Development and Usability of the OHiFamily Mobile App to Enhance Accessibility to Maternal and Infant Information for Expectant Families in Ohio: Qualitative Study.

Background: The Infant Mortality Research Partnership in Ohio is working to help pregnant individuals and families on Medicaid who are at risk for infant mortality and preterm birth. As part of this initiative, researchers at The Ohio State University worked to develop a patient-facing mobile app, OHiFamily, targeted toward, and created for, this population. To address the social determinants of health that can affect maternal and infant health, the app provides curated information on community resources, health care services, and educational materials in a format that is easily accessible and intended to facilitate contact between families and resources.

Preliminary Effectiveness of a One-Week Summer Day Camp for Improving Children's Health Behaviors and Psychosocial Well-Being Outcomes.

Purpose: Summer day camp offers children opportunities to grow knowledge and skills, be physically active, and have fun. Compared to healthy children, at-risk children (i.e.

Clinical performance of existing diagnostic criteria for pneumonia in older emergency patients: A prospective cohort study.

Background: Pneumonia accounts for over half a million older adult emergency department (ED) visits annually, but ED pneumonia diagnosis is inaccurate. Geriatric-specific pneumonia diagnostic criteria exist for other settings; no prospective data exist to determine if application in the older adult ED population is feasible. The objective was to prospectively evaluate the utility of four current diagnostic criteria (Loeb; Modified McGeer; Infectious Disease Society of America/American Thoracic Society; American College of Emergency Physicians) in older adult ED patients.

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification.

Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap along the electron beam trajectory. Here, we introduce a novel method, non-negative matrix factorization based pan-sharpening (PSNMF), to address these limitations.