OBJECTIVES/SPECIFIC AIMS: It has been reported that birth mode affects development, with cesarean section born mice gaining more body weight during development. Since mice C-sections involve fostering and nest change, we sought to determine whether changing only the nest and cage would have an effect on development. METHODS/STUDY POPULATION: A total of 53 mice were born to 9 dams, and 21 babies (4 litters) were exchanged in pairs to foreign cages and nests. Litters were followed for body weight and mothers were observed during periods for maternal and nonmaternal behaviors. RESULTS/ANTICIPATED RESULTS: The results show that average body weight was significantly higher in the experimental group in both genders, with 20% higher body weights at weaning. The mothers from the litters that were changed to a new nest showed significantly more non-maternal behavior in the first 2 days if life, than the controls. DISCUSSION/SIGNIFICANCE OF IMPACT: The results suggest that changes in maternal behavior may be linked to the increased weight gain in their babies.

OBJECTIVES/GOALS: The purpose of this work is to investigate natural buffering capacity of liver tissue and tumors, to understand and exploit differences for therapy. Using this work, we will determine the concentrations of reagents (acids or bases) used in ablation treatment to optimize treatment by increasing tumor toxicity and minimizing healthy tissue toxicity. METHODS/STUDY POPULATION: For this preliminary study, two methods will be used: benchtop pH experiments ex vivo and non-invasive imaging using acidoCEST MRI in vivo. For ex vivo, two types of tissues will be tested: non-cancerous liver and tumor tissue from HepG2 inoculated mice (n = 10). After mice are euthanized, pH will be measured in tissue homogenates at baseline and then the homogenates will be placed in either acidic (acetic acid) or basic (sodium hydroxide) solutions with varied concentrations (0.5–10M) and time recorded until pH returns to baseline. For in vivo imaging, Mia PaCA-2 flank model mice (n = 10) will be imaged with acidoCEST MRI to quantify pH at baseline. Mice will then be injected intratumorally with (up to 100 μL of) acid or base at increasing concentrations and imaged to quantify pH changes in the tumor. RESULTS/ANTICIPATED RESULTS: For this study, buffering capacity is defined as the concentration threshold for which tissue can buffer pH back to within normal range. Non-cancerous tissue is likely to buffer a wider range of concentrations compared to tumor tissue. From the benchtop experiment, comparison of time-to-buffer will be made for each concentration of acid/base for the two tissue types. AcidoCEST MRI will provide in vivo buffering capacity and potentially demonstrate tumor heterogeneity of buffering capacity. For both experiments, a pH vs. concentration curve for the two tissue types will allow for comparison of ex vivo to in vivo experiments, which will differentiate contributions of local tissue buffering capacity from the full body’s natural bicarbonate buffer system that depends on respiration and blood flow. DISCUSSION/SIGNIFICANCE OF IMPACT: The pH of the body must be maintained within a narrow range. With cancer, impairment in regulation of tumor metabolism causes acidosis, lowering extracellular pH in tumors. It remains unclear if pH plays a role in local recurrence or tumor toxicity. This work will determine if acidoCEST MRI can measure deliberate alteration of pH and how this change affects biology.

OBJECTIVES/GOALS: High-grade gliomas (HGG) are among the rarest, most aggressive tumors in neurosurgical practice. We aimed to identify the clinical predictors for 30-day readmission and reoperation following HGGs surgery using the NSQIP database and seek to create web-based applications predicting each outcome. METHODS/STUDY POPULATION: We conducted a retrospective, multicenter cohort analysis of patients who underwent resection of supratentorial HGG between January 1, 2016, and December 31, 2020, using the NSQIP database. Demographics and comorbidities were extracted. The primary outcomes were 30-day unplanned readmission and reoperation. A stratified 80:20 split of the available data was carried out. Supervised machine learning algorithms were trained to predict 30-day outcomes. RESULTS/ANTICIPATED RESULTS: A total of 9,418 patients were included in our cohort. The rate of unplanned readmission within 30 days of surgery was 14.9%.Weight, chronic steroid use, pre-operative BUN, and WBC count were associated with a higher risk of readmission. The rate of early unplanned reoperation was 5.47%. Increased weight, higher operative time, and a longer period between hospital admission and the operation were linked to increased risk of early reoperation. Our Random Forest algorithm showed the highest predictive performance for early readmission (AUC = 0.967), while the XG Boost algorithm showed the highest predictive performance for early reoperation (AUC = 0.985).Web-based tools for both outcomes were deployed: https://glioma-readmission.herokuapp.com/, https://glioma-reoperation.herokuapp.com/. DISCUSSION/SIGNIFICANCE: A high fraction of documented early unplanned readmission and reoperation were considered preventable and related to surgery. Machine learning allows better prediction of resected HGG’s prognosis based on findings from baseline methods leading to more personalized patient care.

OBJECTIVES/SPECIFIC AIMS: Listening effort is needed to understand speech that is degraded by hearing loss and/or a noisy environment. Effortful listening reduces cognitive spare capacity (CSC). Predictive contexts aid speech perception accuracy, but it is not known whether the use of context reduces or preserves CSC. Here, we compare the impact of predictive context and cognitive load on behavioral indices of CSC in elderly, hearing-impaired adults. METHODS/STUDY POPULATION: Elderly, hearing-impaired adults listened in a noisy background to spoken sentences in which sentence-final words were either predictable or not predictable based on the sentence context. Cognitive load was manipulated by asking participants to remember either short or long sequences of visually presented digits. Participants were divided into low or high cognitive capacity groups based on a pretest of working memory. Accuracy and response times were examined for report of both sentence-final words and digit sequences. RESULTS/ANTICIPATED RESULTS: Preliminary results indicate that accuracy and response times for both words and digits were facilitated by sentence predictability, suggesting that the use of predictive sentence context preserves CSC. Response times for both words and digits and accuracy for digits were impaired under cognitive load. Trends were similar across high and low cognitive capacity groups. The preliminary results support the idea that habilitation strategies involving context use could potentially support CSC in elderly, hearing-impaired adults. DISCUSSION/SIGNIFICANCE OF IMPACT: These preliminary results support the concept that habilitation strategies involving context use could potentially support CSC in elderly, hearing-impaired adults.

OBJECTIVES/GOALS: As mortality and morbidity from acute COVID-19 decline, the impact of COVID-19 on short- and long-term quality of life (QoL) becomes critical to address. We assessed the impact of re-purposed COVID-19 therapies on QoL as a secondary outcome measure in ACTIV-6, a decentralized platform trial. METHODS/STUDY POPULATION: Adults aged ≥30 with mild-to-moderate COVID-19 enroll in ACTIV-6 online or through a study site. Patients are randomized to a medication of interest or placebo. Medications are mailed and symptoms are tracked using electronic diaries. QoL is measured#_msocom_1 using the PROMIS-29 questionnaire. Adjusted Bayesian logistic regression models are used to measure effects of treatment on the seven PROMIS-29 QoL domains at days 7, 14, 28#_msocom_2 and 90. Covariates are treatment, age, gender, symptom duration and severity, vaccination status, geographic region, call center#_msocom_3#_msocom_4, and calendar time. Treatment effects are described using ORs, 95% credible intervals, and posterior probabilities of efficacy, P(eff). RESULTS/ANTICIPATED RESULTS: There are 5,362 patients included, representing four of the study arms in ACTIV-6. We report results where P(eff)<0.025 and P(eff)>0.975 in the table below. Table 1. Scale Day: OR* (95% credible interval, P(eff)) Therapy Physical Anxiety Depression Fatigue Sleep Social Pain Ivermectin 400 — Ivermectin 600 D7: 0.77 (0.61-0.96, 0.01) D14: 0.65 (0.49-0.85, <0.01) D28: 0.69 (0.52-0.92, 0.01) — D7: 0.79 (0.64-0.97, 0.01) — D14 0.78 (0.60-1.00, 0.02) D28: 0.66 (0.50-0.87, <0.01) Fluticasone - D14: 0.77 (0.60-0.99, 0.02) — D7: 0.76 (0.62-0.93, <0.01) D90: 0.79 (0.64-0.98, 0.01) — D7: 0.74 (0.59-0.93, 0.01) Fluvoxamine D7: 0.66 (0.51-0.84, 0.01) — D28: 1.38 (1.02, 1.85, 0.98) D7: 0.78 (0.63-0.97, 0.01) D7: 0.77 (0.62-0.95, 0.01) — *OR > 1 favors active intervention DISCUSSION/SIGNIFICANCE: Results suggest fluvoxamine may improve depression scores by day 28, while placebo is favored in several other scales across treatments. Differences between treatment and placebo are not seen at most other timepoints. This trial is ongoing and future work will include results from additional ACTIV-6 study arms.

Objectives/Goals: The overarching objective of this study is to inform clinicians, patients, and other stakeholders of the level of evidence and the real-world risk-to-benefit profiles associated with older antidepressant drugs that are frequently used off-label. Methods/Study Population: A PubMed literature review was performed to identify clinical trials conducted in the USA between 2013 and 2023 for trazodone and 2000 and 2023 for escitalopram and citalopram. These studies were examined for robustness, due to sample size, study design, and generalizability. Findings were compared with information provided on UpToDate® LexiDrug™, a primary database used by clinicians to inform prescribing practice. To explore risks associated with off-label use, the FDA adverse event reporting system was probed to identify adverse events reported for each drug; results were systematically categorized by reason for use. To compare the volume of on-label to off-label prescriptions, data will be extracted from electronic health records from University of Southern California-affiliated hospitals. Results/Anticipated Results: Studies conducted on off-label prescriptions of these drugs show primarily small sample sizes, pointing to a limitation in generalizability. For citalopram (N = 77) and trazodone (N = 42), over half of their off-label studies had samples of 50 participants or less. These two drugs also showed low evidence rating for off-label prescription on LexiDrug due to limited power studies. Multiple health agencies recommend against off-label prescriptions for trazodone due to insufficient evidence. There is limited data in the US regarding the volume of off-label prescriptions; however, trazodone’s FAERS analysis indicated a large proportion of adverse event reports (1099/7239) come from cases where trazodone was used for insomnia, an off-label indication, compared to depression, the on-label indication (464/7239). Discussion/Significance of Impact: With 1 in 6 Americans taking antidepressants and 40%–80% of these psychiatric prescriptions being employed off-label, there is a serious and present risk for patients regarding the safety and efficacy of these medications. Awareness must be brought to clinicians to protect patients and encourage evidence-based practice.

OBJECTIVES/SPECIFIC AIMS: Dependence and abuse of prescription opioid pain medication has substantially increased over the last decade. The consistent rise in opioid dependence contributes to the rising prescription drug overdose deaths over the last decade. The study of the distribution and determinants of opioid dependence among patients who are treated with chronic pain medications prescribed by their healthcare providers would aid in answering some key questions about potential abuse and overdose on opioids. The descriptive epidemiology of opioid dependence would help in identifying the vulnerable age group, race, ethnicity, and type of opioid pain medications that more commonly result in dependence. METHODS/STUDY POPULATION: We implemented an Observational Medical Outcomes Partnership/Observational Health Data Sciences and Informatics (OMOP/OHDSI) database, to hold structured EHR data from our Allscripts patient records. We also created a high-throughput phenotyping, natural language processing system that can parse 7,000,000 clinical notes in 1.5 hours. This runs as a web service and provides a modular component based NLP system. After the full semantic parse, we match the content against any number of ontologies. For each match we tag it as either a positive, negative, or uncertain assertion. We then perform automated compositional expressions. The codes are stored in a Berkley database (BDB) NOSQL database and the compositional expressions are stored in Neo4J (a graph database) and Graph DB (a triple store). This flexibility allows rapid retrieval of complex questions in real time. The High-Throughput Phenotyping (HTP) Natural Language Processing (NLP) Subsystem (HTP-NLP) is software that produces, given biomedical text, semantic annotations of the text. The semantic annotations identify conceptual entities—their attributes, the relations they have with other entities and the events they participate in, as expressed in the input text. The conceptual entities, relations, attributes, and events identified are specified by various knowledge representations (KRs) as documented in Coding Sources. Examples of coding sources are medical terminologies [eg, SNOMED CT, RxNorm, LOINC and open biomedical ontologies (OBO) foundry ontologies, eg, gene ontology (GO), functional model of anatomy, OBI, and others]. The annotation results may be displayed or output in formats suitable for further processing. Entity identified is assigned a truth value from 0 to 1. Values from the text are assigned to entities from ontologies such as SNOMED CT. The retrospective analysis of EHR data from local clinic patients was performed using queries on the problem list, demographic data, and medication list of all the patients in the database. The OMOP/OHDSI database was collected from Allscripts EHRs from 2010 to 2015. This common data model helps in the systematic analysis of disparate observational databases of clinic records from the primary care and family medicine clinics in Western New York region. The database contained 212,343 patient records that were parsed and deidentified. Specific research IDs were assigned to each of the patient records and stored in a secure firewall device for data analytics. The entire 212,343 records were queried for opioid dependence from the ICD-9 and 10 diagnostic codes and SNOMED CT codes mapped to both the clinical notes and the problem list for each patient based on the mapped ICD and SNOMED CT codes. In total, 1356 patients were identified as to having opioid dependence. The records were stratified into 7 age groups from age 18 to 28 and ending with age 79–89 years. RESULTS/ANTICIPATED RESULTS: Of the 212,343 patients in the database 1356 patients revealed opioid dependence on the problem list, ICD9-10 codes and prescription opioid pain medication with or without Buprenorphine and Naloxone (Suboxone) in the medication list. The prevalence of opioid dependence in the clinic population was 0.64% (95% CI: 0.61%–0.67%) over a 5-year period. The 7,000,000 patient records generated 750,000,000 SNOMED CT codes (on average 107 codes per record). The highest numbers of opioid dependence were seen in the 29 to 38 years’ age group. That comprised 39.38% (95% CI: 36.78%–41.98%) of the total opioid dependent population but accounted for only 2.03% of whole clinic population in this age group (95% CI: 1.86% to 2.2%). The subjects were then stratified by race and ethnicity. There were 1005 patients with opioid dependence, in the non-Hispanic population (total number 108,402). Among the White non-Hispanic or Latino population with opioid dependence, 41.33% (95% CI: 38.27%–44.39%) were 29–38 years old. The next common age group among the White Non-Hispanic opioid dependent subjects was 19–28 years, comprising of 22.48% (95% CI: 19.88%–25.08%) of the total number of White non-Hispanic or Latino opioid dependent population. Among the total clinic population Hispanics comprise 51.24%, but they comprise only 2.58% (95% CI: 1.74%–3.42%) of the total opioid dependent population. The non-Hispanic population comprise 51.05% of total clinic population while the percent of people who are opioid dependent is 83.26% (95% CI: 83.04%–83.48%) of the total 1356 opioid dependent population. DISCUSSION/SIGNIFICANCE OF IMPACT: The trends of opioid dependence among the clinic population in the study indicate that the prevalence is more in a certain section of the population. The predominance is among the non-Hispanic White population in the 19–38 years of age. The prevalence in younger age implies that the complications related to opioid dependence would be there for a longer duration of time. The prevalence of dependence in this clinic population would be rising if this trend continues. Interventions at curbing prescription opioid dependence is necessary for the vulnerable population. The findings suggest that a broad based approach is necessary to address this problem. The distribution of opioid dependence in this patient population indicate the need for special attention to these specific age group and race ethnicities. The young age of many of the addicted patients demonstrate the risks of legitimate opioid prescriptions in leading this age group towards addiction and implies the need for routine screening for substance abuse. The evidence of complications of opioid overdose among long-term opioid users and risk of abuse with other agents including illicit agents makes the need for an approach that uses real-time interventions in addition to effect long-term improvement in addiction rates. A potentially cost-effective approach to implement monitoring programs and clinical decision support tools would be to develop inter operable linkage from the EHRs to the state Department of Healths’ prescription monitoring programs.

OBJECTIVES/GOALS: Metabolic syndrome (MetS), defined as a cluster of cardiometabolic disease risk factors, is seldom coded using the ICD-10 system in electronic medical records (EMR). The goal of this study was to use EMR to construct MetS with a digital phenotype to amplify the pool of patients available to assess risk for neurocognitive disease in this population. METHODS/STUDY POPULATION: A digital phenotype using the EMR platform TriNetX (n=38 million patients between age 50 and 80) was created by clustering codes for the individual components of MetS (insulin resistance, hypertension, dyslipidemia, and central adiposity). The research network database on TriNetX was utilized to elucidate risk ratios for neurocognitive decline, Alzheimer’s disease and related dementias (ADRDs), and cerebrovascular disease between a preclinical sample of older adults with and without MetS. Propensity score matching was used to match cohorts on demographic variables, medication use, and relevant comorbidities. Risk ratios (RR) and confidence intervals (95% CI) were presented for all outcomes. RESULTS/ANTICIPATED RESULTS: The digital phenotype for MetS expanded the sample from 29,830 to 274,703, a 10-fold increase. Sensitivity to the standard MetS ICD-10 code was 95.1%, showing strong agreement between coding schema. Older adults with MetS had higher risk of cognitive decline (RR: 1.30, 95% CI: 1.15–1.48, p <0.001), ADRDs (RR: 1.48, 95% CI: 1.25–1.75, p <0.001), and cerebrovascular issues (RR: 1.62, 95% CI: 1.55–1.70, p <0.001) when controlling for demographics, medication, and comorbidities. MetS individuals with cerebrovascular dysfunction had even greater risks for neurocognitive decline (RR: 1.70, 95% CI: 1.38–2.08, p < 0.001) and ADRDs (RR: 2.09, 95% CI: 1.56–2.80, p < 0.001) than those with only MetS. DISCUSSION/SIGNIFICANCE: Implementing a digital MetS phenotype in EMR effectively increased sample size and power for analyses. Older adults with MetS have higher risk for neurocognitive decline, especially among those with cerebrovascular dysfunction, highlighting a critical intervention window prior to overt cardiometabolic disease.

ABSTRACT IMPACT: Identifying an important pathway in treatment resistant TNBC will allow for the future development of clinical therapeutics specific for this disease. OBJECTIVES/GOALS: Triple Negative Breast Cancer (TNBC) is a subtype of breast cancer characterized by negative expression of estrogen receptor, progesterone receptor, and HER2/neu amplification. It resists therapies and has a high recurrence rate after resection. The goal of my research is to identify & characterize a TNBC pathway for future development of therapies. METHODS/STUDY POPULATION: The project uses a combination of cell lines, patient derived xenograft (PDX) models, as well as patient databases. Standard cellular and molecular biology techniques will be used including: Cell culture, qPCR, western blotting, and flow cytometry. RESULTS/ANTICIPATED RESULTS: LKB1 is a master kinase that activates 14 possible downstream kinases. The signaling pathway has been demonstrated to play a role in energy homeostasis and metabolism. Mutation of LKB1 signaling results in Peutz-Jeghers Syndrome and is associated with neoplasias of the lung, pancreas, and breast. Based on preliminary analysis, overexpression of LKB1 by shRNA in TNBC cell lines results in suppression of EMT and reduction of the cancer stem cell population. Additional studies show that LKB1 overexpression has no effect on growth rate in 2D culture while significant reduction in 3D mammosphere formations can be seen. Downstream studies using commercially available SIK1 inhibitor HG-9-91-01 is able to induce a larger fraction of CSC from reduced LKB1 overexpression as well as from baseline levels. DISCUSSION/SIGNIFICANCE OF FINDINGS: Overall, our results suggest that LKB1 acts through SIK1 to suppress EMT and the generation of cancer stem cells. This results in reduced cancer functionality, as evidenced by inhibition of mammosphere formation. These results establishes a foundation for future mechanistic studies on the LKB1 axis and its mechanisms in TNBC.

OBJECTIVES/SPECIFIC AIMS: Determine if synthetic or endogenously produced butyrate can delay Parkinson’s disease (PD) progression, attenuate PD associated GI dysfunction, and impact the gut-microbiota in mice expressing human mutant aSyn. METHODS/STUDY POPULATION: Two transgenic mouse models expressing human mutant alpha-synuclein (aSyn) will be used. Transgenic mice expressing aSyn A53T display GI dysfunction before motor deficit onset and will be used to investigate treatment impact on PD associated GI dysfunction. Mice expressing aSyn Y39C more accurately recapitulate age-related neuropathology and behavioral deficits and will be used to assess treatment impact on PD-associated neuropathology, motor, and cognitive function. Mice will receive a synthetic sodium butyrate, sodium phenylbutyrate, or a synbiotic treatment regimen for 3 months. Disease progression will be assessed by aSyn brain and gut neuropathology, brain and gut inflammatory status, behavioral deficits, and gastrointestinal function. In addition, fecal and gut-microbiota composition and neuroprotective gene expression in the brain will be investigated. RESULTS/ANTICIPATED RESULTS: Our preliminary data shows that both sodium butyrate and sodium phenylbutyrate delay disease progression in aSyn Y39C mice. Butyrate-treated mice have reduced aSyn oligomerization, reduced Lewy body formation, and improved motor and cognitive function compared to placebo-treated mice. 16S rRNA sequencing did not reveal fecal-microbiota shifts between treatment groups or with age progression. Further analysis assessing expression levels for genes with anti-oxidant and protein degradation roles will be performed to determine if sodium butyrate and sodium phenylbutyrate similarly impact cellular mechanisms to delay neurodegeneration. Our future experiments will focus on comparing sodium butyrate and synbiotic treatment outcomes in aSyn A53T mice. DISCUSSION/SIGNIFICANCE OF IMPACT: Our lab developed a Tg mouse model that more accurately recapitulate age-related symptoms, pathology, and mechanisms observed in PD patients compared with animal models onset by neurotoxins. Our use of an age-dependent model of a severe form of Parkinsonism, DLB, will better predict clinical outcomes in PD populations. We will be the first to assess if elevating select microbial product production enhances neuroprotective brain activity in a PD model. Results obtained will further characterize gut-brain axis communication mechanisms. These proposed experiments will be the first to determine if elevating microbial products improves GI deficits associated with PD and may lead to insight on the gut-brain axis role in PD. Overall, this proposal will be the first to investigate a novel, highly accessible treatment with the potential to delay PD progression and target motor, cognitive, and GI deficits associated with PD. Due to the current FDA approval of probiotics and prebiotics that enhance butyrate production, results obtained may be quickly translated for clinical use.

OBJECTIVES/GOALS: Utilize polymer-based fiber scaffolds and machine learning methods applied to patient biomarker data to enhance and personalize T cell expansion and production for T cell therapy in chronic lymphocytic leukemia. METHODS/STUDY POPULATION: Scaffolds are 1) generated from a co-polymer blend of PDMS and PCL with controlled fiber diameters and pore size, 2) coated with activating antibodies to CD3 and CD28, and 3) used to stimulate T cells from both healthy donors and CLL patients. CLL patients have pre-annotated mutation burdens and clinical biomarkers. T cell populations will be analyzed for exhaustion markers and phenotypes before, during, and after expansion. Cell functionality will be measured by cytokine secretion, cell cycle analysis, and fold expansion, with respect to platform parameters, and analyzed with inputs of disease markers and exhaustion profile of isolated T cells using regression and random forest classifiers. RESULTS/ANTICIPATED RESULTS: We previously showed that engineering the mechanical rigidity of activating substrates can enhance and rescue T cell expansion from exhausted populations. Now we aim to study a broader range of compositions and geometry of scaffolds with respect to capacity to expand CLL T cells. Preliminary data with fiber diameters ranging from 300 nm to 6 um confirm the effect of geometry in modulating expansion. A biorepository of T cells from 80 CLL patients have been isolated concurrently. Anticipated results include correlating exhaustion profile of T cells with clinical biomarkers and identifying markers associated with expansion on panel of platform parameters. DISCUSSION/SIGNIFICANCE OF IMPACT: T cell therapy has shown particular promise in treating blood cancers, yet significant percentage of T cells isolated from patients undergoing treatments are unresponsive to activation. A powerful tool is to predict if and how patient T cells can be robustly expanded on a personalized approach.

OBJECTIVES/GOALS: Caregivers of patients with ventricular assist devices (VAD) are invaluable contributors to patient health, but they may experience psychological symptoms or worsening quality of life related to caregiving. We aimed to explore VAD caregiving burden and resilience comparing high and low burden patient-caregiver dyads. METHODS/STUDY POPULATION: Using a convergent mixed-methods design, we conducted semi-structured interviews and collected self-reported surveys from patient-caregiver dyads. Patients (n=10, mean age 55.8 years) were mostly white males implanted as destination therapy. Caregivers (n=10, mean age 52.6) were mostly white females. Most dyads were spousal (n=6). Caregiver burden was measured with the Zarit Burden Interview Scale, using the median score to categorize lower and higher burden dyads (median = 12, IQR = 8). Data were analyzed using deductive and inductive coding for thematic analysis. RESULTS/ANTICIPATED RESULTS: Lower and higher burden dyads described the shared burden experience of 1) hyper-vigilance and uncertainty immediately post-implant and 2) changing relationship roles. Lower burden dyads were more likely to able to take on responsibilities outside of caregiving. Contextual differences among higher burden dyads included higher financial strain, less social support, worse quality of life (both patients and caregivers) and worse heart failure severity compared to lower burden dyads. All dyads were resilient, using coping strategies like cognitive reframing, positivity, and adaptation to new roles. Higher burden dyads were more vulnerable to coping fatigue and were more likely to express that the VAD had irreversibly changed their life. DISCUSSION/SIGNIFICANCE: VAD caregiver-patient dyads differ in their levels of caregiver burden. Burden experiences and resilience mechanisms may inform future interventions to reduce caregiver burden in diverse VAD dyads. Knowledge of the caregiver experience and factors contributing to burden can inform the healthcare team in providing essential caregiver support.

OBJECTIVES/GOALS: At least 15 stroke thrombectomies per proceduralist per year are required for Thrombectomy-Capable and Comprehensive Stroke Centers. We sought to estimate the potential impact of these minimum volume thresholds on geographic access to stroke thrombectomy (ST). METHODS/STUDY POPULATION: Using the Florida State Inpatient Database, we will identify patients discharged with acute ischemic stroke from 2015-2019 and all non-federal facilities that performed ≥1 ST procedure per year. We will then calculate the proportion of stroke patients who live within 20, 65, 115, 165 and 200 miles (correlating with estimated ground transport times of 1, 2, 3, 4 and 5 hours, respectively) of centers that perform ST using ArcGIS software and evaluate the impact of varying the threshold ST volume required by each facility on this proportion. We will then perform multiple two proportion z-tests to compare proportions of patients within driving distance over time. RESULTS/ANTICIPATED RESULTS: We hypothesize that over time, and particularly after the pivotal trials of ST were published in 2015, that the number of facilities that perform ST have increased over time, which would increase the geographic access to ST. We also hypothesize that since the Joint Commission set the minimum procedural volume for proceduralists at Thrombectomy-Capable and Comprehensive Stroke Center to 15 per year, this would work to increase regionalization and could work to decrease geographic access to ST. However, we hope to elucidate the net impact of the interplay between these two opposing factors on regionalization of care over time which is currently unclear. DISCUSSION/SIGNIFICANCE: Current ST volume thresholds have focused on technical proficiency but may impact regionalization of care and geographic access to ST. Since access to ST is time-sensitive, a data-driven approach and better coordination on a regional level may be necessary to ensure timely access to ST.

Objectives/Goals: We aim to establish a systematic approach to distinguish translational science from translational research. Our goal is to create a simple tool that would enable individuals with different backgrounds and levels of expertise to readily determine whether a study truly features translational science. Methods/Study Population: Participants were recruited from a Clinical and Translational Science Award (CTSA) program hub and randomly divided into 2 groups. One group was asked, with minimal guidance, to categorize whether publications described translational science or translational research. The group met to resolve disagreements and identify key indicators and challenges in determining whether a study involves translational science. They provided input on a set of guiding questions intended to facilitate the identification of translational science. The second group did not participate in discussion or tool development. Both groups reviewed a new set of publications, using the tool to guide their assessments. Results/Anticipated Results: Based on publication assessments, we will assess the percent agreement among reviewers in each group for each publication and across the set. We anticipate that the first group will exhibit higher agreement for its second round of review than its first, owing to the benefit of discussion with colleagues and provision of guiding questions. We anticipate that the tool will also promote higher agreement among the second group in their first round of review. We predict that both groups will exhibit high rates of agreement when reviewing with the support of guiding questions. Discussion/Significance of Impact: This study will help us understand interpretations of translational science, a term that has sparked debate and disagreement within CTSA hubs. If successful, the guiding questions will provide CTSAs a tool to improve training, proposal responsiveness, and review for translational science projects.

OBJECTIVES/GOALS: A disease-agnostic translational science framework for data mining is proposed for use across disciplines to: Answer clinical questions, justify future clinical research recruitment, and explore under-represented populations. As a case example, male puberty demonstrates utility of the framework. METHODS/STUDY POPULATION: As a case example using the generalizable framework, the following interdisciplinary question was asked: Does early pubertal timing increase the risk of developing type II diabetes (T2d) in boys? A digital phenotype of males < 18 years old was created in the TriNetX Diamond Network utilizing Boolean operator data queries. TriNetX contains patient electronic health record information (ICD-10 diagnoses, anthropometric measures). A case control analysis leveraging patient counts from various digital phenotypes allowed for outcome (T2d) comparison of boys diagnosed with precocious puberty (E30.1, ICD code for early pubertal timing) to those without, while controlling for body mass index (BMI). RESULTS/ANTICIPATED RESULTS: Subjects (N=12,996,132) displayed the following digital phenotype: Male, < 18 years old, without ever having a BMI documented >85th percentile. Boys diagnosed with precocious puberty (E30.1) were 6.89 times more likely to develop T2d when aged 14-18 years old than those without (OR 6.89, 95%CI: 5.17-9.19, p. DISCUSSION/SIGNIFICANCE: Boys are under-represented in the early pubertal timing literature, justifying future human subjects research on male puberty. This case example demonstrates a broader disease-agnostic framework which can be adapted across disciplines. Opportunities may include public health digital phenotyping.

ABSTRACT IMPACT: Our data reveal a histone modifying enzyme involved in regulating inflammation that may be a novel target for treating non-healing diabetic wounds. OBJECTIVES/GOALS: We investigate molecular mechanisms that regulate the inflammatory phenotype of macrophages in normal and diabetic wound healing. Our goal is to identify novel pathways that may be used to better treat diabetic patients with non-healing wounds. METHODS/STUDY POPULATION: We utilize normal and transgenic murine models on standard chow or high-diet to identify chromatin modifying enzymes involved in regulating macrophage function during wound healing. We validate our murine studies with human blood monocytes or wound macrophages from diabetic patients undergoing limb amputation surgery. RESULTS/ANTICIPATED RESULTS: We have identified the histone methyltransferase SETDB2 as a regulator inflammation in normal and diabetic wound macrophages. We found that SETDB2 was dependent on IFNβ singaling and that both IFNβ and Setdb2 expression were impaired in diabetic wound macrophages. Further, we show that SETDB2 regulates inflammatory response and immune cell trafficking pathways. We also show that SETDB2 genomic localization is dependent on *NFκΒ deposition of the promoter. DISCUSSION/SIGNIFICANCE OF FINDINGS: Our results indicate that SETDB2 is a regulator of macrophage plasticity and that SETDB2 expression is impaired in diabetic wound macrophages leading to hyper-inflammatory response and delayed wound healing. These data provide a novel potential therapeutic pathway for treating non-healing diabetic wounds.

OBJECTIVES/SPECIFIC AIMS: Psoriasis is one of the most common inflammatory diseases of the skin, affecting about 2%–3% of the US population. Despite its high prevalence, its pathogenesis remains poorly understood. The ability of the microbiome to modify host immunity and metabolism suggests that it may contribute to the development of psoriasis and its cardiometabolic comorbidities. This study aims to characterize the psoriatic skin microbiome and understand the functional role that these bacteria may play. METHODS/STUDY POPULATION: 16s rRNA sequencing of site-matched skin swabs from 8 psoriasis patients and 8 healthy controls was used to identify bacteria and determine their relative abundance and microbial community diversity in the sample. PICRUSt was used to infer the functional roles of the bacteria from 16s rRNA amplicon data. RESULTS/ANTICIPATED RESULTS: Lesional psoriasis skin had lower α diversity (p=0.04), less Actinobacteria (p=0.0001), but higher Firmicutes (p=0.009) compared with controls. At the genus level, lesional skin had more Alloiococcus (p=0.01) and Aerococcus (p=0.01) and demonstrated a trend towards lower Propionibacterium (p=0.08) and higher Gallicola (p=0.09) compared to controls. Interestingly, Alloiococcus (p=0.003) and Gallicola (p=0.04) were also higher in nonlesional skin compared with controls. Furthermore, lesional and nonlesional skin shared an increased abundance of Acinetobacter sp., Staphylococcus pettenkoferi, and Streptococcus sp., relative to controls. Lesional and nonlesional psoriasis skin did not differ significantly in microbiome composition. Predictive functional analysis revealed that both the healthy and psoriatic skin microbiome were enriched with bacteria capable of amino acid and carbohydrate metabolism suggest these functions might have a general role in host-microbe interaction. DISCUSSION/SIGNIFICANCE OF IMPACT: These data reveal intriguing differences in the cutaneous microbiome of psoriatic individuals and healthy controls and suggest that bacterial metabolism may play an important role in host-microbe interaction.

OBJECTIVES/SPECIFIC AIMS: Hepatitis C viral (HCV) infections are rising significantly both in young adults and as newly diagnosed cases in “baby boomers.” New HCV therapeutics cure over 95% of cases, and a call has been made for elimination of the epidemic by 2030; yet major HCV cascade of care (CoC) barriers exist. We secured CTSA pilot funding to obtain preliminary data for an innovative clinical trial utilizing big data modeling toward HCV elimination. METHODS/STUDY POPULATION: Our pilot work has developed a coordinated, real-time clinical data management process across 3 major CTSA affiliated hospital systems (MedStar Health, Emory-Grady, and UT-Southwestern), and additional data will be obtained from a pragmatic clinical trial. Electronic medical records data will be mapped to the OHDSI model, securely transmitted to Oak Ridge National Laboratory, Knoxville, TN and exposed to integrated data, analytics, modeling and simulation (IDAMS). RESULTS/ANTICIPATED RESULTS: Our U01 CTSA application proposes that HCV-IDAMS will model modifications to the established HCV CoC at community and population levels and thus simulate future outcomes. As data volume increases, system knowledge will expand and recursive applications of IDAMS will increase the accuracy of our models. This will reveal real-world reactions contingent upon population dynamics and composition, geographies, and local applications of the HCV CoC. DISCUSSION/SIGNIFICANCE OF IMPACT: Only an innovative, integrated approach harnessing pragmatic clinical data, big data and supercomputing power can create a realistic model toward HCV elimination.