Matching Items (31)
ContributorsMozart, Wolfgang Amadeus, 1756-1791 (Composer)
ContributorsMozart, Wolfgang Amadeus, 1756-1791 (Composer)
Description
Influenza remains a constant concern for public health agencies across the nation and worldwide. Current methods of surveillance suffice but they fall short of their true potential. Incorporation of evolutionary data and analysis through studies such as phylogeography could reveal geographic sources of variation. Identification and targeting of such sources for public health initiatives could yield increased effectiveness of influenza treatments. As it stands there is a lack of evolutionary data available for such use, particularly in the southwest. Our study focused on the sequencing and phylogeography of southwestern Influenza A samples from the Mayo Clinic. We fully sequenced two neuraminidase genes and combined them with archived sequence data from the Influenza Research Database. Using RAxML we identified the clade containing our sequences and performed a phylogeographic analysis using ZooPhy. The resultant data were analyzed using programs such as SPREAD and Tracer. Our results show that the southwest sequences emerged from California and the ancestral root of the clade came from New York. Our Bayesian maximum clade credibility (MCC) tree data and SPREAD analysis implicates California as a source of influenza variation in the United States. This study demonstrates that phylogeography is a viable tool to incorporate evolutionary data into existing forms of influenza surveillance.
ContributorsTurnock, Adam Ryan (Author) / Scotch, Matthew (Thesis director) / Halden, Rolf (Committee member) / Pycke, Benny (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2013-05
Description
Despite a continuously growing body of evidence that they are one of the major causes of pregnancy loss, preterm birth, pregnancy complications, and developmental abnormalities leading to high rates of morbidity and mortality, viruses are often overlooked and underestimated as teratogens. The Zika virus epidemic beginning in Brazil in 2015 brought teratogenic viruses into the spotlight for the public health community and popular media, and its infamy may bring about positive motivation and funding for novel treatments and vaccination strategies against it and a variety of other viruses that can lead to severe congenital disease. Lymphocytic choriomeningitis virus (LCMV) is famous in the biomedical community for its historic and continued utility in mouse models of the human immune system, but it is rarely a source of clinical concern in terms of its teratogenic risk to humans, despite its ability to cause consistently severe ocular and neurological abnormalities in cases of congenital infection. Possibilities for a safe and effective LCMV vaccine remain difficult, as the robust immune response typical to LCMV can be either efficiently protective or lethally pathological based on relatively small changes in the host type, viral strain, viral dose, method of infection/immunization, or molecular characteristics of synthetic vaccination. Introducing the immunologically unique state of pregnancy and fetal development to the mix adds complexity to the process. This thesis consists of a literature review of teratogenic viruses as a whole, of LCMV and its complications during pregnancy, of LCMV immunopathology, and of current understanding of vaccination against LCMV and against other teratogenic viruses, as well as a hypothetical experimental design intended to initially bridge the gaps between LCMV vaccinology and LCMV teratogenicity by bringing a vaccine study of LCMV into the context of viral challenge during pregnancy.
ContributorsHarris, Maryl (Author) / Blattman, Joseph (Thesis director) / Scotch, Matthew (Committee member) / Luna, Evelyn (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Introduction: Diabetes Mellitus (DM) is a significant health problem in the United States, with over 20 million adults diagnosed with the condition. Type 2 Diabetes Mellitus, characterized by insulin resistance, in particular has been associated with various adverse conditions such as chronic kidney disease and peripheral artery disease. The presence of Type 2 Diabetes in an individual is also associated with various risk factors such as genetic markers and ethnicity. Native Americans, in particular, are more susceptible to Type 2 Diabetes Mellitus, with Native Americans having over two times the likelihood to present with Type 2 DM than non Hispanic whites. Of worry is the Pima Indian population in Arizona, which has the highest prevalence of Type 2 DM in the world. There have been many risk factors associated with the population such as genetic markers and lifestyle changes, but there has not been much research on the utilization of raw data to find the most pertinent factors for diabetes incidence.
Objective: There were three main objectives of the study. One objective was to elucidate potential new relationships via linear regression. Another objective was to determine which factors were indicative of Type 2 DM in the population. Finally, the last objective was to compare the incidence of Type 2 DM in the dataset to trends seen elsewhere.
Methods: The dataset was uploaded from an open source site with citation onto Python. The dataset, created in 1990, was composed of 768 female patients across 9 different attributes (Number of Pregnancies, Plasma Glucose Levels, Systolic Blood Pressure, Triceps Skin Thickness, Insulin Levels, BMI, Diabetes Pedigree Function, Age and Diabetes Presence (0 or 1)). The dataset was then cleaned using mean or median imputation. Post cleaning, linear regression was done to assess the relationships between certain factors in the population and assessed via the probability statistic for significance, with the exclusion of the Diabetes Pedigree Function and Diabetes Presence. Reverse stepwise logistic regression was used to determine the most pertinent factors for Type 2 DM via the Akaike Information Criterion and through the statistical significance in the model. Finally, data from the Center of Disease Control (CDC) Diabetes Surveillance was assessed for relationships with Female DM Percenatge in Pinal County through Obesity or through Physical Inactivity via simple logistic regression for statistical significance.
Results: The majority of the relationships found were statistically significant with each other. The most pertinent factors of Type 2 DM in the dataset were the number of pregnancies, the plasma glucose levels as well as the Blood Pressure. Via the USDS Data from the CDC, the relationships between Female DM Percentage and the obesity and inactivity percentages were statistically significant.
Conclusion: The trends found in the study matched the trends found in the literature. Per the results, recommendations for better diabetes control include more medical education as well as better blood sugar monitoring.With more analysis, there can be more done for checking other factors such as genetic factors and epidemiological analysis. In conclusion, the study accomplished its main objectives.
Objective: There were three main objectives of the study. One objective was to elucidate potential new relationships via linear regression. Another objective was to determine which factors were indicative of Type 2 DM in the population. Finally, the last objective was to compare the incidence of Type 2 DM in the dataset to trends seen elsewhere.
Methods: The dataset was uploaded from an open source site with citation onto Python. The dataset, created in 1990, was composed of 768 female patients across 9 different attributes (Number of Pregnancies, Plasma Glucose Levels, Systolic Blood Pressure, Triceps Skin Thickness, Insulin Levels, BMI, Diabetes Pedigree Function, Age and Diabetes Presence (0 or 1)). The dataset was then cleaned using mean or median imputation. Post cleaning, linear regression was done to assess the relationships between certain factors in the population and assessed via the probability statistic for significance, with the exclusion of the Diabetes Pedigree Function and Diabetes Presence. Reverse stepwise logistic regression was used to determine the most pertinent factors for Type 2 DM via the Akaike Information Criterion and through the statistical significance in the model. Finally, data from the Center of Disease Control (CDC) Diabetes Surveillance was assessed for relationships with Female DM Percenatge in Pinal County through Obesity or through Physical Inactivity via simple logistic regression for statistical significance.
Results: The majority of the relationships found were statistically significant with each other. The most pertinent factors of Type 2 DM in the dataset were the number of pregnancies, the plasma glucose levels as well as the Blood Pressure. Via the USDS Data from the CDC, the relationships between Female DM Percentage and the obesity and inactivity percentages were statistically significant.
Conclusion: The trends found in the study matched the trends found in the literature. Per the results, recommendations for better diabetes control include more medical education as well as better blood sugar monitoring.With more analysis, there can be more done for checking other factors such as genetic factors and epidemiological analysis. In conclusion, the study accomplished its main objectives.
ContributorsKondury, Kasyap Krishna (Author) / Scotch, Matthew (Thesis director) / Aliste, Marcela (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Alzheimer’s disease (AD) is a neurodegenerative disease resulting in loss of cognitive function and is not considered part of the typical aging process. Recently, research is being conducted to study environmental effects on AD because the exact molecular mechanisms behind AD are not known. The associations between various toxins and AD have been mixed and unclear. In order to better understand the role of the environment and toxic substances on AD, we conducted a literature review and geospatial analysis of environmental, specifically wastewater, contaminants that have biological plausibility for increasing risk of development or exacerbation of AD. This literature review assisted us in selecting 10 wastewater toxic substances that displayed a mixed or one-sided relationship with the symptoms or prevalence of Alzheimer’s for our data analysis. We utilized data of toxic substances in wastewater treatment plants and compared them to the crude rate of AD in the different Census regions of the United States to test for possible linear relationships. Using data from the Targeted National Sewage Sludge Survey (TNSSS) and the Centers for Disease Control and Prevention (CDC), we developed an application using R Shiny to allow users to interactively visualize both datasets as choropleths of the United States and understand the importance of this area of research. Pearson’s correlation coefficient was calculated resulting in arsenic and cadmium displaying positive linear correlations with AD. Other analytes from this statistical analysis demonstrated mixed correlations with AD. This application and data analysis serve as a model in the methodology for further geospatial analysis on AD. Further data analysis and visualization at a lower level in terms of scope is necessary for more accurate and reliable evidence of a causal relationship between the wastewater substance analytes and AD.
GitHub Repository: https://github.com/komal-agrawal/AD_GIS.git
GitHub Repository: https://github.com/komal-agrawal/AD_GIS.git
ContributorsAgrawal, Komal (Author) / Scotch, Matthew (Thesis director) / Halden, Rolf (Committee member) / College of Health Solutions (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The 2017-2018 Influenza season was marked by the death of 80,000 Americans: the highest flu-related death toll in a decade. Further, the yearly economic toll to the US healthcare system and society is on the order of tens of billions of dollars. It is vital that we gain a better understanding of the dynamics of influenza transmission in order to prevent its spread. Viral DNA sequences examined using bioinformatics methods offer a rich framework with which to monitor the evolution and spread of influenza for public health surveillance. To better understand the influenza epidemic during the severe 2017-2018 season, we established a passive surveillance system at Arizona State University’s Tempe Campus Health Services beginning in January 2018. From this system, nasopharyngeal samples screening positive for influenza were collected. Using these samples, molecular DNA sequences will be generated using a combined multiplex RT-PCR and NGS approach. Phylogenetic analysis will be used to infer the severity and temporal course of the 2017-2018 influenza outbreak on campus as well as the 2018-2019 flu season. Through this surveillance system, we will gain knowledge of the dynamics of influenza spread in a university setting and will use this information to inform public health strategies.
ContributorsMendoza, Lydia Marie (Author) / Scotch, Matthew (Thesis director) / Hogue, Brenda (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Phylogenetic analyses that were conducted in the past didn't have the ability or functionality to inform and implement useful public health decisions while using clustering. Models can be constructed to conduct any further analyses for the result of meaningful data to be used in the future of public health informatics. A phylogenetic tree is considered one of the best ways for researchers to visualize and analyze the evolutionary history of a certain virus. The focus of this study was to research HIV phylodynamic and phylogenetic methods. This involved identifying the fast growing HIV transmission clusters and rates for certain risk groups in the US. In order to achieve these results an HIV database was required to retrieve real-time data for implementation, alignment software for multiple sequence alignment, Bayesian analysis software for the development and manipulation of models, and graphical tools for visualizing the output from the models created. This study began by conducting a literature review on HIV phylogeographies and phylodynamics. Sequence data was then obtained from a sequence database to be run in a multiple alignment software. The sequence that was obtained was unaligned which is why the alignment was required. Once the alignment was performed, the same file was loaded into a Bayesian analysis software for model creation of a phylogenetic tree. When the model was created, the tree was edited in a tree visualization software for the user to easily interpret. From this study the output of the tree resulted the way it did, due to a distant homology or the mixing of certain parameters. For a further continuation of this study, it would be interesting to use the same aligned sequence and use different model parameter selections for the initial creation of the model to see how the output changes. This is because one small change for the model parameter could greatly affect the output of the phylogenetic tree.
ContributorsNandan, Meghana (Author) / Scotch, Matthew (Thesis director) / Liu, Li (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Title: A Mobile Health Application for Tracking Patients' Health Record Abstract Background: Mobile Health (mHealth) has recently been adopted and used in rural communities in developing countries to improve the quality of healthcare in those areas. Some organizations use mHealth application to track pregnancy and provide routine checkups for pregnant women. Other organizations use mHelath application to provide treatment and counseling services to HIV/AIDs patients, and others are using it to provide treatment and other health care services to the general populations in rural communities. One organization that is using mobile health to bring primary care for the first time in some of the rural communities of Liberia is Last Mile Health. Since 2015, the organization has trained community health assistants (CHAs) to use a mobile health platform called Data Collection Tools (DCTs). The CHAs use the DCT to collect health data, diagnose and treat patients, provide counseling and educational services to their communities, and for referring patients for further care. While it is true that the DCT has many great features, it currently has many limitations such as data storage, data processing, and many others. Objectives: The goals of this study was to 1. Explore some of the mobile health initiatives in developing countries and outline some of the important features of those initiatives. 2. Design a mobile health application (a new version of the Last Mile Health's DCT) that incorporates some of those features that were outlined in objective 1. Method: A comprehensive literature search in PubMed and Arizona State University (ASU) Library databases was conducted to retrieve publications between 2014 and 2017 that contained phrases like "mHealth design", "mHealth implementation" or "mHealth validation". For a publication to refer to mHealth, the publication had to contain the term "mHealth," or contains both the term "health" and one of the following terms: mobile phone, cellular phone, mobile device, text message device, mobile technology, mobile telemedicine, mobile monitoring device, interactive voice response device, or disease management device. Results: The search yielded a total of 1407 publications. Of those, 11 publications met the inclusion criteria and were therefore included in the study. All of the features described in the selected articles were important to the Last Mile Health, but due to issues such as internet accessibility and cellular coverage, only five of those features were selected to be incorporated in the new version of the Last Mile's mobile health system. Using a software called Configure.it, the new version of the Last Mile's mobile health system was built. This new system incorporated features such as user logs, QR code, reminder, simple API, and other features that were identified in the study. The new system also helps to address problems such as data storage and processing that are currently faced by the Last Mile Health organization.
ContributorsKarway, George K. (Author) / Scotch, Matthew (Thesis director) / Kaufman, David (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Mosquito population data is a valuable resource for researchers and public health officials working to limit the spread of deadly zoonotic viruses such as Zika Virus and West Nile Virus. Unfortunately, this data is currently difficult to obtain and aggregate across the United States. Obtaining historical data often requires filing requests to individual States or Counties and hoping for a response. Current online systems available for accessing aggregated data are lacking essential features, or limited in scope. In order to make mosquito population data more accessible for United States researchers, epidemiologists, and public health officials, the MosquitoDB system has been developed. MosquitoDB consists of a JavaScript Web Application, connected to a SQL database, that makes submitting and retrieving United States mosquito population data much simpler and straight forward than alternative systems. The MosquitoDB software project is open source and publically available on GitHub, allowing community scrutiny and contributions to add or improve necessary features. For this Creative Project, the core MosquitoDB system was designed and developed with 3 main features: 1) Web Interface for querying mosquito data. 2) Web Interface for submitting mosquito data. 3) Web Services for querying/retrieving and submitting mosquito data. The Web Interface is essential for common end users, such as researchers and public health officials, to access historical data or submit new data. The Web Services provide building blocks for Web Applications that other developers can use to incorporate data into new applications. The current MosquitoDB system is live at https://zodo.asu.edu/mosquito and the public code repository is available at https://github.com/developerDemetri/mosquitodb.
ContributorsJones-Shargani, Demetrius Paul (Author) / Scotch, Matthew (Thesis director) / Weissenbacher, Davy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12