Objectives/Goals: The goal of this project was to engineer 3D lung models by embedding human epithelial cells and fibroblasts within hybrid-hydrogels containing human decellularized extracellular matrix (dECM) from healthy and fibrotic lungs. This platform will enable us to study cell–matrix interactions involved lung fibrosis pathogenesis. Methods/Study Population: To incorporate dECM into hybrid-hydrogels it must be digested and functionalized. We determined the best conditions for pepsin digesting dECM from healthy and fibrotic human lung by collecting samples every 12 hours up to 96 hours and measuring total protein (BCA assay), total amine concentration (ninhydrin assay), and protein fragment size (SDS PAGE). Next, several molar excesses of Traut’s reagent were tested and functionalization was verified by comparing amine content (ninhydrin assay) to thiol content (Ellman’s assay). Hydrogel stiffness was measured initially and after stiffening using parallel-plate rheology. Results/Anticipated Results: The dECM was successfully pepsin-digested, with the 48-hour time point yielding the highest free amine levels. A 75-molar excess of Traut’s reagent was best for converting free amines to thiols. Dynamic stiffening allowed the creation of hybrid-hydrogels mimicking both healthy (1–5 kPa) and fibrotic (>10 kPa) lung microenvironments. We anticipate that this model will demonstrate differential fibroblast activation based on hybrid-hydrogel dECM source (healthy or fibrotic), microenvironmental stiffness, and cell source (healthy or fibrotic). Validation of this 3D co-culture system could accelerate drug discovery by providing a more accurate in vitro platform for high-throughput screening. Discussion/Significance of Impact: This work advances pulmonary fibrosis modeling by creating human dECM-based hydrogels that recapitulate the cellular and mechanical microenvironment of healthy and diseased lung, potentially enabling us to uncover novel therapeutic targets and improving drug efficacy testing in vitro.

Objectives/Goals: 1) Discuss process of pilot integration of Community Health Worker (CHW) services as a component of patient-centered healthcare service delivery in 3 clinic models. 2) Summarize profiles of patients who self-select to utilize CHW services. 3) Discuss social determinants of health impacts of underserved and historically marginalized populations. Methods/Study Population: The priority population consists of individuals living in Mobile AL at or below poverty level. USA Health Center for Healthy Communities (CHC) piloted the integration of CHW services at USA Health Stanton Road Clinic (SRC), at USA Student Run Free Clinic (SRFC), and as part of a Medi Hub Outreach Clinic with the historically underserved MOWA Choctaw native American population. SRC is a high-utilization clinic for uninsured or underinsured patients across the breadth of the Gulf Coast. The other 2 sites serve similar clientele. Social determinants of health (SDOH) screenings at intake facilitate CHW referral for a clients’ unique needs for support at healthcare or social care agencies. Referral summaries can then be used to guide planning, community collaborating partner intervention, and clinical quality certification, Results/Anticipated Results: Results include identification of referrals process by which CHWs are able to provide culturally competent support to persons accessing healthcare services at the 3 clinic models identified. Identification of top SDOH needs that preclude access to care among the patients served during a 24-month pilot period, e.g., (i) housing insecurity, (ii) food insecurity, (iii) transportation, (iv) health Ins, and (v) pharmacy access and payment assistance. Discussion of beneficial impacts for health care service delivery with other members of the multidisciplinary clinical teams as recorded referrals can be used to guide planning, clinic certification efforts Discussion/Significance of Impact: Patient utilization of CHW services though self-selective offers opportunities for equity in access to care services from direct SDOH impacts, where CHWs act as responsive resource coordinators within the multi-disciplinary service delivery team.

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

Invasive plants’ ability to extend their range depends upon their local environments and both positive and negative interactions with native species. Interactions between invasive and native plants may be indirectly linked to the soil fungal community, which may enhance or suppress invasion through mutualism or parasitism. Many invasive plants preferentially select fungal communities or change soil chemistry to gain a competitive advantage, and such changes can remain even after the invader is removed, known as legacy effects. Yellow toadflax (Linaria vulgaris Mill.) is an invasive forb that is aggressive in the western United States but is nonaggressive in the midwestern United States. We evaluated the relationship between soil abiotic properties, nitrogen (N) enrichment, arbuscular mycorrhizal fungal (AMF) community composition, and L. vulgaris invasion in aggressive (CO) and nonaggressive (IL) populations. We collected soil from uninvaded and invaded sites in Gothic, CO, and near Chicago, IL, and sequenced AMF community composition in each site. Using the same soil, we grew L. vulgaris and native species in pots for 120 d, with half of the pots receiving N fertilization, and harvested biomass. We also injected a 15N-labeled tracer in pots and analyzed plant tissue for 15N enrichment and net uptake rates (NUR). In CO soil, L. vulgaris rhizomes sprouted more in invaded soil, whereas in IL soil, L. vulgaris only sprouted in uninvaded soil. N fertilization had no impact on biomass, and NUR did not differ significantly between any treatments. AMF communities differed between the two sites but were not significantly influenced by invasion history. Our results suggest that L. vulgaris leaves legacy effects but that these effects are different between aggressive and nonaggressive populations. Legacy effects may facilitate reinvasion in CO, but we did not find conclusive evidence of legacy effects in IL, and differences between the sites could be shaped by endemic AMF communities.

This paper presents a mapping of theory use in the design discipline based on the corpus of the published ICED and DESIGN conference papers since 2010. We searched the resulting 4,451 papers for occurrences of theories and compared them with an existing ontology of named theories through natural language processing (NLP). The results yielded a variety of analyses, illustrating, for example, the most-used theories and which disciplines these theories stem from. This paper presents a rich overview of the theories relevant to the design discipline and a novel approach to bibliometric analyses.

The paper emphasizes the need to consider negative aspects of creativity, especially in design, where it can have significant societal impacts. It calls for a more comprehensive view of creativity that includes both positive and negative effects and proposes a research approach to assess the potential negative consequences of creative work.

The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

Transitions into an assisted living home (ALH) are difficult and may impact the well-being of older adults. A thematic analysis guided by grounded theory was employed to better understand how a transition into an ALH influenced older adults’ overall well-being. Individual, face-to-face interviews were conducted with a convenience sample of 14 participants at an ALH in the rural, southeastern U.S. Two central findings that influenced well-being during the transition process were revealed: loss of independence (sub-themes include loss of physical and mental health and loss of driving) and downsizing in space and possessions. The themes support and broaden the Hierarchical Leisure Constraints Theory, a Modified Constraints to Wellbeing model is proposed, and implications for older adult health care practitioners in ALHs are recommended. Further research is needed on the Modified Constraints to Wellbeing model and how to better describe these constraints to older adults’ well-being when relocating into ALHs.

Generative AI algorithms that are able to generate creative output are progressing at tremendous speed. This paper presents a research agenda for Generative AI-based support for designers. We present examples of existing applications and thus illustrate the possible application space of Generative AI reflecting the current state of this technology. Furthermore, we provide a theoretical foundation for AI-supported design, based on a typology of design knowledge and the concept of evolutionary creativity. Both concepts are discussed in relation to the changing roles of AI and the human designer. The outlined research agenda presents 10 research opportunities for possible AI-support to augment the designer of the future. The results presented in this paper provide researchers with an introduction to and overview of Generative AI, as well as the theoretical understanding of potential implications for the future of the design discipline.

Rapid advances in precision medicine promise dramatic reductions in morbidity and mortality for a growing array of conditions. To realize the benefits of precision medicine and minimize harm, it is necessary to address real-world challenges encountered in translating this research into practice. Foremost among these is how to choose and use precision medicine modalities in real-world practice by addressing issues related to caring for the sizable proportion of people living with multimorbidity. Precision medicine needs to be delivered in the broader context of precision care to account for factors that influence outcomes for specific therapeutics. Precision care integrates a person-centered approach with precision medicine to inform decision making and care planning by taking multimorbidity, functional status, values, goals, preferences, social and societal context into account. Designing dissemination and implementation of precision medicine around precision care would improve person-centered quality and outcomes of care, target interventions to those most likely to benefit thereby improving access to new therapeutics, minimize the risk of withdrawal from the market from unanticipated harms of therapy, and advance health equity by tailoring interventions and care to meet the needs of diverse individuals and populations. Precision medicine delivered in the context of precision care would foster respectful care aligned with preferences, values, and goals, engendering trust, and providing needed information to make informed decisions. Accelerating adoption requires attention to the full continuum of translational research: developing new approaches, demonstrating their usefulness, disseminating and implementing findings, while engaging patients throughout the process. This encompasses basic science, preclinical and clinical research and implementation into practice, ultimately improving health. This article examines challenges to the adoption of precision medicine in the context of multimorbidity. Although the potential of precision medicine is enormous, proactive efforts are needed to avoid unintended consequences and foster its equitable and effective adoption.