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.