Matching Items (103)
Description
This thesis explores and analyzes the emergence of for-profit stem cell clinics in the United States, specifically in the Phoenix metropolitan area. Stem cell therapy is an emerging field that has great potential in preventing or treating a number of diseases. Certain companies are currently researching the application of stem cells as therapeutics. At present the FDA has only approved one stem cell-based product; however, there are a number of companies currently offering stem cell therapies. In the past five years, most news articles discussing these companies offering stem cell treatments talk of clinics in other countries. Recently, there seems to be a number of stem cell clinics appearing in the United States. Using a web search engine, fourteen stem cell clinics were identified and analyzed in the Phoenix metropolitan area. Each clinic was analyzed by their four key characteristics: business operations, stem cell types, stem cell isolation methods, and their position with the FDA. Based off my analysis, most of the identified clinics are located in Scottsdale or Phoenix. Some of these clinics even share the same location as another medical practice. Each of the fourteen clinics treat more than one type of health condition. The stem clinics make use of four stem cell types and three different isolation methods to obtain the stem cells. The doctors running these clinics almost always treat health conditions outside of their expertise. Some of these clinics even claim they are not subject to FDA regulation.
ContributorsAmrelia, Divya Vikas (Author) / Brafman, David (Thesis director) / Frow, Emma (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
As life expectancy increases worldwide, age related diseases are becoming greater health concerns. One of the most prevalent age-related diseases in the United States is dementia, with Alzheimer’s disease (AD) being the most common form, accounting for 60-80% of cases. Genetics plays a large role in a person’s risk of developing AD. Familial AD, which makes up less than 1% of all AD cases, is caused by autosomal dominant gene mutations and has almost 100% penetrance. Genetic risk factors are believed to make up about 49%-79% of the risk in sporadic cases. Many different genetic risk factors for both familial and sporadic AD have been identified, but there is still much work to be done in the field of AD, especially in non-Caucasian populations. This review summarizes the three major genes responsible for familial AD, namely APP, PSEN1 and PSEN2. Also discussed are seven identified genetic risk factors for sporadic AD, single nucleotide polymorphisms in the APOE, ABCA7, NEDD9, CASS4, PTK2B, CLU, and PICALM genes. An overview of the main function of the proteins associated with the genes is given, along with the supposed connection to AD pathology.
ContributorsRichey, Alexandra Emmeline (Author) / Brafman, David (Thesis director) / Raman, Sreedevi (Committee member) / School of International Letters and Cultures (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as “economic epidemiology” or “epidemiological economics,” the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.
ContributorsPerrings, Charles (Author) / Castillo-Chavez, Carlos (Author) / Chowell-Puente, Gerardo (Author) / Daszak, Peter (Author) / Fenichel, Eli P. (Author) / Finnoff, David (Author) / Horan, Richard D. (Author) / Kilpatrick, A. Marm (Author) / Kinzig, Ann (Author) / Kuminoff, Nicolai (Author) / Levin, Simon (Author) / Morin, Benjamin (Author) / Smith, Katherine F. (Author) / Springborn, Michael (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / W.P. Carey School of Business (Contributor) / Economics (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2015-12-01
Description
Preserving a system’s viability in the presence of diversity erosion is critical if the goal is to sustainably support biodiversity. Reduction in population heterogeneity, whether inter- or intraspecies, may increase population fragility, either decreasing its ability to adapt effectively to environmental changes or facilitating the survival and success of ordinarily rare phenotypes. The latter may result in over-representation of individuals who may participate in resource utilization patterns that can lead to over-exploitation, exhaustion, and, ultimately, collapse of both the resource and the population that depends on it. Here, we aim to identify regimes that can signal whether a consumer–resource system is capable of supporting viable degrees of heterogeneity. The framework used here is an expansion of a previously introduced consumer–resource type system of a population of individuals classified by their resource consumption. Application of the Reduction Theorem to the system enables us to evaluate the health of the system through tracking both the mean value of the parameter of resource (over)consumption, and the population variance, as both change over time. The article concludes with a discussion that highlights applicability of the proposed system to investigation of systems that are affected by particularly devastating overly adapted populations, namely cancerous cells. Potential intervention approaches for system management are discussed in the context of cancer therapies.
ContributorsKareva, Irina (Author) / Morin, Benjamin (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2015-02-01
Description
Background
In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.
Methodology
We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.
Conclusions
We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.
In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.
Methodology
We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.
Conclusions
We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.
ContributorsTowers, Sherry (Author) / Afzal, Shehzad (Author) / Bernal, Gilbert (Author) / Bliss, Nadya (Author) / Brown, Shala (Author) / Espinoza, Baltazar (Author) / Jackson, Jasmine (Author) / Judson-Garcia, Julia (Author) / Khan, Maryam (Author) / Lin, Michael (Author) / Mamada, Robert (Author) / Moreno, Victor (Author) / Nazari, Fereshteh (Author) / Okuneye, Kamaldeen (Author) / Ross, Mary (Author) / Rodriguez, Claudia (Author) / Medlock, Jan (Author) / Ebert, David (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Mary Lou Fulton Teachers College (Contributor) / Educational Leadership and Innovation (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Global Security Initiative (Contributor)
Created2015-06-11
Description
Background
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.
Conclusions
Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.
Conclusions
Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.
ContributorsNishiura, Hiroshi (Author) / Chowell-Puente, Gerardo (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2011-03-24
Description
Background
Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts.
Methods
Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event.
Conclusions
We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.
Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts.
Methods
Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event.
Conclusions
We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.
ContributorsTowers, Sherry (Author) / Gomez-Lievano, Andres (Author) / Khan, Maryam (Author) / Mubayi, Anuj (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-07-02
Description
Cell viability is an important assessment in cell culture to characterize the health of the cell population and confirm if cells are alive. Morphology or end-line assays are used to determine cell viability of entire populations. Intracellular pO2 levels is indicative of cell health and metabolism that can be used as a factor to asses cell viability in an in-line assay. Siloxane based pO2 sensing nanoprobes present a modality to visualize intracellular pO2. Using fluorescent lifetime imaging microscopy (FLIM), pO2 levels can be mapped intracellular as a highly functional in-line assay for cell viability. FLIM is an imaging modality that reconstructs an image based of its fluorescent lifetime. Nanoprobes were synthesized in different manufacturing/storage conditions. The nanoprobes for both long- and short-term storage were characterized in a cell free environment testing for changes in fluorescent intensity, average and maximum nanoprobe diameter. The nanoprobes were validated in two different culture systems, 2D and microcarrier culture systems, for human derived neural progenitor cells (NPCs) and neurons. Long- and short-term storage nanoprobes were used to label different neuronal based culture systems to asses labeling efficiency through fluorescent microscopy and flow cytometry. NPCs and neurons in each culture system was tested to see if nanoprobe labeling effected cellular phenotype for traits such as: cell proliferation, gene expression, and calcium imaging. Long-term and short-term storage nanoprobes were successfully validated for both NPCs and neurons in all culture systems. Assessments of the pO2 sensing nanoprobes will be further developed to create a highly functional and efficient in-line test for cell viability.
ContributorsLeyasi, Salma (Author) / Brafman, David (Thesis director) / Kodibagkar, Vikram (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Over 5.8 million people are currently living with Alzheimer’s disease (AD), with the sixth highest mortality rate in the United States. No known cure or substantially life-extending treatment exists. With the growing aging population these numbers are only expected to increase to about 13.8 million by the year 2050. Alzheimer’s is a multifactorial disease, giving rise to two main types: familial AD (FAD) and sporadic AD (SAD). Although there are different factors associated with each type of the disease, both FAD and SAD result in neuronal and synaptic loss and remain difficult to model in-vitro and treat overall.
Current advances in cellular models of neurodegenerative diseases overcome a variety of limitations possessed in animal and post-mortem human models. Human-induced pluripotent stem cells (hiPSCs) provide a platform with cells that can self-renew and differentiate into mature and functional cell types. HiPSCs are at the forefront of neurodegenerative disease research because of their ability to differentiate into neural cell types. Apolipoprotein E (ApoE) is a protein encoded by the APOE gene found on chromosome 19 of the human genome. There are three common polymorphisms in the APOE gene, resulting from a single amino acid change in the protein. The presence of these polymorphisms are studied as associated risk factors of developing AD. Different combinations of these alleles closely relate to the risk a patient has in developing Alzheimer’s disease. The risk associated effects of this gene are primarily investigated, however the protective effects are not examined to the same extent.
This research aims to overcome the existing limitations in cell differentiations and improve cell population purity that limits the variables present in the culture. To do this, this study optimized a differentiation protocol by separating and purifying neuronal cell populations to study the potential protective effects associated with ApoE, a risk factor seen in SAD. This platform aims to use a purified cell population to effectively analyze cell type specific affects of the ApoE risk factor, specifically in neurons.
Current advances in cellular models of neurodegenerative diseases overcome a variety of limitations possessed in animal and post-mortem human models. Human-induced pluripotent stem cells (hiPSCs) provide a platform with cells that can self-renew and differentiate into mature and functional cell types. HiPSCs are at the forefront of neurodegenerative disease research because of their ability to differentiate into neural cell types. Apolipoprotein E (ApoE) is a protein encoded by the APOE gene found on chromosome 19 of the human genome. There are three common polymorphisms in the APOE gene, resulting from a single amino acid change in the protein. The presence of these polymorphisms are studied as associated risk factors of developing AD. Different combinations of these alleles closely relate to the risk a patient has in developing Alzheimer’s disease. The risk associated effects of this gene are primarily investigated, however the protective effects are not examined to the same extent.
This research aims to overcome the existing limitations in cell differentiations and improve cell population purity that limits the variables present in the culture. To do this, this study optimized a differentiation protocol by separating and purifying neuronal cell populations to study the potential protective effects associated with ApoE, a risk factor seen in SAD. This platform aims to use a purified cell population to effectively analyze cell type specific affects of the ApoE risk factor, specifically in neurons.
ContributorsFrisch, Carlye Arin (Author) / Brafman, David (Thesis director) / Tian, Xiaojun (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Effectively modeling Alzheimer’s disease will lend to a more comprehensive
understanding of the disease pathology, more efficacious drug development and
regenerative medicine as a form of treatment. There are limitations with current
transgenic mouse models of Alzheimer’s disease and the study of post mortem brain tissue of Alzheimer’s diseases patients. Stem cell models can overcome the lack of clinical relevance and impracticality associated with current models. Ideally, the use of stem cell models provides the foundation to study the biochemical and physiological aspects of Alzheimer’s disease, but at the cellular level. Moreover, the future of drug development and disease modeling can be improved by developing a reproducible and well-characterized model of AD that can be scaled up to meet requirements for basic and translational applications. Characterization and analysis of a heterogenic neuronal culture developed from induced pluripotent stem cells calls for the understanding of single cell identity and cell viability. A method to analyze RNA following intracellular sorting was developed in order to analyze single cell identity of a heterogenic population
of human induced pluripotent stem cells and neural progenitor cells. The population was intracellularly stained and sorted for Oct4. RNA was isolated and analyzed with qPCR, which demonstrated expected expression profiles for Oct4+ and Oct4- cells. In addition, a protocol to label cells with pO2 sensing nanoprobes was developed to assess cell viability. Non-destructive nanoprobe up-take by neural progenitor cells was assessed with fluorescent imaging and flow cytometry. Nanoprobe labeled neurons were cultured long-term and continued to fluoresce at day 28. The proof of concept experiments demonstrated will be further expanded upon and utilized in developing a more clinically relevant and cost-effective model of Alzheimer’s disease with downstream applications
in drug development and regenerative medicine.
understanding of the disease pathology, more efficacious drug development and
regenerative medicine as a form of treatment. There are limitations with current
transgenic mouse models of Alzheimer’s disease and the study of post mortem brain tissue of Alzheimer’s diseases patients. Stem cell models can overcome the lack of clinical relevance and impracticality associated with current models. Ideally, the use of stem cell models provides the foundation to study the biochemical and physiological aspects of Alzheimer’s disease, but at the cellular level. Moreover, the future of drug development and disease modeling can be improved by developing a reproducible and well-characterized model of AD that can be scaled up to meet requirements for basic and translational applications. Characterization and analysis of a heterogenic neuronal culture developed from induced pluripotent stem cells calls for the understanding of single cell identity and cell viability. A method to analyze RNA following intracellular sorting was developed in order to analyze single cell identity of a heterogenic population
of human induced pluripotent stem cells and neural progenitor cells. The population was intracellularly stained and sorted for Oct4. RNA was isolated and analyzed with qPCR, which demonstrated expected expression profiles for Oct4+ and Oct4- cells. In addition, a protocol to label cells with pO2 sensing nanoprobes was developed to assess cell viability. Non-destructive nanoprobe up-take by neural progenitor cells was assessed with fluorescent imaging and flow cytometry. Nanoprobe labeled neurons were cultured long-term and continued to fluoresce at day 28. The proof of concept experiments demonstrated will be further expanded upon and utilized in developing a more clinically relevant and cost-effective model of Alzheimer’s disease with downstream applications
in drug development and regenerative medicine.
ContributorsKnittel, Jacob James (Author) / Brafman, David (Thesis director) / Salvatore, Oddo (Committee member) / School of Life Sciences (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05