Roshan Pillai

SAVR- Stroke and Cardiovascular Risk Assessment using Wearable Technology

Presented By Roshan Pillai, BASIS Scottsdale

Cardiovascular risk is the risk of a person developing cardiovascular diseases, often sudden. Heart disease is the leading cause of death for both men and women. Most of the current warning tools utilize the threshold level wherein, a warning is given when a patient’s symptom crosses a threshold. Unfortunately, this threshold method does not take into account other signs and symptoms demographic, or social factors, nor does it attain a 360-degree view of a patient’s
risk. Cardiovascular issues can appear rapidly and without warning. Heart attacks (Myocardial Infarction) and Strokes (Cerebrovascular Accidents) can cause irreversible damage and focus on prevention or early diagnosis is still needed.

Also, the current risk calculators and available tools do not provide results based on input from wearables/ live data and also do not incorporate the latest technology that’s available to the users to include information from their medical records. I created an app that utilizes an interoperable based approach where a patient/user can take their everyday data from a wearable, integrate the information available on their health app/health records and with user input, including their reported medical symptoms to screen and find out their relative risk for stroke/cardiovascular disease.

My design incorporated integration with Wearable Technology, the ability to integrate live data, and allow user input, as well as the ability to access health records (via PHI: Protected Health Information) in a HIPAA compliant manner to protect patient privacy. I formulated a hierarchical project architecture to create this application. My first layer was the data collection layer. Once I had obtained the data, I converted the data to .csv files for easier analysis. The next layer was the analytical model layer where I utilized machine learning, created a statistical model, and then combined the two models to create my algorithm. Once my algorithm generated both a viable and accurate structure for prediction, the next step was implementation. Lastly, I constructed an Intelligence Layer to access Wearable Technology as well as create SAVR (Stroke and Vascular Risk), my mobile application. For my machine learning algorithm, my unique approach was to mesh both statistical analyses through the use of Ensembling Models. The machine learning method used was XGBoost, an open-source, R and Python-based software library which is a versatile tool made to work through most regression, classification, and ranking problems as well as user-built objective functions. I chose XGBoost because it’s a library designed and optimized for boosted tree algorithms which are specifically tuned towards training an algorithm for software functionality. Also, XGBoost is known for its model performance/ accuracy and execution speed; both critical for this project. I then did the statistical model to add another layer to my algorithm accuracy. I did a linear regression and created a statistical model algorithm using Python. The algorithm plots all of its analyzed data then finds the most accurate linear line to its predictions. Then, I performed a linear regression to find out how my application should weigh different factors. After I generated both a machine learning model and a statistical model, I used Ensembling Models to combine the two. The combined algorithm revealed how different risk factors must be designed to create an accurate prediction for a patient.

Lastly, to build the SAVR Application, I used Ionic, a hybrid app development platform. Ionic is an open-source Java-Script-based Language. With Ionic, I combined the Analytical Model Layer and the Intelligence Layer to create the Presentation Layer, where the user could input their data and get a predicted Risk Score. This was SAVR, the wearable app that I hope that one day will be a tool that helps save lives.

Yakeleen Almazan and Asher Bankhead

Can Forensic Quality DNA be Extracted from Discarded Gum?

Presented by Yakeleen Almazan and Asher Bankhead, Pueblo High School

The purpose of our research is to determine whether forensic scientists can accurately use gum samples to extract Deoxyribonucleic Acid (DNA) and identify suspects. This research is especially helpful for forensic scientists who extract DNA to solve cases.

Evidence is a scarce resource for forensic scientists. We wish to determine if a mundane source like chewing gum can yield stable forensic quality DNA, and at what time frame. Furthermore, this involves the stability of the mitochondrial Cytochrome B gene over time.

DNA from the chewing gum was isolated and specific primers were used to amplify a part of Cytochrome B gene. Electrophoresis was performed with a gene ruler that allowed us to measure the gene product (measured by base pairs) and see if we isolated the correct gene. Other samples were sequenced and compared to the isolated part of the Cytochrome B gene.

The first set of results we got were our electrophoresis gels. There were two qualifiers that made the data forensic quality. The first qualifier being the amount of base pairs in our product. Nearly every sample was close to 230 base pairs. The other qualifier was the amount of DNA, which was determined by the brightness of the electrophoresis bands (products) which were variable. To judge them based on brightness, we created a scale from 1-5 based on the luminosity. The amount of DNA over time didn’t decrease by any meaningful amount. After visual analysis, we also received DNA sequencing on our gum samples. The sequencing found a 99% similarity to the part of the Cytochrome B gene we isolated. With the amount of DNA in the electrophoresis not decreasing, and our isolated genes being nearly identical, it is likely that gum found in DNA doesn’t decrease in quality up to a year, making it useful for forensic scientists.

Although we were focused on the mitochondrial DNA of the gum sample, it is unethical to use such analysis in a classroom because our work could potentially point us to an individual who has not committed a crime. Therefore, we used the cytochrome B gene in the mitochondrial DNA because its sequence is highly conserved and stable among humans. If we were to do forensic analysis on our gum samples, we would look at the variable region of the Mitochondrial DNA which would connect us to Haplogroups and eventually, a suspect. In the future we hope to conduct this experiment in various environments to examine how these variables impact the DNA stability.

Rebecca Jernigan

New Structural Insights into the Function of the Active Full-Length Human Taspase1: A Novel Anticancer Therapeutic Target

Presented by Rebecca Jernigan (Arizona State University – Graduate Student)

Taspase1 (threonine aspartase 1) is an endopeptidase overexpressed in primary human cancers that has been identified as a novel potentially potent anticancer drug target. Loss of Taspase1 activity has been demonstrated to disrupt proliferation of human cancer cells in vitro and in mouse tumor xenograft models of glioblastoma. By functioning as a non-oncogene addiction protease, Taspase1 coordinates cancer cell proliferation, invasion and metastasis. Taspase1 encodes a highly conserved 50 kDa inactive proenzyme that undergoes auto-proteolytic cleavage becoming an active heterodimer that displays an overall αββα structure. The crystallographic structures of the proenzyme and a truncated version of activated Taspase1 are known. In this study, the crystallographic structure of the full length active human Taspase1 is shown to 3.1Å. For the first time, a key structure element has been identified: a long helix of about 50 residues that was missing in previous reported structures of the activated enzyme. Previously his helix was predicted to have a helix-turn-helix conformation lying right on top the catalytic site of Taspase1; however the crystallographic structure of the full-length taspase1 shows a straight helix conformation. This opens new insights in the enzymatic mechanisms for possible substrate recruitment of Taspsae1 and suggest the long fragment as a novel target for the design of medical drugs that inhibit the function of Taspase1 enzyme.

Caitlin Moffett

Repurposing Exhaled CO2 for Spacecraft

Presented By Caitlin Moffett , University of Arizona (Graduate Student)

Life support is a vital component in any manned space mission, and some waste production is a small price to pay for keeping astronauts safe and healthy in space. Historically, life support on the ISS and other manned spacecraft have produced a methane by-product that is vented to space. Honeywell’s methane pyrolysis reactor now provides the opportunity for this waste methane to be reclaimed, transforming it into useful hydrogen and a solid carbon composite material that will grow in quantity with the progression of a space mission. If a viable application can be found for this carbon, it will greatly lower waste and raise the efficiency of the life support system as a whole. Our team, tasked with the challenge of finding such an application, has designed a storage box constructed out of the carbon material with little additional resources. It will be constructed out of 6 square carbon pieces adhered to a fabric base, which can be folded up to create a cube shaped container.

We have endeavored to create a design that succeeds on multiple levels: it not only employs the carbon material in an application that is appropriate to the spacecraft environment and can be used by astronauts on a trip home from Mars, but can also make use of other waste products such as astronaut clothing that has reached the end of its designated wearable lifetime. The box design is simple, collapsible for easy storage, and easily scalable depending on intended use. With this design, it is our hope that the first astronauts to walk on Mars will be able to store experimental samples, food, and other items in carbon boxes, produced while breathing into a life support system that is nearly zero-waste.

Subhadeep Dutta

Radiation-responsive Nanosensor Gel (RaNG): A New way to Monitor Cancer Radiotherapy Doses

Presented By Subhadeep Dutta, Arizona State University Graduate Student

Radiotherapy using ionizing radiation (e.g. X rays) still remains a mainstay of treatment modalities in clinic for different types of cancers. Overexposure to radiation can induce toxicity and damage to healthy tissue, whereas underdosing can lead to poor efficacies of tumor ablation. Robust radiation sensors are in critical demand to ensure effective delivery of radiation to patients undergoing radiotherapy. Currently existing sensors possess several inherent limitations in rapid radiation detection because of their sensitivity to light and heat, fragility, long processing times, dose dependence, complexity of application process and / or high costs. Commonly used NanodotsTM require separate read-out devices, do not conform to tissue morphologies and are expensive as well. We developed a novel easy-to-use Radiation-responsive Nanosensor Gel (RaNG)-technology, based on the formation of maroon-colored gold nanoparticles from their colorless precursor formulations, with exposure to different levels of ionizing radiation. By measuring the intensity of color developed with a simple bench-top absorbance spectrophotometer, we were able to precisely detect doses typically administered in fractional cancer radiotherapy (i.e. ranging from 0-5Gy) and beyond. Topographical distribution of delivered radiation doses was qualitatively and quantitatively determined and efficacy of the hydrogel sensor was validated using different ionizing radiation sources including protons, electrons, photons and radioactive isotopes. Translational capability of the nanosensor was demonstrated using anthropomorphic phantoms and in live canine patients undergoing clinical radiotherapy treatments (in collaboration with Banner MD Anderson Cancer Center). Our results manifest a new generation of low-cost nanosensor for colorimetric detection of radiation dose to improve patient safety in clinical radiotherapy and trauma care.

Ghena Krdi

Characterizing Primary Mesothelioma Cell Lines by Exome Sequencing

Presented by Ghena Krdi, University of Arizona (Graduate Student)

Malignant Pleural Mesothelioma is a rare and aggressive type of lung cancer. Currently, its main cause is asbestos exposure. It is usually discovered at an advanced stage which makes treatment harder as there is no cure. This is due to the lack of knowledge about early symptoms of the disease. Although tumor development takes a long time, there has not been enough research about reliable early disease biomarkers to look for in patients who are at risk. Along with that, mesothelioma in vitro research is usually conducted on commercial cell lines which do not yield accurate results due to the difference in the environment and metabolism of the cells. Primary cell lines are proven to yield more reliable in vitro results in mesothelioma research because the cells come from real patients. Although research is still conducted in vitro, primary cell lines are considered a better representation of mesothelioma because they show more accurate cell metabolic profiles and responses to drugs tested.

Besides using primary cell lines, DNA exome sequencing, a time efficient and inexpensive technique, is used for identifying specific DNA mutations which are important for identifying possible disease biomarkers including the expression of certain genes or transcription factors that are common for mesothelioma tumor cells. Computational analysis of exome sequencing data can be used to make conclusions about copy number variation and genes associated with tumor progression.

Isabele Ross

Green Infrastructure Impacts on Carbon Cycling: Evaluating Changes in Soil Microbial Composition and Function

Presented by Isabel Ross, Cienega High School, Vail, Arizona

Green Infrastructure (GI) redirects water into the soil, affecting soil microbial diversity, decomposition, and stabilization of carbon. Soil microbes drive decomposition and carbon stabilization, making them important for carbon cycling. Decomposition by soil microbes releases carbon dioxide, a greenhouse gas, into the soil and atmosphere which is important for climate change. This project focuses on how GI systems change soil microbial community composition and function to understand how GI affects the carbon cycle. We used a phylogenetic gene marker and FAPROTAX to identify bacteria taxa and function. We observed changes in microbial composition and key functions with rainwater harvesting techniques. Although decomposition also creates more nutrients for carbon-absorbing plants, increased decomposition observed in this study could lead to additional sources of greenhouse gases.

Suraj Puvvadi

Validation of Predictions From a Novel Small-Sample Statistic of Valley Fever and Dysregulated Pathways and Increasing Statistical Power of Metabolite Identifiers Through Biocuration

Presented by Suraj Puvvadi, BASIS Scottsdale

Precision medicine is an approach to patient care that optimizes the efficiency and personalizes treatments based on transcriptomic, genomic, and metabolomic profiling. Rigorous curation of high throughput data is the first step towards efficient precision medicine. The purpose of this research is to validate findings from a novel small-sample valley fever statistic and to increase the statistical power of a precision medicine framework.

Disseminated coccidioidomycosis (DCM) is a chronic and fungal form of valley fever that’s prevalent in the southwestern parts of the United States. Currently, no anti-fungal treatment exists for DCM which makes it a lethal disease. Through literature biocuration and non-deterministic ratings of associations between DCM and ontological medical conditions, significant dysregulated molecular pathways were unveiled. Patient associations were ranked based on types of limitations, experimental nuances, and environmental setup. Interrater agreement scores and hypothesis testing were used to authenticate association ratings. A 71.4% rate of the novelty of dysregulated pathways was calculated which indicated the specific pathway relationships that need to be studied to create individualized treatments for DCM.

Metabolomic profiling is part of the foundation of precision medicine. An existing N-of-1 pathways framework uses transcriptomic data to create patient profiles targeting molecular pathways that need to be treated. Creating a metabolomic layer to the N-of-1 framework increases the accuracy and clarity of a treatment profile. After the systematic biocuration of unique serum metabolites, these novel metabolite identifiers are linked to molecular pathways from high-throughput databases which increases the statistical power of dysregulated pathways used in the N-of-1 framework. Following the metabolite curation, a significant statistical boost was observed. These new metabolites will be further investigated through literature biocuration, as in the DCM component.

Overall, this project emphasizes the importance of biocuration techniques and is the first step in producing individualized treatments to heal humanity.

Katherine Wei

How Single Nucleotide Polymorphisms (SNPs) in Apolipoprotein (APOE) Impact Alzheimer’s Disease (AD) Pathology

Presented By Katherine Wei, BASIS High School, Chandler, Arizona

Alzheimer’s Disease (AD) is a neurodegenerative disease caused by beta-amyloid plaques that build up in the brain as well as tau protein which form tangles around brain cells. Currently, there is no cure for AD but there are some therapeutic treatments to slow down the process. Apolipoprotein E (APOE) is a protein that has been associated with Alzheimer’s and cardiovascular diseases because of its role in metabolism of fats in the body. This experiment will use several different databases in order to analyze the effects of single nucleotide polymorphisms (SNP) in the function of the APOE gene. Specifically, analysis of structure and function of different SNPs that may lead to changes in the gene will be conducted and comparisons between the expression of the APOE4 gene and cognitive impairment will be evaluated to determine their relationship. Using datasets that have the frequency of patients who suffer from AD as well as SNPs that are present in the patient’s genes, visualizations and graphs will be drawn to evaluate the relationship between SNPs and AD. Common SNPs in the APOE gene that may induce late-onset Alzheimer’s Disease (LOAD) are rs7412, rs429358, and rs769455. The purpose of this research is to understand how these SNPs in APOE correlate to Alzheimer’s Disease and how they may play a role in the production of beta-amyloid plaques. Understanding these concepts is fundamental in getting a better grasp of the functionality of the APOE4 gene in Alzheimer’s and possibilities in new therapeutic treatments.

Devin Bowes

SARS-CoV-2 Monitoring in Wastewater as an Early Warning Signal for COVID-19 Presence in Communities

Presented By Devin Bowes, Arizona State University

The COVID-19 global pandemic caused by the virus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has highlighted several challenges within our healthcare system, such as accessible testing and rapid reporting. Wastewater-based Epidemiology (WBE) has been proven to serve as an effective, near real-time tool to understand human health and behavior at population-scale by analyzing raw sewage for human biomarkers excreted via urine and/or feces. The study within showcases the first ever longitudinal investigation of monitoring SARS-CoV-2 in municipal wastewater at the neighborhood level to track trends throughout the COVID-19 global pandemic. Untreated wastewater samples were collected from within the sewer collection system throughout Tempe, Arizona and analyzed for SARS-CoV-2 using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Preliminary results indicate peak loadings of the virus observed in wastewater precede reported peaks of positive new clinical cases, COVID-related hospitalizations, and COVID-related deaths. This study holds great promise for the future of population-level diagnostics; acting as an early warning system for emerging infectious diseases and allowing more time for hospital preparation, resource deployment, and targeted educational interventions throughout communities.