OBJECTIVES/GOALS: More young adults (age <50 years) are diagnosed with metastatic gastric cancer (mGC) every year. We will evaluate the association between environmental risk factors (including historical racial residential segregation) clinical, pathologic, molecular features and H.pylori associated early-onset mGC (mE-GC). METHODS/STUDY POPULATION: This retrospective matched case-control study of patients (1:2 by diagnosis year) with mGC (early-onset [E-GC; <50 years]; vs older-onset [O-GC; >50 years]) from 2000-2022 from the Los Angeles Cancer Surveillance Program (LA-CSP) will be enriched by a chart-abstracted cohort from USC Norris Comprehensive Cancer (NCCC). This annotated database captures sociodemographic, medical, and pathologic features of patients treated for mGC at NCCC. It will link to LA-CSP data exploring neighborhood features (obesity rate, poverty, insurance, access to care, ethnic enclave, historical segregation, etc.) of cases and controls to identify, characterize, and compare geographic “hotspot” neighborhoods of mE-GC. We hypothesize younger Hispanic and Asian patients are at higher risk of H.pylori associated mGC. RESULTS/ANTICIPATED RESULTS: From 2000-2022, 339 patients (mE-GC n = 113; mO-GC n=226) were treated at NCCC. We will have characterized clinical and pathological features of mE-GC vs mO-GC. We determined the proportion of H. pylori associated mE-GC vs mO-GC. We will have established the geographical distribution of patients with mE-GC vs mO-GC to identify high-risk neighborhoods. We will link neighborhood risk factors such as food scarcity, poverty, health care access, ethnic enclaves, to the distinct clinical and pathological features of mE-GC, including H. pylori status. Descriptive statistics, chi-square, t-tests, and multivariable regression will be used to compare mE-GC to mO-GC. After controlling for underlying demographics and tumor features, we anticipate clusters of mE-GC and mO-GC in areas of historical racial segregation. DISCUSSION/SIGNIFICANCE: Linking neighborhood and individual risk factors for mE-GC will inform early detection and prevention efforts for vulnerable individuals in high-risk neighborhoods. Building community partnerships within these neighborhoods is essential for developing interventions targeting H. pylori treatment to reduce health disparities in mE-GC.

OBJECTIVES/GOALS: The Colorado Community Engagement Alliance Against COVID-19 Disparities (CO-CEAL) Team has worked with 5 racial/ethnic communities in Colorado to understand COVID-19 vaccine hesitancy and develop messages, materials and dissemination strategies to impact vaccine hesitancy. Relationships have been key facilitators of this work. METHODS/STUDY POPULATION: CO-CEAL was funded for one year as a part of the national CEAL Teams effort. CO-CEAL engaged urban Hispanic/LatinX, rural Hispanic/LatinX, urban African/American, rural African Immigrant and urban American Indian/Alaska Native communities through Community Connectors in each community. Our Community Connectors led recruitment of community members for our community survey data collection (goal of 200 surveys from each community) and participation in our 5 Community (Boot Camp) Translations. Where possible we worked with Community Connectors and leveraged community relationships with whom we had worked over multiple years and projects. In two communities we worked to quickly build and establish new relationships with Community Connectors and their communities. RESULTS/ANTICIPATED RESULTS: The rapid nature of the CO-CEAL project, its timeline and the unusual nature of the funding mechanism required adaptations to usual ways of working on both the academic and community side.In our CO-CEAL partner communities where there was a history of working together, concerns, possible barriers and other issues tended to be rapidly addressed. The prior existing, long-standing relationships fostered trust that enabled open airing of concerns and an ability to address issues as they arose. In CO-CEAL partner communities where new relationships were being forged, trust needed to be build from the ground up. This made adaptations more challenging and the ability to address concerns and issues as they arose more problematic. Although the work has moved forward in all communities, extra care has been required in some. DISCUSSION/SIGNIFICANCE: When crises necessitate rapid action in communities, the depth of prior investments in building and maintaining relationships makes a difference in how rapidly and successfully partners can work together to address a crisis like the COVID-19 pandemic. Funders and Academic researchers should invest broadly in communities to enable rapid responses.

OBJECTIVES/SPECIFIC AIMS: Allogeneic hematopoietic stem cell transplantation (allo-HCT) is a curative procedure for hematological malignancies. Chronic graft Versus host disease (cGVHD) is a lethal complication that often develops after allo-HCT. Fli-1 is an aberrantly expressed protein in cancers including erythroleukemia and melanoma, while being implicated in pathogenesis of systemic lupus in mice and humans, a disease with marked similarity to cGVHD. METHODS/STUDY POPULATION: cGVHD was induced using hematopoietic cells from conditional knock-out mice deficient for the fli-1 gene specifically on T cells and progression of cGVHD in murine allo-HCT recipients was monitored using a clinical scoring system, and changes in activation status of hematopoietic cell populations were quantified using flow cytometry. RESULTS/ANTICIPATED RESULTS: Recipients transplanted with fli-1 deficient T cells exhibited reduced cGVHD clinical scores compared with littermate wild-type controls. Donor-grafts containing fli-1 deficient T cells were associated with restrained T-cell responses including reduced Interferon-y cytokine production, PD-1 expression, and differentiation into follicular helper T cells. fli-1 T-cell deficient donor-grafts also improved donor B-cell reconstitution and reduced plasma cells in allo-HCT recipients relative to littermate wild-type control donor-graft recipients. DISCUSSION/SIGNIFICANCE OF IMPACT: Thus, inhibiting Fli-1 represents a promising therapeutic strategy for the goal of preventing cGVHD after allo-HCT while also directly targeting cancers which aberrantly express Fli-1.

OBJECTIVES/GOALS: The Diabetes Prevention Program (DPP) has been shown to reduce diabetes risk by 58%. Men, particularly men of color, are underrepresented in DPP, while they experience higher diabetes-related morbidity. We examine whether race, ethnicity, and gender disparities in engagement are associated with the risk of referral to DPP in primary care METHODS/STUDY POPULATION: Using electronic health record (EHR) from a large urban health system in the Bronx, NY, with an in-house DPP, we examined patient, visit, referral data for DPP-eligible, adult patients with a primary care visit between July 2015 and December 2017. Eligibility included: hemoglobin A1c between 5.7-6.4%; a body mass index (BMI)≥24 kg/m2 (≥22 if Asian); and having no prior diagnosis of diabetes. A total of 26987 patients were included in this study. We examined patient race, ethnicity, preferred language, visit and prescription history, and health payer, among other characteristics. SPSS was used for univariate and bivariate analyses to examine associations between patient characteristics and referral followed by a logistic regression to examine the multivariate association between predictors and referrals. RESULTS/ANTICIPATED RESULTS: Of all DPP-eligible patients, 49% were Hispanic/Latino, and 39% were non-Hispanic Black. Around one-third (34%) of all eligible patients were men. Among all eligible patients in the sample, only 10% were referred to DPP. There were significant differences in the proportion of eligible patients who were referred versus those who were not referred. Women were referred at more than twice the prevalence of men (8.26% to 2.41%), with Hispanic women being referred most frequently (3.59%), and non-Hispanic white men being referred least frequently (.07%) Ethnicity, race, sex, age, number of provider visits, and number of chronic conditions all impacted healthcare provider referral rates to DPP. The health system’s unique free-of-charge DPP likely influenced the lack of significance of patient health insurance. DISCUSSION/SIGNIFICANCE: Given the implementation of DPP at-scale there is an urgent need to understand the patient and systems-level factors that are associated with referring individuals in the DPP. By detecting characteristics of health systems and patients that warrant special attention, we can improve equitable access to evidence-based diabetes prevention.

A crucial reckoning was initiated when the COVID-19 pandemic began to expose and intensify long-standing racial/ethnic health inequities, all while various sectors of society pursued racial justice reform. As a result, there has been a contextual shift towards broader recognition of systemic racism, and not race, as the shared foundational driver of both societal maladies. This confluence of issues is of particular relevance to Black populations disproportionately affected by the pandemic and racial injustice. In response, institutions have initiated diversity, equity, and inclusion (DEI) efforts as a way forward. This article considers how the dual pandemic climate of COVID-19-related health inequities and the racial justice movement could exacerbate the “time and effort tax” on Black faculty to engage in DEI efforts in academia and biomedicine. We discuss the impact of this “tax” on career advancement and well-being, and introduce an operational framework for considering the interconnected influence of systemic racism, the dual pandemics, and DEI work on the experience of Black faculty. If not meaningfully addressed, the “time and effort tax” could contribute to Black and other underrepresented minority faculty leaving academia and biomedicine – consequently, the very diversity, equity, and inclusion work meant to increase representation could decrease it.

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/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: 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/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.

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.