OBJECTIVES/GOALS: The objective of this research was to learn how the oncogenic transcription factor, ERG, is regulated in prostate cancer. If we could learn how ERG is regulated and which genes are important for its oncogenic phenotype in prostate cells, we could design new therapeutic strategies against ERG, which has proven to be difficult to target. METHODS/STUDY POPULATION: We conducted an shRNA screen in prostate cells to determine candidate genes and pathways that are important for ERG function. To validate the findings of the screen, we performed a variety of cell-based functional assays, including trans-well migration, wound healing, and clonogenic survival assays. To further investigate the mechanism between ERG and the genes revealed by the screen, we performed biochemical and molecular biology experiments such as Western blotting and qRT-PCR for protein and mRNA expression, co-immunoprecipitation assays to determine protein-protein interactions, and chromatin immunoprecipitation (ChIP-qPCR) to determine transcription factor binding to DNA sites. RESULTS/ANTICIPATED RESULTS: The screen revealed that genes involved in the toll-like receptor 4 (TLR4) pathway are important for ERG-mediated migration. We tested the effect of a TLR4 inhibitor on ERG function and observed decreased migration and clonogenic survival exclusively in ERG-positive cells. Expression of pMEK and pERG was reduced when TLR4 was inhibited, which suggests a mechanism in which TLR4 upregulates pMEK, leading to the phosphorylation and activation of ERG. This is supported by functional assays in which cells expressing a phosphomimetic ERG are resistant to the TLR4 inhibitor. We demonstrated that ERG drives the transcription of TLR4 and its endogenous ligands HSPA8 and BGN. Therefore, ERG can sensitize the cell to TLR4 activation by increasing the number of receptors as well as providing the ligands needed for stimulation. DISCUSSION/SIGNIFICANCE OF IMPACT: This research provides a new therapeutic pathway for treating ERG-positive patients through TLR4 inhibition. This can be beneficial because many patients become resistant to the standard therapy, leaving very few treatment options. TLR4-based therapies could provide an alternative for patients who have developed resistance.

OBJECTIVES/GOALS: Our objective is to address the gaps in our epidemiologic understanding of Echinococcosis, a neglected and emergent zoonotic parasite in the US. Comparing regional differences in human cases with known Echinococcus genotypes in canine reservoirs will reveal landscape-level transmission risks. METHODS/STUDY POPULATION: Retrospective analysis of de-identified diagnosis codes for human echinococcosis in the US from 2002-2022 in a federal multi-healthcare network database (TriNetX). Analyses included Kaplan-Meier survival and specific parasite species cohort comparisons with descriptive summaries of demography and co-morbidities, and odds ratios for interventions (surgery, antiparasitics, cyst aspiration) and complications (anaphylaxis, sepsis, etc.) Mitochondrial genomes of Echinococcus spp. from next-generation sequencing ova-positive archival animal scat (National Park Service), along with sequences from published sources in the US, will be used to compare regional genotypic and reservoir host differences. RESULTS/ANTICIPATED RESULTS: Over 36,000 US patients were diagnosed with Echinococcosis in the past twenty years. Most cases had an unspecified parasite species, primarily affecting pediatrics (average age 16y +/- 10y) in the South/Southeast US, with very few interventions or medical complications reported. Patients with specific parasite species diagnoses (E. granulosus or E. multilocularis, N=500) had higher rates of mortality (11.7% at 10y), surgical (8.8%) and medical (18.2%) interventions, and complications (2%-17.2%), with demographic differences noted. Sequencing data and analysis pending for 47 Echinococcus positive wolf samples, with 78 additional sequences available for two types of Echinococcus spp. in wildlife, and lacking data for domestic dog-adapted strains in the US. DISCUSSION/SIGNIFICANCE: This study revealed a higher-than-expected burden of Echinococcosis in the US, confirming it is an under-recognized neglected and emergent disease. Future studies will address gaps regarding the origin of unspecified infections, with targeted surveillance of domestic dogs in high-risk areas.

OBJECTIVES/GOALS: Since 2013, managers of community engagement (CE) programs across the Clinical and Translational Science Award (CTSA) consortium funded by the National Center for Advancing Translational Sciences (NCATS) have convened monthly to build connections, share knowledge, and enable collaboration. METHODS/STUDY POPULATION: Notable for focus on staff leadership, the CE Brokers group has been central to the ongoing success of CTSA community engagement partnerships and approaches to research. In early 2022, a survey of the 139-member group asked about their roles and responsibilities, the ways the CE Brokers network has contributed to their hubs adoption and development of best practices and innovations, resources and lessons learned, and the creation of opportunities for members to collaboratively conduct and disseminate original research, and research on the science of community engagement. The survey also asked CE Brokers if they or their community partners are part of an underrepresented community. RESULTS/ANTICIPATED RESULTS: These demographic data will be shared, along with analyses of data on growth of the group over time, evolving themes, and a SWOT analysis completed in 2021. DISCUSSION/SIGNIFICANCE: This will provide a platform to explore new avenues for the CE Brokers and their impact within the NCATS CTSA consortium, in line with the evolving direction of the clinical and translational research enterprise.

ABSTRACT IMPACT: This work may provide new targets for vaccine and immunotherapeutic development against MRSA infections. OBJECTIVES/GOALS: Staphylococcus aureus is the leading cause of skin and skin structure infection (SSSI), a primary portal of entry for invasive infection. Patients with SA SSSI have a high 1-year recurrence. We have shown innate memory protects mice against SA SSSI. The goal of this project is to determine epigenetic mechanisms of protective memory against SA SSSI. METHODS/STUDY POPULATION: We have shown macrophages (Mf) afford protective memory against recurrent SA SSSI in mice. Priming by prior infection reduced skin lesion size and MRSA burden, which correlated with increased Mf in abscesses and lymph nodes. Priming potentiated the opsonophagocytic killing of SA by bone-marrow derived Mf (BMDM) in vitro, and their adoptive transfer into naive skin afforded protective efficacy in vivo. Here, we investigated epigenetic mechanisms of anti-SA efficacy in BMDMs. BMDM from naive (uninfected) or primed (SA SSSI) wild-type C57Bl/6 mice were cultured ex vivo. DNA from BMDM groups were isolated and analyzed for methylation changes using reduced representation bisulfite sequencing (RRBS). Pathway analyses of methylation changes were determined with Panther. RESULTS/ANTICIPATED RESULTS: Present findings indicate the protective memory afforded by BMDM was mediated by epigenetic modifications of the DNA. Using RRBS, we profiled differentially methylated regions (DMR) in DNA from naive vs. primed BMDM. Primed BMDM exhibited significantly different DMRs as compared to naive BMDM. Proximity to known genes were mapped using GREAT. Pathway analyses revealed DMRs predominant in genes integral to immune modulation, such as integrin signaling, cytokine/chemokine networks, and growth regulation. For example, SA-primed BMDM were hypermethylated proximate to GIMAP8 versus naive BMDM, suggesting repression of this protein. Gimap family ligands are small GTPase immune-associated proteins expressed in immune cells known to regulate macrophage lysosomal fusion during parasite infection. DISCUSSION/SIGNIFICANCE OF FINDINGS: These findings reveal epigenetic mechanisms of macrophage innate memory against recurrent MRSA infection. Functional testing of these genes in response to SA infection is needed to confirm their protective role. These insights may provide new targets for vaccine and immunotherapeutic development against MRSA.

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

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/SPECIFIC AIMS: Allostatic load, the chronic stress-induced wear and tear on the body, has a cumulative deleterious effect in individuals over their lifetime. Recent studies have suggested that socio-economic status, psychological determinants, and biomedical health cumulatively contribute to allostatic load in young adults. Although these finding individually suggest that African American children may be particularly susceptible to the effects of allostatic loading due to racially-based discrimination and economic instability, few studies have shown the effect of exposure to violence on the allostatic load carried by young African Americans. METHODS/STUDY POPULATION: The Biological and Social Correlates of Drug Use in African American Emerging Adults (BADU) data set is composed of young African Americans (n=557 individuals) living in the Washington, DC area, collected from 2010 to 2012. Study participants were sought equally between males and females (n=283, n=274, respectively). This data set provides a rich source of information on the behavioral, mental, and physical health of African American young adults (18–25 year olds) living in the Washington, DC area. Analysis of 6 biomedical markers were measured in BADU study participants: C-reactive protein, cortisol, Epstein-Barr virus IgG, IgE, IgA, and IgM, known to be markers of immune stress and allostatic load. Naive Bayes was used to identify participant responses that were correlated to elevated stress biomarker levels. RESULTS/ANTICIPATED RESULTS: Violence was most closely correlated to elevated EBVVCA IgM and IgE levels. Elevated IgE levels correlated to increased experience of familial violence and sexual abuse; familial drug abuse and depression; violence and community violence. Cortisol is positively correlated to reported emotional state (R=0.072) and perceived individual discrimination (R=0.059). DISCUSSION/SIGNIFICANCE OF IMPACT: Allostatic load appears to be high in individuals who self-report exposure to violence. Both perceived mental health and violence were correlated to elevated stress biomarkers. When Epstein-Barr virus viral capsid antigen IgM was compared with violence features characterized in the data set, we found that internalization of environmental stressors were most strongly correlated to elevated allostatic load markers. This work suggests that internalization of experienced violence may be as important as the actual violence experience.

ABSTRACT IMPACT: Our proposed jaw-specific control mechanism of tooth development is expected to address the site-specific prevalence of tooth agenesis in humans. OBJECTIVES/GOALS: To determine the molecular mechanisms that control jaw-specific tooth development. To identify the molecular basis of the site-specific prevalence of humans tooth agenesis cases. METHODS/STUDY POPULATION: We used three different genetically engineered mouse lines: Msx1 ^’/ ^’, Dkk2 ^’/ ^’, and Sostdc1 ^’/ ^’ mice. We used developmental mouse genetics approaches, basically generating different combinations of compound mutant mice. We examined their tooth development by using gross, histology, and mRNA expression analyses. RESULTS/ANTICIPATED RESULTS: We identified that Sostdc1, a secreted Wnt inhibitor, also plays an important role in regulating the Msx1-dependent odontogenic pathway. Sostdc1 mRNA showed similar expression patterns in the developing tooth germs between control and Msx1-null molar buds. Remarkably, by deleting the Sostdc1 gene, as well as the Dkk2 gene, in the Msx1-null background mouse, molar tooth development was rescued in the maxillary jaw, but not in the mandibular jaw. Furthermore, tooth developmental rescue could be achieved in both the maxillary and mandibular molars by combinedly deleting Dkk2 and Sostdc1 in Msx1-null mice. DISCUSSION/SIGNIFICANCE OF FINDINGS: Our study demonstrates that secreted Wnt inhibitors Dkk2 and Sostdc1 synergistically regulate the Msx1-dependent odontogenic pathway and further control early tooth morphogenesis. These mouse model will be used to further address the site-specific prevalence of tooth agenesis in humans.

OBJECTIVES/GOALS: COVID-19 has taken a big toll on health, mental health and community well-being. COVID-19 has also presented unique opportunities for community engagement programs to sustain their work within communities. Low-tech methods of engagement might provide opportunities for success. METHODS/STUDY POPULATION: When we could not be in the field due to COVID-19, we recontacted our 12.4k HealthStreet members by phone and in 12 months successfully contacted 3.2k. By contrast, over the two COVID affected years (2019 & 2020), our in-person recruitment efforts in the community fell to 832 from 1817 in the two prior years (2017 & 2018). Reconnecting with existing members is an important aspect of sustainable community engagement. RESULTS/ANTICIPATED RESULTS: While 8/10 of the 3.2 K sample had internet access in their homes, 9/10 said having it was somewhat to very important. Importantly, 1 in 3 people who had additional needs or suggestions were looking for better COVID-19 knowledge or ways to reduce the threat of illness, 1 in 4 wanted resources; and 1 in 5 encouraged us to keep doing what we were doing. Among recontacted members, 30.2% indicated a high stress level (8 & over on a 10 point scale) while 15.2% reported a high level of loneliness (8 & over on a 10 point scale). High stress was significantly more likely among those identifying as African American (23.1%) compared to non-African American (16.6%). Loneliness did not vary (10.1% among both). DISCUSSION/SIGNIFICANCE: Sustainable community engagement was important to reach community members during the pandemic, especially given the high rates of stress and loneliness among members contacted. In-person connections were reduced, but phone contact provided an opportunity for successful engagement.

OBJECTIVES/GOALS: As of 2021, the lung transplantation waiting list has a mortality rate of 7.6 deaths per 100 patient-years. Bioengineered human organs is an emerging field of tissue engineering with a goal of developing suitable organs for transplantation. The focus of the project is to evaluate the efficacy of bioengineered lungs using a human-to-swine model. METHODS/STUDY POPULATION: This project will involve designing and assessing the bioengineered lung by establishing a human-to-pig xenotransplantation survival model. The project aims to evaluate how well the bioengineered lung functions within a living model. The bioengineered lung is constructed using swine connective tissue scaffolding, which has been recellularized with human cells. Anatomically, the lung will resemble a swine lung but will possess the immunological and cellular markers of human tissue. The proposed model will initially assess the immunological response of swine to human lung tissue. Lung function will be assessed during surgery using pulmonary vein gas samples and tissue sampling. Following the end of the study, additional tissues samples will be taken to evaluate the immunological response to the tissue. RESULTS/ANTICIPATED RESULTS: Xenotransplantation and bioengineered organs are two new emerging fields of research that have just begun to enter the large animal testing phase. This model will provide a novel human-to-pig xenotransplant survival model that will be used to test the efficacy of bioengineered lungs function in a dynamic living organism. The design has taken the principles of immunology learned from the current clinical and xenotransplant research and has incorporated this knowledge into the known pig-to-pig transplant models. We anticipate that this model design will be easily reproducible and can be expanded to other bioengineered organs as an effective means to test functionality. DISCUSSION/SIGNIFICANCE: The COVID-19 pandemic’s aftermath may lead to an increased demand for lung transplants. Bioengineered lungs could provide an additional source of organs to supplement current availability. This novel approach has the potential to offer a means to test several different types of bioengineered organs in the future.

OBJECTIVES/SPECIFIC AIMS: Neurological injury remains as the main limiting factor for overall recovery after cardiac arrest (CA). Currently available indicators of neurological injury are inadequate for early prognostication after return of spontaneous circulation (ROSC). High diversification of brain mitochondrial cardiolipins (CL) makes them unique candidates to quantify brain injury and to predict prognosis early after ROSC. METHODS/STUDY POPULATION: CL content in plasma in 39 patients within 6 hours of ROSC and 10 healthy subjects as well as CL content in human heart and brain specimens were quantified using a high-resolution liquid chromatography mass spectrometry method. The quantities of brain-type CL species were correlated with clinical parameters of brain injury severity permitting derivation of a cerebral CL score (C-score) using linear regression. C-score and a single CL species (70:5) were evaluated in patients with varying neurological injury and outcome. Using a rat model of CA, CL was quantified in the plasma and brain of rats using similar methods and results compared with the controls. RESULTS/ANTICIPATED RESULTS: We found that brain and the heart fell on extreme ends of the CL diversity spectrum with 26 species of CL exclusively present in human brain not heart. Nine of these 26 species were present in plasma within 6 hours of ROSC with quantities correlating with greater brain injury. The C-score correlated with early neurologic injury and predicted discharge neurologic/functional outcome. CL (70:5) emerged as a potential point-of-care marker that alone was predictive of injury severity and outcome nearly as well as C-score. Using a rat CA model we showed a significant reduction in hippocampal CL content corresponding to CL released from the brain into systemic circulation. C-score was significantly increased in 10 minute Versus 5 minute no-flow CA and naïve controls. DISCUSSION/SIGNIFICANCE OF IMPACT: CA results in appearance and accumulation of CL in plasma, proportional to injury severity. Quantitation of brain-type CL species in plasma can be used to prognosticate neurological injury within 6 hours after ROSC.

Objectives/Goals: To contextualize existing knowledge in translational science, women’s health research can benefit from artificial intelligence (AI) methods for obtaining insights out of massive scientific literature. This study explored the feasibility of making an AI model answer diverse questions based on a set of our institution-specific publications. Methods/Study Population: We developed an automated pipeline to retrieve PubMed publications on women’s health that acknowledged the Mayo Clinic Clinical and Translational Science Award of the past two cycles. The publications need to be indexed with MeSH terms (as a major topic) including “Women’s Health”, “Obstetrics”, “Gynecology”, “Female Urogenital Diseases and Pregnancy Complications”, and “Perinatal Care”, etc. In addition, we enforced the species to be “Human” and the demographics to include “Female.” The metadata of every article including title, abstract, publication year, journal, and author affiliation were saved into a single JSON file. The JSON file was then used as the input context along with a few test prompts submitted to the gemini-2.5-flash model. All the experiments were conducted in a Google Colab environment. Results/Anticipated Results: A total of 237 articles on women’s health were retrieved for the experiment. The AI model correctly summarized the top 5 journals but did not obtain the correct article counts until explicitly requested to heed accuracy. When asked to list three prominent departments and their focus areas, the model gave sensible answers aligned with our knowledge. When asked to identify 10 articles that best represent the T4 stage, the model returned appropriate answers that demonstrated an understanding of the implied emphasis on population health and widespread implementation. Lastly, the model provided well-organized advice with annotated experts and strategies, when asked to recommend collaborators for doing AI research in women’s health. Discussion/Significance of Impact: Based on a literature dataset, the general-purpose AI model delivered impressive results when asked to fulfill tasks that involved information extraction, summarization, reference to external knowledge, and strategy consultation. The ongoing work aims to enhance scalability and accessibility of the tool while adding more rigorous evaluations.

OBJECTIVES/GOALS: SalmonellaTyphi primarily persists in human chronic carriers by forming biofilms on gallstones in the gallbladder (GB). We developed a mouse model of GB chronic carriage, and using this model, aim to detect Salmonella in living mice and track the progression of GB carriage with bioluminescent S.Typhimurium and in vivoimaging. METHODS/STUDY POPULATION: S.Typhimurium 14028 (WT) was transduced with the lux operon from the S. Typhimurium Xen33 strain from Perkin Elmer©, creating 14028lux. 129X1/SvJ mice were fed a lithogenic diet for 6 weeks to induce gallstone formation. After cessation of diet, these mice were infected with 5x103-1x104 colony forming units (CFU) of either the 14028lux isolate, WT (non-luminescent) isolate, or an equal volume of sterile saline. Mice were serially imaged (IVIS SpectrumCT) every 2-3 days for up to 63 days. Images were quantified by measuring average radiance over selected regions of interest. The presence of bioluminescent bacteria in specific organs was confirmed by imaging the abdominal cavity post-mortem. Organs were homogenized and CFUs per mg of tissue were quantified and compared between each group. RESULTS/ANTICIPATED RESULTS: Compared to the controls, mice infected with 14028lux showed luminescence in the abdomen as early as three days post-infection. Within 15 days, the resolution was sufficient to discriminate signal in specific organs, notably the gallbladder, liver, spleen, and cecum. The presence of bacteria was confirmed in these organs via direct imaging and by quantifying CFUs in the tissues. At 63 days post-infection, we identified >103 CFUs and significant luminescence in the GB of a portion of 14028lux-infected mice. For all days post-infection, 14028lux-infected mice that lacked observable luminescence had <100 CFUs/mg tissue. DISCUSSION/SIGNIFICANCE: We have developed a technique using bioluminescent S.Typhimurium and in vivo imaging that, without sacrificing infected mice, enables us to reliably distinguish between mice that have maintained gallbladder chronic carriage >60 days and those that have cleared infection.

OBJECTIVES/GOALS: Our goal was to assess the ability of a 3D-Printed dual cover-core design alveolar ridge bone graft, to withstand the average maximum masticatory force of a healthy person. To this end, we characterized the materials, ran a finite element analysis (FEA) model, and validated it using a resin 3D-printed version tested under compression with strain gauges. METHODS/STUDY POPULATION: A tricalcium-phosphate/hydroxyapatite paste and mixed methacrylated alginate-gelatin were used for the core, and polycaprolactone for the cover. These were characterized using ASTM standards D695 and D638 for compression, tensile, and rheological testing. Then we converted cone CT-scan images of a mandibular alveolar ridge defect to an .stl file, and designed the cover and core in Meshmixer. The model was then imported into ANSYS 11.0, and a downward compression force of 500 N, the maximum masticatory force of a healthy adult, was applied on the graft and mandible’s top ridge. The different models included solid and porous covers and cores, as well as comparing screws on one or both sides of the cover, then validated by compressing a resin 3D-printed versions. RESULTS/ANTICIPATED RESULTS: The FEA model provided maximum displacements, Von Mises stress (VMS), and stress/strain values for each model. The highest maximum displacement was found on the solid covers with a combination of both buccal and lingual screws, at 0.162 mm. The lowest maximum displacement was found in the porous cover at 0.085 mm. All VMS values were below the tensile yield strength, meaning that the materials would not yield. The highest maximum stress was found on the porous cover at 13.52 MPa, the lowest was 1.06 MPa on the cover with no screws. The highest strain was found on the porous model at 0.010, which was 5.6x higher than the solid cover. The porous cover also showed less stress shielding, thus allowing a beneficial mechanical stimulation of the bone, and the lowest maximum displacement, possibly due to flexion through the pores. DISCUSSION/SIGNIFICANCE: Preliminary FEA models demonstrated that for the considered materials, a cover-core design of the mandibular implant would sustain the desired 500 N of force without yielding. The porous cover provides the most benefits, causing the least stress shielding and allowing diffusion of biological factors to support the osteoinductive role of the core.

OBJECTIVES/SPECIFIC AIMS: The objective of this study is to determine the degree to which the use of a contralesionally-controlled brain-computer interface for stroke rehabilitation drives change in interhemispheric motor cortical activity. METHODS/STUDY POPULATION: Ten chronic stroke patients were trained in the use of a brain-computer interface device for stroke recovery. Patients perform motor imagery to control the opening and closing of a motorized hand orthosis. This device was sent home with patients for 12 weeks, and patients were asked to use the device 1 hour per day, 5 days per week. The Action Research Arm Test (ARAT) was performed at 2-week intervals to assess motor function improvement. Before the active motor imagery task, patients were asked to quietly rest for 90 seconds before the task to calibrate recording equipment. EEG signals were acquired from 2 electrodes—one each centered over left and right primary motor cortex. Signals were preprocessed with a 60 Hz notch filter for environmental noise and referenced to the common average. Power envelopes for 1 Hz frequency bands (1–30 Hz) were calculated through Gabor wavelet convolution. Correlations between electrodes were then calculated for each frequency envelope on the first and last 5 runs, thus generating one correlation value per subject, per run. The chosen runs approximately correspond to the first and last week of device usage. These correlations were Fisher Z-transformed for comparison. The first and last 5 run correlations were averaged separately to estimate baseline and final correlation values. A difference was then calculated between these averages to determine correlation change for each frequency. The relationship between beta-band correlation changes (13–30 Hz) and the change in ARAT score was determined by calculating a Pearson correlation. RESULTS/ANTICIPATED RESULTS: Beta-band inter-electrode correlations tended to decrease more in patients achieving greater motor recovery (Pearson’s r=−0.68, p=0.031). A similar but less dramatic effect was observed with alpha-band (8–12 Hz) correlation changes (Pearson’s r=−0.42, p=0.22). DISCUSSION/SIGNIFICANCE OF IMPACT: The negative correlation between inter-electrode power envelope correlations in the beta frequency band and motor recovery indicates that activity in the motor cortex on each hemisphere may become more independent during recovery. The role of the unaffected hemisphere in stroke recovery is currently under debate; there is conflicting evidence regarding whether it supports or inhibits the lesioned hemisphere. These findings may support the notion of interhemispheric inhibition, as we observe less in common between activity in the 2 hemispheres in patients successfully achieving recovery. Future neuroimaging studies with greater spatial resolution than available with EEG will shed further light on changes in interhemispheric communication that occur during stroke rehabilitation.

Objectives/Goals: In 2018, a novel analytic resource navigation process was developed at Duke University to connect potential collaborators, leverage resources, and foster a community of quantitative researchers and scientists. We provide information about how this process works along with guidance for academic medical centers to develop similar initiatives. Methods/Study Population: Quantitative and qualitative scientists with expertise in data science, biostatistics, epidemiology, and related fields play a critical role in data collection, study design, analysis, interpretation, and implementation. The analytic resource navigation process connects researchers with quantitative scientists and relies on strong institutional knowledge of methodological expertise, understanding of research goals, educating researchers, and ongoing evaluation to understand unmet needs. University staff serve as navigators to help researchers identify the needed expertise, find potential collaborators, and track outcomes. Duke University’s tracking system for this navigation process, implemented in 2019, underwent a nearly five-year evaluation (November 2019 – September 2024). Results/Anticipated Results: In the nearly five-year evaluation of the process, 1247 requests from 813 unique researchers were navigated with a success rate of 93.8%. A total of 323 requests (256 unique researchers) were navigated in year 1, 285 requests (239 unique researchers) in year 2, 210 requests (179 unique researchers) in year 3, and 247 requests (192 unique researchers) in year 4. In the current year (partial year 5, 11/1/2023 – 9/18/2024), 182 requests have been navigated (159 unique researchers). Unsuccessful linkages occurred in 35 requests (2.8%) and 42 requests (3.4%) were withdrawn. Among the cases of unsuccessful navigation, 26 failed due to effort (e.g., insufficient effort available to meet the researcher’s deadline), 2 failed due to lack of expertise at the institution, and 4 failed due to a lack of sufficient funding. Discussion/Significance of Impact: The navigation process provides a critical resource for researchers who need to develop collaborations and a method for institutions to understand collaboration needs. Implementation requires training knowledgeable navigators, maintaining updated information about quantitative and qualitative methodologists, and institutional support.

Objectives/Goals: The goal of this study is to resolve the complexity of the adipose precursor cells and identify potential therapeutic targets/mechanism to treat obesity, diabetes, cancer cachexia, and related metabolic conditions in both white and brown adipose tissues. Methods/Study Population: White and brown adipose precursor cells were isolated from neonatal P0 mice and expanded in culture for single-cell RNA sequencing analysis. Unsupervised machine learning was used to unbiasedly cluster and categorize sequenced cells. Differentially expressed genes were used to identify populations via pathway analysis. Populations were then validated with published datasets via integration, reference mapping, and module scoring to ensure our dataset is reflective of known literature. Then, white and brown datasets were combined and unbiasedly clustered. Finally, signaling inferences using CellChat was used to identify significant signals being sent to and received from each cluster based on ligand–receptor pairs. Results/Anticipated Results: ScRNAseq revealed 7 subclusters in both white and brown adipose tissues. Differential expression and trajectory inferences revealed that white and brown precursors develop into two distinct fates: committed adipogenic precursors (CAPs), where these cells will be mature lipid-laden adipocytes; or fibro-adipogenic precursors-like (FAPLs), where these cells preferably stay in a fibroblast-like, antiadipogenic phenotype. Integrating white and brown cells with subsequent reclustering reveals that white and brown FAPLs are highly similar to one another by being clustered together. Cell signaling inferences and pathway analysis reveal that white and brown FAPLs may participate in the regulation of adipogenesis and angiogenesis of the adipose tissue. Discussion/Significance of Impact: Our results demonstrated that brown and white precursor cells share a common regulatory subpopulation with similar gene expression profiles, highlighting a more interconnected regulatory landscape in adipose tissue than previously understood. These findings reveal novel mechanisms of systemic metabolism and provide new therapeutic targets.

Objectives/Goals: Cerebral palsy (CP) results in impaired selective motor control (SMC), or the ability to move joints independently. This study leverages computer vision algorithms to create a scalable, low-cost, and accessible tool that automatically quantifies SMC behavior in infants born preterm, allowing for earlier screening and intervention for CP. Methods/Study Population: In single-camera videos of 600 15-week-old infants, all of whom have standardized clinical SMC scores available, human pose estimation algorithms will be used to derive 3D joint trajectories and kinematic metrics. These features will be used to train and cross-validate a machine learning model aimed at automating clinical scoring of SMC from single-camera video recordings of infants. Then, this model will be used to automatically score the SMC in longitudinal single-camera videos of 47 preterm infants (collected every two weeks from birth until 16 weeks of age). The SMC score trajectories between infants who eventually received a CP diagnosis and those who did not will be compared to establish an inflection point at which SMC development begins to diverge, establishing an optimal timepoint for intervention. Results/Anticipated Results: Preliminary results show that 3D angles of 22 joints across the infant’s body can be estimated in each frame of a 1 to 3 minute single-camera video. We anticipate that the error between the 3D angle estimated from a custom fitted kinematic model and the 2D angle calculated using human pose estimation measures will be less than 5 degrees for all joints measured. With a dataset of over 1,500 videos from over 600 infants, we expect that the trained machine learning model will achieve a sensitivity and specificity of over 90% in predicting the clinical SMC score. Finally, we expect that the automated, longitudinal SMC score trajectories will begin to significantly deviate in infants with a later CP diagnosis, from those without, at around term age (40 weeks gestational age), based on recent clinical findings. Discussion/Significance of Impact: We provide the first automated, quantitative analysis of SMC from video recordings that can be used within real-world settings such as the NICU, at home, or at daycare. This work will result in a cost-effective, precise, smartphone-accessible tool that empowers earlier, more equitable diagnosis and treatment planning.

ABSTRACT IMPACT: Our work unveils a novel mechanism of ischemia repurfusion injury driven by pre-existing autoimmunity following lung transplant and a potential therapeutic strategy for blocking complement-dependent injury thereby reducing risk of lung transplant rejection. OBJECTIVES/GOALS: Our goal was to determine if pre-existing autoimmune autoantibodies, such as those resulting from cigarette smoke (CS), contribute to graft rejection in lung transplantation (LTx) and if autoreactive-mediated graft injury is complement-dependent. METHODS/STUDY POPULATION: For in vivo experiments, we utilized our emphysema mouse model. Briefly, eight-week-old C57BL/6J mice are exposed to 3R4F reference cigarette smoke 5 hours per day, 5 days a week for 6 months. Upon completion, cigarette smoked (CS) mice and control (NS) mice received syngeneic orthotopic left-lung transplant from age-matched C57BL/6J donors. To determine if pre-existing autoreactivity mediated graft injury was complement-dependent we treated CS-LTx mice with a novel, bifunctional complement inhibitor. Autoantibody levels were measured by ELISA and lung injury was assessed by blinded histopathological analyses. Complement inhibition was verified by immunofluorescence. RESULTS/ANTICIPATED RESULTS: We found that CS-exposure leads to production of autoreactive antibodies towards extracellular matrix (ECM) components and contributes to graft injury. Interestingly, LTx into CS exposed mice further increased de-novo ECM autoantibody development. Lastly, treatment with our novel, bifunctional complement inhibitor blocked autoantibody spreading and significantly reduced graft rejection. DISCUSSION/SIGNIFICANCE OF FINDINGS: These data demonstrate that smoking induces pre-LTx autoreactivity to ECM proteins that promotes graft injury following LTx. Furthermore, complement inhibition reduces autoantibody production and protects the graft from injury.

OBJECTIVES/SPECIFIC AIMS: Our objective was to develop and evaluate a machine learning pipeline that uses electronic health record (EHR) data to predict acute kidney injury (AKI) during rehospitalization for patients who did not have an AKI episode in their most recent hospitalization. METHODS/STUDY POPULATION: The protocol under which this study falls was given exempt status by our institutional review board. The fully deidentified data set, containing all adult hospital admissions during a 2-year period, is a combination of administrative, laboratory, and pharmaceutical information. The administrative data set includes International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, Current Procedural Terminology, 4th Edition (CPT-4) procedure codes, diagnosis-related grouping (DRG) codes, locations visited in the hospital, discharge disposition, insurance, marital status, gender, age, ethnicity, and total length of stay. The laboratory data set includes bicarbonate, chloride, calcium, anion gap, phosphate, glomerular filtration rate, creatinine, urea nitrogen, albumin, total protein, liver function enzymes, and hemoglobin A1c. The pharmacy data set includes, for each medication, a description, pharmacologic class and subclass, and therapeutic class. Data preprocessing was performed using Python library Pandas (McKinney, 2011). Top-level binary representation (Singh, 2015) was used for diagnosis and procedure codes. Categorical variables were transformed via 1-hot encoding. Previous admissions were collapsed using rules informed by domain expertise (eg, the most recent age or sum of assigned diagnosis codes were retained as elements in the feature vector). We excluded any patient without at least 1 rehospitalization during the time window. We excluded any admission with or without AKI where AKI was also present in the most recent hospitalization. For comparison, we do not exclude such admissions for an identical experiment in which we considered any AKI event as a positive sample (regardless of AKI presence in the most recent hospitalization). We defined an AKI event as an assignment of any of the acute kidney failure (AKF) ICD-9 codes [584.5, AKF with lesion of tubular necrosis, 584.6, AKF with lesion of renal cortical necrosis, 584.7, AKF with lesion of renal medullary (papillary) necrosis, 584.8, AKF with other specified pathological lesion in kidney, or 584.9, AKF, unspecified]. Since diagnosis codes are believed to be specific but not sensitive for AKI (Waikar, 2006), we supplemented them using creatinine for patients who had laboratory values. Diagnosis was made according to the Kidney Disease: Improving Global Outcomes (KDIGO) Practice Guidelines (AKI defined as a 1.5-fold or greater increase in serum creatinine from baseline within 7 d or 0.3 mg/dL or greater increase in serum creatinine within 48 h). We report preliminary model discrimination via area under the receiver operating characteristic curve (AUC) using k-fold cross validation grouped by patient identifier (to ensure that admissions from the same patient would not appear in the training and validation set). It was confirmed that the prevalence of positive samples in the entire data set was maintained in each fold. Python library Sci-kit Learn (Pedregosa, 2011) was used for pipeline development, which consisted of imputation, scaling, and hyper-parameter tuning for penalized (l1 and l2 norm) logistic regression, random forest, and multilayer perceptron classifiers. All experiments were stored in IPython (Pérez, 2007) notebooks for easy viewing and result reproduction. RESULTS/ANTICIPATED RESULTS: There were 107,036 adult patients that accounted for 199,545 admissions during a 2-year window. Per admission, there were at most 54 ICD-9 diagnoses, 38 ICD-9 procedures, 314 CPT-4 procedures, and 25 hospital locations visited. The admissions were 55% female, the average age was 46±standard deviation 20, and average length of stay was 2.5±8.0 days. We excluded 2360 admissions that involved an AKI event that directly followed an admission with an AKI event and 4130 admissions that did not involve an AKI event but directly followed an admission with an AKI event. In total, there were 4561 (5.3%) positive samples (AKI during rehospitalization without AKI in the previous stay) generated by 3699 unique patients and 81,458 negative samples (non-AKI during rehospitalization without AKI in the previous stay) generated by 31,831 unique patients. When using any AKI event as a positive sample (regardless of whether or not AKI was in the most recent stay), the prevalence was 7.3% (6921 positive samples generated by 4395 unique patients and 85,588 negative samples generated by 33,287 unique patients). Best results were achieved with a code precision of 3 digits for which we had a total of 4556 features per patient. Fitted hyper-parameters corresponding to each classifier were logistic regression with l1 penalty C as 2×10−3; logistic regression with l2 penalty C as 1×10−6; random forest number of estimators as 100, maximum depth as 3, minimum samples per leaf as 50, minimum samples per split as 10, and entropy as the splitting criterion; and multilayer perceptron l2 regularization parameter α as 15, architecture as 1 hidden layer with 5 units, and learning rate as 0.001. Five-fold stratified cross validation on the development set yielded AUC for logistic regression with l1 penalty average 0.830±0.006, logistic regression with l2 penalty 0.796±0.007, random forest 0.828±0.007, and multilayer perceptron 0.841±0.005. In an identical experiment for which an AKI event was considered a positive sample regardless of AKI presence in the most recent stay, we had 4592 features per sample with the same code precision. Five-fold stratified cross validation on the development set with identical settings for the hyper-parameters yielded AUC for logistic regression with l1 penalty average 0.850±0.004, logistic regression with l2 penalty 0.819±0.006, random forest 0.853±0.004, and multilayer perceptron 0.853±0.006. DISCUSSION/SIGNIFICANCE OF IMPACT: Our objective was to investigate the feasibility of using machine learning methods on EHR data to provide a personalized risk assessment for “unexpected” AKI in rehospitalized patients. Preliminary model discrimination was good, suggesting that this approach is feasible. Such a model could aid clinicians to recognize AKI risk in unsuspicious patients. The authors recognize several limitations. Since our data set corresponds to a time-window sample, patients with high frequency of hospital utilization are likely overrepresented. Similarly, our data set contains records from only 1 hospital network. Although we supplement with laboratory-based diagnosis, using diagnosis codes as labels is problematic as numerous reports suggest low sensitivity of codes for AKI. Future work includes calibration analysis, incremental updating (“online learning”), and a representation learning-based (“deep learning”) extension of the model.