Identifying Diabetes Non-invasively
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BluCircle is interested in non-invasively assessing patients living in rural areas where detection of diabetes is historically low. Also, BluCircle looks to raise awareness of diabetes and preventative measures in these areas. Currently, the target area for our work is Belize, where 14.2% of people have diagnosed diabetes and there are an estimated 9,400 undiagnosed cases. After a year of research on glucose detection, we found work being done at the Indian Institute of Technology Delhi which showed a correlation between glucose levels in saliva and long-term glucose levels. Our design consists of filter paper coated in a glucose immobilizer, which can detect changes in pH and therefore glucose concentration. The RBG profile of the strips are analyzed with a Raspberry Pi and compared to a calibration curve to determine a numerical glucose level. Using a known “at-risk” glucose concentrations, patients can be referred to a doctor. This year, BluCircle is working to produce the first prototype of the saliva glucose reader and hopes to test it during summer 2017 in Belize. The trip will provide feedback on the prototype, including areas for improvement for a second prototype. Blucircle hopes that implementation of the portable, self-sufficient device would provide a low-cost, non-invasive option for diabetes detection both domestically and abroad.
BluCircle’s mission is to develop an innovative, non-invasive blood glucose detector targeted towards rural and underdeveloped areas with a high potential risk for Type 2 Diabetes. BluCircle also works to raise awareness on diabetes and preventative measures.
Worldwide, there are many areas where Type 2 Diabetes levels are believed to be very high, but these levels remain unknown because of inabilities to test and diagnose civilians. A non- invasive, portable, and sustainable diagnostic method would greatly improve consciousness and treatment.
Our proposed solution is to develop an optical, non-invasive glucose biosensor for salivary analysis. Saliva causes a color change on filter paper strips that have been saturated with a co-immobilize glucose oxidase solution and a pH sensitive dye. We will analyze the color change by finding the mean intensity of the RGB (red-green-blue) profile. Then, by correlating the intensities with different glucose concentrations, we can deduce a biosensor calibration curve which we will serve as a basis of comparison for experimental data. The final step of the process will to be to implement this system onto a Raspberry Pi, which will be self- contained within a box for accuracy and precision.
This simple approach of taking a RGB profile of paper strip for glucose monitoring is not only novel, but is cost-effective and convenient for at risk patients to be diagnosed and seen by a doctor.