Developing EDTA-Polymerized Cyclodextrin as a Drug-Delivering Polymer For Use in a Coronary Drug-Eluting Stent Coating

Overview

Horst A. von Recum Case Western Reserve University, Department of Biomedical Engineering Grace Burkhart Valparaiso University, Valparaiso IN. Drug-eluting stents (DES) release anti-proliferative drugs to prevent in-stent restenosis. However, current DES do not have sufficient periods of drug release to treat long-term restenosis. Cyclodextrin polymers (pCD) can slow drug release due to unique affinity for small hydrophobic drugs. Ethylenediaminetetraacetic acid (EDTA)- crosslinked pCD, which can chelate to metal stent surfaces, were tested as drug delivery polymers for DES coatings. This study looks at the attachment of these polymers to stent surfaces and compares the drug release patterns of various EDTA-crosslinked pCD particles to verify affinity-based release. It was expected that CD particles with higher degrees of crosslinking would slow release the most due to tighter concentration of cyclodextrins. Chelation tests with CoCl2, FTIR, SEM, and EDS were used to characterize particles and coatings. Drug release studies were carried out using drugloaded particles, and UV-Vis spectroscopy was used to quantify released drug. Sirolimus was used as a model drug. Color change and FTIR data confirmed chelating activity of EDTA-pCD. SEM indicated a texture difference between coated vs. uncoated stents, and EDS showed an increase of 4% in carbon composition, which comes from the coating. Cumulative drug release within 24 hours from highly- crosslinked EDTA-pCD was 82.4% lower than that of control nonaffinity EDTA-dextran particles, a reduction attributed to affinity. Compared to highlycrosslinked EDTA-pCD, however, less-crosslinked EDTA-pCD and control EDTA-dextran exhibited longer drug release. These preliminary results indicate that EDTA-pCD particles can chelate to metal stent surfaces, and that less-crosslinked EDTA-pCD particles and EDTAdextran particles have promising levels of extended drug release. Creation of Novel Molecular Biosensors Nikhita Mudium James Clemens High School Madison, Alabama Supervising Scientist: Dr. Luis Cruz-Vera University of Alabama at Huntsville Molecular biosensors are essential in biomedical research as well as monitoring disease progression, environmental changes, drug discovery, and food control. Some are synthetically engineered and others utilize organisms’ existing biochemical pathways, such as the amino acid Tryptophan’s catabolic pathway. In the last case the TnaC molecular sensor detects the amino acid Tryptophan. If the TnaC sequence is changed to produce multiple variants, then these multiple variants would detect other molecules than L-tryptophan. Through the use of mutagenesis of this tnaC gene and selection, a total of 6 mutants were tested for these characteristics by using the TetR gene for selection and tryptophan to test for induction. The numbers of the mutants correspond to the variant number assigned to them. They were tested against the wild type of the the tnaC gene and their corresponding stop codon variants. tnaC2 and tnaC5 were indicative of detecting other molecules, but need additional testing. Additionally, it was found that tnaC4 is in fact a new molecular biosensor. Multi-Analyte Precision Nanoparticle Sensor for Wound Management utilizing Lindenmayer Systems Research Summary Anushka Naiknaware Lake Oswego Senior High School Lake Oswego, Oregon Every year on average there are about 165 million injuries in the United States, both acute and chronic, which require wound treatment. Chronic wounds follow delayed healing patterns often due to preexisting medical conditions (Singer & Clark, 1999). Worldwide in excess of USD $50 Billion are spent on the treatment of chronic wounds alone (Fife & Carter, 2012). The number of patients with chronic wounds in the United States is greater than the number of patients with leukemia, colon, lung and breast cancer combined (AAWC, 2014). There is no cost-effective, precision, multi-analyte, mass manufacturable wound dressing which can monitor the conditions continually as well as keep the foreign pathogens out (Scognamiglio, Antonacci, Lambreva, Litescu, & Rea, 2015). Changing the dressing bed too early, or too late, can lead to the worsening of the wound and more frequent dressing changes. Also, infection cannot be detected without opening the wound and doing a direct physical examination of the affected area (McColl, MacDougall, Watret, & Connolly, 2009). This research has created a sensor where physicians will able to track the status of the wound through one or many variables including temperature, pH, moisture and oxygen level; quorum sensing regulation; and synthesized molecules such as pyocyanin. Continuously sensed, the detailed status is then wirelessly viewed over a smart- phone, and is connected to the Internet for remote monitoring and intelligent data analytics. Which subsequently allows for autoregulation of the wound condition in real-time. These sensors are created using inkjet printing, and are miniature, inexpensive, accurate, reliable and mass manufacturable. The approach uses biopolymer chitosan (Dai, Tanaka, Huang, & Hamblin, 2011; Muzzarelli, El Mehtedi, & MattioliBelmonte, 2014) in conjunction with single- and multi-layer carbon nanoparticles to effectively obtain all the required features including biocompatibility. The systematic ink formulations is further enhanced by making it capable of carrying quantum- dot nanocrystal to target complex analytes. These sensors are optimized over four generations. A fully functional prototype is built to demonstrate the mobile and cloud connectivity. Precision enhancement for patterning and controllability is obtained by line fractals created using Lindenmayer systems (L-Systems)(Sagan, 1994). The data obtained through characterization in a controlled environment show successful meeting of all the objectives. Identifying Tablets using Neural Networks Isha Narang (2nd Place Mathematics & Computer Science) Ardrey Kell High School Charlotte, North Carolina According to the FDA, approximately 1.3 million people are injured due to medicine errors annually in the United States. A large percentage of these people are the elderly and people with multiple medical conditions. So, the purpose of my project was to utilize data analytics and machine learning methods to provide a simple way to identify tablets, thus reducing medicine errors. First, I took pictures of different tablets and applied various filters to them in WEKA. Then, I ran the Decision Trees algorithm on features generated by each filter and selected the Auto Color Correlogram filter because its features resulted in the highest classification accuracy of 88.75%. The accuracy of this filter with Neural Networks (NN) was 97.5%. With this evidence of NN being a good classifier, I ran the algorithm available at Teachable Machine on my dataset to generate an NN model using TensorFlow Lite. I imported this model and embedded it into an Android app, which I named ‘Tablet Identifier’. I downloaded this app onto a virtual phone, and connected a webcam to my desktop in order to test the app. It correctly identified tablets with 99% accuracy. When published, more medications can be added for numerous medical conditions. Thus, anyone with a mobile phone can download the app, and identify any tablet when their phone’s camera is pointed towards it. As a result, people will be able to use this user friendly app at home before taking their medication, which will lower the number of medicine errors. Coral Grief: Machine Learning on Crowd-sourced Data to Highlight an Ecological Crisis Rithika Narayan Elwood John Glenn High School Elwood, New York Supervising Scientist: Anthony Pellicano Angion Biomedica Corp. Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached and/or diseased corals enables implementation of measures to halt or retard the same. Benthic cover analysis as a standalone measure of reef health is insufficient for identification of coral bleaching and/or disease. Pr oposed herein is a solution that couples machine learning with crowd-sourced data - images from government archives, citizen science projects, and personal images collected by tourists - to build a model capable of identifying healthy, bleached, and/or diseased coral. The student researcher collected hundreds of images of corals with various health conditions from open sources such as the National Oceanic and Atmospheric Administration’s records and the XL Catlin Seaview Survey and annotated these images using the image annotation platform Labelbox in order to highlight the regions of interest: healthy, bleached, black band disease, dark spot disease, white syndrome, or yellow band disease. These annotations were then used to build, train, and validate a Python-based model, adapted from an open source Mask R-CNN (region-based convolutional neural network) algorithm, within an Amazon Web Services EC2 remote computer. Use of the model on a test set of coral images yields over 85% accuracy in distinguishing healthy versus unhealthy coral. This machine learning-based model has the potential to rapidly analyze a large and growing database of images to identify coral bleaching and/or coral disease around the world, thereby enabling effective allocation of resources for preservation of our marine ecosystem.