Here we present a novel approach to evaluate peripheral blood mononuclear cell vascular adhesion using a microfluidic model designed to approximate the complexity of a human arteriole. While EC monolayer assays are commonly used to investigate leukocyte-EC interactions, we hypothesized that our single channel arteriole (SCA) on a chip would recapitulate the microvasculature more accurately and provide additional insight into the initial stages of atherogenesis.

This model is comprised of stromal cells embedded in a hydrogel surrounding a channel lined by endothelial cells (EC) that has an inner diameter approximating a small arteriole. Under physiologic shear conditions, the EC take on a phenotype distinct from monolayer cultures, including alignment with the direction of flow.

Significant differences were found between the SCA and monolayer cultures in the expression of key EC and stromal cell markers, including ICAM-1, VCAM-1, PDGFB, aSMA, and KLF2. Indeed, flow-induced PDGFB expression likely mediated the recruitment and differentiation of αSMA-positive cells to the vessel wall. Importantly, the vessels were responsive to stimulation by inflammatory mediators, showing both increased leukocyte adhesion and increased permeability. Finally, mechanically mediated protrusion of the vessel wall into the lumen disrupted flow, producing increased shear over the vessel wall.

In summary, our studies demonstrate the utility of the SCA model for studies of small vessel physiology under both normal and disrupted flow and to lay the groundwork for further development into a model for atherosclerosis. Additionally, our data emphasize the advantages of complex 3D assays over more traditional 2D cultures.

Rural communities make up 19% of the US population, yet are underrepresented in clinical trials. Community engagement methods can facilitate collaboration and trust with local healthcare personnel to enhance enrollment. The purpose of this manuscript is to describe community engagement methods and their impact on enrollment in a pragmatic clinical trial.

We describe a variety of methods used in the Fibromyalgia TENS in Physical Therapy Study (FM-TIPS) to enhance enrollment in rural communities and low-enrolling clinics. Community engagement methods were implemented partway through the trial for selected groups: Targeted Rural (TR) (n = 10), Targeted Low Enrolling (TLE) (n = 6), and compared to Untargeted Groups (UT) (n = 13). The impact of these methods on inquiries, screening, and enrollment were evaluated by comparing actual enrollment to projected enrollment.

We trained and employed community engagement coordinators to implement strategies in TR and TLE physical therapy clinics. These included, posting flyers, community events, physician outreach, social media ads, and direct mailing. These methods increased study inquiries, screening and enrollment in the study. Specifically, when compared to projected values there were increases in enrollment for both the TR and the TLE groups, but not the UT group. Of those that passed screening 99% of rural and 32% of urban residents enrolled in the study.

A multi-pronged and individualized community engagement approach can increase enrollment of rural residents in clinical trials. Building strong relationships and partnering with community clinics and local communities is essential to success.

Asthma exacerbations are frequent causes of pediatric hospital admissions. We sought to develop a time series algorithm to forecast next-day daily asthma hospitalizations.

Daily hospitalizations for asthma were collected at Cincinnati Children’s from January 1, 2016, to December 31, 2023. We evaluated Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Prophet, and Ensemble models to forecast next-day asthma hospitalizations validated on 2023 data, considering varying historical training data lengths. Forecasts were calibrated to identify days exceeding a 5% high-risk threshold of historical totals and considered multiple validation years and years before and during the COVID-19 pandemic.

A total of 5,593 hospital admissions were recorded for asthma. Over 2,922 days, 166 days met the 5% high-risk threshold equating to 6 or more admissions. The Ensemble (Median Absolute Percentage Error (MAPE): 46.7%; Positive Predictive Value (PPV): 0.278; Negative Predictive Value (NPV): 0.942; Area Under the ROC Curve (AUC): 0.740; Sensitivity: 0.800; Specificity: 0.656) model achieved higher accuracy of high-risk days than ARIMA (MAPE: 46.5%; PPV: 0.278; NPV: 0.942; AUC: 0.709; Sensitivity: 0.760; Specificity: 0.571), ETS (MAPE: 47.2%; PPV: 0.222; NPV: 0.939; AUC: 0.711; Sensitivity: 0.800; Specificity: 0.668), and Prophet (MAPE: 48.9%; PPV: 0.444; NPV: 0.951; AUC: 0.732; Sensitivity: 0.680; Specificity: 0.741) models.

Our Ensemble model of mean predictions from ARIMA, ETS, and Prophet models was the most accurate in forecasting future asthma hospitalizations. Integrating forecasting techniques with clinical operations could enable proactive prevention through enhanced population care management.

Academic Medical Centers (AMCs) with Clinical and Translational Science Awards (CTSAs) offer a range of resources to support clinical and translational research and science. However, research professionals often face challenges in navigating these resources effectively.

Our study sought to examine research navigation services across CTSA hubs to identify successful strategies, common challenges, and best practices for supporting research teams.

We conducted interviews with representatives from ten CTSA hubs and performed a landscape analysis to explore the types of research navigation services available, the methods of advertising and orienting faculty and staff, and the challenges faced in launching and maintaining these services.

Our analysis identified three primary types of research navigation services offered at CTSA hubs: online resource libraries, personalized research navigation with dedicated staff, and interdisciplinary research “studios” for protocol development. Despite these offerings, challenges such as low awareness, difficulties coordinating across siloed university systems, and limited metrics for evaluating navigation services persist.

Effective research navigation requires a combination of web-based applications and in-person support, backed by institutional commitment to foster engagement and streamline access. Key strategies for successful navigation services include proactive advertising, integration with orientation programs, and cross-departmental collaboration. Our findings offer actionable recommendations for enhancing research navigation at AMCs, ultimately aiming to increase research productivity and collaboration.

Although decentralized research is being used more frequently, few data are available regarding barriers for potential subjects related to engaging in decentralized research with remote biospecimen collection, especially within pregnancy and birth cohorts that include individuals of diverse racial and ethnic backgrounds.

Focus groups and individual interviews with pregnant and postpartum women were conducted in English and Spanish. Thematic analysis was used to identify motivators and barriers to participation in decentralized research involving biospecimens.

Sixty women (35% Hispanic/Latino, 23% Black, 18% Asian, 15% non-Hispanic White) participated in 10 focus groups (English = 8, Spanish = 2) and 11 individual interviews (English = 7, Spanish = 4). Three themes emerged about factors that could promote participation in decentralized biospecimen collection: 1) convenience, 2) autonomy, and 3) benefit (to self, community or society). Four themes emerged about potential barriers: 1) lack of interaction with trained professionals, 2) inability to coordinate with existing clinical care, 3) discomfort and invasiveness, and 4) concerns about data transparency and security. Overall, participants felt more comfortable providing biospecimens for themselves compared to their child and with biospecimens perceived as less painful or invasive to obtain.

Our findings suggest that transparency about the purposes and use of collecting biospecimen and clear instructions (such as written and instructional videos) could improve biospecimen collection in decentralized pregnancy and birth cohorts. Additionally, opportunities for virtual interaction with study staff and options related to collection of certain biospecimens such as blood (mobile collection unit with trained staff versus a self-collection device) may also improve participant engagement.

Effective recruitment techniques are essential for researchers to recruit and retain potential participants in studies, particularly as recruitment numbers into clinical trials have decreased. While recruitment techniques have been investigated, there is a gap in understanding the perspectives of clinical trials recruiters. This paper examines recruiters’ usage and perceived effectiveness of various recruitment techniques, as well as their perspectives on related ethical issues.

We conducted a cross-sectional survey of 381 clinical trials recruiters. Closed-ended items examined whether recruiters had used 31 pre-defined recruitment techniques and their perceptions of the effectiveness of each technique. For techniques perceived to be highly effective or ineffective, open-ended items examined recruiter reasoning. The multiple methods analysis integrated the closed-ended and open-ended data.

Recruitment techniques such as reassured potential participants about confidentiality (96.3%) and reassured about data sharing (95.8%) had high usage, while techniques like having the PI approach and enroll had a high average perceived effectiveness (M = 4.23, SD = 0.91). Recruiters often rated techniques as more highly effective when they had prior experience using them. They also identified concerns about professionalism, ethics, and transparency in standard practice recruitment techniques.

Our findings indicate that there is significant variation in the usage of clinical trial recruitment techniques and how different recruiters view the effectiveness of each technique. The unique perspectives of those who recruit into clinical trials can help inform future decisions regarding which recruitment techniques to utilize, along with how and when to use particular recruitment techniques in an ethical manner.

Understanding how different symptoms co-occur and are correlated may provide insights into the pathophysiology of disease. The lack of group-to-individual generalizability of co-occurrence of symptoms was recently demonstrated by comparing intra-individual and inter-individual correlations in several psychological studies. Here, we investigate this phenomenon for lower urinary tract symptoms (LUTS).

We analyzed data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network Recall Study. Participants responded to questions about their urinary symptoms for 25 consecutive days. These questions queried urologic symptoms including storage (urinary urgency, frequency, nocturia, and urinary incontinence), voiding (slow/weak stream), and post-micturition (incomplete emptying and post-micturition dribble) symptoms. We calculated Pearson correlation coefficients and cosine similarity measures and compared distributions of intra-individual and inter-individual (cohort) metrics.

Among 234 participants, distributions of intra-individual measures were 10-fold wider than those of inter-individual correlations. There are pairs of questions with distributions of correlations and cosine similarities containing individuals with extreme positive (>0.8) and extreme negative values (<–0.8). There are groups of participants with strong positive and negative correlations of urinary frequency and nocturia, urinary incontinence and weak flow, as well as strong negative and positive correlations of urinary frequency and dribbling. Information on these extreme groups is averaged out and lost in the inter-individual correlations.

Lack of group-to-individual generalizability previously shown for psychological symptoms is confirmed for LUTS. Wealth of information on the co-occurrence and co-evolution of LUTS in the intra-individual correlations and cosine similarities corroborates heterogeneity of LUTS and can be useful for deep phenotyping and for identifying personalized treatments of LUTS.

The National Institutes of Health (NIH) Institutional Development Award (IDeA) program was created to build capacity and enhance research in states with historically low levels of NIH funding. IDeA Clinical and Translational Research (CTR) networks are focused on building statewide and regional capacity to conduct biomedical research. The tracking and evaluation component of each CTR is tasked with collecting data to facilitate continuous improvement and measure impact.

This paper presents findings from a survey conducted with IDeA-CTR evaluators examining the following questions: 1) To what extent do evaluators use meta-evaluative practices and how does meta-evaluation inform their evaluation? and 2) What challenges evaluators face in their evaluation planning and implementation?

Findings show that 50% of CTRs conducted some form of meta-evaluation. Further, quantitative and qualitative responses tell a compelling story of the challenges in translational research evaluation. The most prominent were the development of feasible and useful data management systems, the selection and endorsement of program-wide impact metrics, and the promulgation of realistic expectations regarding feasibility and utility for recipients of the evaluation, including expectations for project impacts that lead to systemic change.

Findings suggest the importance of internally adopting a participatory, collaborative approach to evaluation and externally sharing insights with and adopting strategies from fellow evaluators within a learning community. This study promotes the value of conducting meta-evaluation in CTR settings, demonstrates means for and results from doing so, and shares best practices for addressing challenges encountered by many CTR evaluators.

Interdisciplinary collaboration is essential for addressing scientific challenges, particularly in integrated fields like mobile health (mHealth), which combines computer communication and medicine to deliver healthcare services. The formation of collaborative relationships in such field is an emerging topic, with conversations among interdisciplinary scholars serving as a critical indicator of relationship development. This study aims to examine the specific effects of different conversation types (research or social oriented) on interdisciplinary collaboration and explore the impact of communication mode.

We tracked conversations among interdisciplinary scholars participating in a 15-day hybrid mHealth training program, which uniquely captures both scholars’ conversation networks and the conversation quality. Three types of conversation networks were recorded (topics about current research, future research, or small talk). Using longitudinal network models, we compared the effect of different types of conversation quality on network formation and evaluated the interaction between conversation quality and communication mode (in-person or online).

We found that the quality of social conversations on non-research-related topics had robust effects in promoting the formation of interdisciplinary communications. In-person communication is more conducive for current and future research conversations, while online communication is valued for small talk.

This study highlights the power of perception of personal conversation in interdisciplinary collaboration formation. The diverse effects of communication mode on different conversation networks are revealed. Our findings offer valuable insights for the event designs of interdisciplinary training program.

Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network.

We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit.

The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility.

This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

Rural cancer survivors have worse outcomes than their urban counterparts. To improve outcomes, it is essential that rural survivors participate in research, yet they are underrepresented in cancer research. The aim of this study was to assess urban-rural differences in participation in a cancer survivorship survey and differences in mode of participation (mail, online, or phone) by rurality and age.

We developed a survivorship needs assessment survey and invited cancer survivors to participate by mail, online, or phone. We compared participation between rural and urban invitees and examined differences in mode of participation by rurality and age.

A quarter (25.47%) of invited rural patients and 27.84% of invited urban patients participated in the survivorship study. The probability of participation by urban survivors was approximately 1.09 times higher than for rural survivors (χ2(1) = 4.31, p = 0.038). Rural survivors were more likely to participate by mail (average difference [Rural-Urban] = 9.64%, p < 0.001), while urban survivors were more likely to participate online (average difference [Urban-Rural] = 8.77%, p < 0.001). As participant age increased, the likelihood of survey participation by mail increased (1.16% per year of age, p < 0.001) while the probability of participating online decreased by 1.20% per year of age (p < 0.001).

To ensure equitable access to research for rural and older cancer survivors, researchers should design studies with a range of participation modes. Non-digital methods, such as mailed paper surveys, appear to promote participation among rural and older survivors.

Recruiting and retaining racial/ethnic minorities in research remains a significant challenge, often due to mistrust in clinical research and cultural misconceptions related to specific conditions. Despite the anonymity provided by technology-based intervention studies, difficulties in participant recruitment and retention in these studies remain. This paper addresses practical issues in recruiting and retaining Asian American breast cancer survivors with pain and depressive symptoms in a technology-based intervention study.

To identify practical issues in participant recruitment and retention, a content analysis was conducted on all recorded materials, including research diaries of individual research team members, weekly team meeting minutes, and research team members’ posts on Microsoft Teams.

Analysis identified six practical issues: (a) strict inclusion/exclusion criteria; (b) multiple stigmas associated with cancer, depressive symptoms, and pain; (c) lack of interest in research participation; (d) closed Asian American communities/groups; (e) frequent technological issues; and (f) potential unauthentic cases.

Addressing these recruitment and retention issues can inform the design of future culturally tailored, technology-based intervention studies for racial and ethnic minority populations.