This paper is part of the Environmental Scan of Adaptive Capacity and Preparedness of Clinical and Translational Science Award (CTSA) hubs, illuminating challenges, practices, and lessons learned related to CTSA hubs’ efforts of engaging community partners to reduce the spread of the virus, address barriers to COVID-19 testing, identify treatments to improve health outcomes, and advance community participation in research. CTSA researchers, staff, and community partners collaborated to develop evidence-based, inclusive, accessible, and culturally appropriate strategies and resources helping community members stay healthy, informed, and connected during the pandemic. CTSA institutions have used various mechanisms to advance co-learning and co-sharing of knowledge, resources, tools, and experiences between academic professionals, patients, community partners, and other stakeholders. Forward-looking and adaptive decision-making structures are those that prioritize sustained relationships, mutual trust and commitment, ongoing communication, proactive identification of community concerns and needs, shared goals and decision making, as well as ample appreciation of community members and their contributions to translational research. There is a strong need for further community-engaged research and workforce training on how to build our collective and individual adaptive capacity to sustain and improve processes and outcomes of engagement with and by communities—in all aspects of translational science.

Neonatal sepsis is a major cause of death and disability in newborns. Commonly used biomarkers for diagnosis and evaluation of treatment response lack sufficient sensitivity or specificity. Additionally, new targets to treat the dysregulated immune response are needed, as are methods to effectively screen drugs for these targets. Available research methods have hitherto not yielded the breakthroughs required to significantly improve disease outcomes, we therefore describe the potential of zebrafish (Danio rerio) larvae as preclinical model for neonatal sepsis. In biomedical research, zebrafish larvae combine the complexity of a whole organism with the convenience and high-throughput potential of in vitro methods. This paper illustrates that zebrafish exhibit an immune system that is remarkably similar to humans, both in terms of types of immune cells and signaling pathways. Moreover, the developmental state of the larval immune system is highly similar to human neonates. We provide examples of zebrafish larvae being used to study infections with pathogens commonly causing neonatal sepsis and discuss known limitations. We believe this species could expedite research into immune regulation during neonatal sepsis and may hold keys for the discovery of new biomarkers and novel treatment targets as well as for screening of targeted drug therapies.

Our drug development process has produced many life-saving medications, but patients experiencing rare diseases and similar conditions often are left with limited options for treatment. For an approved treatment to be developed, research on a new candidate or existing drug must validate safety and efficacy based on contemporary research expectations. Randomized clinical trials are conducted for this purpose, but they are also costly, laborious, and time-consuming. For this reason, The 21st Century Cures Act mandates that the US Food and Drug Administration look for alternative methods for approving drugs, in particular exploring the uses of real-world data and evidence. Expanded access (“compassionate use”) is a pathway for the clinical treatment of patients using drugs that are not yet approved for prescribing in the United States. Using real-world evidence generated from expanded-access patients presents an opportunity to provide critical data on patient outcomes that can serve regulatory approval in conjunction with other observational datasets or clinical trials, and in limited circumstances may be the best data available for regulatory review. In doing so, we may also support and encourage patient-centered care and a personalized medicine approach to drug development.

The All of Us Research Program is an historic effort to gather data over 10+ years from one million or more people living in the United States to accelerate research and advance precision medicine. There is a particular focus on populations historically underrepresented in biomedical research who are often served by Federally Qualified Health Centers (FQHCs). However, FQHCs face significant challenges in participating in research. This paper addresses three common barriers faced by FQHCs and describes a management model that was used to support a group of FQHCs participating in the All of Us Research Program. Specifically, the paper addresses the challenges of building FQHC research capacity to operationalize and manage research activities, transforming and sharing Electronic Health Records and other data, and recruiting and retaining research participants. The central coordination management model, which was used to support the FQHCs, is a generalizable framework and can serve as an exemplar of how to engage FQHCs in other longitudinal research efforts. To date, the FQHCs have enrolled more than 10,000 participants in the All of Us Research Program. Their success is an indicator that with the proper support, FQHCs can successfully implement a complex biomedical research program in the context of their health centers.

OBJECTIVES/SPECIFIC AIMS: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects one in 68 children. Children with ASD have 2 core areas of difficulty: social communication skills and restricted and repetitive interests and patterns of behavior. Children with social skills deficits are at higher risk of developing mental health problems, and underdeveloped social skills predict poorer quality of life in adulthood. Therapies have been developed to help people with ASD improve social abilities in childhood, often involving a clinician directly teaching social skills lessons, either one-on-one or in a group setting. However, children with ASD can become anxious when interacting with other people and have an intrinsic motivation to interact with technology. To capitalize on this interest, this research team developed a robot, the socially animated machine (SAM) to teach social skills to children with ASD. Previous research found that this intervention was feasible and enjoyable for children with ASD and average cognitive ability, and participants improved in complex emotion recognition following intervention. The purpose of this study was to determine whether participants of all IQ levels were motivated by the SAM intervention, and whether they improved on emotion identification, facial recognition, social skills, and adaptive behavior. METHODS/STUDY POPULATION: This study recruited 20 children with ASD ages 5–14. Children completed tasks measuring ASD symptoms, IQ, receptive language, facial recognition, and emotion identification and were assigned to the control group (nonemotion dance games with SAM robot) or the intervention group (emotion games with SAM robot). Parents and teachers completed questionnaires about the child’s social skills. Following the robot intervention, facial recognition, emotion identification, and social skills were measured again, and parents and children rated participant enjoyment during the robot interaction. RESULTS/ANTICIPATED RESULTS: Overall, parents and children in both groups rated the robot interaction as highly enjoyable and motivating (parent ratings: M=26.4 out of 30, child ratings: M=17.5 out of 20). There were no differences between groups on post-test measures when controlling for pre-test scores (all p>0.05). Both groups improved over time on emotion identification accuracy (intervention: M=13.0% improvement, t=2.57, p<0.05; control: M=10.2% improvement, t=2.38, p<0.05) and parent-rated social skills (intervention: pre-test M=113.8, post-test M=100.6, t=−3.37, p=0.01; Control: pre-test M=107.9, post-test M=89.0, t=−2.83, p<0.05; decrease in scores indicates improvement). Teachers saw a decrease in problem behaviors for the intervention group (pre-test M=127.4, post-test M=119.6, t=−3.79, p<0.01, decrease in scores indicates improvement). DISCUSSION/SIGNIFICANCE OF IMPACT: This study shows that children with ASD and all levels of cognitive ability enjoyed and were motivated by the SAM robot intervention. This is particularly important for children with ASD who often have difficulty with attention and motivation. Children who are intrinsically motivated by the learning process will be more likely to benefit from it; therefore, continuing to pursue the methodology of robot-based interventions with this population is a worthwhile endeavor.

The shift in learning environments due to the COVID-19 pandemic necessitates a closer look at course design, faculty approaches to teaching, and student interaction, all of which may predict learner achievement and satisfaction. Transitioning to an online environment requires the reinvention, reimagining, and applying of “e-flavors” of general learning theory. With this shift to online learning comes the opportunity for misunderstandings and “myths” to occur, which may stand in the way of faculty embracing online learning and fully realizing its potential. This article seeks to address several myths and misconceptions that have arisen in higher education during the rapid shift to online teaching and learning. While not comprehensive, these myths represent a snapshot of common challenges. These are we can transfer our in-person course design to online; adult learners do not need an empathetic approach; and online teaching and learning is socially isolating. Through an appreciative inquiry framework, we present each myth in the context of relevant literature and invite faculty with varied online teaching experience to share their own case studies that illustrate how they have “busted” these myths with the goal to identify existing examples of locally effective practices for the express purpose of replication that leads to positive change.

OBJECTIVES/SPECIFIC AIMS: The objectives for the Rigor, Reproducibility, and Transparency course within KAIZEN-Edu was to provide a platform that allows essential training, in a novel and customizable approach, for a large number of students across the multiple institutions within the UAB CCTS Partner Network. Successful implementation across this geographically diverse of partner institutions would serve as proof of concept to future dissemination across the CTSA consortium. METHODS/STUDY POPULATION: We used the “build a game” tools within Kaizen-Edu to design the “Rigor and Reproducibility Game.” The games consisted of four modules, with 20 questions designed to test participant knowledge, and edify learners on particular concepts through a multimedia approach (embedded video, text, and hyperlinks to articles) with content provided as questions released over 4 weeks. Researchers from across the UAB CCTS Partner Network developed comprehensive modules for (1) How Scientists Fool Themselves/Scientific Premise, (2) Authentication of Chemical and Biologic Resources and Sex and Other Biologic Variables, (3) Statistical Rigor, and (4) Comprehensive Review. A typical week began with review articles (1–2) sent to each participant. The participants are informed that 5 questions will be released midweek testing the key concepts from the papers. When ready, the participant logs into Kaizen-Edu and starts to answer questions/play the game. Immediately, the articles are opened for reference, followed by a brief 4–5 minute video which reinforces key concepts and then timed questions begin. A typical question is allowed 3 minutes (visible countdown clock). Accurate responses result in the addition of points, with double points awarded for correct answers within the questions time limit. No points are awarded for incorrect answers. After each question, a detailed explanation reviews and reinforces the key concepts. Each participants’ points contribute to both their individual score and team scores, which influences their position on the Rigor and Reproducibility game leaderboard. RESULTS/ANTICIPATED RESULTS: Within 2017, the Rigor Reproducibility, and Transparency course was conducted 5 times. A total of 126 researchers across 9 institutions were enrolled. A total of 87 enrollees completed the full course, with 80% passing (answering ≥75% of questions correctly) on their first attempt and an additional 20% passing on a second attempt. The distribution of completers across the CCTS Network was UAB=48, Auburn=13, Pennington=10, University of Alabama=5, Hudson Alpha=5, Tulane=4, University of South Alabama=1, LSU=2, and Southern Research=1. Researchers throughout at Partner Institutions represent 46% of the total population trained. DISCUSSION/SIGNIFICANCE OF IMPACT: This software based, gamification-enhanced course was broadly accepted with each session fully enrolled, and learners spread almost evenly between our institution and various Partner Network sites. Our pilot proved that gamification was an effective technique to engage users and produced a high pass rate, suggesting that the content both engaged learners and was effectively internalized. Educational interventions, imbued with principles of gamification provide educators powerful tools that use competition and/or collaboration to disseminate knowledge, engage learners with content, and save educator time as created game content can be reused in additional educational sessions. Analyses of the data trail provided by users engaging with such electronic learning tools will provide educators will insights on how to maximize learning, opening the door to an era of educational analytics.

Rapid development and deployment of diagnostic testing for COVID-19 have been a key component of the public health response to the pandemic. Out of necessity, academic and other clinical laboratories developed laboratory testing innovations for COVID-19 to meet clinical testing demands. In addition to constraints on local testing supplies and equipment, a rapidly changing regulatory framework created challenges for translational scientists. Illustrative examples of approaches used to develop laboratory tests during the early stages of the COVID-19 pandemic demonstrate effective team science approaches to this challenging clinical care and public health emergency. These experiences and the associated lessons learned are relevant to the development of public health response plans for future pandemics.

The University of Arkansas for Medical Sciences (UAMS), like many rural states, faces clinical and research obstacles to which digital innovation is seen as a promising solution. To implement digital technology, a mobile health interest group was established to lay the foundation for an enterprise-wide digital health innovation platform. To create a foundation, an interprofessional team was established, and a series of formal networking events was conducted. Three online digital health training models were developed, and a full-day regional conference was held featuring nationally recognized speakers and panel discussions with clinicians, researchers, and patient advocates involved in digital health programs at UAMS. Finally, an institution-wide survey exploring the interest in and knowledge of digital health technologies was distributed. The networking events averaged 35–45 attendees. About 100 individuals attended the regional conference with positive feedback from participants. To evaluate mHealth knowledge at the institution, a survey was completed by 257 UAMS clinicians, researchers, and staff. It revealed that there are opportunities to increase training, communication, and collaboration for digital health implementation. The inclusion of the mobile health working group in the newly formed Institute for Digital Health and Innovation provides a nexus for healthcare providers and researches to facilitate translational research.