Wearable Sensors Improve Prediction of Post-Stroke Walking Function Following Inpatient Rehabilitation.

OBJECTIVE: A primary goal of acute stroke rehabilitation is to maximize functional recovery and help patients reintegrate safely in the home and community. However, not all patients have the same potential for recovery, making it difficult to set realistic therapy goals and to anticipate future needs for short- or long-term care. The objective of this study was to test the value of high-resolution data from wireless, wearable motion sensors to predict post-stroke ambulation function following inpatient stroke rehabilitation. METHOD: Supervised machine learning algorithms were trained to classify patients as either household or community ambulators at discharge based on information collected upon admission to the inpatient facility (N=33-35). Inertial measurement unit (IMU) sensor data recorded from the ankles and the pelvis during a brief walking bout at admission (10 meters, or 60 seconds walking) improved the prediction of discharge ambulation ability over a traditional prediction model based on patient demographics, clinical information, and performance on standardized clinical assessments. RESULTS: Models incorporating IMU data were more sensitive to patients who changed ambulation category, improving the recall of community ambulators at discharge from 85% to 89-93%. CONCLUSIONS: This approach demonstrates significant potential for the early prediction of post-rehabilitation walking outcomes in patients with stroke using small amounts of data from three wearable motion sensors. CLINICAL IMPACT: Accurately predicting a patient's functional recovery early in the rehabilitation process would transform our ability to design personalized care strategies in the clinic and beyond. This work contributes to the development of low-cost, clinically-implementable prognostic tools for data-driven stroke treatment.

Graphene Oxide Functionalized Biosensor for Detection of Stress-Related Biomarkers.

A graphene oxide (GO)-based cortisol biosensor was developed to accurately detect cortisol concentrations from sweat samples at point-of-care (POC) sites. A reference electrode, counter electrode, and working electrode make up the biosensor, and the working electrode was functionalized using multiple layers consisting of GO and antibodies, including Protein A, IgG, and anti-Cab. Sweat samples contact the anti-Cab antibodies to transport electrons to the electrode, resulting in an electrochemical current response. The sensor was tested at each additional functionalization layer and at cortisol concentrations between 0.1 and 150 ng/mL to determine how the current response differed. A potentiostat galvanostat device was used to measure and quantify the electrochemical response in the GO-based biosensor. In both tests, the electrochemical responses were reduced in magnitude with the addition of antibody layers and with increased cortisol concentrations. The proposed cortisol biosensor has increased accuracy with each additional functionalization layer, and the proposed device has the capability to accurately measure cortisol concentrations for diagnostic purposes.

Serum Metabolite Profiles Are Altered by Erlotinib Treatment and the Integrin α1-Null Genotype but Not by Post-Traumatic Osteoarthritis.

The risk of developing post-traumatic osteoarthritis (PTOA) following joint injury is high. Furthering our understanding of the molecular mechanisms underlying PTOA and/or identifying novel biomarkers for early detection may help to improve treatment outcomes. Increased expression of integrin α1ß1 and inhibition of epidermal growth factor receptor (EGFR) signaling protect the knee from spontaneous OA; however, the impact of the integrin α1ß1/EGFR axis on PTOA is currently unknown. We sought to determine metabolic changes in serum samples collected from wild-type and integrin α1-null mice that underwent surgery to destabilize the medial meniscus and were treated with the EGFR inhibitor erlotinib. Following (1)H nuclear magnetic resonance spectroscopy, we generated multivariate statistical models that distinguished between the metabolic profiles of erlotinib- versus vehicle-treated mice and the integrin α1-null versus wild-type mouse genotype. Our results show the sex-dependent effects of erlotinib treatment and highlight glutamine as a metabolite that counteracts this treatment. Furthermore, we identified a set of metabolites associated with increased reactive oxygen species production, susceptibility to OA, and regulation of TRP channels in α1-null mice. Our study indicates that systemic pharmacological and genetic factors have a greater effect on serum metabolic profiles than site-specific factors such as surgery.