Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patients using an automated pipeline. The dataset is divided into training (357 cases), calibration (63 cases), and testing (105 cases). Our probabilistic model is a convolutional neural network (CNN) with dropout regularization. It generates a prediction set containing high-probability correct predictions using conformal prediction techniques, which add an uncertainty layer to the CNN. Additionally, gradient-based localization mapping is employed to identify brain regions associated with low uncertainty cases. Results/Anticipated Results: The performance of the computational conformal model is evaluated using training and testing data with varying dropout rates from 0.1 to 0.5. The best results are achieved with a dropout rate of 0.5, yielding a fivefold cross-validated average precision of 72.19% and an F1-score of 70.66%. Additionally, the model provides probabilistic uncertainty quantification along with gradient-based localization maps that identify key brain regions, including the temporal lobe, putamen, caudate, and occipital lobe, relevant to obesity prediction. Comparisons with standard segmented brain atlases and existing literature highlight that our model’s uncertainty quantification mapping offers complementary evidence linking obesity to structural brain regions. Discussion/Significance of Impact: This research offers two significant advancements. First, it introduces a probabilistic model for predicting obesity from structural magnetic resonance imaging data, focusing on uncertainty quantification for reliable results. Second, it improves interpretability using localization maps to identify key brain regions linked to obesity.

Objectives/Goals: Opioid use disorder (OUD) at delivery increased between 1999 and 2014. Clinical guidelines include medication for OUD (MOUD) for pregnant women with OUD and is associated with better fetal outcomes. Few large studies have compared prenatal MOUD outcomes to no MOUD. We evaluated the association of documented MOUD prescription during pregnancy with maternal outcomes. Methods/Study Population: We utilized aggregated electronic health records using the TriNetX platform to conduct a retrospective cohort study of females, aged 1249 years with a childbirth CPT code and documented opioid use via ICD-10 codes in the nine months before delivery between 2014 and 2020, comparing patients with MOUD prescription of buprenorphine or methadone during the nine months before delivery to demographically matched patients without any documented MOUD, using hazard ratios and 95% CIs for outcomes occurring one week to one or three years after childbirth. Results/Anticipated Results: MOUD cohort (n = 6,945, 85.33% White; 82.77% Non-Hispanic or Latino) was associated with significantly higher subsequent documented MOUD prescription (HR, 9.26 [95% CI, 7.98–10.76]; 6.21 [95% CI, 5.60–6.88]) and new remission codes (HR, 2.79 [95% CI, 2.15–3.62]; 2.85 [95% CI, 2.38–3.40]) at one and three years, lower ED visits at one year (HR, 0.88 [95% CI, 0.81–0.96]), no significant association of ED visits at three years (0.95 [95% CI, 0.89–1.02]), higher outpatient visits (HR, 1.26 [95% CI, 1.20–1.32]; HR, 1.27 [95% CI, 1.21–1.33], and no significant association of inpatient visits at one and three years (HR, 0.93 [95% CI, 0.813–1.06]; 1.06 [95% CI, 0.96–1.18]) than the never-MOUD cohort (n = 4,708, 76.11% White; 75.68% non-Hispanic or Latino). Discussion/Significance of Impact: A documented prescription for MOUD during pregnancy is associated with newly documented remission of OUD, increased outpatient visits, decreased ED visits, and additional documented MOUD prescriptions suggestive of increased access to continuity care. Efforts to increase MOUD use in pregnancy may improve maternal outcomes.

Objectives/Goals: Manual skin assessment in chronic graft-versus-host disease (cGVHD) can be time consuming and inconsistent (>20% affected area) even for experts. Building on previous work we explore methods to use unmarked photos to train artificial intelligence (AI) models, aiming to improve performance by expanding and diversifying the training data without additional burden on experts. Methods/Study Population: Common to many medical imaging projects, we have a small number of expert-marked patient photos (N = 36, n = 360), and many unmarked photos (N = 337, n = 25,842). Dark skin (Fitzpatrick type 4+) is underrepresented in both sets; 11% of patients in the marked set and 9% in the unmarked set. In addition, a set of 20 expert-marked photos from 20 patients were withheld from training to assess model performance, with 20% dark skin type. Our gold standard markings were manual contours around affected skin by a trained expert. Three AI training methods were tested. Our established baseline uses only the small number of marked photos (supervised method). The semi-supervised method uses a mix of marked and unmarked photos with human feedback. The self-supervised method uses only unmarked photos without any human feedback. Results/Anticipated Results: We evaluated performance by comparing predicted skin areas with expert markings. The error was given by the absolute difference between the percentage areas marked by the AI model and expert, where lower is better. Across all test patients, the median error was 19% (interquartile range 6 – 34) for the supervised method and 10% (5 – 23) for the semi-supervised method, which incorporated unmarked photos from 83 patients. On dark skin types, the median error was 36% (18 – 62) for supervised and 28% (14 – 52) for semi-supervised, compared to a median error on light skin of 18% (5 – 26) for supervised and 7% (4 – 17) for semi-supervised. Self-supervised, using all 337 unmarked patients, is expected to further improve performance and consistency due to increased data diversity. Full results will be presented at the meeting. Discussion/Significance of Impact: By automating skin assessment for cGVHD, AI could improve accuracy and consistency compared to manual methods. If translated to clinical use, this would ease clinical burden and scale to large patient cohorts. Future work will focus on ensuring equitable performance across all skin types, providing fair and accurate assessments for every patient.

Objectives/Goals: To address the manual, time-consuming processes of validating IRB compliance and ensuring the secure delivery of i2b2 data, this project automates compliance checks, streamlines Protected Health Information (PHI) access, and provides timely, secure data availability while reducing administrative burdens and non-compliance risks. Methods/Study Population: This project enhances the i2b2 application to automate compliance processes and facilitate secure data delivery through integration with REDCap. By linking i2b2 with the IRB system, the application performs automatic compliance checks for project requests, verifying GCP and HIPAA certifications, only allowing the release of IRB-approved PHI variables, safeguarding against unauthorized data access. Manual signatures confirm non-automated compliance processes. Once verified, the application automatically creates a REDCap project, assigns user access, and securely delivers data, ensuring compliance with HIPAA regulations. Results/Anticipated Results: The automated system successfully streamlined IRB compliance checks and data delivery for i2b2 requests. Validation of certifications like GCP and HIPAA, now occurs automatically, significantly reducing the risk of non-compliance. Personnel access to data is limited to IRB-approved PHI, ensuring data security and adherence to institutional standards. The integration with REDCap has reduced manual processes, cutting data request processing time to approximately 30 minutes. Researchers and administrative staff experienced a notable decrease in administrative burden, with faster, more efficient access to approved data while maintaining full compliance with IRB and HIPAA regulations. Discussion/Significance of Impact: The lessons learned can be adapted by institutions to improve compliance efficiency and reduce administrative overhead. Implementing similar automation of certification checks and data delivery, sites can enhance data security, minimize errors, and ensure faster, compliant access to research data.

Objectives/Goals: Endometrial cancer is one of the few cancers that has both a rising incidence and mortality rate. Molecular classification is becoming more important for the management of endometrial cancer but the ability to translate this into clinical practice remains constrained. Our goal is to use AI to predict the molecular subtype from histopathology slides. Methods/Study Population: We utilized the open source endometrial cancer datasets from The Cancer Genome Atlas (TCGA) (N = 387) and Cancer Proteomics Transcriptomic Tumor Analysis Consortium (CPTAC) (N = 135) to develop and train a vision transformer AI model. We used a proprietary cohort of patients (N = 548) for external validation. Whole slide images (WSI) and molecular subtype data were collected. Subtypes include POLE ultramutated (POLE), microsatellite instability (MSI-H), copy-number low (CNV-L), and copy-number high (CNV-H). WSI were preprocessed, and features were extracted. Modified STAMP protocol was used in training, utilizing a pretrained foundation transformer model (Virchow2). Cross-validation of the TCGA was used for initial training, followed by testing on the CPTAC dataset and validation on our proprietary cohort. Results/Anticipated Results: Fivefold cross-validation of the TCGA database (60% training, 20% testing, and 20% validation) developed a best overall model with a mean AUC of 0.74 (POLE 0.78, MSI-H 0.76, CNV-H 0.86, CNV-L 0.77). Overall precision 0.58, recall 0.55. CNV-H was the subtype with the most accurate prediction. CPTAC holdout testing revealed moderately high AUC (POLE 0.63, MSI-H 0.62, CNV-H 0.98, and CNV-L 0.76). Overall precision 0.54 and recall 0.58. Again, CNV-H was the most accurate prediction. Validation on our proprietary cohort revealed a drop in performance with overall mixed results by AUC (POLE 0.50, MSI-H 0.69, CNV-H 0.78, and CNV-L 0.61). Overall precision 0.57, recall 0.45. Again, CNV-H with the most accurate prediction but F1 score dropped from 0.77 in the CPTAC to 0.47 on validation. POLE was the least accurate prediction subtype. Discussion/Significance of Impact: The CNV-H subtype demonstrated robust performance, suggesting the model effectively captures the features associated with this subtype. CNV-L had moderate performance. MSI-H and POLE were notably lower. WSI-based AI models show translational potential for subtype prediction in the management of endometrial cancer but more work is necessary.

Objectives/Goals: This pilot study aims to assess the implementation of the DataDay app in memory clinics for patients with MCI or dementia, focusing on usability, user satisfaction, and impact on health outcomes. We seek to identify barriers and facilitators to implementation and evaluate its effect on reducing unnecessary hospital stays. Methods/Study Population: This mixed-methods study will involve 50 participants, 25 diads of patients with MCI or mild-to-moderate dementia and their caregivers from the community. Participants will use DataDay for 12 weeks, receiving reminders to log daily activities such as nutrition, mood, cognition, and physical activity. Baseline demographic data will be collected from self-reported surveys. Participants will receive training on app use, with follow-up interviews at 4, 8, and 12 weeks to gather feedback. Quantitative data analysis will include repeated measures analysis of variance to compare pre- and post-intervention outcomes, such as medication use and ER visits. Thematic analysis will be conducted on interview transcripts to understand user experiences. Results/Anticipated Results: We anticipate the study will demonstrate the feasibility of the DataDay app for self-management in individuals with MCI or dementia. Expected outcomes include improved medication adherence, reduced emergency room visits, and increased user engagement with daily health monitoring. Qualitative feedback is expected to highlight user satisfaction with the app’s reminders and ease of integration into daily routine. We also expect potential challenges to be identified such as initial learning difficulties and technology-related frustration. The data will help refine the app for better usability and inform strategies for widespread implementation in memory assessment clinics. Discussion/Significance of Impact: The study will provide insights into the practicality of implementing DataDay in memory clinics. The results will highlight necessary adjustments and provide key factors for successful adoption in other clinics. DataDay aims to allow individuals with MCI or dementia to manage their condition at home and enhance their quality of life.

Objectives/Goals: Ischemic stroke treatments assist in restoring blood flow, but do not guarantee good outcomes. Since extracellular vesicles (EVs) able to cross the blood brain barrier, total (nonspecific) and astrocyte enriched EVs (TEVs, AEVs, respectively) from plasma may emerge as plasma biomarkers for prognostication and targeted therapeutics. Methods/Study Population: “Blood and Clot Thrombectomy Registry and Collaboration” (BACTRAC; NCT03153683) is a human stroke biobank at the University of Kentucky that collects samples at the time of mechanical thrombectomy during emergent large vessel occlusions (ELVO; ischemic stroke). EVs were isolated, via size exclusion chromatography, from unbanked plasma and concentrated resulting in TEVs. AEVs were immunoprecipitated with anti-EAAT1 (GLAST), an astrocyte-specific transmembrane glycoprotein. Isolated protein was sent to Olink and ran on their metabolic panel. Demographics and medical histories of the subjects were exported from REDcap and investigators were blinded during EV analysis. Results/Anticipated Results: ELVO subjects (8 females/ 5 males) were an average age of 71.1 ± 11.7 years. Lower TEV enolase 2, a neuronal glycolysis enzyme, associated with increased stroke severity (NIHSS; rs =  -0.7819, p = 0.0476). Higher systemically TEV quinoid dihydropteridine reductase (QDPR), essential co-factor enzyme, was associated with more severe strokes (NIHSS; rs =  0.8486, p = 0.0123) and lower cognition (MoCA; r2 =  0.7515, p = 0.0254). Interestingly, higher intracranial AEVs QDPR was associated with lower infarct volumes (rs =  -0.7333, p = 0.0202), less severe strokes (NIHSS; rs =  -0.6095, p = 0.0388), and better cognition (MoCA; r2 =  0.6095, p = 0.0388). Increased AEV nicotinamide adenine dinucleotide kinase another essential co-factor enzyme, intracranially also correlated to higher cognition (MoCA; rs =  0.8356, p = 0.0298). Discussion/Significance of Impact: Plasma TEV and AEV metabolic proteins correlate with the progression of stroke outcomes and should be investigated as target therapies during MT to improve outcomes.

Objectives/Goals: Pharmacogenomic (PGx) testing identifies genetic variations affecting medication response but is not yet in routine clinical whole-genome sequencing (WGS) workflows. We aimed to establish a streamlined bioinformatics pipeline for incorporating PGx reporting into clinical WGS and to determine clinical implications for medication treatment. Methods/Study Population: A PGx profiling pipeline based on existing WGS data was developed, integrating three WGS-based PGx calling tools: Aldy, PyPGx, and Cyrius (CYP2D6 only), to provide genotype calls for 17 key pharmacogenes. The pipeline was validated using WGS data from 70 individuals with diverse backgrounds (36% European, 27% African, 27% Asian, and 10% admixed) from the Genetic Testing Reference Materials Coordination Program (GeT-RM). Results were manually reviewed against published data. The validated pipeline was then applied to 144 clinical patients previously screened for neurodevelopmental disorders or suspected hereditary diseases, followed by diplotype-to-phenotype translation and preemptive PGx-guided medication recommendations based on consensus guidelines and FDA labeling for commonly used medications. Results/Anticipated Results: Congruent phenotype call rates for GeT-RM samples were 100% for 13 genes (CFTR, CYP2B6, CYP2C19, CYP2C9, CYP3A4, CYP4F2, DPYD, G6PD, IFNL3, NAT2, NUDT15, TPMT, and VKORC1), 99% for three genes (CYP3A5, SLCO1B1, UGT1A1), and 97% for CYP2D6, indicating strong pipeline performance. Among 144 clinical patients, 99.3% had at least one clinically actionable PGx results relevant to 36 of top 300 medications in the USA across psychotropic, cardiovascular, musculoskeletal, gastrointestinal, and other therapeutic areas. The most prevalent drug–gene interactions involved sertraline and CYP2B6, affecting 49% patients: 41% were intermediate metabolizers who may require slower titration and lower maintenance doses, while 8% poor metabolizers may benefit from a lower starting dose or alternative antidepressants. Discussion/Significance of Impact: Our validated WGS-based PGx profiling pipeline successfully extracted actionable PGx data from clinical WGS. By aligning PGx profiles with guideline-recommended clinical actions, we demonstrated the clinical value of integrating PGx reporting in WGS workflows, improving personalized medication management.

Objectives/Goals: Many left hemisphere stroke survivors have a reading disorder (alexia), which is experienced as decreasing well-being. Therapies produce inconsistent results, demonstrating a need for treatment response predictors. We identify neural correlates of a computational model of reading, which may provide biomarkers to improve therapeutic outcomes. Methods/Study Population: Left hemisphere stroke survivors (LHSS) (n  =  52) performed an oral reading task and tests of semantic and phonological processing. Artificial neural network (ANN) models, mapping between orthography (visual word form), phonology (auditory word form), and semantics (word meaning), were trained to read single words at an adult reading level. Stroke was simulated by removing percentages (in 10% intervals) of the connections into and out of semantics, phonology, and the combination thereof. The lesioned model producing the smallest average Euclidean distance over word and pseudoword reading accuracy to each LHSS was selected as the matched model. Two voxelwise lesion-symptom mapping (VLSM) analyses identified the neural correlates of the percent of phonological and semantic links removed in the matched models. Results/Anticipated Results: Model reading was correlated with LHSS reading (high-frequency regular words, r(48) = 0.96; high-frequency irregular words, r(48) = 0.94; low-frequency regular words, r(48) = 0.97); low-frequency irregular words, r(48) = 0.85; all p’s Discussion/Significance of Impact: Our results show that ANN models of reading, when closely matched to LHSS reading performance, directly connect cognitive processes to the brain. Using matched models as a precision medicine framework to predict therapy response or to identify targets for neurostimulation provides a valuable route toward improving poststroke language outcomes.

Objectives/Goals: Diabetic kidney disease (DKD) affects 40% of diabetic patients, leading to renal failure, yet the molecular drivers remain elusive. MicroRNAs, noncoding regulators of gene expression, may hold the key. This study aims to identify key miRNAs in DKD, providing crucial insights for early intervention. Methods/Study Population: miRNA sequencing was conducted on kidneys from 8-week old male BTBR wild type and BTBR ob/ob mice. BTBR ob/ob mice lack the hormone leptin and spontaneously develop type 2 diabetes, with morphological renal lesions characteristic of human DKD. Total RNA was extracted from whole kidney sections and processed using the QIAseq miRNA library kit. Sequencing was performed on an Illumina NextSeq 550 platform. GeneGlobe analysis was used to identify differentially expressed miRNA functional pathways, while ingenuity pathway analysis (IPA) was employed to predict master regulators and causal networks involved in DKD. Results/Anticipated Results: miRNA sequencing identified significantly differentially expressed miRNAs (p < 0.05) between 8-week-old BTBR WT and BTBR ob/ob male mice, including miR-34a (-6.86 fold), miR-122 (-5.01 fold), miR-129 (-2.23 fold), miR-142a (+2.78 fold), miR-346 (+4.66 fold), miR-547 (-2.49 fold), miR-592 (+11.81 fold), miR-802 (-6.95 fold), and miR-6539 (-7.93 fold). Qiagen GeneGlobe analysis revealed biological processes potentially targeted by these miRNAs, including endocytosis, phagocytosis, hyperglycemia (p = 7.59e-3), and insulin-dependent diabetes (p = 4.32e-4). IPA predicted activation of RRAS, a small GTPase regulating cell growth and signaling (Z-score +2), with miR-34a and miR-122 targeting MYC, PI3K, and TGF-β in DKD progression in BTBR ob/ob mice. Discussion/Significance of Impact: We identified kidney miRNA expression in BTBR ob/ob mice at a pivotal disease stage. miR-34a, miR-122, and RRAS emerged as key drivers in DKD progression, showing remarkable early biomarker potential. These findings lay the groundwork for early detection and innovative therapies to halt DKD and improve patient outcomes.

Objectives/Goals: This study aims to explore the relationship between plasma biomarkers (GFAP, NF-L, and IL-1β) and cognitive impairment in moderate to severe TBI patients. We will assess biomarker levels and their link to neurocognitive outcomes at acute and chronic stages of injury. Methods/Study Population: We will recruit 100 patients aged 21 years and older with moderate to severe TBI (Glasgow Coma Score 3–12) from a trauma hospital. Blood samples will be collected at 24–72 hours post-injury and again at 3 and 6 months. Plasma levels of GFAP, NF-L, and IL-1β will be measured using multiplex ELISA. Neurocognitive tests will be administered at 3 and 6 months to assess cognitive function. Correlations will be made between biomarker levels, neurocognitive performance, and disability scores (Disability Rating Scale and Glasgow Outcome Scale). Exosome isolation from plasma will allow for detailed analysis of astrocyte-derived biomarkers and their association with long-term cognitive impairment and recovery. Results/Anticipated Results: We anticipate that plasma levels of GFAP, NF-L, and IL-1β will be elevated in the acute phase of moderate to severe TBI and will correlate with injury severity. At 3 and 6 months, higher levels of IL-1β, in particular, are expected to be strongly associated with cognitive deficits. We also anticipate that biomarkers in astrocyte-derived exosomes will provide more specific insights into long-term neuroinflammation and its impact on cognitive function. These findings could pave the way for targeted, personalized interventions to improve recovery in TBI patients. Discussion/Significance of Impact: This research focuses on inflammation’s role in cognitive impairment and disability in TBI patients. We propose using multiple biomarkers – GFAP, IL-1β, NF-L – paired with advanced techniques like exosomes and multiplex analyses to identify novel therapeutic targets, aiming for personalized treatment strategies, as well as prognosis.

Objectives/Goals: pT217-tau is a novel fluid biomarker that predicts onset of Alzheimer’s disease (AD) symptoms, but little is known about how pT217-tau arises in brain, as soluble pT217-tau is dephosphorylated postmortem in the humans. Aging macaques naturally develop tau pathology with the same qualitative pattern and sequence as humans, including cortical pathology. Methods/Study Population: The etiology of pT217-tau in aging brains can be probed in rhesus macaques, where perfusion fixation allows capture of phosphorylated proteins in their native state. We utilized multi-label immunofluorescence and immunoperoxidase and immunogold immunoelectron microscopy to examine the subcellular localization of early-stage pT217-tau in entorhinal cortex (ERC) and dorsolateral prefrontal cortex (dlPFC) of aged rhesus macaques with naturally occurring tau pathology and assayed pT217-tau levels in blood plasma using an ultrasensitive nanoneedle approach. Results/Anticipated Results: pT217-tau labeling is primarily observed in postsynaptic compartments, accumulating in: 1) dendritic spines on the calcium-storing smooth endoplasmic reticulum spine apparatus near asymmetric glutamatergic-like synapses and 2) in dendritic shafts, where it aggregated on microtubules, often “trapping” endosomes associated with Aβ42. The dendrites expressing pT217-tau were associated with autophagic vacuoles and dysmorphic mitochondria, indicative of early neurite degeneration. We observed trans-synaptic pT217-tau trafficking between neurons within omega-shaped bodies and endosomes, specifically near excitatory, but not inhibitory synapses. We also examined pT217-tau in blood plasma in macaques across age-span and observed a statistically significant age-related increase in pT217-tau. Discussion/Significance of Impact: We provide direct evidence of pT217-tau trafficking between neurons near synapses to “seed” tau pathology in higher brain circuits, interfacing with the extracellular space to become accessible to CSF and blood. The expression of pT217-tau in dendrites with early signs of degeneration may help to explain why this tau species can herald future diseases.

Objectives/Goals: Transgender women who have sex with men (TGWSM) have higher HIV risk. The rectal mucosal (RM) immune environment of TGWSM who choose feminizing hormone therapy (FHT) has been shown to be distinct from the RM of cisgender men who have sex with men (MSM). We studied the impact of FHT on the adaptive immune cellular composition of the RM. Methods/Study Population: We sampled cross-sectional and longitudinal cohorts of TGWSM and cisgender MSM from The Silom Clinic in Bangkok, Thailand from December 2020 to December 2023. We included participants aged >18 years, all cisgender MSM and TGWSM with FHT levels in the therapeutic range for cisgender women. We performed RM biopsies and analyzed the adaptive immune cell characteristics via flow cytometry. We will perform binary linear regression to assess the association between systemic FHT levels and the percentage of CD4+ T cells expressing key biomarkers. Primary outcomes include the percentage of CD4+ T cells that express CCR5, with a secondary outcome of the percentage of CD4+ T cells that express Ki67. Results/Anticipated Results: The cross-sectional cohort included 100 TGWSM on FHT and 50 cisgender MSM. The longitudinal cohort included 25 TGWSM who were initiating FHT. Similar primary and secondary outcomes are to be elucidated in both cohorts. We anticipate the RM environment of TGWSM using FHT in both cohorts compared to the RM environment of cisgender MSM in the cross-sectional cohort will be associated with greater percentages of activation/co-receptor expression of CD4+ T cells that express biomarkers of interest. In the longitudinal cohort, we similarly anticipate increased percentages and activation/co-receptor expression of CD4+ T cells expressing biomarkers of interest in TGWSM after in comparison to before initiating FHT. Discussion/Significance of Impact: This is the largest study of its kind to compare HIV target cells in RM of TGSWM, which challenges prevailing perspectives suggesting to group cisgender MSM with TGWSM. Anticipated results will inform HIV prevention strategies and future vaccine studies in this high-risk population.

Objectives/Goals: NAD+ synthesis is enhanced in glioblastoma (GBM) allowing GBM to resist chemotherapy. NQO1 is upregulated in GBM and may be targeted by β-lapachone (β-lap) to induce NAD-depletion and cell death. This project investigates NQO1 as a selective target for GBM and the contributions of glucose and uridine to NAD+ synthesis. Methods/Study Population: Survival Studies and NQO1 expression. RNA-seq and survival data from TCGA of glioma patients (n = 667) was obtained using the UCSC Xena platform. Western blots were utilized to determine expression levels of NQO1 and NAMPT in normal human astrocytes, U87 cells, and patient-derived GBM cell lines. Immunocytochemistry: γ-H2AX staining was used to evaluate β-lap induced DNA damage. NQO1-dependence was evaluated with the NQO1-inhibitor dicoumarol. Cytotoxicity measurements. Cells were exposed to β-lap and other inhibitors, and cell survival was determined by trypan-blue exclusion assay. Co-culturing experiments were performed with fluorescently labeled U87 cells and unlabeled astrocytes. NAD+ quantification. Intracellular NAD+ was acid extracted and quantified by an enzyme-cycling reaction. Results/Anticipated Results: NQO1 overexpression is linked to decreased survival in glioma patients. In glioma patients, high NQO1 expression was associated with a decreased overall survival and high-grade tumors. β-lap induces selective NAD+ depletion and cell death in NQO1-expressing GBM cells. Western blots demonstrate NQO1 expression to be elevated in GBM cell lines compared to normal human astrocytes. β-lap induces NQO1-dependent NAD+ depletion and cell death in GBM compared to astrocytes in mono- and co-culture experiments. Glucose and uridine facilitate NAD+ regeneration in GBM. We demonstrate extracellular glucose and uridine facilitate NAD+ regeneration and cell survival in β-lap exposed GBM cells. Utilizing inhibitors, we determined that glucose and uridine facilitate NAD+ regeneration through the NAD+ salvage pathway. Discussion/Significance of Impact: GBM is the most common primary adult CNS tumor with a median survival of 14 months. Despite significant research in therapeutic strategies, treatment has not improved in 2 decades. There is a significant need to discover new targets that may improve GBM treatment. We demonstrate here that targeting NQO1 with β-lap induces selective GBM toxicity.

Objectives/Goals: Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer (BC) with limited treatment options. Mortality rate is especially high in African American (AA) women of reproductive age. High levels of intracellular calcium (Ca2+) have been shown in TNBC cells. This study is to investigate Ca2+ channel blockers (CCBs) as therapy for TNBC. Methods/Study Population: Two human TNBC cell lines obtained from ATCC – HCC1806, and MDA-MB-453 are treated with CCBs, Cilnidipine (Cil), and Mibefradil (Mib), in a concentration- and time-dependent manner. Cell proliferation assays are performed by the MTS cell viability assay. Intracellular Ca2+ levels are measured using the fluorescent dye: Fluro 4-AM. Apoptosis is determined by flow cytometry using Annexin V staining and mitochondrial permeability will be assessed by the Mito JC-1 assay. Expression of Ca2+ signaling genes will be quantitated by real-time polymerase chain reaction (RT-PCR). Potential pathways of CCB efficacy will be identified by ingenuity pathway analysis (IPA). Results/Anticipated Results: Our findings show both CCBs decrease cell proliferation in a concentration- and time-dependent manner to a maximum of 80% vs. control in both TNBC cells. Flow cytometry findings on both TNBC cells treated with both drugs at 20 µM for 24 hours depicts late apoptosis. Interestingly, Mib did not change the intracellular Ca2+ level in HCC1806 cells yet decreased in MDA-MB-453 cells by fivefold, while Cil increased the intracellular Ca2+ level in both cells almost twofold. It is anticipated that Mito JC-1 assay depict decreased mitochondrial potential in both cells. For reverse transcription polymerase chain reaction, it is anticipated that CCB treatment will increase transient receptor potential Ca2+ channels and decrease voltage-gated Ca2+ channels in both cells. IPA analysis is expected to show apoptotic pathways are involved in TNBC via CCB treatment. Discussion/Significance of Impact: TNBC lacks the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Treatment options for TNBC remain severely limited. Our findings that both Cil and Mib can inhibit proliferation of human breast cancer cell lines indicate repurposing CCBs as treatment for TNBC warrants further investigation.

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: 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: Our lab’s novel adoptive cellular therapy (ACT) significantly improves survival in brain tumor models. However, there is a lack of biomarkers to assess immunotherapy responses. Our objective is to use gold nanorods to track hematopoietic stem cell migration, a critical arm of ACT, and validate it as a prognostic biomarker. Methods/Study Population: Hematopoietic stem cells (HSCs) were isolated from the bone marrow of 6-week-old C57BL/6J mice and co-cultured with varying gold nanorod (GNR) concentrations and time points. GNR uptake in HSCs was evaluated with inductive coupled plasma mass spectrometry, two-photon luminescence, and tissue histology. After GNR co-culture, HSC viability and differentiation were quantified with flow cytometry and colony forming unit assays. To evaluate the impact of GNRs on HSC reconstitution, mice received myeloablative total body irradiation and intravenously received GNR-labeled HSCs. Computed tomography (CT) contrast of GNRs will be confirmed through microCT. Lastly, mice will intracranially receive KR158b glioma and GNR-labeled HSC bio-distribution will be measured after ACT and correlated with survival outcomes. Results/Anticipated Results: We have demonstrated that GNRs are readily taken up by HSCs within 30 minutes, and retained within intracellular compartments, via TPL. Incubation of GNRs with HSCs did not significantly alter cell viability or differentiation, supporting the GNR’s favorable biosafety profile. Colony-forming unit assays revealed that GNR incubation did not significantly disrupt the total number of colonies formed and qualitatively, colonies did not demonstrate significant lineage differences. GNR-labeled HSCs demonstrated significant reconstitution after myeloablative total body irradiation in mice. We expect that GNR-labeled HSCs will distribute to the glioma microenvironment and draining lymph nodes, positively correlating with long-term survival after ACT. Discussion/Significance of Impact: GNRs harbored high biosafety and feasibility for tracking HSC migration after ACT. We seek to translate this theranostic tool into the current first-in-human clinical trials at our institution for patients diagnosed with neuroblastoma and diffuse intrinsic pontine glioma to improve immunotherapies against brain malignancies.

Objectives/Goals: Opioid use disorder (OUD) in pregnancy and its implications on the maternal-fetal interface has been relatively understudied. Here, we aimed to uncover the impact of maternal OUD on placental structure, function, and inflammatory responses and further stratified our findings by maternal hepatitis C (HCV) infection. Methods/Study Population: To address this knowledge gap, we collected placental tissue from healthy pregnancies (control) and those with opioid use disorder with and without maternal HCV infection. First, placental development was assessed by gross and histological examination of the placenta. Immune cells were then isolated from decidua (maternal) and chorionic villous (fetal) placental tissues, and the frequency and phenotype of immune subsets were determined by flow cytometry. Markers of inflammation, placental perfusion, growth factors, tissue remodeling, and vascularization were measured in placental tissue homogenate by multiplex Luminex assay. Finally, gene expression alterations in placental architecture were assessed by Visium spatial transcriptomics, integrating transcriptomic data with spatial information. Results/Anticipated Results: Our results indicate that maternal OUD impairs placental perfusion/development and is accompanied by increased markers of inflammation in the decidua (IL-1Ra, IL-2, IL-18, IP-10, MIP-1β, and TNFα) and villous (IL-6 and IL-8). Furthermore, markers of angiogenesis and placental development are altered in the decidua, including increased EGF and IL-6Ra, but decreased FLT-1, FLT-4, and bFGF. The abundance of placental immune cells is varied with OUD/HCV, including decreased frequencies of decidual macrophages and NK cells, critical for blood supply to the fetus, and increased abundance of infiltrating maternal macrophages in fetal chorionic villous. Finally, spatial transcriptomics revealed aberrant infiltration of activated immune cells and modified processes associated with inflammation and angiogenesis. Discussion/Significance of Impact: Altogether, these findings suggest a profound impact of maternal OUD with and without maternal HCV infection on the structure, function, and immune landscape of the maternal–fetal interface that can alter fetal development and maturation.

Objectives/Goals: We investigated the risk of trauma in the form of fractures and traumatic brain injuries (TBIs) among Medicare beneficiaries with incident Parkinson’s disease (PD) age ≥67 compared to population-based controls. Secondarily, we examined the risk of death following a fracture in PD cases compared to controls. Methods/Study Population: We identified incident PD cases (N = 94,317) within a population-based sample of 2017 Medicare beneficiaries. Controls (N = 471,585) were matched 5:1 on month and year. We obtained claims data from 2017 to 2019 to follow cases and controls to identify new fractures treated in a hospital. Our primary outcome was any fracture. We also considered fracture type and TBI. We compared frailty level between cases and controls. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between trauma and PD after adjusting for the following covariates: selected medical comorbidities, age, sex, race/ethnicity, smoking, and use of care. We used Cox regression to estimate hazard ratios (HRs) and 95% CI for trauma in cases compared to controls using the same covariates. Results/Anticipated Results: Compared to controls, PD patients who developed a fracture were more likely to have a history of falls (OR = 2.20, 95% CI 2.08–2.34) and difficulties in walking (OR = 2.66, 95% CI 2.50–2.82). Compared to controls with a fracture, PD patients with a fracture were more likely to be moderately frail (OR = 1.43, 95% CI 1.25–1.64). PD cases had a higher risk of all fracture types, including hip (OR = 1.93, 95% CI 1.85, 2.01), spine (OR = 1.90, 95% CI 1.79, 2.02), upper extremity (OR = 1.69, 95% CI 1.58–1.80), and other traumas such as a TBI (OR = 2.14, 95% CI 1.88–2.43). PD patients had greater mortality following a fracture (HR = 1.18, 95% CI 1.13–1.24) than controls. Discussion/Significance of Impact: The burden of trauma in the first two years immediately after PD diagnosis is high and warrants the initiation of early fall and fracture prevention strategies, in addition to aggressive treatment of PD symptoms by all providers caring for patients with PD.