Abstract:An insulin biosensor prototype capable of detecting insulin in physiological range without complex data normalization was developed. This prototype will be the ground works of a multimarker platform sensor technology for future all-in-one glycemic management sensors.
“…In the future clinicians will be working within a "Digital Diabetes Ecosystem" that combines the Internet of Medical Things (connected physiological and behavioral sensors embedded within multiple medical devices worn or used by an individual) and the new smart pens to support insulin therapy with continuous access to the Internet. 10 We expect that insulin dosing data combined with real time continuous plasma insulin data, 11 together enhanced with the use of artificial intelligence and machine learning to support dose calculations, will eventually predict and prevent adverse events such as hypoglycemia. These predictions will be possible at a much earlier time when an intervention is more likely to be successful.…”
“…In the future clinicians will be working within a "Digital Diabetes Ecosystem" that combines the Internet of Medical Things (connected physiological and behavioral sensors embedded within multiple medical devices worn or used by an individual) and the new smart pens to support insulin therapy with continuous access to the Internet. 10 We expect that insulin dosing data combined with real time continuous plasma insulin data, 11 together enhanced with the use of artificial intelligence and machine learning to support dose calculations, will eventually predict and prevent adverse events such as hypoglycemia. These predictions will be possible at a much earlier time when an intervention is more likely to be successful.…”
“…5,6 To prevent diabetes related complications, blood glucose levels must be tested and corrected several times a day. [7][8][9][10] The standard practice for testing blood glucose levels involves using a self-monitoring blood glucose (SMBG) device. SMBG devices are the most widely used monitoring technology for individuals with diabetes.…”
Section: Technology Reportmentioning
confidence: 99%
“…5,6 To prevent diabetes related complications, blood glucose levels must be tested and corrected several times a day. 7-10…”
This work represents a preliminary proof-of-concept design and verification of a 3D-printed glucose biosensor. The proof of concept presented is the first example of glucose dehydrogenase sensor fabricated by a 3D-printer while maintaining similar features to current lab-industry standards. The sensor was verified to detect physiological glucose concentrations between 0 and 400 mg/dL with a linear coefficient as high as .97. This study showed that it was possible to use 3D-printed technology to create a biosensor sensitive to glucose detection. As availability and functionality of 3D-printers expands, this technology has the potential to be an option for diabetes management. This preliminary study shows that the 3D-printed sensor platform holds promise for sensitive glucose detection.
“…Last, EIS offers the user rapid results; the results are displayed in 90 seconds or less, all while the process stays manufacturer-friendly. 2 Most important, EIS has demonstrated novel success in multimarker detection on a single electrode surface 3 with the potential for continuous monitoring. 4 Multimarker detection is achieved via optimal binding frequency (OBF) based off the biological specificity from molecular recognition elements (MRE).…”
Section: Letter To the Editormentioning
confidence: 99%
“…Verification and determination of OBFs specific to glycemic management has determined different OBF values for individual biomarkers. Shown in Figure 1A is the OBF for markers such as insulin at 810.5 Hz, 2 glucose at 1170 Hz, 1,5-anhydroglucitol 3,710 Hz, 6 glycated albumin at 1.18 Hz, and glycated hemoglobin of 547 Hz. Knowing the OBF allows for the use of a single electrode with multiple markers as shown in Figure 1B.…”
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