Health Sciences – Page 20 – UROP Spring Symposium 2021

Health Sciences

Predicting Pathogenicity of Clinical Mutations through Deep Learning

Recent developments in gene sequencing and personalized medicine provide a remarkable opportunity to revolutionize healthcare. Many single-nucleotide polymorphisms (nsSNPs) are associated with disease-causing mutations. Because these mutations are often eliminated from the gene pool through purifying selection, the opportunity to determine their relationship to human disease is rare. Previous studies have developed neural networks that can identify pathogenic mutations to an adequate accuracy, but success with solely human variants is still lacking. Here we will train a deep neural network with a large data set of clinically annotated human variants from the dbSNP database. Each input layer is comprised of a sequence containing a clinically annotated variant from the dbSNP database, as well as an evolutionary profile of closely homologous sequences generated from multiple sequence alignment. This input is fed through multiple layers of feature extraction to achieve a final output determination of benign or pathogenic. The final trained network will then be tested on a smaller, separate data set of disease variants to gauge the efficacy and accuracy of the training. Our aim is to develop a neural network that is able to identify pathogenic mutations in human disease patients with high accuracy. This will hopefully allow future studies to utilize this network in the diagnosis or treatment of rare disease patients.

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A Deeper Look into the World of Addiction

The United Community Addiction Network (UCAN) empowers those in the community of Genesee County, Michigan struggling with substance use disorders and addiction. UCAN networks with hospitals, law enforcement, the judicial system, schools, and other systems to evolve how people are treated and how professionals treat co-occurring disorders. They combine the community and clinical aspects of treatment to empower people and to help them find sobriety and reach their potential. Less than 10% of people in need of treatment are being treated for co-occurring disorders alongside their substance use disorder, which causes too many people to relapse and to lack the treatment and resources they need. UCAN is creating an interdisciplinary model for treatment across the world that gives people the resources they need and makes the recovery process easier and more manageable. Through this research project and the current work of UCAN, by analyzing data, we are able to identify gaps in multiple systems and create a cohesive program that addresses every aspect of substance abuse and provides every resource to those who need it.

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A Deeper Look into the World of Addiction

The United Community Addiction Network or UCAN is a community organization based in Genesee County, Michigan that is working to evolve the way addiction is addressed and treated. The project “A Deeper Look into the World of Addiction” sought to aid the planning, development, and implementation of a new treatment program combining community and clinical services for a pathway to recovery. The focus of the new program is to use evidence based practices in order to treat co-occurring substance use and mental health disorders more effectively. Some evidence based practices include consumer-driven treatment, family and community involvement, and having cross-trained specialists. Alongside the development of a new addiction treatment program, UCAN’s other initiatives work to instill lasting change by including medical examiners, emergency responders, local law enforcement, and a new K-9 unit that aims to identify substances for a drug-free environment. In a coordinated effort with community members, UCAN aims to see the implementation of a co-occuring disorder facility as well as other initiatives to be replicated across the state, country, and around the world.

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In silico drug design for pain and addiction therapeutics

Traditional opioids target the mu opioid receptor and remain the de facto treatment for pain management. However, they are responsible for a myriad of unwanted side effects, such as addiction and respiratory depression, limiting long-term clinical utility. The kappa opioid receptor has emerged in recent years as a viable drug target as a means to avoid the undesirable side effects stemming from the mu opioid receptor. Thus, identifying a novel kappa opioid receptor agonist would be of great therapeutic value. To better understand the preference of the kappa opioid receptor’s preference for agonists or antagonists, opioids known to target this receptor were subjected to molecular docking against the agonist- and antagonist-bound structures to predict whether either had preference for an opioid type. After ranking ordering based on the docking score, no preference was observed for either receptor. Additional optimization of the docking protocol, such as the use of pharmacophore constraints, would potentially improve results. Structurally, a better understanding of opioid preference for the kappa opioid receptor would aid in the selection of compounds following a virtual screen for experimental validation.

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Improving the accuracy of detecting single-exon deletions in human cancers

Current algorithms that analyze data from Next-generation sequencing(NGS) systems have a blind spot for Copy Number Variants(CNVs) involving one or two exons. This involves the insertion or deletion of entire exons or coding regions of the DNA. This is an important complication because deletions involving one or two exons are usually associated with the deactivation of an entire gene. The purpose of this research project is to design a novel algorithm which can detect single exon CNVs and quantify how common these aberrations are in human cancers. This project utilizes the R programming language and popular R packages such as Bioconductor to design the algorithm and parse genetic data. The algorithm will be built using existing statistical techniques such as the Hall and von Neumann estimates for variance. The data is obtained from a database of hundreds of samples in order to accurately describe the per-exon variance. Results are expected that allow for more consistent identification of single exon CNVs in human cancers. It is also possible that this increase in sensitivity and accuracy will be associated with a decrease in specificity when compared to current algorithms. This research can improve the ability to characterize different cancers which can help inform clinical diagnostics and ultimately improve outcomes for patients.

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Identification of novel genetic drivers of pediatric tumors

Acute lymphoblastic leukemia (ALL) is the most common cancer among pediatric patients and is characterized by fewer mutations than adult cancers, suggesting that aberrant gene expression is a critical factor in treatment and prognosis. Gene expression data can identify targeted therapy pathways by identifying more precise cancer profiles based on genetic expression. To assess effectiveness of RNA expression analysis at identifying target genes, mRNA copy counts from tumor samples of 32 pediatric ALL patients were gathered. Sample grouping was explored using principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. The samples were then split according to expression levels of known neuroblastoma driver ALK and then analyzed for differentially expressed genes using the DESeq2 and limma workflows. As part of the preliminary findings, differential expression analysis revealed differences within the group in expression of known neuroblastoma drivers MYCN, ALK, PHOX2B, and TERT. This also revealed limitations of RNAseq analysis due to noise at low expression levels and outliers that skewed mean expression data. Analysis of RNA expression data shows promising results by identifying groups of similar cancer profiles and identifying differentially expressed genes as therapeutic targets.

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Impact of a Web-Based Naloxone Training on First Responders

In the midst of the pandemic, the United States continues to experience an opioid epidemic. Fatal opioid overdoses have risen significantly during the pandemic. Naloxone is an opioid antagonist, and is an effective public health intervention to reduce opioid overdoses. Law enforcement officers are often first to arrive at the scene of an overdose. To equip law enforcement officers to respond to opioid overdoses, a web-based naloxone training program was developed in collaboration with our community partners (www.overdoseaction.org). The purpose of this study is to evaluate the impact of our First Responder web-based naloxone training program. We aim to use descriptive statistics and paired samples t-test to analyze pre-test and post-test data to assess the law enforcement officers’ improvements in knowledge, confidence, and attitudes towards naloxone and overdoses. We anticipate our results will show an increase in knowledge, confidence, and attitudes among law enforcement officers who participate in our web-based naloxone training.

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Consumer Response to Self-Sampling Methods for Cervical Cancer Screening

Cervical cancer is one of the most prominent cancers in the world, as it is the third most prevalent cancer in women across the globe. However, it is also deemed as one of the most treatable cancers as long as there is early detection and diagnosis because of the primary and secondary stages of prevention: “the HPV vaccine and cervical cancer screening have made it one of the most preventable cancers” (CDC.gov). The problem is that most people are unaware that they are infected with the Human Papillomavirus (HPV), and that many women between the recommended ages do not receive a Pap smear since it is not an accessible screening for everyone. Consequently, no treatment will be given to the patient, resulting in a greater risk of obtaining cervical cancer if the patient is infected with high-risk HPV. In this MISSH study, we observe the consumer response to two self-sampling kits used to detect the presence of high-risk HPV. One kit includes the urine sampling method (ColliPee), and the other includes the vaginal method (Eve brush). Women who volunteered to participate in this study were given the options of either using one method of their choice, or using both kits. These self-sampling kits are sent through the mail; and once the sampling is done, extensive phone interviews were conducted to inquire about the patient’s preferences. Through this, we were able to learn what adjustments/accommodations are needed to be made in order to make the self-sampling methods more widely accepted, taking into account that more women feel more comfortable and are more likely to get screened with a self-sampling method.

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The Relationship between COVID-19 Racial Discrimination and Mental Health among Asian Americans

Racism and discrimination is a persisting dilemma that has affected people of color in the United States on many levels, including employment, housing, and health. Specifically, there is multiple evidence that indicates cultural racism (including stereotyping) connected to an increased prevalence of mental health concerns and poor overall wellness. Since the beginning of the COVID-19 pandemic, there has been an influx of anti-Asian racism and violence towards Asian American populations. In addition to the implicit and explicit bias that Asian populations have been facing prior to the COVID-19 pandemic, this current anti-Asian sentiment has the potential to have a toll on their mental and physical health. This research project explores the relationships between encountered racism during the COVID-19 pandemic and mental state among Asian American populations. The method used in this study is a quantitative survey; data collected were based on online survey questions that include demographics, experiences related to racism and/or discrimination after the start of the COVID-19 outbreak, mental state, and actions taken as results of racism experiences. The results and conclusion of the survey are yet to be reviewed and determined. I expect to identify an association between negative COVID-19 racism encounters/heightened fear for one’s safety and worsened mental health. With these results, we can address the need for prevention and intervention strategies to eliminate acts of racism/discrimination and to provide safe and comfortable environments for Asian Americans.

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Vision Zero implementation and how to improve its effectiveness in underserved communities

The Center for Health Disparities Innovation and Studies (CHDIS) team at EMU has begun working with the city of Hamtramck, a predominately ethnic and minority city on the outskirts of Detroit, in a mission to address public health deficiencies and disparities in underserved communities through policy change, community outreach/education, and general health promotion.  A core part of their proposal to the city is the implementation of a Vision Zero (VZ) strategy to combat the growing numbers of traffic accidents that has disproportionately affected the pedestrians/civilians of Hamtramck. VZ requires a complete overhaul on the relationship shared between road/transportation infrastructure and pedestrians/.

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