Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population.

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

Automated Urinal-Based Specific Gravity Measurement Device for Real-Time Hydration Monitoring in Male Athletes.

Acute and chronic hydration status is important for athlete safety and performance and is frequently measured by sports scientists and performance staff in team environments via urinalysis. However, the time required for urine collection, staff testing, and reporting often delays immediate reporting and personalized nutrition insight in situations of acute hydration management before training or competition. Furthermore, the burdensome urine collection and testing process often renders chronic hydration monitoring sporadic or non-existent in real-world settings. An automated urinalysis device (InFlow) was developed to measure specific gravity, an index of hydration status, in real-time during urination. The device was strongly correlated to optical refractometry with a mean absolute error of 0.0029 (±0.0021). Our results show this device provides a novel and useful approach for real-time hydration status via urinalysis for male athletes in team environments with high testing frequency demands.

Digital microfluidics for spheroid-based invasion assays.

Cell invasion is a key process in tissue growth, wound healing, and tumor progression. Most invasion assays examine cells cultured in adherent monolayers, which fail to recapitulate the three-dimensional nuances of the tissue microenvironment. Multicellular cell spheroids have a three-dimensional (3D) morphology and mimic the intercellular interactions found in tissues in vivo, thus providing a more physiologically relevant model for studying the tissue microenvironment and processes such as cell invasion. Spheroid-based invasion assays often require tedious, manually intensive handling protocols or the use of robotic liquid handling systems, which can be expensive to acquire, operate, and maintain. Here we describe a digital microfluidic (DµF) platform that enables formation of spheroids by the hanging drop method, encapsulation of the spheroids in collagen, and the exposure of spheroids to migration-modulating agents. Collagen sol-gel solutions up to 4 mg mL(-1), which form gels with elastic moduli up to ∼50 kPa, can be manipulated on the device. In situ spheroid migration assays show that cells from human fibroblast spheroids exhibit invasion into collagen gels, which can be either enhanced or inhibited by the delivery of exogenous migration modulating agents. Exposing fibroblast spheroids to spheroid secretions from colon cancer spheroids resulted in a >100% increase in fibroblast invasion into the collagen gel, consistent with the cancer-associated fibroblast phenotype. These data show that DµF can be used to automate the liquid handling protocols for spheroid-based invasion assays and create a cell invasion model that mimics the tissue microenvironment more closely than two-dimensional culturing techniques do. A DµF platform that facilitates the creation and assaying of 3D in vitro tissue models has the potential to make automated 3D cell-based assays more accessible to researchers in the life sciences.