Impact: This study highlights the transformative power of AI in translational oncology research with applications in drug development and personalized patient care in cancer treatment and research.

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

Objectives/Goals: This study aims to evaluate common features of mobile health (mHealth) apps and their role in helping caregivers make health decisions for children. Methods/Study Population: A scoping review of literature on caregivers’ use of mHealth apps (published since 2008) was conducted across 5 databases (i.e., Embase, PubMed, CINAHL, Clinicaltrials.gov, and IEEE Xplore). Selected papers were categorized based on app purposes, target users, and mHealth agile development phases. Common features were also identified and analyzed along with users’ pros and cons. Further, primary feature requests were summarized to inform future development. Results/Anticipated Results: This review included 62 studies. Most apps were about maternity and infant care and specific diseases. Major users were caregivers and pregnant women. Around 20% of papers covered multiple phases in the mHealth agile development lifecycle. The effectiveness/clinical trial (phase III) was the most common. E-learning, personalization and customization, and health tracking features were the three most common features of mHealth apps included in this review. More positive feedback was found regarding features than concerns. Caregivers perceived apps as helpful and empowered them to make informed decisions. Concerns were mainly over 1) technical issues, 2) inappropriate design, and 3) ambiguous terms. Requested new features included content comprehensiveness, user engagement, and usage flexibility. Discussion/Significance of Impact: To our knowledge, this is the first review to investigate the usability of mHealth app features in this area. The results offer feasible strategies for developers to improve the effectiveness of apps for caregiver decision-making.

Objectives/Goals: Oral health is an important and understudied part of overall health. Poor oral health is linked to many systemic conditions, but little has been done to explore these issues in large electronic health records data sources that include dental health records. Here we report on our exploration of data readiness and completeness of three of these data sources in the Clinical and Translational Science Awards (CTSA) network. Methods/Study Population: Three CTSAs from the Consortium of Rural States (CORES) with diverse geographies, demographics, and data ecosystems can integrate medical and dental records, but it is unknown if the target population having both dental and medical records have sufficient completeness and similarity to enable dental/medical health studies. Here we use descriptive analytics to characterize the demographics, and the “complete data” approach presented by Weber et al. to evaluate differences between the completeness of the general populations and the one having both dental/medical records. We accomplish this by identifying patients with dental records in commonly used research networks and performing empirical patient statistics in comparison to the entire population available at the three institutions. Results/Anticipated Results: This poster will present the results of using the Weber et al. approach to compare the completeness of records of the general patient population in the Iowa, Kentucky, and Utah medical/dental health care systems to those for which they have also dental records. The completeness of the records of these two subpopulations is also associated with different demographic characteristics, as it has been established that the populations served by the dental clinics is biased by dental insurance considerations. The work will show what retrospective studies can (or not) be done using these populations when taking into account that it is well established that studies of populations with different level of completeness can be inconsistent. Discussion/Significance of Impact: This study provides an informatics framework to assess similarity and completeness of patient records with and without dental records. Establishing the level of similarity and completeness in these patient populations is critical to justify the validity of studies that utilize a combined record.

Objectives/Goals: Identifying and indexing rare disease studies is labor intensive, especially in research centers with a large number of trials. To address this gap, we applied natural language processing (NLP) and visualization techniques to develop an efficient pipeline and user-friendly web interface. Our goal is to offer the rare disease study identification (RDSI) tool for adoption by other sites. Methods/Study Population: The RDSI retrieves study information (short and long titles, study abstract) from the IRB system. These descriptive fields are then processed by the MetaMap Lite NLP program for identifying disease terms and standardizing them to UMLS concepts. By terminology identifier mapping, the diseases intersecting with concepts in rare disease databases (Genetic and Rare Disease program and Orphanet) are further scored to pinpoint studies that focus on a rare disease. The web interface displays a scatter bubble chart as an overview of all the rare diseases, with each bubble size proportional to the number of studies for that disease. In addition to the visual navigation, users can search studies by disease name, PI, or IRB number. Search results contain detailed study information as well as the evidence used by algorithms of the pipeline. Results/Anticipated Results: The RDSI identification results and functions were verified manually and spot-checked by several study investigators. The web interface is a self-contained solution available to our staff for various use cases like reporting or environment scan. We have built in a versioning mechanism that logs the date of each major result in the process. Therefore, even as the rare disease data sources evolve over time, we will be able to preserve any historical context or perform updates as needed. The RDSI outputs are replicated to Mayo Clinic’s enterprise data warehouse daily, allowing tech-savvy users to leverage any useful intermediate results at the backend. We anticipate the performance of the rare disease identification to be further enhanced by employing the advancements in AI technology. Discussion/Significance of Impact: The RDSI represents an informatics solution that offers efficiency in identifying and navigating rare disease clinical studies. It features the use of public databases and open-source tools, manifesting return on investment from the broad translational science ecosystem. These considerations are informative and adoptable by other institutions.

Objectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping visualizes regions of heightened neural activity for OUD. We conduct fivefold cross-validation classification experiments (OUD vs. HC) to assess the discriminative power of functional network features with and without incorporating demographic features. Demographic features are ranked based on ML classification importance. Follow-up Boruta analysis is performed to study the medial prefrontal cortex (mPFC), posterior cingulate cortex, and temporoparietal junctions in the DMN. Results/Anticipated Results: Boruta ML analysis identifies the DMN as the most salient functional network for differentiating OUD from HC, with 33% of DMN features found significant (p < 0.05), compared to 10% and 0% for the SN and ECN, respectively. The Boruta ML algorithm identifies age and education as the most significant demographic features. Brain activity mapping shows heightened neural activity in the DMN for OUD. The DMN exhibits the greatest discriminative power, with a mean AUC of 69.74%, compared to 47.14% and 54.15% for the SN and ECN, respectively. Fusing DMN BOLD features with the most important demographic features improves the mean AUC to 80.91% and the F1 score to 73.97%. Follow-up Boruta analysis highlights the mPFC as the most important functional hub within the DMN, with 65% significant features. Discussion/Significance of Impact: Our study enhances the understanding of OUD neurobiology, identifying the DMN as the most significant network using ML rs-fMRI BOLD feature analysis. Ethnicity, education, and age rank are the most important demographic features and the mPFC emerges as a key functional hub for OUD. Future research can build on these findings to inform treatment of OUD.

Objectives/Goals: Aspiration causes or aggravates lung diseases. While bedside swallow evaluations are not sensitive/specific, gold standard tests for aspiration are invasive, uncomfortable, expose patients to radiation, and are resource intensive. We propose the development and validation of an AI model that analyzes voice to noninvasively predict aspiration. Methods/Study Population: Retrospectively recorded [i] phonations from 163 unique ENT patients were analyzed for acoustic features including jitter, shimmer, harmonic to noise ratio (HNR), etc. Patients were classified into three groups: aspirators (Penetration-Aspiration Scale, PAS 6–8), probable (PAS 3–5), and non-aspirators (PAS 1–2) based on video fluoroscopic swallow (VFSS) findings. Multivariate analysis evaluated patient demographics, history of head and neck surgery, radiation, neurological illness, obstructive sleep apnea, esophageal disease, body mass index, and vocal cord dysfunction. Supervised machine learning using five folds cross-validated neural additive network modelling (NAM) was performed on the phonations of aspirator versus non-aspirators. The model was then validated using an independent, external database. Results/Anticipated Results: Aspirators were found to have quantifiably worse quality of sound with higher jitter and shimmer but lower harmonics noise ratio. NAM modeling classified aspirators and non-aspirators as distinct groups (aspirator NAM risk score 0.528+0.2478 (mean + std) vs. non-aspirator (control) risk score of 0.252+0.241 (mean + std); p Discussion/Significance of Impact: We report the use of voice as a novel, noninvasive biomarker to detect aspiration risk using machine learning techniques. This tool has the potential to be used for the safe and early detection of aspiration in a variety of clinical settings including intensive care units, wards, outpatient clinics, and remote monitoring.