ABSTRACT IMPACT: The importance of this study is to evaluate the responses following the exposure of opioid drugs in young adults to address the current opioid epidemics OBJECTIVES/GOALS: To compare the proportion of patients between 18 and 25 years of age, who develop an opioid abuse disorder following dental surgery, to those following other surgical procedures, when an opioid drug is used for acute postoperative pain control. METHODS/STUDY POPULATION: We fashioned an IRB-sponsored retrospective cohort study of patients, ages 18 to 25 years old, who presented for dental surgery or other medical surgical procedures, at Strong Memorial Hospital Medical Center, at the University of Rochester and received opioid drug treatments, for acute surgical pain management. Patients with the diagnosis of acute surgical pain were included in the study. However, those with chronic pain or other related abnormalities were excluded, even if a diagnosis of acute surgical pain was present in the electronic chart. The clinical data were retrieved from electronic medical records and NYS-iStop records. All statistics were significant at the level of <0.1 RESULTS/ANTICIPATED RESULTS: We identified 167 subjects, of whom, only 150 subjects met inclusion criteria (n=100) in dentistry and (n=50) in other medical specialties. Patients were followed up in a 7-year period. Most of the subjects were females (n=91), Caucasian (n=80), and lived in a suburban location (n=78). The most frequently prescribed opioid included hydrocodone (n=119). A significantly higher proportion (8.7%) of patients developed opioid abuse disorders in the control group, compared to 1% of subjects in the experimental group (p 0.02). Those in the control group received marginally significant higher doses of MMEs 447.2 +/-644.8 mg vs 306.2 +/-354.7mg in the experimental group (p 0.086). Those in the control group had significantly longer periods of opioid treatment 10 (+/-12) compared to 6(+/-6) days in the experimental group DISCUSSION/SIGNIFICANCE OF FINDINGS: It is paramount to evaluate the morphine milligram equivalents, and duration of opioid treatment given to the young population with acute surgical pain, to prevent opioid abuse disorders in this vulnerable cohort.

ABSTRACT IMPACT: If successful, this program can provide a scalable, patient-centered intervention to help patients taper off opioid medications in primary care settings. OBJECTIVES/GOALS: Tapering of chronic opioid therapy is often desirable but challenging in primary care and specialty clinics that lack behavioral health expertise. The objective of this pilot study is to determine the feasibility of testing a peer-delivered pain self-management program to assist primary care patients through an opioid taper. METHODS/STUDY POPULATION: To provide critical support to patients and providers during opioid medication tapering, we propose to conduct a 40 patient randomized controlled pilot of a 12-week telecare collaborative care program administered by a psychiatrist and peer recovery specialist team. The intervention will incorporate a validated positive psychology intervention for treating chronic pain. Additionally, participants will be invited to participate in semi-structured individual interviews to discuss their experience in the trial, what worked well, what could be improved, and potential strategies to bolster recruitment of additional patients in future studies. RESULTS/ANTICIPATED RESULTS: Our primary aim is to determine the effectiveness of our intervention in facilitating opioid medication weaning, with reduction in opioid dose as the primary outcome. Our secondary aims will be to assess pain outcomes, adherence to tapering, patient satisfaction, and barriers to adherence as described by patients. DISCUSSION/SIGNIFICANCE OF FINDINGS: This trial proposes a novel collaborative care approach for opioid weaning using proven, easy-to-deliver positive psychology tools for pain management that, if successful, could be implemented broadly in many clinics struggling to safely reduce opioid prescribing.

ABSTRACT IMPACT: The findings from this study will inform the development of an FDA-approved patient-rated outcome measure of drug and alcohol craving that can be used in clinical trials aimed at developing or testing effective treatments for substance use disorder. OBJECTIVES/GOALS: Craving is a potential target of investigative medications to reduce drug use due to the strong link between craving and drug use. We will identify all existing craving measures as the first step for developing an FDA-approved patient-rated outcome measure for use in clinical trials. METHODS/STUDY POPULATION: Following PRISMA guidelines, we will update Rosenberg’s (2009) craving review by conducting a systematic review of all existing published and unpublished measures of craving for alcohol, nicotine, cannabis, opioid, and stimulant use. Electronic database (i.e., Ovid MEDLINE, Embase, PsycINFO, Web of Science, Cochrane), forward, backward, and author searches will be conducted. We will also request unpublished craving measures on major listservs (e.g., Research Society on Alcoholism, the Collaborative Perspectives on Addiction, and the College on Problems of Drug Dependence). All papers included in Rosenberg’s (2009) review through September 2020 will be included. RESULTS/ANTICIPATED RESULTS: The findings from this review will provide a comprehensive summary of the construct of craving and its hypothesized and tested domains. This review will elucidate whether the literature suggests there are components of craving unique to alcohol, nicotine, cannabis, opioid, and/or stimulant use, and whether there are key elements of craving common across the disorders. Therefore, these findings will inform whether a single patient-rated outcome measure of craving can be developed for use across substances or if unique patient-rated outcome measures of craving need to be developed for each substance. DISCUSSION/SIGNIFICANCE OF FINDINGS: While many different measures of craving exist, none have gone through the developmental steps required to qualify as an FDA-approved patient-rated outcome measure on which drug treatment labeling can be based. Completing this systematic review is the first step in this process.

ABSTRACT IMPACT: Investigation of tumor-associated blood vessels may serve as an imaging biomarker of response to systemic therapy and cancer outcomes. OBJECTIVES/GOALS: Aberrancies in the tumor microvasculature limit the systemic delivery of anticancer agents, which impedes tumor response. Using human intravital microscopy (HIVM), we hypothesized that HIVM would be feasible in patients with peritoneal carcinomatosis (PC) and generate clinical utility. METHODS/STUDY POPULATION: During cytoreductive surgery with hyperthermic intraperitoneal chemotherapy for PC, HIVM was performed in both tumor and non-tumor areas. The primary outcome was HIVM feasibility to measure vessel characteristics. We secondarily evaluated associations between HIVM vessel characteristics and oncologic outcomes (RECIST response to neoadjuvant therapy and disease-specific survival). RESULTS/ANTICIPATED RESULTS: Thirty patients with PC were enrolled. Nineteen patients (63.3%) received neoadjuvant therapy. HIVM was feasible in all patients. Compared to non-tumor (control) areas, PC areas had a lower density of functional vessels, higher proportion of non-functional vessels, smaller lumenal diameters, and lower blood flow velocity. Qualitative differences in these vessel characteristics were observed among patients who had partial response, stable disease, or progressive disease after receiving neoadjuvant therapy. However, no statistically significant relationships were found between HIVM vessel characteristics and oncologic outcomes. DISCUSSION/SIGNIFICANCE OF FINDINGS: These novel findings comprise the first-in-human, real-time evidence of the microscopic differences between normal and tumor-associated vessels and form the basis for our larger, ongoing clinical trial appropriately powered to determine the clinical utility of HIVM (NCT03823144).

ABSTRACT IMPACT: Our research has the potential to impact human health by identifying gender specific neural markers of emotion regulation in alcohol use disorder. OBJECTIVES/GOALS: Emotion dysregulation is known to be mediated by altered functional organization of the limbic system in addiction. This preliminary study sought to identify gender effects in the association between emotion regulation and resting-state functional connectivity (rsFC) of a negative affect network. METHODS/STUDY POPULATION: 55 individuals receiving treatment for alcohol use disorder (˜2 weeks of abstinence) were recruited for this study and included in this analysis (N=55; Age: M=41.78, SD=10.66; 21 females). RsFC within a network involved in the withdrawal/negative affect stage of addiction and Personality Inventory for DSM-5 (PID-5) metrics were collected from all participants. RsFC data were preprocessed using the Human Connectome Project pipelines. Correlations between (a) rsFC within the withdrawal/negative affect network and the (b) scores of the negative affect subscale of the PID-5 instrument were conducted for each gender separately. RESULTS/ANTICIPATED RESULTS: Independent samples t-test showed a statistically significant gender difference in the PID-5 negative affect scores (Males: M=1.02, SD=0.66; Females: M=1.53, SD=0.51); t(55)=-3.002, p=0.004. Only females showed a significant correlation between rsFC within the withdrawal/negative affect network and negative affect scores of the PID-5 (r=0.51, p<0.05). Fisher r-to-z test showed significant gender differences (z=-1.91; p=0.03, 1-tailed) in correlations coefficients representing the relationship between rsFC of the withdrawal/negative affect network and negative affect (PID-5 subscale). DISCUSSION/SIGNIFICANCE OF FINDINGS: Preliminary findings suggest that the relationship between neural networks mediating emotion regulation and negative affect is only found in females. These results provide valuable data to inform personalized chemical dependency treatment that targets emotion regulation specific to females.

ABSTRACT IMPACT: Our research has the potential to impact human health by identifying a neural network that can be used to predict time to relapse in individuals with alcohol use disorder. OBJECTIVES/GOALS: Preoccupation towards alcohol use (e.g. craving, rumination, and poor executive control) is a maladaptive behavior associated with relapse risk. We investigated whether alterations in resting state networks known to mediate preoccupation could predict time to relapse in alcohol use disorder (AUD). METHODS/STUDY POPULATION: 50 participants with alcohol use disorder (AUD) (Age: M=41.76, SD=10.22, 19 females) were recruited from an addiction treatment program at ˜2 weeks of abstinence. fMRI data were preprocessed with the Human Connectome Project pipeline. Strength of resting state functional connectivity (RSFC) within two networks known to mediate the ‘Preoccupation go’ (PG) and ‘Preoccupation stop’ (PS) stages of addiction were calculated. T-tests were conducted to compare RSFC between subsequent abstainers and relapsers (after 4 months). Linear regressions were conducted to determine whether RSFC (of PG and PS networks) can predict time to relapse. Craving measures were included in the model. RESULTS/ANTICIPATED RESULTS: 19 AUD relapsed during the 4-month follow-up period. There were no RSFC group effects (subsequent abstainers and relapsers) in the PG or PS networks. Number of days to relapse could be predicted by PG RSFC (F(1,17)=14.90, p=0.001, r 2=0.47). Time to relapse increased by 13.19 days for each PG RSFC unit increase. Number of days to relapse could be predicted by PS RSFC (F(1,17)=9.39, p=0.002, r ²=0.36). Time to relapse increased by 12.94 days for each PS RSFC unit increase. After adding a self-report craving measure (i.e. Penn Alcohol Craving Scale) in the prediction model, both PG and PS RSFC still significantly predicted time to relapse. Craving metric did not predict time to relapse. DISCUSSION/SIGNIFICANCE OF FINDINGS: RSFC in preoccupation networks during short-term abstinence predicted time to relapse. These preliminary findings highlight promising targets for AUD neuromodulation interventions aimed to reduce relapse. Future larger scale studies that examine the effects of covariates and mediators are needed.

ABSTRACT IMPACT: We are adapting a global telehealth platform and model of care to the U.S. context in order to solve the problem of undertreatment of age-related hearing loss and, in turn, facilitate healthy aging and social engagement among older adults. OBJECTIVES/GOALS: Intelehealth is a nonprofit startup that provides medical care to last-mile populations in India by equipping frontline health workers with an open-source digital assistant and telemedicine platform. Here, we explore how this technology and model of care might be adapted to address health inequities in the context of the U.S. healthcare system. METHODS/STUDY POPULATION: We first sought to identify a specific healthcare need that we could address as a case study on applying the Intelehealth model more broadly in the U.S. context. We began with a needs assessment, wherein we conducted primary ethnographic research, expert interviews, and literature review to identify problems in the general areas of health disparities, community health workers, and telemedicine accessibility. We then scored each need on clinical impact, feasibility, business potential, and strategic fit. After a top need was selected, a root cause analysis was performed. Brainstorming and solution concepting will be followed by prototyping, iterative design with primary stakeholder feedback, usability testing, and finally implementation and validation of the solution. RESULTS/ANTICIPATED RESULTS: Of 106 needs, the most highly scored was undertreatment of age-related hearing loss (ARHL). The third most common chronic condition in the U.S., ARHL presents a significant barrier to healthy aging and the single largest modifiable risk factor for dementia; yet only 15% of those with ARHL regularly use hearing aids. Thus, a large market segment - nearly 30 million Americans - is underserved by the current hearing care paradigm. Root cause analysis revealed that the primary reasons for hearing aid non-use include stigma around aging, denial of hearing loss, poor awareness of resources, and insufficient education around proper use and maintenance. These barriers, being primarily sociocultural in nature, may be optimally addressed by community health workers, making ARHL an ideal fit for the Intelehealth model. DISCUSSION/SIGNIFICANCE OF FINDINGS: We have identified ARHL as an optimal test market for Intelehealth in the U.S. By developing a targeted intervention to improve hearing aid access and acceptability among older adults, we will create a generalizable model for delivering care through community health workers equipped with a decision support and telemedicine platform.

ABSTRACT IMPACT: ReacStick concussion testing and monitoring can serve as a 'vital sign for the brain', allowing for an immediate, objective assessment on the field or at the bedside. This project examines the entrepreneuship process from invention to commercialization. OBJECTIVES/GOALS: ReacStick is the first objective, portable, measure of concussion likelihood and severity and uses simple and complex reaction time testing. We detail the entrepreneurship process from product invention through its current mid-stage (patented, 20+ publications, etc.) to future commercialization for diverse applications. METHODS/STUDY POPULATION: ReacStick was invented in 2010 and underwent extensive testing and validation of the underlying innovations. The regulatory landscape of the product was examined, and 510(k) was found to be the best pathway. Competitive analysis was done examining alternative products and comparing against the current gold standards. A customer discovery process was undertaken, and stakeholders were interviewed for feedback and iteration. Testing and validation were completed with athletes, older adults, and people taking medications. An overview of the necessary commercialization concepts is: market opportunity/monetization, intellectual property considerations, regulatory processes, commercialization plan. RESULTS/ANTICIPATED RESULTS: ReacStick accurately predicts concussion and time to recovery and was patented through UM Tech Transfer in 2010, with 10 years currently remaining on the patent. Through customer discovery processes, athletics was determined to be the most viable first market to enter. Next steps include seeking additional patent protection, capital investors, delivery of minimum viable product followed by iteration and improvement for military, emergency medicine and acute care use. The current remaining timeline involves 12-18 months to commercialization and includes regulatory approval, additional patent protection, collaboration with regulatory consultants, capital fundraising and product production. DISCUSSION/SIGNIFICANCE OF FINDINGS: The research team has gone through a lengthy process toward commercialization of ReacStick. Proof of concept and extensive validation of the underlying technology have been completed and the regulatory process has been mapped. Our experience can serve as a model of many of the steps and challenges that lie on the path from lab to sale to end users.

ABSTRACT IMPACT: The results from this study will improve needs assessment practices. OBJECTIVES/GOALS: The discovery phase in project development is necessary to better understand the needs and requirements of the intended market. This paper compares the outcomes of two virtual data collection methodologies, NSF I-Corps Customer Discovery interviews and REDCap surveys, for a needs assessment. METHODS/STUDY POPULATION: Clinical and Translational Science Award (CTSA) Directors and Academic Administrators across the Consortium were asked about the types of skills needed to assess clinical research professional competencies and the need for a competency-based self-assessment tool (CBST). Parallel methods were used to extract qualitative and quantitative data. The first approach was to conduct interviews using I-Corps customer discovery guidelines, and data was collected using Innovation Within software. Targeted requests were sent via cold email outreach to 102 individuals within 63 CTSA hubs. The second approach involved the use of the NJ ACTS Training and Education Offering Inventory REDCap Survey which was distributed via LISTSERV to 63 CTSA hubs. Response rates and user insights from each method were compared. RESULTS/ANTICIPATED RESULTS: Twenty-one of 63 CTSA hubs responded to the survey (response rate: 33%) while 18 of 63 hubs participated in an interview (response rate: 28%). Twenty-two individuals out of 102 were interviewed (response rate: 21%). Fifty-nine percent of interviewees and 62% of survey respondents indicated a clear need for a CBST; types of responses varied. Forty user insights were obtained from ten interviews. Two insights were gained in the survey from the eight who were prompted to fill out the free-text response. Both survey participants and interviewees indicated that communication and team science soft skills were the most important competencies. Regarding hard skills, interviewees preferred written skills while survey participants favored ‘scientific design and concept’ skills. DISCUSSION/SIGNIFICANCE OF FINDINGS: Results suggest the use of a survey or an interview for a needs assessment is dependent on several factors: need for insights, burden of time, desire to obtain quantitative vs. qualitative data, and question format. The interview was more effective than the survey in addressing the key question and obtaining insights from the intended market.

ABSTRACT IMPACT: Improved non-biased matching of clinical research professionals to PI needs will accelerate time to active project engagement for new hires. OBJECTIVES/GOALS: An ongoing challenge for HR recruiters when matching applicants to open job positions is the time-consuming screening effort, which relies on imprecise semantic searching. We propose building a precision-based matching tool using Natural Language Processing to automate the accurate and non-biased identification of suitable job candidates. METHODS/STUDY POPULATION: We conducted 30-45’ interviews with HR administration/recruitment specialists to delineate the recruitment and hiring process used to match CRC resumes to job descriptions (n=7). Next, CRC applicant resumes were evaluated by experts, first by independent review, followed by consensus and assignment of a final rating, 0= not qualified; 1= CRC1; 2= CRC2; 3= CRC3; 4= CRC4. Guidelines evolved after reviewing 6 batches of 50 unique resumes (300 total) and were based on applicant qualifications & experiences by job level, CRC 1-4. Using final guidelines an additional 3,145 resumes were rated. For uniform input into the NLP model, resume formats were converted and text contents extracted into multiple sections, i.e., education, professional experiences, etc. RESULTS/ANTICIPATED RESULTS: Guideline development: Rater agreement improved over time with poor agreement when no guidelines were present (.161- Kappa) to good agreement for final guidelines (.608- Kappa). Spearman’s rho correlation between guideline iterations and Kappa is large and positive (rho 0.886) indicating significant rater agreement. NLP Model: Resume to job description matching indicated a third of applications were qualified, a third overqualified, and a third underqualified, suggesting the majority of applicants were unable to identify their ‘best fit’ by job level. Our NLP model matched the candidate resume to CRC level with 73.3% accuracy; and achieved 79.2% accuracy when matching the applicant resume to the CRC job description. Refinement of the NLP Model is ongoing. DISCUSSION/SIGNIFICANCE OF FINDINGS: A precision-based NLP matching tool will improve applicant targeting for the hire of great, qualified candidates. Improved applicant to job matching offers several advantages, i.e., reduced bias with greater diversity and inclusion; reduced time-to-hire; ability to anticipate training needs; and a reduced time to active project engagement.

ABSTRACT IMPACT: Improved radiation treatment will yield higher doses at the tumor site, while reducing damage to healthy tissue, which will improve clinical outcomes. OBJECTIVES/GOALS: Development of gold nanoparticles covalently linked to a photosensitizer for use to enhance radiation therapy. The particles will be thoroughly characterized and the mechanism uncovered. The efficacy of these particles will be tested in a murine system. METHODS/STUDY POPULATION: Gold nanoparticles were synthesized and coated with amine-terminated poly(ethylene) glycol then covalently conjugated to chlorin e6, a known, FDA approved photosensitizer. The system was characterized using UV-Vis spectroscopy, transmission electron microscopy, and nanoparticle tracking analysis. The generation of reactive oxygen species following X-irradiation was measured. Enhanced cell killing was measured clonogenically and in vivo efficacy and tumor pathology was assessed in a murine system. Further studies will determine the optimum combination of particle shape, photosensitizer structure, and ratio of components, as well as the optimal dosing schedule. RESULTS/ANTICIPATED RESULTS: Conjugation of the particle to the photosensitizer was successfully achieved, and the molecule was detectable by UV-Vis spectroscopy. TEM and NTA showed no aggregation of the particles, and an increase in reactive oxygen species generation was observed. The conjugates significantly increased cell killing during radiation treatment, while neither the particle alone or the photosensitizer significantly affected clonogenic survival at the same concentrations. Pathology of breast tumors grown in immunocompetent mice showed a significant increase in necrotic tissue following a single 20 gy treatment when the conjugate was present. DISCUSSION/SIGNIFICANCE OF FINDINGS: Radiation therapy is widely used clinically and it is a highly localized form of treatment. However, the total dose of radiation is limited largely to prevent injury to adjacent normal tissue. This conjugate has the potential to increase the effective dose in the tumor thereby reducing damage to healthy tissue and providing a more effective therapy.

ABSTRACT IMPACT: The purpose of the T1-T4 in 3 Minutes program is to improve trainees’ capacity for communication of complex to a non-scientific audience as well as to ensure that our community stakeholders have access to, and understanding of, ongoing clinical and translation research OBJECTIVES/GOALS: The T1-T4 in 3 Program: ο Increases knowledge of research across institution; ο Increases capacity of trainees to convey complex science to lay audiences, funders, colleagues, and the media; ο Increases health and scientific literacy; ο Bridges gaps between trainees and potential entrepreneurial mentors METHODS/STUDY POPULATION: T1-T4 in 3 (Minutes) is an adaptation of the University of Queensland’s Three Minute Thesis competition in which PhD students present their thesis in 3 minutes or less to a lay audience. The competition enables them to cogently communicate their ideas and research findings to a non-specialist audience. Our adapted version, T1-T4 in 3, requires a presentation in three minutes or less to a lay audience, but rather than a thesis, the topics are on trainees’ research, and in this particular case, an idea for a commercial venture. The competition provides awards for the first- and second-best projects as determined by a panel of judges, and a ‘people’s choice’ award determined by a lay audience. RESULTS/ANTICIPATED RESULTS: This exercise is anticipated to improve trainees’ capacity for communications as well as ensure that community stakeholders and research and business community partners have access to, and understanding of, ongoing clinical and translation research with potential commercial applications. Further, the increased ability of our faculty and trainees to effectively communicate complex science to the public and other audiences’‘ including potential funders’‘ supports additional stakeholder dissemination mechanisms by increasing their confidence in their abilities to converse with non-specialists about their research, thus increasing the likelihood of participation in other community-based activities. DISCUSSION/SIGNIFICANCE OF FINDINGS: To increase ITS commercialization efforts, we envision involving numerous external partners to educate, fund, and support new ventures. T1-T4 in 3 judges will include commercialization scholars from regional and national institutions as well as pharmaceutical entities and regional angel investors.

ABSTRACT IMPACT: This work aims to identify best practices for university-based asset development programs to improve commercialization throughput, which in turn will drive innovation in the biomedical space and directly contribute to improved human health. OBJECTIVES/GOALS: University technology transfer exhibits a high rate of failure, often due to a lack of researcher experience or early-stage financial capital. The LEAP program at Washington University (WUSTL) was created to address these needs. The goal of this study is to assess the performance of LEAP against similar gap funds and further improve program operations. METHODS/STUDY POPULATION: The goals of LEAP are achieved by providing university inventors with individualized consulting and feedback from industry experts, as well as awarding funding to the most promising projects. To determine whether these activities are impactful, we distributed an awardee report form to collect data on all funded LEAP projects, and then combined the results with project registration information. We also collected records Office of Technology Management, including invention disclosures, licenses, and startup creations. The resulting dataset was used to calculate program metrics and then evaluated against comparable gap funds. Sentiment data from participant surveys were also analyzed to assess perceived program value and knowledge transfer. RESULTS/ANTICIPATED RESULTS: As of the Sp2020 cycle, LEAP has funded 76 projects. Resubmitted projects had a funding rate of 52%, vs. 34% for new projects. Of the startups founded off of WUSTL intellectual property since 2016, nearly two-thirds had previously participated in LEAP. Funded LEAP projects also had a 29% licensing rate, which is comparable to similar gap funds. Lastly, participants self-reported an increase in knowledge across a range of commercialization areas. DISCUSSION/SIGNIFICANCE OF FINDINGS: The increased repeat funding rate and self-reported knowledge suggest that LEAP is impactful in building commercialization proficiency. The licensing rate and prevalence of LEAP projects in WUSTL startups also indicate that LEAP is indeed promoting tech transfer. Together, these results suggest that LEAP could be a model for other institutions.

ABSTRACT IMPACT: This work will standardize necessary image pre-processing for diagnostic and prognostic clinical workflows dependent on quantitative analysis of conventional magnetic resonance imaging. OBJECTIVES/GOALS: Conventional magnetic resonance imaging (MRI) poses challenges for quantitative analysis due to a lack of uniform inter-scanner voxel intensity values. Head and neck cancer (HNC) applications in particular have not been well investigated. This project aims to systematically evaluate voxel intensity standardization (VIS) methods for HNC MRI. METHODS/STUDY POPULATION: We utilize two separate cohorts of HNC patients, where T2-weighted (T2-w) MRI sequences were acquired before beginning radiotherapy for five patients in each cohort. The first cohort corresponds to patients with images taken at various institutions with a variety of non-uniform acquisition scanners and parameters. The second cohort corresponds to patients from a prospective clinical trial with uniformity in both scanner and acquisition parameters. Regions of interest from a variety of healthy tissues assumed to have minimal interpatient variation were manually contoured for each image and used to compare differences between a variety of VIS methods for each cohort. Towards this end, we implement a new metric for cohort intensity distributional overlap to compare region of interest similarity in a given cohort. RESULTS/ANTICIPATED RESULTS: Using a simple and interpretable metric, we have systematically investigated the effects of various commonly implementable VIS methods on T2-w sequences for two independent cohorts of HNC patients based on region of interest intensity similarity. We demonstrate VIS has a substantial effect on T2-w images where non-uniform acquisition parameters and scanners are utilized. Oppositely, it has a modest to minimal impact on T2-w images generated from the same scanner with the same acquisition parameters. Moreover, with a few notable exceptions, there does not seem to be a clear advantage or disadvantage to using one VIS method over another for T2-w images with non-uniform acquisition parameters. DISCUSSION/SIGNIFICANCE OF FINDINGS: Our results inform which VIS methods should be favored in HNC MRI and may indicate VIS is not a critical factor to consider in circumstances where similar acquisition parameters can be utilized. Moreover, our results can help guide downstream quantitative imaging tasks that may one day be implemented in clinical workflows.

ABSTRACT IMPACT: This work will be used to improve the design of engineered dermal replacements that can be used to treat difficult-to-heal wounds such as burns or ulcers. OBJECTIVES/GOALS: Wounds of the skin are among the most common and costly medical problems experienced. Engineered dermal replacements have been developed to improve outcomes, but the optimal design features are unknown. Here we describe a hypothesis-driven study of scaffold parameters using a computational model of wound healing to simulate a variety of treatments. METHODS/STUDY POPULATION: The computational model, which was informed by animal data, was used to simulate cell, cytokine, and collagen density fields. There are reciprocal mechanobiological interactions between the cells and collagen that guide the wound healing process. We analyzed initial wound properties such as scaffold stiffness, microstructure, degradation, and wound geometry by running a one-at-a-time order-of-magnitude parameter change. We then conducted a derivative-based local sensitivity analysis for simulated experimental conditions and constructed a surrogate model of wound contraction using Gaussian process regression. RESULTS/ANTICIPATED RESULTS: We conducted finite element model simulations of scaffolds that varied in physical properties. A sensitivity analysis demonstrated that wound contraction was highly sensitive to collagen fiber stiffness and density. Wound contraction rate was also dependent on initial wound size and surface area. Collagen fiber orientation in the scaffolds affected contraction directionality and the orientation of the final wound area. A Gaussian process regression model was fit to the simulation results for use in rapid prototyping of scaffolds for design optimization. The Gaussian process model was able to reproduce the wound contracture for training and test cases. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work further analyzes a computational model of wounds treated with collagen scaffold dermal replacements. The hypothesis driven analysis of the model suggested several key design features of scaffolds. The model surrogate will further be used for the purposes of prediction and optimization of tissue regeneration outcomes.

ABSTRACT IMPACT: Current, complete, unbiased, and accurate information, which includes patient social and environmental context, is necessary to understand health outcomes. OBJECTIVES/GOALS: Literacy in technology empowers patients in improving their health. We hypothesize that by integrating this information into clinical information, obfuscated relationships may become apparent. To test this, we have combined technological literacy elements with clinical data and test results from patients at risk for severe COVID-19 reactions. METHODS/STUDY POPULATION: Zip level data was appended to approximately 55,000 clinical records of COVID-19 positive and negative patients with comorbidities linked to high illness severity (e.g., diabetes, heart disease). The patient zip code was matched to zip code level health and technology interest indicators. Health interest indicates the level of interest in health such as research, exercising, better dieting, preventive care, etc., and is ranked from 0 to 9. The technology interest indicator is a binary flag indicating technology adopters. These lifestyle factor data points were obtained from survey data and purchasing patterns using transactional and response information from self-reported sources. For each zip code, the index values were represented by a percentage of that population. RESULTS/ANTICIPATED RESULTS: There is a pronounced difference between urban and rural areas with respect to interest in health and technology. In neighborhoods where most residents are interested in both health and technology, the percentage of COVID-19 cases was smaller. A Wilcoxon signed-rank test indicated that the distributions were statistically different (p-value = 4.606e-06) when evaluating the low interest values for health and technology, and (p-value = 1.069e-09) when there was high interest in health and technology against COVID-19 results. In addition, the health and technology indicator variables are not correlated with income at the zip code level. At the low index values, interest in health and technology, the correlation was -0.0856, and at the high end, the correlation was -0.0436. DISCUSSION/SIGNIFICANCE OF FINDINGS: This result is significant for COVID-19 research because it describes a methodology for identifying patients who may be at higher risk for contracting the disease. This relationship was reflected in electronic health record data only after zip-level information was added. Moreover, this was true at across income levels.

ABSTRACT IMPACT: A one-stage Bayesian multilevel model for meta-analysis integrating different survival data is introduced to complete the information synthesis without assuming proportional hazard. OBJECTIVES/GOALS: To develop a general modeling approach to perform efficient and robust meta-analyses using aggregated data (AD) for survival type endpoint and apply to a meta-analysis to examine the association between measurable residual disease (MRD) and disease-free survival (DFS) and overall survival (OS) in patients with acute myeloid leukemia (AML). METHODS/STUDY POPULATION: A Bayesian semi-parametric hierarchical model with a time-varying HR effect was presented. Three common types of survival information, including reconstructed survival data, the hazard ratio (HR) estimates with corresponding CIs and survival rates at specific time points, are synthesized such that all literature from the systematic review can contribute properly to the estimation and uncertainty quantification of the model parameters. The time-varying effects was modeled by assuming piecewise hazard risk and piecewise constant hazard ratio. The heterogeneity across studies was expressed by an additive random study effect and a random treatment-by-study interaction. The method was applied to a systematic review of 81 publications reporting on 11,151 AML patients. RESULTS/ANTICIPATED RESULTS: In simulation studies that the proportional hazard assumption is either valid or violated, the proposed method was efficient to achieve comparable performance to IPD meta-analysis, a gold standard approach, in estimating the survival rates, the restricted mean survival time at specific time points and median survival time with the point estimates close to the true values. When HR is not proportional over time, the proposed method was robust in estimating HR and significantly outperformed the classical random-effects meta-analysis. In the application to AML study, the average HR for achieving MRD negativity was 0.36 (95% CrI, 0.33-0.39) for OS and 0.37 (95% CrI, 0.34-0.40) for DFS. The association of MRD negativity with OS and DFS was significant and consistent across all subgroups. DISCUSSION/SIGNIFICANCE OF FINDINGS: The proposed novel method provided a flexible framework for meta-analysis of survival data, to accommodate various types of survival data in one model without assuming proportional hazards assumption. The findings of AML meta-analysis suggest that achievement of MRD negativity is associated with superior DFS and OS in patients with AML.

ABSTRACT IMPACT: This work will discuss informatics methods enabling the use of exposure health data in translational research. OBJECTIVES/GOALS: 1. Characterize gaps and formal informatics methods and approaches for enabling use of exposure health in translational research. 2. Education of informatics methods enabling use of exposure health data in translational research. METHODS/STUDY POPULATION: We performed a scoping review of selected literature from PubMed and Scopus. In addition we reviewed literature and documentation of projects using exposure health data in translation research. RESULTS/ANTICIPATED RESULTS: Primary challenges to use of exposure health data in translational research include: (1) Generation of comprehensive spatio-temporal records of exposures, (2) Integration of exposure data with other types of biomedical data, and (3) Uncertainties associated with using data as exact quantifications of exposure which are dependent on both - the proximity of measurement to subject under consideration and the capabilities of measuring devices. We identified 9 major informatics methods that enable incorporation and use of exposure health data in translational research. While there are existing and ongoing efforts in developing informatics methods for ease of incorporating exposure health in translational research, there is a need to further develop formal informatics methods and approaches. DISCUSSION/SIGNIFICANCE OF FINDINGS: Depending on the source about 50 - 75% of our health can be quantified to be a contribution of our environment and lifestyles. In this presentation, we summarize the studies and literature we identified and discuss our key findings and gaps in informatics methods and conclude by discussing how we are covering these topics in an informatics courses.

ABSTRACT IMPACT: Improve healthcare of patients with Cystic Fibrosis by reducing the time needed to generate results. OBJECTIVES/GOALS: We developed an automated framework capable of segmenting the lungs, extract the airways, and create a skeletonize map of the airways from CT scans of Cystic Fibrosis patients. As future expansion, the framework will be expanded to measure the airways diameters, detect the abnormal airways, and count the number of visible airways generations. METHODS/STUDY POPULATION: For this study, 35 CT scans from CF patients with different levels of severity were used to test the developed framework. The lungs segmentation was performed using an algorithm based on Gaussian Mixture Models for mild cases, and for severe cases a technique that uses convex hull and the recurrent addition of ‘dots’ was implemented. The airways extraction was performed using a 26-points connected components algorithm in conjunction with a curve fitting technique over the histogram of voxel values. Medial axis transform was used to perform the skeletonization of the extracted airways, and airways diameters determined via ray-casting. RESULTS/ANTICIPATED RESULTS: The framework was able to correctly obtain the segmented lungs in all 35 sample volumes regardless of disease severity. In contrast, it tends to fail to skeletonize the airways for severe cases where the framework is unable to differentiate between abnormal lungs conditions and dilated airways. Fine tuning is required to achieve better results. The expected result of the future implemented sections of the framework are focused to characterize the extracted airways by: 1) measuring the airways diameters; 2) detect and count the number of abnormal airways sizes; and 3) count the number of visible airways branching which will permit determination of stage and grade of the lungs of CF patients. DISCUSSION/SIGNIFICANCE OF FINDINGS: The proposed framework allows a fast and reproducible way to segment the lungs and create a skeletonized map of the airways that are independent of clinical training. In addition, this framework will be extended to obtain measurements of airway dilation and branching level, which could provide a deeper insight of the airways in CF patients.

ABSTRACT IMPACT: We explored the use of machine learning to explore how multi-pollutant air quality is related to type 2 diabetes, which is more representative than the single pollutant models often employed to assess this relationship. OBJECTIVES/GOALS: Single pollutant air pollution models have correlated air pollution components with type 2 diabetes mellitus (DM). However, air pollution is a complex mixture, therefore, we explored the relationship between multi-pollutant air quality and DM incidence using machine learning. METHODS/STUDY POPULATION: Annual diabetes incidence from the CDC for each US county was downloaded for the years 2007-2016. Daily air pollution concentrations for PM2.5, PM10, CO, SO2, NO2, and O3 were downloaded from the US EPA for the years 2006-2015. K-means clustering, an unsupervised machine learning method, was employed to partition all air pollution components, for each day and county monitored, into the optimal number of clusters. Change in DM incidence was matched to air pollution clusters by county, lagged by one year. Additionally, NASA satellite-derived air pollution data will be compared to EPA data to inspect as a potential source for future clustering analysis of counties that do not have an EPA monitor. RESULTS/ANTICIPATED RESULTS: The largest increase of annual DM incidence was associated with the cluster having the highest average PM10, PM2.5, and CO, and the second greatest average NO2 concentrations. Inversely, the most significant decrease of annual DM incidence was associated with the cluster having the lowest PM10, PM2.5, and CO. While average PM10, PM2.5, SO2, NO2, and CO showed a rising tendency with elevating change of DM incidence, ozone did not show any such trend. It is anticipated that the NASA satellite-derived air pollution data will approximate the EPA air quality data and will be usable in assessing the air pollution-DM relationship for areas currently not monitored by the EPA. DISCUSSION/SIGNIFICANCE OF FINDINGS: Using an unsupervised k-means algorithm, we showed multiple ambient air components were related to increased incidence of T2DM even when average concentrations were below the National Ambient Air Quality Standards. This work could help guide policy making regarding air quality standards in the future.