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.

Effective mentoring is a key mechanism propelling successful research and academic careers, particularly for early career scholars. Most mentoring programs focus on models pairing senior and early career researchers, with limited focus on peer mentoring. Peer mentoring may be especially advantageous within emerging areas such as implementation science (IS) where challenges to traditional mentoring may be more prevalent. This special communication highlights the value of peer mentoring by describing a case study of an early career IS peer mentoring group. We delineate our curriculum and structure; support and processes; and products and outcomes. We highlight important group member characteristics to consider during group formation and continuation. The group’s long-term (6 years) success was attributed to the balance of similarities and differences among group members. Members were in a similar career phase and used similar methodologies but studied different health topics at different institutions. Group members gave and received instrumental and psychosocial support and shared resources and knowledge. Peer mentoring can serve an important function to provide emotional, logistical, and professional development support for early career scholars. Our case study highlights strategies to foster peer mentoring groups that provide a generalizable blueprint and opportunity for improved outcomes for early career professionals.

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.

Youth are an understudied population requiring additional safeguards when participating in research. Their input is necessary to facilitate participation and interest in studies. To address this, Mayo Clinic established one of the first pediatric advisory boards (PAB) comprised of 18 diverse youth aged 11–17. The PAB members participated in quarterly meetings (in person and then by video conference with the advent of COVID-19) where they provided feedback to researchers on recruitment strategies, study materials, and procedures. The PAB meetings fostered bidirectional conversations with researchers on several health research topics, including mental health. Youth advisory boards can promote engagement in pediatric research.

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.

The commercialization of medical devices and biotechnology products is characterized by high failure rates and long development lead times particularly among start-up enterprises. To increase the success rate of these high-risk ventures, the University of Massachusetts Lowell (UML) and University of Massachusetts Medical School (UMMS) partnered to create key academic support centers with programs to accelerate entrepreneurship and innovation in this industry. In 2008, UML and UMMS founded the Massachusetts Medical Device Development Center (M2D2), which is a business and technology incubator that provides business planning, product prototyping, laboratory services, access to clinical testing, and ecosystem networking to medical device and biotech start-up firms. M2D2 has three physical locations that encompass approximately 40,000 square feet. Recently, M2D2 leveraged these resources to expand into new areas such as health security, point of care technologies for heart, lung, blood, and sleep disorders, and rapid diagnostics to detect SARS-CoV-2. Since its inception, M2D2 has vetted approximately 260 medical device and biotech start-up companies for inclusion in its programs and provided active support to more than 80 firms. This manuscript describes how two UMass campuses leveraged institutional, state, and Federal resources to create a thriving entrepreneurial environment for medical device and biotech companies.

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.