OBJECTIVES/GOALS: The glucocorticoid receptor (GR) is a ubiquitous steroid hormone receptor that is emerging as a mediator of breast cancer metastasis. We aim to better understand the biology associated with phospho-GR species in TNBC and their contribution to tumor progression. METHODS/STUDY POPULATION: To better understand how p-S134 GR may impact TNBC cell biology, we probed GR regulation by soluble factors that are rich within the tumor microenvironment (TME), such as TGFβ. TNBC cells harboring endogenous wild-type or S134A-GR species were created by CRISPR/Cas knock-in and subjected to in vitro assays of advanced cancer behavior. RNA-Seq was employed to identify pS134-GR target genes that are uniquely regulated by TGFβ in the absence of exogenously added GR ligands. Direct regulation of selected TGFβ-induced pS134-GR target genes was validated accordingly. Bioinformatics tools were used to probe publicly available TNBC patient data sets for expression of a pS134-GR 24-gene signature. RESULTS/ANTICIPATED RESULTS: In the absence of GR ligands, GR is transcriptionally activated via p38-MAPK-dependent phosphorylation of Ser134 upon exposure of TNBC cells to TME-derived agents (TGFβ, HGF). The ligand-independent pS134-GR transcriptome primarily encompasses gene sets associated with TNBC cell survival and migration/invasion. Accordingly, pS134-GR was essential for TGFβ-induced TNBC cell migration, anchorage-independent growth in soft-agar, and tumorsphere formation, an in vitro readout of breast cancer stemness properties. Finally, a 24-gene pSer134-GR-dependent signature induced by TGFβ1 predicts shortened survival in breast cancer. We expect to find similar results using an in-house tissue microarray. DISCUSSION/SIGNIFICANCE OF IMPACT: Phospho-S134-GR is a critical downstream mediator of p38 MAPK signaling and TNBC migration, survival, and stemness properties. Our studies define GR as a required effector of TGFβ1 signaling and nominate pS134-GR as a biomarker of elevated risk of breast cancer dissemination.

Objectives/Goals: The objective of this study is to explore strategies for AI-physician collaboration in diagnosing acute respiratory distress syndrome (ARDS) using chest X-rays. By comparing the diagnostic accuracy of different AI deployment methods, the study aims to identify optimal strategies that leverage both AI and physician expertise to improve outcomes. Methods/Study Population: The study analyzed 414 frontal chest X-rays from 115 patients hospitalized between August 15 and October 2, 2017, at the University of Michigan. Each X-ray was reviewed by six physicians for ARDS presence and diagnostic confidence. We developed a deep learning AI model for detecting ARDS and explored the strengths, weaknesses, and blind spots of both physicians and AI systems to inform optimal system deployment. We then investigated several AI-physician collaboration strategies, including: 1) AI-aided physician: physicians interpret chest X-rays first and defer to the AI model if uncertain, 2) physician-aided AI: the AI model interprets chest X-rays first and defers to a physician if uncertain, and 3) AI model and physician interpreting chest X-rays separately and then averaging their interpretations. Results/Anticipated Results: While the AI model (84.7% accuracy) had higher accuracy than physicians (80.8%), we found evidence that AI and physician expertise are complementary. When physicians lacked confidence in a chest X-ray’s interpretation, the AI model had higher accuracy. Conversely, in cases of AI uncertainty, physicians were more accurate. The AI excelled with easier cases, while physicians were better with difficult cases, defined as those where at least two physicians disagreed with the majority label. Collaboration strategies tested include AI-aided physician (82.4%), physician-aided AI (86.9%), and averaging interpretations (86%). The physician-aided AI approach had the highest accuracy, could off-load the human expert workload on the reading of up to 79% chest X-rays, allowing physicians to focus on challenging cases. Discussion/Significance of Impact: This study shows AI and physicians complement each other in ARDS diagnosis, improving accuracy when combined. A physician-aided AI strategy, where the AI defers to physicians when uncertain, proved most effective. Implementing AI-physician collaborations in clinical settings could enhance ARDS care, especially in low-resource environments.

OBJECTIVES/GOALS: The project investigates the role that resiliency may play within individual, interpersonal, social, and structural contexts in protecting against acute lethal stimulant (meth/amphetamine or cocaine) toxicity. Identifying preventative factors is crucial in developing and implementing risk reduction strategies for people who use stimulants. METHODS/STUDY POPULATION: This is a qualitative study involving in-depth interviews via questionnaire assessing resiliency factors among persons living in San Francisco who have used primarily either meth/amphetamine or cocaine for at least 5 years. The interviews will be coded for salient and recurrent themes and analyzed for code frequency, cooccurrences, clustering of themes and representative excerpts to highlight emergent themes as well as stressors and resilience factors at multiple levels. We aim to assess for substance use patterns, multiple domains of resiliency, medical and psychiatric complaints, and risk reduction strategies. We will recruit participants to match recent decedents from acute stimulant toxicity in various domains including salient demographic information and neighborhood characteristics. RESULTS/ANTICIPATED RESULTS: The anticipated results include a qualitative interview guide for living persons using stimulants in San Francisco to be used to gain insight into the community, illustrate participants’ substance use practices, and allow for better characterization of several discrete resiliency factors that have protected the participants and other community members from suffering lethal stimulant toxicity. We expect to identify individual components (e.g. use patterns, use of harm reduction supplies), interpersonal/social factors (e.g. drug using network, friendships, community connection), and structural influences (e.g. access to care, safe use sites, house and economic stability) that all play a role in resiliency against lethal stimulant toxicity. DISCUSSION/SIGNIFICANCE: Stimulant use is common, along with rising deaths involving stimulants in urban counties and in smaller rural/non-metro counties which are disproportionately affected, posing a public health challenge. We will find discrete, modifiable risk and resiliency factors that can be manipulated to minimize the chances of outcomes like overdose and death.

Multi-site and multi-organizational teams are increasingly common in epidemiologic research; however, there is a lack of standards or best practices for achieving success in collaborative research networks in epidemiology. We summarize our experiences and lessons learned from the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a collaborative agreement between the Centers for Disease Control and Prevention and research teams at Drexel University, New York University, Johns Hopkins University and Geisinger, and the University of Alabama at Birmingham. We present a roadmap for success in collaborative epidemiologic research, with recommendations focused on the following areas to maximize efficiency and success in collaborative research agreements: 1) operational and administrative considerations; 2) data access and sharing of sensitive data; 3) aligning network research aims; 4) harmonization of methods and measures; and 5) dissemination of findings. Future collaborations can be informed by our experiences and ultimately dedicate more resources to achieving scientific aims and efficiently disseminating scientific work products.

In recent times, there have been calls from within the developing nations for increased ownership by governmental research bodies and universities of the priority research setting and research that aligns with national health strategies. This is a review paper of the studies that have been published on clinical trials in developing countries, with a focus mainly on Pakistan. The literature review used online databases such as PubMed, Scopus, and Google Scholar, World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP), and ClinicalTrials.gov trial registries to search for clinical trials conducted in Pakistan between January 2000 and December 2022 and analyzed. The results revealed that clinical research in Pakistan is hindered by a number of barriers, including a lack of funding, skilled personnel, and regulatory issues. Lack of funding is a common obstacle, and the majority of funding for clinical trials originates from Western countries or pharmaceutical companies established in the West. In conclusion, clinical studies in developing countries, especially in Pakistan, are hindered by a plethora of barriers, and to improve the current state, increasing funding, streamlining ethical approval procedures, simplifying regulatory systems, addressing cultural and religious concerns, and participating in global efforts to bridge the gap in health-based research are crucial.

The COVID-19 pandemic had an immediate impact on the lives and work of early-career researchers. We leveraged a cluster-randomized trial and compared survey data collected over two timepoints to explore whether these impacts persisted. Although more than a year had passed, 74% of participants reported that their research was affected in multiple ways in both 2020 and 2021. These data suggest that the effects of the pandemic on early-career researchers may be prolonged. Our findings additionally serve as an impetus to identify and implement solutions to early-career challenges that undoubtedly existed before the pandemic, but which COVID-19 brought into the spotlight.

Objectives/Goals: This study aimed to enhance clinical trial data management through large language model information retrieval and generation techniques within the clinical trial reporting workflow. We focused on improving compliance with reporting, reducing human labor, and promoting standardized reporting structure and data quality oversight. Methods/Study Population: We used approved study protocols from UC Davis IRB-approved investigator-initiated studies compared to the same studies reported to ClinicalTrials.gov. Our baseline data extraction system employs commercial large language models (LLMs) and retrieval augmented generation (RAG) to isolate data sources within the secure extraction environment. We stratified protocol documents into easy, complex, and random categories based on study focus, document complexity, the extent of amendments or modifications, and completion metrics from ClinicalTrials.gov. We developed a pilot web-based architecture to capture variations in categorization, labeling, and reporting style and compared generated extraction data. We primarily focused on qualitative evaluation through a review of expert staff. Results/Anticipated Results: Our results revealed significant variations in reporting quality, with dependencies stemming from multiple authors and stages throughout the clinical trial protocol lifecycle. Based on these variations, we used prompt engineering to improve the pilot application’s output compliance with the protocol registration and results system (PRS) structured data format for various study types. We piloted the assisted workflow with prospective studies by partnering with study investigators and the clinical trial office staff to assist in review and clinical trial reporting creation. Initial studies reported by our system were approved and released to the public by PRS staff. We are refining content generation and workflows to different components of studies and evaluating their use in quality and training areas. Discussion/Significance of Impact: Our system fosters collaboration, efficient review, and compliance with clinical trial reporting standards. It supports the promise of AI-driven assistance in clinical trial management, design, and reporting. We focus on the multiple stakeholders, expertise, and data flows in the organizational management of clinical and translational science.

OBJECTIVES/SPECIFIC AIMS: A brain-machine interface (BMI) is a device implanted into the brain of a paralyzed or injured patient to control an external assistive device, such as a cursor on a computer screen, a motorized wheelchair, or a robotic limb. We hypothesize we can utilize electrical stimulation of subdural electrocorticography (ECoG) electrodes as a method of generating the percepts of somatosensation such as vibration, temperature, or proprioception. METHODS/STUDY POPULATION: There will be 10 subjects, who are informed, willing, and consented epilepsy patients undergoing initial surgery for placement of subdural ECoG electrodes in the brain for seizure monitoring. ECoG will be used as a platform for recording high-resolution local field potentials during real-touch behavioral tasks. In addition, ECoG will also be used to electrically stimulate the human cerebral cortex in order to map and understand how varying stimulation parameters produce percepts of sensation. RESULTS/ANTICIPATED RESULTS: To determine how tactile and proprioceptive signals are integrated in S1, we will perform spectral analysis of the broadband local field potentials to look for increased power in specific frequency bands in the ECoG recordings while touching or moving the hand. To explore generating artificial sensation, the subject will be asked to perform a variety of tasks with and without the aid of stimulation. We anticipate the subject’s performance will be enhanced with the addition of artificial sensation. DISCUSSION/SIGNIFICANCE OF IMPACT: Many patients might benefit from a BMI, such as those with stroke, amputation, spinal cord injury, or brain trauma. The current generation of BMI devices are guided by visual feedback alone. However, without somatosensory feedback, even the most basic limb movements are difficult to perform in a fluid and natural manner. The results from this project will be crucial to developing a closed loop motor/sensory BMI.

OBJECTIVES/GOALS: In this study, we established a high-throughput chemical screening platform to identify small molecules that facilitates efficient differentiation of stem cells derived β (SC-β) cells. Using this platform, we identified several compounds that potentially increase the differentiation efficiency. METHODS/STUDY POPULATION: Differentiation of human embryonic stem cells (HUES8) into SC-β was carried out using previously published protocols in a 3D cell suspension. Single cells were replated in Matrigel-coated well plates at the start of different stages depending on experiments. Differentiation medium supplemented with small molecules at a final concentration of 2 M and 0.2 M was used throughout the stage. All the cells were then fixed and permeabilized. Immunocytochemical staining was performed. Images of each well were taken and analyzed. Numbers of the total cell, insulin-positive cell, NKX6.1-positive cell, and co-positive cell were recorded. Candidate compounds were validated using flow cytometry or ICC. RESULTS/ANTICIPATED RESULTS: We identified several hit compounds that significantly increase the NKX6.1 positive cell percentage compared to the DMSO-treated controls when treated at the PP1 cell stage. Follow up assays demonstrated that at least one of these putative hits reproducibly increased NKX6.1 expression. In addition, we identified other compounds that significantly increase the insulin and NKX6.1 copositive SC-β cell population when treated at the later PP2 cell stage during the differentiation. We expect a dosage-dependent response when the candidate hits are validated using more accurate assays. DISCUSSION/SIGNIFICANCE OF IMPACT: We established a high-throughput screening platform to identify small molecules that increase the efficiency of SC-β direct differentiation. Successful generation of SC-β allows cell replacement therapy in diabetes patients, and a better understanding of pancreatic biology and development.

OBJECTIVES/SPECIFIC AIMS: High on-treatment platelet reactivity (HTPR) with clopidogrel imparts an increased risk for ischemic events in adults with coronary artery disease. Although more potent antiplatelet agents are available, clopidogrel remains the most commonly used P2Y12 inhibitor in Puerto Rico. Platelet reactivity varies with ethnicity and is influenced by both clinical and genetic variables; however, no clopidogrel pharmacogenetic studies with Puerto Rican patients have been reported. Therefore, we sought to identify clinical and genetic determinants of on-treatment platelet reactivity in a cohort of Puerto Rican patients with cardiovascular disease. METHODS/STUDY POPULATION: We performed a retrospective study of 111 Puerto Rican patients on 75 mg/day maintenance dose of clopidogrel. Patients were allocated into 2 groups: Group I, without HTPR; and Group II, with HTPR. Clinical data was obtained from the medical record. Platelet function was measured ex vivo using the VerifyNow® P2Y12 assay and HTPR was defined as P2Y12 reaction units (PRU)≥230. Genotyping of CYP2C19, ABCB1, PON1, PY2R12, B4GALT2, CES1, and PEAR1 was performed using Taqman® Genotyping Assays. RESULTS/ANTICIPATED RESULTS: The mean PRU across the cohort was 203±61 PRU (range, 8–324), and 42 (38%) patients had HTPR. One in four individuals carried at least 1 copy of the CYP2C19*2 variant allele. Hematocrit and PON1 p.Q192R variant were inversely correlated with platelet reactivity (p<0.05). Multiple logistic regression showed that 27% of the total variation in PRU was explained by a history of diabetes mellitus, hematocrit, CYP2C19*2, and PON1 p.Q192R. Body mass index (OR=1.15; CI: 1.03–1.27), diabetes mellitus (OR=3.46; CI: 1.05–11.43), hematocrit (OR=0.75; CI: 0.65–0.87), and CYP2C19*2 (OR=4.44; CI: 1.21–16.20) were the only independent predictors of HTPR. DISCUSSION/SIGNIFICANCE OF IMPACT: In a representative sample of Puerto Rican patients with cardiovascular disease, diabetes mellitus, hematocrit, CYP2C19*2, and PON1 p.Q192R were associated with on-treatment platelet reactivity. These factors may identify a subset of patients at higher risk for adverse events on clopidogrel in the Hispanic population.

OBJECTIVES/SPECIFIC AIMS: The goal of the present study was to advance our understanding of how alcohol use may contribute to physical inactivity among university students by investigating this association at a day-to-day level. METHODS/STUDY POPULATION: In total, 57 university students (Mage=20.27; 54% male) completed daily diary questionnaires using a cellphone application, which prompted them each evening to report minutes of moderate/vigorous physical activity engaged in, and number of alcoholic drinks consumed, as well as intended minutes of physical activity for the following day. Longitudinal mixed-level modeling was used to disentangle within person and between-person effects of alcohol use on physical activity behavior and intentions. Separate models were run to investigate lagged effects of previous day alcohol use. We controlled for sex and age in all models. RESULTS/ANTICIPATED RESULTS: Results indicated that participants’ usual alcohol use (between-person) was not associated with physical activity behavior or intentions. At the within-person level, day-to-day variance in alcohol use was negatively associated with both physical activity behavior (γ=−0.34, p=0.003) and intentions to engage in physical activity the following day (γ=−0.70, p<0.001). The lagged model indicated that previous day alcohol use negatively predicted PA behavior (γ=−0.33, p=0.004). DISCUSSION/SIGNIFICANCE OF IMPACT: Previous studies have largely been constrained to cross-sectional designs, and have surmised that there exists a positive association between alcohol use and physical activity due to trait-level differences between university students. We advance this literature by using ecological momentary assessment to investigate the within-person effects of alcohol use on physical activity at a day-to-day level while controlling for between-person variance. Contrary to existing literature, we found that on days when students consumed relatively more alcohol than they typically report, they: (a) report fewer minutes of physical activity on the same day, (b) plan to engage in relatively less physical activity on the subsequent day, and (c) engage in less physical activity on the subsequent day. By advancing our understanding of how alcohol use may curtail other health behaviors such as physical activity, we inform interventions that aim to target these behaviors in conjunction, or as part of a multiple behavior change intervention.

OBJECTIVES/GOALS: To increase vaccination rates in Black and Latino communities, specifically in South LA and the Eastside of LA. METHODS/STUDY POPULATION: Multimedia campaign combined with vaccine navigation and assistance, led by the SC CTSI and conducted by an interdisciplinary team from 14 schools at USC and CHLA, >160 community partners. RESULTS/ANTICIPATED RESULTS: VaccinateLA was a highly interdisciplinary and multifaceted project that demonstrated increased vaccination rates in Black and Latino communities in South LA and Eastern part of LA. We vaccinated over 1000 people, facilitated vaccines for almost 10k people and reached 1.37M people through our digital campaign. DISCUSSION/SIGNIFICANCE: We have used community vaccine navigators, pop-up clinics, narrative storytelling and other unique approaches to encourage vaccine uptake in communities of color. We plan to expand activities to parents and children in these communities, encourage booster uptake, and evaluate and disseminate the program widely.

OBJECTIVES/GOALS: In 2010, an estimated 65% of antipsychotic prescriptions were given to youth for off-label indications. It is unclear whether subsequent calls for reduced pediatric prescribing led to a decrease in off-label use. This study examined the diagnostic characteristics of the current pediatric population using antipsychotics in the United States. METHODS/STUDY POPULATION: Data from the Clinformatics Data Mart, a database containing longitudinal patient information between 2010-2019 from commercial healthcare insurance claims in the United States was used in this study. We conducted a case-control study, in which antipsychotic initiators were matched 1:1 to non-initiators on age, sex, and insurance enrollment. There were 26,375 included antipsychotic initiators with matched non-initiator controls between the ages of 6-17. Conditional logistic regression was used to examine the odds of being an antipsychotic user among those with past-year psychiatric diagnoses that have been previously found to be associated with antipsychotic use. RESULTS/ANTICIPATED RESULTS: Disorders with psychotic features were associated with the greatest differences in odds of antipsychotic prescription receipt. Specifically, children with bipolar or manic disorders had 145-fold greater odds of antipsychotic receipt than did those without bipolar or manic disorders (OR = 145.45, 95% CI = [95.64, 221.22]), whereas those with schizophrenia had 106-fold greater odds (OR = 105.89, CI = [67.40, 166.37]). However, these disorders occurred in only 12% and 8%, respectively, of the antipsychotic recipients. The most common disorders among those with antipsychotic receipt were depressive disorders and ADHD, which occurred in 46% and 43% of the recipients, respectively. Additionally, conduct disorder was present in 20% of recipients. DISCUSSION/SIGNIFICANCE: As expected, bipolar disorder and schizophrenia were strongly associated with initiation of pediatric antipsychotic treatment. Yet, most initiators did not have a diagnosis for either psychotic disorder or any other FDA-approved indications. This study highlights the critical need for further research on antipsychotic use among youth.

OBJECTIVES/GOALS: The rapid spread of Monkeypox (MPV) across the US may trigger stress reactions that increase the risk of developing secondary trauma among those most at risk of exposure. The present study aimed to investigate the degree to which vicarious MPV exposure (i.e., knowing people who acquired MPV), was associated with symptoms of secondary traumatization. METHODS/STUDY POPULATION: An online survey was administered to 253 participants enrolled in Keeping it LITE, a prospective U.S. cohort study of ethnically diverse, sexually active, sexual, and gender minority persons aged 19–39 in September 2022. Multiple linear regression was used to examine the association between proximity to MPV and secondary traumatic stress (STS) symptoms. RESULTS/ANTICIPATED RESULTS: Study findings demonstrated that MPV morbidity was low (1%); however, 37% of participants reported knowing at least one person diagnosed with MPV. For most individuals, this person was a friend (28%). 16% of participants were found to have at least one indicator of MPV-related STS. Our multiple linear regression results demonstrated a positive association between an individual’s indirect exposure to MPV via their interpersonal relationships and STS symptoms. DISCUSSION/SIGNIFICANCE: Findings suggest that the more adults’interpersonal relationships are saturated with people who have acquired MPV, the more likely they are to develop symptoms of secondary traumatization. These findings provide initial evidence that secondary exposure to MPV via one’s social network may undermine adults’mental health.

OBJECTIVES/SPECIFIC AIMS: Several clinical studies have established a correlation between changes in relative bacterial populations in the gut and Parkinson disease. However, few published experiments have been able to parse out whether these associations are causative or correlative. Our aim is to determine how bacteria in the gut may impact the health and resilience of dopaminergic signaling. Our experiment is designed to serve as a proof-of-principle that controlled alterations to the gut microbiome alters mechanisms in dopamine homeostasis in the midbrain. METHODS/STUDY POPULATION: Bacterial inoculation 8–10-week-old germ-free male mice (C57BL/6) were exclusively used in this experiment. Mice were orally gavaged every 3 days (D0, 3, 6, and 9) with 100 µL novel bacterial suspension (~108 CFU resuspended in PBS with 1.5% NaHCO3) or vehicle and were sacrificed on D11. Tissue preparation—brains were quickly extracted and the striatum was isolated and homogenized in either RIPA buffer with protease inhibitors (for Western blot analysis) or in 0.1 N HClO4 (for HPLC processing). The homogenates were processed through fractional centrifugation to remove cellular debris. Lysate samples were frozen at −80°C until ready for analysis. Protein expression quantification—expression of proteins were measured using intensity of bands from Western blots. Lysates were denatured prior to loading with LB with 10% β-mercaptoethanol and 30-minute incubation at 37°C. All immunoblots were normalized to immunoreactivity to α-tubulin. Immunoblot intensity was determined using the ImageJ software. Dopamine/dopamine metabolite quantification HPLC analysis was used to determine dopamine and dopamine metabolite concentration. Aliquots of the lysate were injected onto a C18 column using a mobile phase consisting of 50 mM H2NaO4P·H2O, 0.72 mM sodium octyl sulfate, 75 µM Na2 EDTA, and 10% acetonitrile (pH 3.0). The mobile phase was pumped through the system at 0.3 mL/minute. RESULTS/ANTICIPATED RESULTS: Measured total dopamine concentration through HPLC analysis in the striatum showed no significant differences in the bacteria-treated group relative to the control group. The metabolites DOPAC and HVA had an elevated measured concentration in the bacteria-treated group relative to the control group. Western blot analysis showed decreased immunoreactivity for DAT and TH in the bacteria-treated group compared with the control group. There was no significance difference in the immunoreactivity for VMAT2. DISCUSSION/SIGNIFICANCE OF IMPACT: This study demonstrates that dopamine signaling dynamics in the midbrain can be altered by changes in the gut flora in mice. These results further substantiate the impact of the gut-brain axis and may even point to a potential avenue of bolstering the resilience of dopaminergic neurons in preventing the onset of PD. Further experiments must be performed to understand the mechanism of the observed changes and to determine if these changes have any salutary effect.

OBJECTIVES/GOALS: This study examined racial-ethnic differences in antipsychotic initiation within psychiatric diagnostic groups. This is a follow-up to our prior work, which reported that, overall, youth from minority backgrounds had 30-65% lower odds of initiating antipsychotics compared to White youth. METHODS/STUDY POPULATION: This study used 2009-2021 data from Optum’s® Clinformatics® Data Mart, a database containing longitudinal patient information from nationwide commercial insurance claims. We created three separate samples of antipsychotic users and matched non-user controls between the ages of 6-17 years old. These groups contained individuals with clinically diagnosed ADHD, conduct disorder, and depressive disorder, respectively. We used conditional logistic regression to estimate the odds of antipsychotic initiation based on race-ethnicity within each diagnostic group. RESULTS/ANTICIPATED RESULTS: There were no racial-ethnic differences in the odds of antipsychotic initiation among youth diagnosed with ADHD. Among youth with depression diagnoses, Asian youth had 19% lower odds of initiating antipsychotics and Hispanic youth had 11% lower odds compared with White youth. Similar results were observed for conduct disorders, with Asian and Black youth having approximately 10% lower odds of initiating antipsychotic treatment and Hispanic youth having 18% lower odds relative to White youth. DISCUSSION/SIGNIFICANCE: Previously observed lower rates of antipsychotic initiation among racial-ethnic minority groups may be at least partially due to factors leading to disparities in diagnosis. Further research is needed to evaluate factors that may lead to differential antipsychotic use, as the disparities may occur upstream of receiving clinical diagnoses.

OBJECTIVES/SPECIFIC AIMS: The goals of this study were (1) to evaluate the effect of proarrhythmic drugs on calcium transient and (2) to use three-dimensional human engineered cardiac tissue (hECT) technology to evaluate cardiac contractile properties in response to pharmacological challenge with proarrhythmic drugs. METHODS/STUDY POPULATION: Calcium transient was measured in subject-specific iPSC-CMs by using the IonOptix system in Sotalol treated vs. untreated conditions. We fabricated human engineered cardiac tissues (hECT) in a custom designed bioreactor using low- and high-sentitive subject-specific iPSC-CMs. Contractile function of the hECT was evaluated at baseline and after Sotalol [300 µM] administration. The change in beat rate was recorded under spontaneous beating conditions; changes in other twitch parameters, including time to relaxation, were recorded under electrical stimulation. Time to relaxation served as an indicator of action potential duration (APD), which has a temporal correlation with the QT interval. RESULTS/ANTICIPATED RESULTS: The low-sensitive iPSC-CM showed a considerable drop in overall peak height of the calcium transient, in the presence of 100 µM Sotalol. The high-sensitive line, however, showed a more pronounced drop in peak height. Sotalol treatment also induced a more pronounced increase in the exponential decay time constant (tau) in the high-sensitive line compared to the low-sensitive line. The hECT fabricated with high sensitive hiPSC-CM showed a larger decrease in spontaneous beat rate in response to Sotalol (0.41 vs 0.23 fold decrease), with a higher increase in time to relaxation (1.8 vs 1.3 fold increase), compared to hECT from low sensitive hiPSC-CM. Moreover, while the low-sensitive hECT showed a positive correlation between time to relaxation and developed force, as expected after Sotalol stimulation; the high-sensitive hECT failed to show a positive inotropic response. DISCUSSION/SIGNIFICANCE OF IMPACT: Our findings suggest subject-specific iPSC-CMs and hECT, can be used to model functional abnormalities observed in diLQTS in response to Sotalol, and offer novel insights into human-based screening assays for toxic drug reactions. Success of this study may help identify key components underlying diLQT susceptibility to ultimately develop novel therapeutic agents.

OBJECTIVES/GOALS: To assemble publicly-available, proprietary, and geocoded datasets about social, environmental, behavioral, and psychological exposures experienced by children with asthma, to provide a technical overview of data aggregation, management, and integration processes utilized, and to build predictive models using sociome and clinical data. METHODS/STUDY POPULATION: Publicly-available data involving census information, crime, green space, building permits, vacant and abandoned buildings, traffic (City of Chicago data portal), pollution and weather (National Oceanic and Atmospheric Administration), and noise (Array of Things project) were assembled. We placed a local instance of the Pelias geocoder on the UChicago Center for Research Informatics HIPAA-compliant infrastructure. The UChicago Clinical Research Data Warehouse will be leveraged to obtain clinical information for children diagnosed with asthma at UChicago Medicine between 2007 and 2021. The address of each child will be subjected to geocoding, and this information will be aligned with imported sociome data. A model will be built to account for each sociome elements contribution to asthma outcomes. RESULTS/ANTICIPATED RESULTS: Here we are creating sustainable and scalable ways for collecting, standardizing, and sharing real-world sociome data, simultaneously linking those data back to patient information. With this work, we aim to demonstrate feasibility of a data-commons-as-a-service for clinical and sociome data and to provide technical specifications and descriptions of processes employed. Creating generalizable and scalable infrastructure to support research of social and environmental impacts on clinical outcomes is critical, and our work will provide a framework to be used in other disease states. Further, this infrastructure will facilitate the application of advanced analytical tools and visualization platforms to accelerate the study of diseases and lead to new insights into factors influencing outcomes. DISCUSSION/SIGNIFICANCE: Beyond focusing on and treating biological mechanisms of disease, advancing health also requires addressing adverse consequences of sociome factors on clinical outcomes. We describe an innovative process to comprehensively codify and quantify such information in a way suitable for large scale co-analysis with biological and clinical data.