Developing A Urinalysis Immunoassay for Cortisol Detection Year 2

Overview

Rockdale Magnet School for Science and Technology In recent years, societal focus has shifted towards maintaining a balanced stress level. Stress is an issue that all people will face in their lifetime, however, few know the severity that results in chronic stress. Cortisol is the hormone that is known as the “biochemical marker of stress.” Numerous health problems occur as a result of increased cortisol and may cause a large range of side effects. Although interest in this field is increasing, tests available for measuring cortisol are limited. This project focuses on the development of a urinalysis immunoassay for cortisol detection. Similar to a pregnancy test, the results of this test show a number of lines depending on a person’s stress level. First, to create the test, a variety of glass fibers were tested for their absorbance ability. Next, a conjugate pad was created by mixing a solution of a purified cortisol antibody and gold nanoparticles. Then, the remainder of the cortisol antibody was mixed with fluorescent dyes and added to the strip. After, the components of the test strip were all put together to create a finalized test. A plastic cassette was designed using a 3D printer. Finally, numerous trials were completed to check accuracy using synthetic urine with altered amounts of cortisol. The results of the test found it to be 96% accurate, completing the original engineering goal. Because an excess of stress is common, the method created in this procedure will assist millions worldwide, allowing them to check their cortisol levels rapidly and inexpensively. An Inexpensive Smartphone-Based Device for Rapid, Non-Invasive, and Point-of-Care Monitoring of Diabetes with Related Ocular and Cardiovascular Complications Kasyap Chakravadhanula BASIS Scottsdale Scottsdale, AZ Supervising Mentor: Madhavi Chakravadhanula Grand Canyon University Diabetic retinopathy is the leading cause of blindness among working class adults, and cardiovascular disease is the leading cause of death worldwide. However, diagnosis is often too late to prevent irreversible damage caused by these linked conditions. The first goal of this project was to create an integrated test, automated and not requiring laboratory blood analysis, for diagnosis/screening of these conditions. First, a random forest model was developed by retrospectively analyzing the influence of various risk factors (obtained quickly and non- invasively) on cardiovascular risk. Next, a deep-learning model was developed for prediction of diabetic retinopathy from retinal fundus images by transfer learning the InceptionV3 model and pre-processing the images via automatic vessel segmentation. Then, a colorimetric sensor was developed to measure saliva acetone concentration to track diabetes, providing a “warning system” and enhancing early intervention for these conditions and many other complications. The models were integrated into a smartphone-based device, combined with the saliva acetone sensor and an inexpensive 3D-printed retinal imaging attachment. Accuracy scores, as well as the receiver operating characteristic curve, the learning curve, and other gauges, were promising. This test is much cheaper and faster, enabling continuous monitoring for diabetes and its complications. It has the potential to replace the manual methods of diagnosing both diabetic retinopathy and cardiovascular risk, which are time consuming and costly processes only done by medical professionals away from the point of care, and to prevent irreversible blindness and heart-related complications through faster, cheaper, and safer monitoring of diabetes.

Awards (1)

• 1st Place Medicine & Health/Behavioral Sciences

Competition history

• JSHS 2020 Category not listed

Resources

• National JSHS abstract book ↗

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