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 illuminates the efforts of a Clinical and Translational Science Award (CTSA) Hub to share stories of its aspirations, challenges, successes, opportunities, and impact when pursuing its complex goals, and how storytelling contributes to the narrative of the translational science work (via storytelling strategies, products, and benefits). Methods/Study Population: We utilized an environmental scan of a CTSA Hub (University of Minnesota Clinical and Translational Science Institute (CTSI)), including case study vignettes of its storytelling practices and products. We triangulated data from diverse data sources: grant applications, reports, and publications; public stories/news related to CTSI activities and impact; scientific publications; organizational/policy documents; and interviews with CTSI stakeholders featured in published sources. Results/Anticipated Results: TS storytelling uses and strategies include communicating the essence of research translation, promoting program utilization, engaging community, reporting to stakeholders, and evaluating for accountability, learning, and improvement. Storytelling challenges include complexity of translation; balancing the scientific rigor with an engaging narrative; identifying appropriate stories that resonate with diverse stakeholders and are at an appropriate level of maturity; and building capacity using storytelling. Facilitators include supportive infrastructure to integrate stories; leadership endorsement of storytelling as a valuable strategy; capable cross-functional teams of communicators, administrators, and researchers to facilitate the integration of data into storytelling. Discussion/Significance of Impact: The environmental scan provides evidence and lessons learned on leveraging storytelling as a useful tool for communicating CTS goals, actions, and findings, engaging stakeholders, building a narrative around scientific discoveries, evaluating and improving programs, and addressing health disparities in translational science.

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: Present a framework for hosting Community Grand Rounds, where community and academic partners showcase completed community-engaged research (CEnR) projects. This highlights innovative dissemination methods, engages diverse audiences, elicits community responses, and advances the translational science of CEnR. Methods/Study Population: Our approach involves planning and outreach to collaborate with promotion of academic and community engaged grantees to develop community dissemination events that translate the science of CE into accessible, relatable, culturally relevant formats for diverse audiences. These events incorporate interactive presentations that encourage active participation and feedback from attendees. Following each event, an evaluation is completed to assess community impact. Key strategies for hosting, facilitating, and utilizing diverse marketing to ensure that events are tailored to culturally diverse community groups, including regional implementation when practical. This collaborative approach meets a critical need and strengthens the bond between researchers and the communities they aim to serve. Results/Anticipated Results: These events create a feedback loop between the community and academic researchers. It was not just about telling people what was found. We created opportunities for community members and academics to build trust, give us feedback, ask questions, and discuss how findings could be practically applied. By presenting the findings in an accessible way within the community, community members are more informed and empowered to make decisions or advocate for changes in their own lives based on the research. Academics also benefited from community feedback, which provided new insights to help refine future research questions and methods. The goal is for shared conversation and understanding between community members and academics to inspire real-world applications and policy change directly informed by the research. Discussion/Significance of Impact: Community Grand Rounds are one dissemination strategy to leverage community–academic collaboration to present tailored research, fostering engagement, understanding, and action between researchers and community members. This approach effectively enhances the translational science of CEnR by involving and benefiting the community.

Objectives/Goals: Our goal for this project is to develop a metric that integrates the intersectional social and structural determinants of health and well-being into the existing policy development framework to impact the integration of such considerations on population mental health. Methods/Study Population: This project was developed from a case study module offered by the Translational Research Program at the University of Toronto. This course was designed to sharpen contextual inquiry skills and further develop a case through employing strategies, including outreach engagement with stakeholders, conducting informational interviews and formulating potential pathways forward based on the integration of insights from interdisciplinary perspectives. Results/Anticipated Results: The anticipated outcome would be improved mental health outcomes as measured by the Mental Health Commission of Canada’s Mental Health Indicators (Mental Health Commission of Canada, 2015) Discussion/Significance of Impact: Although there are established mental health indicators and policy development framework the two operate independently of each other. Our proposal bridge the gap between the sectors so that one may inform the other, and they can collectively formulate reflective and representative policies.

Objectives/Goals: We describe the prevalence of individuals with household exposure to SARS-CoV-2, who subsequently report symptoms consistent with COVID-19, while having PCR results persistently negative for SARS-CoV-2 (S[+]/P[-]). We assess whether paired serology can assist in identifying the true infection status of such individuals. Methods/Study Population: In a multicenter household transmission study, index patients with SARS-CoV-2 were identified and enrolled together with their household contacts within 1 week of index’s illness onset. For 10 consecutive days, enrolled individuals provided daily symptom diaries and nasal specimens for polymerase chain reaction (PCR). Contacts were categorized into 4 groups based on presence of symptoms (S[+/-]) and PCR positivity (P[+/-]). Acute and convalescent blood specimens from these individuals (30 days apart) were subjected to quantitative serologic analysis for SARS-CoV-2 anti-nucleocapsid, spike, and receptor-binding domain antibodies. The antibody change in S[+]/P[-] individuals was assessed by thresholds derived from receiver operating characteristic (ROC) analysis of S[+]/P[+] (infected) versusS[-]/P[-] (uninfected). Results/Anticipated Results: Among 1,433 contacts, 67% had ≥1 SARS-CoV-2 PCR[+] result, while 33% remained PCR[-]. Among the latter, 55% (n = 263) reported symptoms for at least 1 day, most commonly congestion (63%), fatigue (63%), headache (62%), cough (59%), and sore throat (50%). A history of both previous infection and vaccination was present in 37% of S[+]/P[-] individuals, 38% of S[-]/P[-], and 21% of S[+]/P[+] (P<0.05). Vaccination alone was present in 37%, 41%, and 52%, respectively. ROC analyses of paired serologic testing of S[+]/P[+] (n = 354) vs. S[-]/P[-] (n = 103) individuals found anti-nucleocapsid data had the highest area under the curve (0.87). Based on the 30-day antibody change, 6.9% of S[+]/P[-] individuals demonstrated an increased convalescent antibody signal, although a similar seroresponse in 7.8% of the S[-]/P[-] group was observed. Discussion/Significance of Impact: Reporting respiratory symptoms was common among household contacts with persistent PCR[-] results. Paired serology analyses found similar seroresponses between S[+]/P[-] and S[-]/P[-] individuals. The symptomatic-but-PCR-negative phenomenon, while frequent, is unlikely attributable to true SARS-CoV-2 infections that go missed by PCR.

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 research aims to identify genetic alterations influencing congenital anomalies of the kidney and urinary tract (CAKUT) and bridge a fundamental gap in understanding the cellular mechanisms underlying kidney development, with the long-term goal of enhancing treatments for congenital renal anomalies. Methods/Study Population: We will use a loss-of-function approach in combination with immunofluorescent microscopy techniques to determine the influence of Dnmbp perturbation on Daam1 localization, actin assembly, and junctional turnover. Additionally, to establish a foundation for delineating the molecular mechanism of DNMBP during kidney development, we will utilize clinical whole exome sequencing data to identify human DNMBP mutations associated with urogenital anomalies. Furthermore, we will determine whether human DNMBP mutations linked to CAKUT lead to disruptions in nephron development through loss-of-function rescue experiments in Xenopus. Results/Anticipated Results: Here, we evaluate the dynamics of Dnmbp-mediated transport of Daam1 within the developing kidney and show preliminary data suggesting that Dnmbp and Daam1 directly interact to promote adhesive contact formation between nephron progenitor cells. Furthermore, we propose a model in which Dnmbp functions as a critical regulator of epithelial tissue morphogenesis and provides a functional link between the dynamic processes of actin cytoskeleton regulation, intracellular adhesion, and vesicular transport. Future studies will determine whether Dnmbp interaction with Daam1 facilitates junctional actin assembly by directing Daam1 to cell–cell contact sites via Dnmbp-associated vesicle targeting, enhancing our understanding of the cellular mechanisms influencing tubule morphogenesis. Discussion/Significance of Impact: This research will establish a previously unknown role for DNMBP in kidney development and provide a comprehensive understanding of the impacts of simultaneously regulating vesicular transport and actin dynamics in nephrogenesis.

Objectives/Goals: The overall goal of this project is to determine bacterial transcriptional signatures from clinical sputum and assess their potential to monitor treatment response and predict the outcome of drug therapy in patients with tuberculosis (TB). Methods/Study Population: We are developing a novel transcript capture sequencing (TC-Seq) approach to sequence the mRNA of Mycobacterium tuberculosis (Mtb) and analyze transcriptomes from clinical samples containing minimal amounts of bacterial RNA. This protocol generates single-stranded biotinylated probes from Mtb DNA. Probes are hybridized to and allow enrichment of Mtb-specific mRNA within next-generation RNA sequencing libraries. We will apply TC-Seq to sputum samples collected throughout an 18-month Phase II clinical trial investigating response to TB treatment to compare the transcriptome of Mtb between patients whose treatment results in cure or relapse. Results/Anticipated Results: We have refined a technique to generate biotinylated probes starting from DNA of lab grown Mtb. This protocol achieves robust and unbiased sampling of the Mtb transcriptome from mixed samples containing both human and Mtb RNA. Preliminary sequencing of clinical sputum collected pretreatment has generated 1–4 million Mtb-specific reads, a sequencing depth that allows examination of the entire bacterial transcriptome. We will measure differential gene expression before and during treatment as well as between cure and relapse cases. These results will allow us to characterize bacterial response to treatment and identify bacterial markers that correlate with relapse. Discussion/Significance of Impact: Understanding Mtb activity during treatment will offer new ways to assess the efficacy of different treatment regimens. Crucially, identifying clear bacterial markers that demarcate a cure or relapse outcome will have a significant impact on determining patient eligibility for shorter drug therapy.

Objectives/Goals: Port-site metastasis (PSM), defined as the spread of malignancies to the abdominal wall at the site of surgical ports, poses a significant challenge in cancer management. The objective of this summary overview is to describe the prevalence of and risk factors associated with PSM in various gynecological cancers after laparoscopic surgery. Methods/Study Population: Study design: Systematic Review and Meta analysis Search strategy: All international databases, without language limitations, from January 1990 to December 2023. Inclusion/exclusion criteria: Cohort, case–control, or cross-sectional observational studies reporting the frequency of, or risk factors for PSM in young and middle-aged women will be included (using the PRISMA checklist). Data extraction: two reviewers independently extracting pertinent data (using the STROBE checklist). Quality assessment and risk of bias: The quality of each study will be assessed according to the Quality Assessment Tool for Observational Studies. Results/Anticipated Results: This meta-analysis of 36 studies evaluated the proportion of successes across various populations, with a pooled proportion of 0.02 (95% CI: 0.01–0.02) based on a random-effects model. Significant heterogeneity was identified (I²  =  88.12%), reflecting notable variability between studies. Despite this, the overall effect was statistically significant (p  =  0.00). A subgroup analysis will be conducted to explore potential sources of heterogeneity, considering factors such as cancer stage, diagnostic methods, surgical approach (conventional or robotic), and study type (retrospective/prospective). Discussion/Significance of Impact: By identifying the prevalence of, and the risk factors for, PSM, this study will better inform personalized treatment approaches, surveillance strategies, and surgical decision-making to improve patient-related outcomes and long-term survival in women with gynecological malignancies.

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: This qualitative study evaluated facilitators and barriers to clinical research participation among lower-income, predominately racial/ethnic minority communities to inform a Co-Learning series. The Co-Learning series of community members, stakeholders, and researchers developed strategies to improve diversity in research. Methods/Study Population: Community focus group (FG) participants were recruited from urban communities experiencing poverty, whereas stakeholder FGs included diverse community leaders and researchers. The Social Ecological Model was used as the guiding theoretical framework throughout the qualitative design. Directed content analysis using three independent coders reaching 100% consensus identified prominent factors for increasing diversity in research. The co-learning series was comprised of community members, stakeholders, and researchers who discussed the prominent FG factors over seven in-person meetings to develop a toolkit to increase diversity in research. Results/Anticipated Results: Community (n = 51) and stakeholder (n = 50) FG participants were 54.0±16.0 years of age, 81.2% female, 39.6% Black/African American, 51.5% Hispanic, and 60% had Discussion/Significance of Impact: Due to the lack of inclusivity in clinical research, findings are typically not applicable to health disparity populations limiting the benefit of research to all. This study provides practical community-driven strategies to increase diversity in clinical research, highlighting the role of trust, relationships, and cultural relevancy.

Objectives/Goals: Glucagon-like peptide-1 (GLP1) use prior to bariatric surgery may represent a novel approach to treating obesity. The objectives of this study were to describe trends in pre-bariatric GLP1 use, investigate social and clinical factors associated with their use, and evaluate differences in clinical outcomes based on preoperative GLP1RA use. Methods/Study Population: Patients who underwent bariatric surgery at three Indiana hospitals from 2018 to 2023 were identified. Patients who utilized GLP1 in the year preceding surgery were compared to those who did not. Social determinants of health included insurance, income, and unemployment. Outcomes included rates of GLP1 use, 30-day postoperative readmissions, ED visits, and percent total weight lost (%TWL) at one year. Associations between preoperative GLP1 use and outcomes of interest were evaluated using multivariable logistic and linear regressions. Results/Anticipated Results: Of 2,169 patients who underwent surgery, 293 (13.5%) utilized GLP1 preoperatively. The rate of GLP1 utilization increased threefold from 2018 to 2023. Males were more likely to receive preoperative GLP1 (20.1% vs, 12.2%, p<0.001). There were no significant differences in social determinants of health or 30-day postoperative outcomes between patients who did and did not use GLP1RA preoperatively. Similarly, there were no significant differences in %TWL at one year postoperatively between groups (median 25.5% vs. 27.3%, coefficient: -0.78, 95%CI: -2.26–0.70). Discussion/Significance of Impact: Utilization of GLP1 in the year prior to bariatric surgery has significantly increased. Preoperative GLP1 use is not associated with worse 30-day outcomes or differences in %TWL at one year postoperatively. Further work is needed to evaluate whether GLP1 dosing and duration of treatment impact postoperative outcomes.

Objectives/Goals: Professional coaching greatly enhances faculty personal and professional development through fostered empowerment and a culture of continuous growth within the academic environment. The UMN Clinical and Translational Science Institute (CTSI)’s coaching program provides a variety of coaching formats (1:1, group, special programs). Methods/Study Population: CTSI provides professional coaching to faculty in the KAP, K12, KL2, and K-R01 and NLITeS programs. Typically, coaching begins with a HoganLead™ assessment and debrief. Scholars may pursue further coaching through individual sessions (up to 6 sessions during or 1-year post CTSI award) or a monthly peer coaching group facilitated by certified coaches. New URM faculty hires in the Early Career Research Awards (ECRA) program receive monthly coaching support to problem solve and attend to wellbeing. The NLITeS leadership development program for senior and mid-career faculty includes the HoganLead debriefing and 1–2 individual coaching sessions. All sessions are confidential. Results/Anticipated Results: Since 2016/2017, 80 scholars have taken the HoganLead assessment (72 debriefs), and 23 scholars have made use of 217 individual coaching sessions (most use between 6 and 12). Since 2021, 10 Scholars have participated in the Peer Coaching Group with 7 continuing over multiple years. Since 2021, 12 ECRA scholars have been supported in monthly coaching. Frequent coaching topics include self-awareness, work-life balance, overwhelm, negotiation, and leaving academic medicine. Scholar comments include: “I initially approached this professional coaching with some curiosity but fairly minimal expectations that it would change much. It has reshaped the way I approach my academic career and its associated challenges for the better, giving me confidence that I can handle the unique challenges that this career presents…” Discussion/Significance of Impact: The UMN CTSI coaching programs are extensively used and highly valued by participating faculty. Professional coaching with Hogan assessments provides faculty scholars with support that complements the mentoring they receive by going beyond a teach and learn model.

Objectives/Goals: Intra-amniotic inflammation (IAI) is one of the leading causes of maternal/fetal morbidity globally, yet it is undiagnosed in 90–95% of cases. The purpose of this study was to assess precision and accuracy of a novel microsampling device for measuring cytokines in amniotic fluid (AF) to enable noninvasive evaluation of localized inflammation. Methods/Study Population: AF was obtained from discarded amniocentesis samples from 3 deidentified patients without known inflammation. Samples were spiked to 5 concentrations of interleukin-6 (IL-6) and tumor necrosis factor-a (TNF-a), then sampled using the Neoteryx Mitra volumetric microsampling device (20 µL). Dried/reconstituted samples were analyzed using the Luminex R&D Mag XL multiplex (IL-6, TNF-α) and compared to free-flowing AF. Inter-and intra-assay performances were evaluated across 5-runs in 3 standard cytokine concentrations. Recovery/linearity was assessed by a recovery curve and parameter estimates. Precision was assessed between-run and within-run using coefficients of variance (%CV). Accuracy was evaluated as agreement between microsampled and free-flowing AF using Bland–Altman plots. Results/Anticipated Results: TNF-α results were linear for all 3 patients across 5 concentrations. However, accuracy and recovery consistently failed (mean±SD percent recovery 176±21%). TNF-α results had acceptable precision with %CV within-run of 6.8–12.1% and across-runs of 14.0–15.9%. Microsampled TNF-α agreed with free-flowing sample: 16 of 18 (89%) were within 1SD of the mean difference (-24±36 pg/ml). IL-6 results were linear for 1/3 patients and had unacceptable accuracy and recovery for 13/15 samples (mean±SD percent recovery 764±469%). IL-6 results had acceptable precision (%CV within-run 6.2%, 7.5% and 11.2%; across-run .9–15.2%). Microsampled IL-6 agreed with free-flowing sample; 17 of 18 (94%) were within 1SD of the mean difference (-148±180pg/ml). Free-flowing vs. microsampling methods agreed best at low concentration. Discussion/Significance of Impact: This study provides preliminary support for noninvasive measurement of cytokines leveraging small amounts of leaking AF providing a promising alternative to amniocentesis and potential for assessing inflammation intrapartum. Clinical application requires development of reference ranges and association between cytokine levels and outcomes.

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: The NIH Common Fund launched Community Partnerships to Advance Science for Society (ComPASS) to study ways to reduce health disparities by addressing underlying structural factors within communities. ComPASS was designed for community organizations to lead research that addresses community needs. Methods/Study Population: ComPASS awarded five health equity research hubs (Hubs) to provide specialized technical support to ComPASS research projects led by community organizations. Expertise provided by the Hubs to the community-led projects will focus on specific facets of community health, including nutrition access, health care access, and built environment. The Hubs support community-led piloting and testing of structural interventions within community settings by providing subject matter expertise in areas including structural and multilevel intervention study design and methods, implementation science, and community engagement. Results/Anticipated Results: The Hubs will provide expertise and support to the community-led research projects around one or more social determinants of health domains: health care access and quality, education access and quality, economic stability, social and community context, and neighborhood and built environment. The Hubs will help identify strategies for measuring health outcomes and assessing the effects of structural and contextual factors on intervention outcomes. We anticipate the ComPASS program will lead to a better understanding of how structural interventions that leverage multi-sectoral partnerships can advance health equity. Discussion/Significance of Impact: Through community-led research, ComPASS projects are implementing structural interventions to address social determinants and advance health equity. The technical scientific support rooted in health equity provided by the Hubs is essential to the success of these research projects.

Objectives/Goals: Longitudinal studies capture dynamic host–environment interactions crucial to microbiome research. Commonly used mixed-effects models can struggle with small clinical samples, like many endometrial cancer studies. We will compare them to simpler fixed-effects models, aiming to develop a new fixed-effects differential abundance alternative. Methods/Study Population: Starting with simulated data for linear models with a single treatment effect, we will incrementally increase complexity to reflect real-world microbiome data. To observe conditions of misspecification, simulated data of repeated measures will be generated using different distributions, while varying parameters and inter-class correlation. In each condition, linear fixed- and mixed-effects models will be fitted 1000 times to empirically determine p-value, type 1 error rate, and power. We will increase the verisimilitude of our approach by using endometrial cancer data from our lab as a real background, while adding simulated signals. This data will be used to assess extant differential abundance models against our novel fixed-effects model. Results/Anticipated Results: Our initial findings indicate that fixed-effects models maintain control over type 1 error rates in small samples, while mixed-effects models do not perform as reliably. We are now exploring the effects of confounders on type 1 error rates and power in linear models, while our next step will evaluate generalized linear models in a differential abundance context. We expect that the fixed-effects models will continue to be as reliable as mixed-effects models, while being less computationally complex. After establishing our theoretical basis, our novel model should perform comparably to mixed-effects models on pseudo-simulated endometrial cancer data and better at small sample sizes. We will finally apply our model to real endometrial cancer data to identify microbial markers of disease predisposition. Discussion/Significance of Impact: By characterizing and improving these statistical tools, we seek to increase the robustness and power of microbiome study results (especially at small sample sizes), ultimately supporting better-informed translational research decisions. This work underscores the importance of statistical methods in advancing microbiome research.

Objectives/Goals: We investigated the risk of trauma in the form of fractures and traumatic brain injuries (TBIs) among Medicare beneficiaries with incident Parkinson’s disease (PD) age ≥67 compared to population-based controls. Secondarily, we examined the risk of death following a fracture in PD cases compared to controls. Methods/Study Population: We identified incident PD cases (N = 94,317) within a population-based sample of 2017 Medicare beneficiaries. Controls (N = 471,585) were matched 5:1 on month and year. We obtained claims data from 2017 to 2019 to follow cases and controls to identify new fractures treated in a hospital. Our primary outcome was any fracture. We also considered fracture type and TBI. We compared frailty level between cases and controls. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between trauma and PD after adjusting for the following covariates: selected medical comorbidities, age, sex, race/ethnicity, smoking, and use of care. We used Cox regression to estimate hazard ratios (HRs) and 95% CI for trauma in cases compared to controls using the same covariates. Results/Anticipated Results: Compared to controls, PD patients who developed a fracture were more likely to have a history of falls (OR = 2.20, 95% CI 2.08–2.34) and difficulties in walking (OR = 2.66, 95% CI 2.50–2.82). Compared to controls with a fracture, PD patients with a fracture were more likely to be moderately frail (OR = 1.43, 95% CI 1.25–1.64). PD cases had a higher risk of all fracture types, including hip (OR = 1.93, 95% CI 1.85, 2.01), spine (OR = 1.90, 95% CI 1.79, 2.02), upper extremity (OR = 1.69, 95% CI 1.58–1.80), and other traumas such as a TBI (OR = 2.14, 95% CI 1.88–2.43). PD patients had greater mortality following a fracture (HR = 1.18, 95% CI 1.13–1.24) than controls. Discussion/Significance of Impact: The burden of trauma in the first two years immediately after PD diagnosis is high and warrants the initiation of early fall and fracture prevention strategies, in addition to aggressive treatment of PD symptoms by all providers caring for patients with PD.

Objectives/Goals: Micro-credentialing programs provide a rapid solution to the growing shortage of clinical research professionals (CRPs) by expanding the applicant pool and improving training efficiency. This study evaluates the impact of a micro-credential course on CRP education and its potential to reduce staffing shortages. Methods/Study Population: To address the CRP staffing shortage, new and existing clinical research staff at the Rutgers Cancer Institute of New Jersey (CINJ) participated in a micro-credential badging course developed by NJ ACTS and Rutgers’ Master’s in Clinical Research Management Program. The course focused on key clinical research topics designed to equip participants with foundational knowledge. Post-completion, surveys were administered to both participants and CINJ management to assess the program’s effectiveness. The survey measured perceived knowledge gains, interest in further opportunities, and the program’s potential to alleviate staffing shortages. Survey results were analyzed to determine the program’s overall impact on CRP education and staffing challenges. Results/Anticipated Results: Survey results demonstrated significant knowledge gains among participants, with 85% reporting increased confidence in clinical research topics after completing the micro-credential course. Additionally, 70% of participants expressed interest in pursuing further opportunities in the field. CINJ management reported smoother onboarding processes and noted an improvement in job readiness among new hires. The CRC Badge has since been integrated into CINJ’s formal onboarding process. Overall, the micro-credential program contributed to expanding the CRP applicant pool, improving training efficiency, and offering a short-term solution to alleviate staffing shortages. Discussion/Significance of Impact: This research demonstrates the effectiveness of micro-credentialing in addressing the critical shortage of CRPs. By rapidly equipping staff with essential knowledge, the program broadens the applicant pool, enhances onboarding, and offers an immediate solution to workforce gaps.