Objectives/Goals: Second-generation antipsychotics (SGA) are used to treat mental disorders in youth but are linked metabolic syndrome (MetS). Most data on prescribing practices and risk factors are from short-term studies (6–12 months). We aim to characterize prescribing and identify clinical and genetic predictors of MetS using electronic health records (EHR). Methods/Study Population: EHR data were extracted from Cincinnati Children’s Hospital Medical Center (CCHMC) for patients aged ≤21 years prescribed SGAs from 7/1/2009 and 7/1/2024, identifying prescribing prevalence. Next steps are to create an SGA-MetS case–control dataset 8 weeks after an SGA prescription. A case will be defined by meeting 3 of 5 criteria: 1) BMI ≥95th percentile for age/sex; 2) fasting glucose ≥100 mg/dL or use of anti-diabetics; 3) triglycerides ≥110 mg/dL; 4) HDL-C ≤40 mg/dL; 5) systolic/diastolic BP ≥90th percentile for age/sex or use of antihypertensives. The prevalence of SGA-MetS will be calculated by dividing SGA-MetS cases by total SGA users. Logistic regression will identify clinical predictors of MetS, and we will evaluate the association of polygenic risk scores (PRS) of BMI and type 2 diabetes with SGA-MetS risk. Results/Anticipated Results: Our preliminary analysis identified 30,076 patients who were prescribed SGAs (mean age 12 years, SD = 4; 58.8% female; n =  17685). Most self-identified as non-Hispanic (95%, n = 28,595) and of White race (76%; n =  22,935), with 18.5% self-identifying as Black or African American (n = 5,579). The most commonly prescribed SGAs were risperidone (n = 12,382, 41.1%), aripiprazole (n = 9,847, 32.7%), and quetiapine (n = 5,263, 17.5%), with much lower prescribing rates of other SGA known of their low risk of MetS (e.g., ziprasidone 5.5%, lurasidone 1.4%, paliperidone (n = 316, 1.1%), or others cariprazine (n = 72), asenapine (n = 43), brexipiprazole (n = 39), iloperidone (n = 24), and clozapine (n = 20). Discussion/Significance of Impact: Our analyses found that risperidone, quetiapine, and aripiprazole were the most prescribed SGA, with risperidone/quetiapine linked to a higher risk of MetS. We will present ongoing work identifying risk factors for SGA-MetS and examining the association with PRS. Our work has the potential to identify high-risk patients for personalized treatment.

Objectives/Goals: Originally presented at TS24, we would like to discuss follow-up outcomes and preliminary findings“Building Empowerment Through Fitness” – objectives were designed to address the “whole women” in underserved segments of the population in impoverished and/or underprivileged communities with fewer opportunities and promote economic mobility. Methods/Study Population: A qualitative study design to understand unique cultural context and challenges faced by women residing in the Birmingham Housing Authority, regarding physical activity engagement, including sedentarism. The needs assessment approach examined barriers, support systems, and social networks. Semi-structured, pre, mid, and post program focus groups explored challenges and opportunities for health, such as diet and exercise, and are being analyzed by hand with a thematic analysis. The project framework used movement, home repair tools with a life-building/life repair curriculum to influence self-efficacy and program engagement. The study enrollment included 51 women over two cohorts in a 12-week project. Results/Anticipated Results: Preliminary are results showed the women did face heightened health risks due to sedentary lifestyles, educational gaps, and socioenvironmental barriers. We incorporated tool skills, healthy lifestyles, and social/emotional focus group to explore social/emotional perceptions, concerns, and needs. Although our results were on target, there were some unexpected outcomes related to family dynamics, literacy, housing stability, and peer support. Discussion/Significance of Impact: Women are more likely to be sedentary and have poor health. BeFit served as a solution to addressing problems related to economic mobility barriers, poor health, sedentariness, and low self-esteem/self-efficacy. Additionally, exercise/movement programs can be a powerful tool to address disparities and help prioritize health.

Objectives/Goals: Trainees in clinical and translational science (CTS) must learn to effectively communicate their research ideas and findings to a range of audiences. As part of our science communication curriculum, we developed ORAL and WRITTEN science communication rubrics for our trainees to use across their courses and research activities. Methods/Study Population: The Tufts CTS Graduate Program is the training core of the Tufts CTSI and its associated pre- and post-doctoral T32 awards. Approximately 10 trainees with a range of backgrounds (e.g., physicians, medical students, master’s-level researchers, and basic science PhDs) matriculate each year. Faculty members and staff with expertise in science communication and pedagogy formed a committee to develop the rubrics. Because oral and written communication require different skills, we developed separate rubrics for each. We reviewed our current science communication curriculum, reviewed existing communication rubrics, and identified common mistakes students make. Following pilot testing by students and faculty pilot for one semester, we modified the rubrics based on informal feedback. Results/Anticipated Results: Both rubrics include a section to identify the target audience and specific items organized by theme. Oral rubric themes include presentation content, slides, verbal communication, nonverbal communication, and following instructions. Written rubric themes include overall, manuscript/proposal sections, and following instructions. The rubrics serve as feedback tools for faculty and students to evaluate work others produce and as self-evaluation tools. Feedback elements include a 4-point rating for each rubric item, open text feedback for each theme, and an open text holistic assessment. We now use the rubrics in our study design course, which features student presentations of planned research, and in our writing course. We anticipate collecting formal student feedback to further evaluate the rubrics. Discussion/Significance of Impact: Our rubrics can supplement existing science communication training and can be integrated into all CTS coursework and research activities. For future clinical and translational scientists to have the greatest impact, they must learn to effectively communicate findings to multiple audiences, ranging from experts in their field to the general public.

Objectives/Goals: This study aims to gather user feedback from clinical research professionals on the usefulness and relevance of regulatory resources found on a new regulatory web portal, OpenRegSource, to enhance its usability, thereby advancing this project from the initial to the full implementation phase of the implementation science framework. Methods/Study Population: The Regulatory Knowledge and Support core of the Southern California Clinical and Translational Science Institute developed a regulatory web portal called OpenRegSource to help researchers gain basic regulatory information prior to professional and/or paid consultation. Before publicly launching, a virtual focus group (FG) composed of 21 members of the local clinical research workforce was given two weeks to explore the web portal and answer three surveys. Two other research professionals also gave feedback outside of the focus group. The user feedback data was analyzed and discussed by the web portal project team. Updates were then made accordingly. Once the portal was launched, a plan was implemented to collect usage metrics and additional feedback for continuous improvement. Results/Anticipated Results: Of the 21 FG participants, 20 completed the feedback survey specifically for their experience with the web portal. 65% (13/20) said the number of resources was just right. 90% (18/20) found the resources to be very relevant to their respective topics. 85% (17/20) found the resources very useful and somewhat useful to their daily work activities. 75% (15/20) found the organization of the portal to be good or very good. 85% (17/20) found it very easy and somewhat easy to navigate the web portal. 90% (18/20) found the portal to be effective in providing its audience with a basic understanding of regulatory requirements. 95% (19/20) found the portal useful for novice research professionals. 85% (17/20) found the web portal useful overall. Participants were also able to provide commentary feedback for specific pages. Discussion/Significance of Impact: Obtaining stakeholder input during the development of a resource or tool is essential to ensure the final product meets user needs and is effectively utilized. In this case, involving the feedback of clinical researchers will help improve OpenRegSource to better facilitate the advancement of their work.

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: 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: Nearly 42% of adults in the USA have obesity; women are disproportionately affected. Women with obesity that become pregnant are especially prone to developing health condition; thus, aid is needed to achieve appropriate gestational weight gain (GWG). GWG interventions would benefit from examining the effects of social support and stress has on GWG. Methods/Study Population: The proposed study will examine whether expecting mothers’ social support predicts their perceived stress. Also, examine whether receiving aid, in the form of weekly meal delivery, changes perceived stress. Lastly, this study will explore if social support mediates the relationship between meal delivery and perceived stress. The study will use data gathered from a pilot GWG intervention focused on providing pregnant women weekly meal delivery. Participants (N = 14) will complete 2 visits at 16–20- and 35–36-weeks gestation, while receiving weekly meal deliveries. Data from both visits will be used for the purpose of this study. Women’s social support will be measured using the Multidimensional Scale of Perceived Social Support. While stress will be measured via the Perceived Stress Scale. Results/Anticipated Results: Previous behavioral weight loss studies have found that individual who endorse experiencing social support report less perceived stress. Although the relationship has seldomly been examined in pregnant women, similar results are anticipated. Often aid, such as meal delivery, can impact perceived stress and is likely to cause a positive change. To our knowledge, this is the first study to explore whether social support mediates the changes in perceived stress after receiving weekly meal delivery. Therefore, there is no known literature to suggest anticipatory results. Discussion/Significance of Impact: Few studies have examined the association between social support and perceived stress in pregnant women. This study will examine the effects social support has on a pregnant women’s stress. The proposed study will aim to identify if meal delivery changes perceived stress. If so, the study will explore if perceived social support mediates this change.

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: To assess the impact of the MATCH Program on mentees and mentors over the years, we have surveyed both groups on the effectiveness of the mentorship process, how the MATCH program influenced mentees’ future career plans, and their ongoing interest in clinical research. Methods/Study Population: To evaluate impact on mentees and mentors in the most recent cycle, we fielded two program evaluation surveys, for mentors and mentees. The surveys were distributed and collected using Qualtrics in May 2024. The mentee survey collected data on relationship with mentors, quality of mentorship, future career/education plans, and self-assessment of the program impact. The mentor survey collected data on relationship with mentees, mentees’ engagement, and a self-assessment of the program impact. Qualitative analysis was conducted to determine key themes expressed by participants. The responses were compared to assess the effectiveness of the mentoring relationship from both parties. Results/Anticipated Results: Respondents included 15/20 (75%) mentees and 15/20 (75%) mentors. All mentees (100%) and mentors (100%) stated they would like to continue their relationship outside of the program. The majority of mentees 13/15 (87%) and mentors 13/25 (87%) also viewed their mentee/mentor relationship to be excellent or good. Most mentors 10/15 (67%) stated it was their first time serving as a mentor for the program. Mentees also found their experience in the program very beneficial with 6/15 (40%) stating that MATCH changed their career plans. In addition, most mentees 14/15 (93%) indicated that they are community college or four-year college/university bound. Most mentees 11/15 (73%) indicated an interest in pursuing a health or medical career. Also, 10/15 (67%) mentees stated an interest in pursuing a career in research. Discussion/Significance of Impact: Both mentees and mentors have benefited from the program’s daisy chain mentoring and the program has helped facilitate a potential lifelong mentorship between mentees and mentors. The program also demonstrates promise of developing a pre-health pathway for historically underrepresented students in STEM.

Objectives/Goals: We aim to explore the associations of race/ethnicity and socioeconomic status (SES): 1) with grip strength, walking speed, and comorbidity index cross-sectionally and 2) with the change in comorbidity index and mortality risk over four years of follow-up in cancer survivors. Both aims will examine the potential mediating role of cytomegalovirus (CMV) infection. Methods/Study Population: This study includes 1,602 cancer survivors (mean age = 72 years, 10% Black, 54% female) from the Health and Retirement Study (HRS), a nationally representative U.S. sample followed for health outcomes until 2020. HRS measured CMV immunoglobulin G (IgG) antibody levels (from blood samples), grip strength, and walking speed in 2016. We will apply linear regression to examine the associations of race/ethnicity and SES with grip strength, walking speed, and comorbidity index cross-sectionally and with the change in comorbidity index over four years of follow-up. We will apply Cox proportional hazard regression to examine the associations of race/ethnicity and SES with mortality over four years of follow-up. In all models, we will investigate the potential mediating role of CMV infection in these associations. Results/Anticipated Results: We expect that CMV infection mediates the associations of race/ethnicity and SES with age-related health outcomes, including muscle weakness (measured by grip strength), decreased functional performance (measured by walking speed), comorbidity index, and mortality in elderly cancer survivors. Discussion/Significance of Impact: If our hypothesis is confirmed, the findings may inform physicians to closely monitor CMV infection among cancer survivors from socially disadvantaged groups and apply treatment if needed. Several oral medications for CMV exist, and CMV vaccines are currently undergoing testing in clinical trials. This will make the treatment for CMV more accessible.

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: Explore the association of gestational age of LPIs and neurodevelopment delay in children exposed to the neonatal intensive care unit (NICU). Compare between groups if exposure to NICU affects neurodevelopment in children who were born between 34 and 37 weeks of gestation. Methods/Study Population: This is a cross-sectional study design to study the association of gestational age and neurodevelopmental delays in a birth cohort in Puerto Rico of children exposed to the NICU. Their neurodevelopment will be measured with the Bayley III. Statistical analysis will be performed using IBM SPSS Statistics 27.0. Descriptive statistics will be used, and normality distributions among all continuous variables, frequency distribution for categorical variables. We will recruit 30 infants between 18–24 months of age that were born between 34–37 weeks of gestation. Infant will be divided with regard to their exposure to NICU experience. Results/Anticipated Results: We anticipate identifying neurodevelopment delays among children born prematurely between 34 to 37 weeks of gestation. We anticipate that our controlled group will have better outcomes when compared to the controlled expose group. We also expect that gestational age impacts adversely neurodevelopment in children who were born between 34 and 37 weeks of gestation. Discussion/Significance of Impact: Approximately 84% preterm birth are considered LPIs. Prematurity is described as a chronic condition; adverse long-term neurodevelopment consequences. Our study promotes early detection and interventions that can reduce the consequences of the neurodevelopment delays in LPIs.

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: The Research Education and Community Health (REACH) coalition proposes to develop the infrastructure for continuous and comprehensive collection of community health data to drive programs, education, and funding priorities across municipal agencies, institutions, and nonprofit organizations in Galveston County. Methods/Study Population: The workgroup through REACH will organize and adopt a comprehensive community health needs assessment that 1) accumulates existing, readily available data for shared use (e.g., Center for Health Care Data at the UT School of Public Health, the Texas Department of State Health Services Center for Health Statistics, and Epic Cosmos, a data aggregation tool, used by UTMB and other health systems to improve patient care); 2) utilizes data collected throughout the community (i.e. non-profits, municipal agencies, and law enforcement); and 3) applies qualitative data from focus groups and/or key informant interviews, so we can hear directly from community members about what their needs are. By doing so, we hope all can benefit from having access to current and relevant data to drive our programs, education, and funding. Results/Anticipated Results: This Community Health Needs Assessment is being coordinated by a diverse workgroup including community organizations, researchers, and policy makers who will benefit from access to current and relevant data. The Galveston Youth Risk Student Survey, completed every three years and most recently in 2024, revealed lingering health and mental health effects of the COVID-19 pandemic on County youth. This highlighted the need for community access to current, accurate, and ongoing data to drive programming, interventions, and education. The REACH Coalition, made up of 23 UTMB Centers and Institutes and 39 community organizations, is spearheading this effort as a part of its mission to facilitate collaborative research, service, and educational efforts. Discussion/Significance of Impact: Collected data will be used to establish and support ongoing, coordinated interventions in response to identified needs. Shared ownership of data and project implementation optimizes resources and reduces gaps and/or redundancy in community programming.

Objectives/Goals: Cervical cancer is preventable through HPV vaccination and the detection/removal of precancerous lesions. Incidence and mortality rates have only decreased by 3–4% in the past decade. Despite having the tools to prevent all cervical cancers, they are not being fully utilized. Our goal is to identify barriers and design strategies to overcome them. Methods/Study Population: Women in urban (750) and rural (750) settings will be screened for the presence of high-oncogenic risk HPV (hrHPV) by self-vaginal swab, complete the Monitoring Blunting Style Scale, a validation scale to determine attentional style, and a structural barrier to care survey. A subset (Results/Anticipated Results: The study, launched in September 2024 at the Medicine Primary Care Clinic at UMC in New Orleans, has enrolled 16 women. Sample adequacy was high (82%), with 5 women having hrHPV present. Participants expressed high satisfaction and acceptance of the self-administered vaginal swab, with most samples demonstrating high quality. Surveys have been collected, and hrHPV-positive women have been referred for gynecological follow-up. Shreveport site will recruit women across over 20 rural clinical sites using a Mobile Health Unit to increase access in rural and underserved communities. Discussion/Significance of Impact: The baseline study will take 12–18 months. We will identify and address key barriers to follow-up gynecological care, including logistical issues (improving access and navigation), educational needs (developing culturally sensitive materials), and emotional support. We will create a care delivery model to eliminate cervical cancer in Louisiana.

Objectives/Goals: Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with a mean survival of only 11 months even with the most advanced treatment to date. The desmoplastic microenvironment of PDA is thought to play a critical role in therapy resistance. One pathway that might be responsible for resistance to immunotherapy is the CXCR4-CXCL12 axis. Methods/Study Population: In this study, we propose to evaluate the effect of CXCR4-CXCL12 inhibition on dual checkpoint inhibition in KPC mouse model of PDAC and patient-derived explants. PDAC mouse models are made with pancreatic cancer cells driven by loss of TP53 and activation of KRAS. These models are treated with PD1 inhibitor Balstilimab and an FC-modified CTLA4 Botensilimab with or without CXCR4 inhibitor BL8040. In addition, we make explants of patient tumors along with their tumors and autologous peripheral blood mononuclear cells and this model is similarly challenged with BOT/BAL and BL8040. Using immunofluorescence and flow cytometry, we quantify and evaluate the spatial relationships between different cell populations. Most notably, we evaluate the relative abundance of CD8+ T cells in control and treated conditions. Results/Anticipated Results: We expect the inhibition of CXCR4-CXCL12 axis, along with two new potent checkpoint blockers, will lead to infiltration of CD8+ T cells in both the mouse and human PDAC models. We also expect this to translate into more tumor cell killing as demonstrated by Caspase activities and tumor shrinkage. Discussion/Significance of Impact: If our hypothesis is proven in both mouse and human PDAC models, this study will serve as a basis for a phase I/II clinical trial testing this combination of drug.

Objectives/Goals: To develop a personalized computational framework integrating computational fluid dynamics (CFD) and topology optimization for designing intracranial aneurysm implants. The primary objective is to reduce intra-aneurysmal blood flow velocity and enhance thrombus formation for improved treatment outcomes. Methods/Study Population: Patient-specific aneurysm geometries were extracted from pre-treatment rotational angiograms. A CFD-driven topology optimization framework was employed to design implants that reduce intra-aneurysmal flow velocity. The fluid dynamics were modeled using Navier–Stokes equations and the structural integrity of the implants was ensured by linear elasticity equations. The solid isotropic material with penalization (SIMP) method was applied to optimize the implant’s porous architecture, balancing flow reduction with structural support. COMSOL Multiphysics software was used to implement the optimization. Results/Anticipated Results: The optimized implants demonstrated significant reductions in intra-aneurysmal blood flow velocity and improved hemodynamic conditions. Flow velocity within the aneurysm was reduced by 77%, and the fluid energy dissipation ratio showed a 78.9% improvement compared to pretreatment conditions. The optimized porous structures were tailored to the aneurysm’s specific geometry, providing personalized designs that improve flow stasis and thrombus formation. Further validation of the implants will be performed in vitro and in vivo to assess their effectiveness and biocompatibility. Discussion/Significance of Impact: This personalized implant design framework could lead to better treatment outcomes by reducing aneurysm recurrence and complications compared to current devices. It provides a pathway for improved occlusion rates and patient-specific solutions for intracranial aneurysms.

Objectives/Goals: To explore how generative AI and chatbot technologies can transform clinical research administration by improving operational efficiency, reducing administrative burden, and thereby enhancing overall productivity and accuracy in clinical research environments. Methods/Study Population: This explores AI’s application in enhancing clinical research administration. We specifically address AI’s role in QCT/MCA activities, charge master data cleaning, and generating IRB consent forms from award documents. AI algorithms optimize charge master data for accuracy and compliance. Generative AI models are employed to produce IRB consent forms efficiently, incorporating key grant documents. AI also conducts thematic analyses of historical CTSA aims to identify trends and recurring themes. Furthermore, AI-assisted tools enhance study design through innovative approaches to hypothesis generation, sample size calculation, and protocol development. Integrating these AI methods aims to significantly improve efficiency, accuracy, and overall quality in clinical research administration. Results/Anticipated Results: Incorporating AI into clinical research administration will yield improvements in efficiency and accuracy. AI-driven QCT/MCA steps are expected to reduce human error and enhance data integrity. Chargemaster data cleaning via AI prompts will likely result in optimized, error-free data, ensuring compliance with regulations. The use of genAI for creating IRB consent forms from grant documents should significantly streamline the IRB approval process, reducing preparation time and administrative burdens. Thematic analysis of CTSA aims by AI will provide deep insights into historical trends and recurring themes, aiding in strategic planning. AI-assisted study design tools are anticipated to optimize sample estimation, protocol development, and advance the quality of clinical research administration. Discussion/Significance of Impact: The significance lies in enhancing efficiency, accuracy, and quality in clinical research administration. By streamlining processes, reducing errors, and providing strategic insights, AI supports the CTSA mission to accelerate translational research, thus improving public health outcomes and scientific innovation.

Objectives/Goals: Using secure systems for sharing documents with external collaborators is essential for all researchers. These documents may include protected health information (PHI) or sensitive materials like protocols, study reports, DSMB reports, publications, presentations, abstracts, and statistical analysis plans (SAPs). Methods/Study Population: We surveyed the ACTS Biostatistics, Epidemiology, and Research Design Special Interest Group (BERD-SIG) to gather information about the systems they are currently using or have used in the past for document sharing with external collaborators. The survey focused on the security of these systems, particularly in relation to sharing documents containing PHI. In addition, the survey included questions about various system features of interest. These features included version control, simultaneous editing by multiple users, and access rights management, such as the ability to assign different permissions (e.g., read-only, write, and download) to different individuals. We also invited participants to provide feedback on any additional positive or negative aspects of the systems they use. Results/Anticipated Results: We received 28 completed survey responses. Respondents had an option for choosing more than one system. The top current systems reported were Microsoft Teams (OneDrive, SharePoint) (n = 16), Box (n = 11), Google Docs/Drive (n = 10), and Dropbox (n = 6). Among other systems listed individually were Filelocker, REDCap, Slack, Website, Significant Media Shuttle, and Zulip. Notably, 15 responses indicated the respondents were unsure if their system is secure for sharing documents containing PHI. Respondents also offered feedback on both the positive and negative aspects of these systems. For example, a key advantage of Box was its password-controlled access. However, its incompatibility with office tools and the challenges for external collaborators attempting to access the system were noted as drawbacks. Discussion/Significance of Impact: Utilizing secure institutional document-sharing systems and understanding their features significantly affects the effectiveness and security of collaborations among researchers, particularly with external partners. This knowledge is especially crucial when sharing documents containing sensitive patient and study data.

Objectives/Goals: Patients with multiple chronic conditions (MCCs) face care coordination challenges and poorer health outcomes. Outpatient telehealth may be an effective way to enhance MCC patient care given the need for multiple visits and specialists. This study seeks to describe telehealth utilization between 2013 and 2023 in Arkansas. Methods/Study Population: We utilized the Arkansas All-Payer Claims Database (APCD) to identify patients diagnosed with high-prevalence MCCs comprising diabetes with comorbid hypertension, hyperlipidemia, or asthma. We then measured telehealth utilization defined as any claim associated with a telehealth modifier code, a place of service code defining the service as occurring in the patient’s home, or remote patient monitoring. Finally, we created payer-specific (e.g., commercial or Medicaid) yearly measures of the number of any telehealth claims among MCC patients divided by the number of MCC patients for that year. Linear regression was used to measure the difference in utilization during the COVID-19 pandemic (i.e., 2020–2023) versus prior to the pandemic (i.e., 2013–2019). Results/Anticipated Results: Overall, the COVID-19 pandemic era was associated with an increase of telehealth utilization among commercial patients by 1.01 telehealth claims per MCC patient (95% CI: 0.39 to 1.62, p Discussion/Significance of Impact: Variations in telehealth uptake among MCC patients suggest heterogeneity in its suitability and necessity. We will later evaluate whether telehealth use is associated with different levels of inpatient and emergency department utilization. We expect the findings to provide clarity on the suitability of telehealth use by MCC disease status.