Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting ( N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant ( P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.
PowerAmerica sponsored the development by NCSU of a process for manufacturing power MOSFETs and JBS Rectifiers in 2015. This process, named PRESiCETM, was successful in making 1.2 kV rated state-of-the-art 4H-SiC power devices (MOSFETs, BiDFETs, and JBS Rectifiers) in the X-Fab foundry. In addition, we were successful in monolithically integrating a JBS fly-back rectifier into the power MOSFET structure to create the power JBSFET which allows saving significant (~ 40 %) chip area and reducing package count in half. In the second year (2016), NCSU has qualified the process for manufacturing these power devices at X-Fab.
How might it impact on healthcare in the future? ► Integrating the touchless connector system into peritoneal dialysis systems may help reduce peritonitis risks in patients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.