Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study.

Background: Machine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected.

Objective: This study aims to evaluate the ability of machine-learning algorithms to distinguish between participants who are influenza positive and influenza negative in a cohort of symptomatic patients with ILI using wearable sensor (activity) data and self-reported symptom data during the latent and early symptomatic periods of ILI.

Methods: This prospective observational cohort study used the extreme gradient boosting (XGBoost) classifier to determine whether a participant was influenza positive or negative based on 3 models using symptom-only data, activity-only data, and combined symptom and activity data.

One-year trajectories of nutritional status in perimenopausal women: a community-based multi-centered prospective study.

Background: Nutritional status is a modifiable factor associated with perimenopausal women's health and quality of life. Assessing body composition indicators helps to comprehensively understand nutritional status compared with using body mass index (BMI) only. However, few published studies measured the trends in body composition among perimenopausal women.

Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study.

Background: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized.

Objective: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration.

Attachment and optimization of Staphylococcus aureus, Staphylococcus epidermidis, and Pseudomonas aeruginosa biofilms to a 3D printed lattice.

A lattice was designed and fabricated using three-dimensional (3D) printing that allows for the facile transfer of biofilms formed from either Staphylococcus aureus, Staphylococcus epidermidis, or Pseudomonas aeruginosa into a fresh cell culture flask. To enhance biofilm production onto the filaments, three protein-based treatments were compared: fetal bovine serum (FBS), bovine serum albumin (BSA), and fibrinogen (Fb). Protein treatments included either supplementing the growth broths or pre-coating the lattice prior to immersion into the broth.

Building Partnerships and Stakeholder Relationships for HIV Prevention: Longitudinal Cohort Study Focuses on Community Engagement.

Background: African American women bear disproportionate human immunodeficiency virus (HIV) burden in the United States, yet are often underrepresented in clinical research. Community engagement may decrease research mistrust and increase participation. We describe strategies used to engage community partners and female participants in a multisite HIV incidence study, HIV Prevention Trials Network (HPTN) 064.