Matching Items (26)

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In silico analysis suggests interaction between Ebola virus and the extracellular matrix

Description

The worst Ebola virus (EV) outbreak in history has hit Liberia, Sierra Leone and Guinea hardest and the trend lines in this crisis are grave, and now represents a global

The worst Ebola virus (EV) outbreak in history has hit Liberia, Sierra Leone and Guinea hardest and the trend lines in this crisis are grave, and now represents a global public health threat concern. Limited therapeutic and/or prophylactic options are available for people suffering from Ebola virus disease (EVD) and further complicate the situation. Previous studies suggested that the EV glycoprotein (GP) is the main determinant causing structural damage of endothelial cells that triggers the hemorrhagic diathesis, but molecular mechanisms underlying this phenomenon remains elusive. Using the informational spectrum method (ISM), a virtual spectroscopy method for analysis of the protein-protein interactions, the interaction of GP with endothelial extracellular matrix (ECM) was investigated. Presented results of this in silico study suggest that Elastin Microfibril Interface Located Proteins (EMILINs) are involved in interaction between GP and ECM. This finding could contribute to a better understanding of EV/endothelium interaction and its role in pathogenesis, prevention and therapy of EVD.

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  • 2015-02-19

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Evolution of 2014/15 H3N2 Influenza Viruses Circulating in US: Consequences for Vaccine Effectiveness and Possible New Pandemic

Description

A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. Here we propose a new bioinformatics approach for

A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. Here we propose a new bioinformatics approach for analysis of influenza viruses which could be used as an efficient tool for selection of vaccine viruses, assessment of the effectiveness of seasonal influenza vaccines, and prediction of the epidemic/pandemic potential of novel influenza viruses.

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  • 2015-12-22

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Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction

Description

Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model

Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014–15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation.

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  • 2017-02-07

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MosquitoDB

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.

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.

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Date Created
  • 2016-12

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Effects of LCMV Infection on Murine Fetal Development in Immunized Mothers

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

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.

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  • 2020-05

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A Mobile Health Application for Tracking Patients' Health Record

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

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.

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Date Created
  • 2018-05

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Analysis of HIV Risk Groups Using Bayesian Analysis

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

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.

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  • 2018-05

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Phylogeography of Influenza in the Southwest United States

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

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.

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  • 2013-05

The Elucidation of Potential New Factors that Influence and Impact Type 2 Diabetes Mellitus Prevalence in Pima Indian populations

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,

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.

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Date Created
  • 2020-05

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Phylogeography of influenza A H5N1 clade 2.2.1.1 in Egypt

Description

Background
Influenza A H5N1 has killed millions of birds and raises serious public health concern because of its potential to spread to humans and cause a global pandemic. While the

Background
Influenza A H5N1 has killed millions of birds and raises serious public health concern because of its potential to spread to humans and cause a global pandemic. While the early focus was in Asia, recent evidence suggests that Egypt is a new epicenter for the disease. This includes characterization of a variant clade 2.2.1.1, which has been found almost exclusively in Egypt.
We analyzed 226 HA and 92 NA sequences with an emphasis on the H5N1 2.2.1.1 strains in Egypt using a Bayesian discrete phylogeography approach. This allowed modeling of virus dispersion between Egyptian governorates including the most likely origin.
Results
Phylogeography models of hemagglutinin (HA) and neuraminidase (NA) suggest Ash Sharqiyah as the origin of virus spread, however the support is weak based on Kullback–Leibler values of 0.09 for HA and 0.01 for NA. Association Index (AI) values and Parsimony Scores (PS) were significant (p-value < 0.05), indicating that dispersion of H5N1 in Egypt was geographically structured. In addition, the Ash Sharqiyah to Al Gharbiyah and Al Fayyum to Al Qalyubiyah routes had the strongest statistical support.
Conclusion
We found that the majority of routes with strong statistical support were in the heavily populated Delta region. In particular, the Al Qalyubiyah governorate appears to represent a popular location for virus transition as it represented a large portion of branches in both trees. However, there remains uncertainty about virus dispersion to and from this location and thus more research needs to be conducted in order to examine this.
Phylogeography can highlight the drivers of H5N1 emergence and spread. This knowledge can be used to target public health efforts to reduce morbidity and mortality. For Egypt, future work should focus on using data about vaccination and live bird markets in phylogeography models to study their impact on H5N1 diffusion within the country.

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Date Created
  • 2013-12-10