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With the accelerated emergence of telehealth systems being deployed with promises to access unreachable populations in today’s socially distant environment, it is increasingly important to understand the barriers that underprivileged populations face when trying to access healthcare through digital platforms. This research investigates the use of telehealth in social and cultural sub-populations, focusing on how the diverse student population at Arizona State University (ASU) use the recently-launched ASU Telehealth system. Statistical analysis of demographic factors spanning the five categories of social determinants of health were coupled with population studies of the ASU student body to evaluate the reach of services and patient diversity across telehealth and in person health platforms. Results show that insurance, racial and international student identity influence the percentage of students within these demographic categories Also, though the ASU Telehealth patient body reflects ASU’s general student population, the platform did not increase the reach of Health Services and the magnitude of students served. using ASU Telehealth. Due to the COVID-19 pandemic, it is difficult to determine the validity and reliability of these findings. However, the findings and background research point to targeted marketing campaigns, intentional policy decision-making, post-pandemic telehealth resilience, and the continuation of quantitative and qualitative data collection as means to expand the impact and equity of ASU Telehealth into future iterations of the platform. Outputs of this study include web communication materials and qualitative data collection mechanisms for future use and implementation by ASU Health Services.
Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.
Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.
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.
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.
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.