Objectives/Goals: People with insulin-treated diabetes face hypoglycemia risk due to imperfect insulin replacement and impaired counterregulation. We identified the dopamine antagonist, metoclopramide, as a potential treatment. Hypothesis: Treatment with metoclopramide will prevent the development of impaired counterregulatory response to hypoglycemia. Methods/Study Population: In a pre-clinical model, diabetes was induced in 10-week-old Sprague-Dawley rats with streptozotocin (STZ, 65 mg/kg IP). Rats were divided into three groups: 1) diabetic controls (STZ+RS, n = 6), 2) recurrent hypoglycemia (STZ+RH, n = 7), and 3) recurrent hypoglycemia + metoclopramide (STZ+RH+MET, 3 mg/kg IP, n = 7). After 3 days, all rats underwent a hyperinsulinemic (50 mU/kg/min) and hypoglycemic (~45 mg/dl) clamp. In the clinical trial, adults with Type 1 diabetes (age 20–60, ≥5 years duration) were enrolled in a phase II, double-blinded, placebo-controlled trial. Awareness status was assessed via Gold score, and subjects maintained drug regimens and underwent two hyperinsulinemic-hypoglycemic clamps (where blood glucose was lowered to 100, 65, 55, and 45 mg/dl) to assess counterregulation. Results/Anticipated Results: In the pre-clinical model, glucose infusion rates (GIR) to maintain hypoglycemia were higher in STZ+RH (27±0.9 mg/kg/min) than STZ+RS (19±0.8 mg/kg/min, p Discussion/Significance of Impact: Metoclopramide improves glucoregulatory, sympathoadrenal, and counterregulatory responses to hypoglycemia in pre-clinical models, suggesting dopaminergic regulation. While clinical data are still blinded, increased epinephrine and growth hormone responses suggest treatment may preserve or restore counterregulation.

This study investigated associations between childhood neighborhood deprivation and adolescent mental health difficulties, and potential protective factors. Data were utilized from the Millennium Cohort Study (MCS) (born in 2000–2002; N = 5,422; 52% female) and the Environmental Risk (E-Risk) Longitudinal Twin Study (born in 1994–1995; N = 1,920; 53% female). Childhood neighborhood deprivation was measured using the Index of Multiple Deprivation between age 9 months and 14 years (MCS) and at age 12 (E-Risk). Adolescent mental health was assessed using the Strengths and Difficulties Questionnaire at age 17 (MCS) and the Diagnostic Interview Schedule conducted at age 18 with symptoms loading onto general psychopathology, internalizing and externalizing factors (E-Risk). Cross-classified models showed high levels of neighborhood deprivation in childhood were associated with more total problems (estimate = 0.46, 95% CI = 0.04–0.88) and internalizing difficulties (estimate = 0.32, 95% CI = 0.06–0.59) in adolescence within MCS. Being male, having higher self-esteem, greater social support, and a more positive parent-child relationship were associated with fewer total problems (estimates = −0.09–−1.87) and internalizing difficulties (estimates = −0.03–−1.88) at age 17 in the full sample regardless of neighborhood deprivation exposure. However, interactions revealed that higher self-esteem was especially beneficial for children exposed to high neighborhood deprivation (estimate = −0.35, 95% CI = −0.43–−0.27). No significant associations between childhood neighborhood deprivation and adolescent mental health symptoms were found in E-Risk. Interventions focused on improving self-esteem, social support, and parenting may help promote better adolescent mental health in the general population. Those living in the most deprived areas may benefit most from increased self-esteem.

Significant heterogeneity in network structures reflecting individuals’ dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME’s ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.

According to International Union for the Conservation of Nature (IUCN) guidelines, all species must be assessed against all criteria during the Red Listing process. For organismal groups that are diverse and understudied, assessors face considerable challenges in assembling evidence due to difficulty in applying definitions of key terms used in the guidelines. Challenges also arise because of uncertainty in population sizes (Criteria A, C, D) and distributions (Criteria A2/3/4c, B). Lichens, which are often small, difficult to identify, or overlooked during biodiversity inventories, are one such group for which specific difficulties arise in applying Red List criteria. Here, we offer approaches and examples that address challenges in completing Red List assessments for lichens in a rapidly changing arena of data availability and analysis strategies. While assessors still contend with far from perfect information about individual species, we propose practical solutions for completing robust assessments given the currently available knowledge of individual lichen life-histories.

Background: Surgical delays are in common in Canada. Wait times in elective spine surgery and their impact on outcomes remain uncharacterized. Methods: This was a single-center analysis of elective spine surgery data between 2009-2020. Wait times between referral and consultation (T1), consultation and surgical booking (Ti), and booking and surgery (T2) were assessed. Results: 2041 patients were included. Longitudinal analyses were adjusted for age, sex, diagnosis, surgical volume, while outcomes analyses were age and sex-adjusted. Total T1+Ti+T2 increased 8.1% annually (p<0.001). T1 decreased 4.3% annually (p=0.032). It was not associated with adverse events (AEs) or disposition. Every 100 days of T1 was associated with 1.0% longer hospitalization (p=0.001). Ti increased 21.0% annually (p<0.001). Every 100 days of Ti was associated with 2.9% increased odds of an adverse event (p=0.002), 1.8% longer hospitalization (p<0.001), and 15.9% increased likelihood of discharge home (p<0.001). T2 increased 7.0% annually (p<0.001) and was not associated with AEs. Every 100 days of T2 was associated with 11.6% longer hospitalization (p<0.001) and 76.5% increased likelihood of discharge home (p<0.001). Conclusions: Total wait times for elective spine surgery have increased between 2009-2020. Notably, Ti increased ninefold and was associated with AEs. This study highlights areas of delay and targets for healthcare optimization.

OBJECTIVES/GOALS: Contingency management (CM) procedures yield measurable reductions in cocaine use. This poster describes a trial aimed at using CM as a vehicle to show the biopsychosocial health benefits of reduced use, rather than total abstinence, the currently accepted metric for treatment efficacy. METHODS/STUDY POPULATION: In this 12-week, randomized controlled trial, CM was used to reduce cocaine use and evaluate associated improvements in cardiovascular, immune, and psychosocial well-being. Adults aged 18 and older who sought treatment for cocaine use (N=127) were randomized into three groups in a 1:1:1 ratio: High Value ($55) or Low Value ($13) CM incentives for cocaine-negative urine samples or a non-contingent control group. They completed outpatient sessions three days per week across the 12-week intervention period, totaling 36 clinic visits and four post-treatment follow-up visits. During each visit, participants provided observed urine samples and completed several assays of biopsychosocial health. RESULTS/ANTICIPATED RESULTS: Preliminary findings from generalized linear mixed effect modeling demonstrate the feasibility of the CM platform. Abstinence rates from cocaine use were significantly greater in the High Value group (47% negative; OR = 2.80; p = 0.01) relative to the Low Value (23% negative) and Control groups (24% negative;). In the planned primary analysis, the level of cocaine use reduction based on cocaine-negative urine samples will serve as the primary predictor of cardiovascular (e.g., endothelin-1 levels), immune (e.g., IL-10 levels) and psychosocial (e.g., Addiction Severity Index) outcomes using results from the fitted models. DISCUSSION/SIGNIFICANCE: This research will advance the field by prospectively and comprehensively demonstrating the beneficial effects of reduced cocaine use. These outcomes can, in turn, support the adoption of reduced cocaine use as a viable alternative endpoint in cocaine treatment trials.

Minority populations are largely absent from clinical research trials. The neglect of these populations has become increasingly apparent, with escalating cancer burdens and chronic disease. The challenges to recruitment of minorities in the United States are multiple including trust or lack thereof. Keys to successful recruitment are responding to community issues, its history, beliefs, and its social and economic pressures. The strategy we have used in many low-income, sometimes remote, communities is to recruit staff from the same community and train them in the required basic research methods. They are the first line of communication. After our arrival in the Texas Rio Grande Valley in 2001, we applied these principles learned over years of global research, to studies of chronic diseases. Beginning in 2004, we recruited and trained a team of local women who enrolled in a cohort of over five thousand Mexican Americans from randomly selected households. This cohort is being followed, and the team has remained, acquiring not only advanced skills (ultrasound, FibroScan, retinal photos, measures of cognition, etc.) but capacity to derive key health information. Currently, we are participating in multiple funded studies, including an NIH clinical trial, liver disease, obesity, and diabetes using multiomics aimed at developing precision medicine approaches to chronic disease prevention and treatment.

Background: Mountain biking (MTB) is an increasingly popular sport that has been associated with serious spinal injuries, which can have devastating effects on patients and significant impacts on healthcare resources. Herein, we characterized the occurrence of these MTB spinal injuries over a 15-year period and analyzed the affiliated acute-care hospital costs. Methods: Patients seen at Vancouver General Hospital for MTB spinal injuries between 2008-2022 were retrospectively reviewed. Demographics, injury details, treatments, outcomes, and resource requirements for acute hospitalization were collected. The Canadian Institute for Health Information was referenced for cost analysis. Results: Over the 15 years of analysis, 149 MTB spinal injuries occurred. The majority (87.2%) were male. 59 (39.6%) were associated with spinal cord injury; most of these were in the cervical spine (72.3%) and majority were AIS Grade A (36.1%). 102 patients (68.5%) required spine surgery; 26 (17.4%) required intensive care; 34 (22.8%) required inpatient rehabilitation. Mean length of stay was 13.5 days and acute admission costs for the healthcare system averaged $35,251 (95% CI $27,080-$43,424). Conclusions: MTB spinal injuries are associated with significant medical, personal, and financial burden. As injury prevention remains paramount, further investigation of the roles of education and safety measures is recommended.

Exclusion of special populations (older adults; pregnant women, children, and adolescents; individuals of lower socioeconomic status and/or who live in rural communities; people from racial and ethnic minority groups; individuals from sexual or gender minority groups; and individuals with disabilities) in research is a pervasive problem, despite efforts and policy changes by the National Institutes of Health and other organizations. These populations are adversely impacted by social determinants of health (SDOH) that reduce access and ability to participate in biomedical research. In March 2020, the Northwestern University Clinical and Translational Sciences Institute hosted the “Lifespan and Life Course Research: integrating strategies” “Un-Meeting” to discuss barriers and solutions to underrepresentation of special populations in biomedical research. The COVID-19 pandemic highlighted how exclusion of representative populations in research can increase health inequities. We applied findings of this meeting to perform a literature review of barriers and solutions to recruitment and retention of representative populations in research and to discuss how findings are important to research conducted during the ongoing COVID-19 pandemic. We highlight the role of SDOH, review barriers and solutions to underrepresentation, and discuss the importance of a structural competency framework to improve research participation and retention among special populations.

We present GECKOS (Generalising Edge-on galaxies and their Chemical bimodalities, Kinematics, and Outflows out to Solar environments), a new ESO VLT/MUSE large program. The main aim of GECKOS is to reveal the variation in key physical processes of disk formation by connecting Galactic Archaeology with integral field spectroscopic observations of nearby galaxies. Edge-on galaxies are ideal for this task: they allow us to disentangle the assembly history imprinted in thick disks and provide the greatest insights into outflows. The GECKOS sample of 35 nearby edge-on disk galaxies is designed to trace the assembly histories and properties of galaxies across a large range of star formation rates, bulge-to-total ratios, and boxy and non-boxy bulges. GECKOS will deliver spatially resolved measurements of stellar abundances, ages, and kinematics, as well as ionised gas metallicities, ionisation param- eters, pressure, and inflow and outflow kinematics; all key parameters for building a complete chemodynamical picture of disk galaxies. With these data, we aim to extend Galactic analysis methods to the wider galaxy population, reaping the benefits of detailed Milky Way studies, while probing the diverse mechanisms of galaxy evolution.

Background: Mean arterial pressure augmentation is one current established practice for management of patients with SCI. We present the first data investigating the effectiveness of Intrathecal Pressure (ITP) reduction through CSF drainage (CSFD) in managing patients with acute traumatic SCI at a large academic center. Methods: Data from 6 patients with acute traumatic SCI were included. A lumbar intrathecal catheter was used to monitor ITP and volume of CSFD. CSFD was performed and recorded hourly. ITP recordings were collected hourly and the change in ITP was calculated (hour after minus before CSFD). 369 data points were collected and change in ITP was plotted against volume of CSFD. Results: Data across all patients showed variability in the ITP over time without a significant trend (slope=0.016). We found no significant change in ITP with varying amounts of CSFD (slope=0.007, r2=0.00, p=0.88). Changes in ITP were not significantly different across groups of CSFD but the variation in the data decreased with increasing levels of CSFD. Conclusions: We present the first known data on changes in ITP with varying degrees of CSFD in patients with acute traumatic SCI. These results may provide insight into the complexity of ITP changes in patients post-injury and help inform future SCI management.

Background: Length of stay (LOS) is a surrogate for care complexity and a determinant of occupancy and service provision. Our primary goal was to assess changes in and determinants of LOS at a quaternary spinal care center. Secondary goals included identifying opportunities for improvement and determinants of future service planning. Methods: This is a prospective study of patients admitted from 2006 to 2019. Data included demographics, diagnostic category (degenerative, oncology, deformity, trauma, other), LOS (mean, median, interquartile range, standard deviation) and in-hospital adverse events (AEs). Results: 13,493 admissions were included. Mean age has increased from 48.4 (2006) to 58.1 years (2019) (p=<0.001). Mean age increased overtime for patients treated for deformity (p=<0.001), degenerative pathology (p=<0.001) and trauma (p=<0.001), but not oncology (p=0.702). Overall LOS has not changed over time (p=0.451). LOS increased in patients with degenerative pathology (p=0.019) but not deformity (p=0.411), oncology (p=0.051) or trauma (p=0.582). Emergency admissions increased overtime for degenerative pathologies (p=<0.001). AEs and SSIs have decreased temporally (p=<0.001). Conclusions: This is the first North American study to analyze temporal trends in LOS for spine surgery in an academic center. Understanding temporal trends in LOS and patient epidemiology can provide opportunities for intervention, targeted at the geriatric populations, to reduce LOS.