In the middle of the COVID-19 epidemic, flaws in the SARS-CoV-2 diagnostic
test were identified by the impending supply shortages of nasopharyngeal swabs and nucleic acid isolation and purification kits. The ASU Biodesign Clinical Testing Lab (ABCTL), which converted from a research lab to SARS-CoV-2 testing lab, was not an exception to these shortages, but the consequences were greater due to its significant testing load in the state of Arizona. In response to the shortages, researchers at The Department of Epidemiology of Microbial Diseases, at the Yale School of Public Health created SalivaDirect method, which is an epidemic effective test, that accounts for limitations of materials, accessibility to specialized lab equipment, time per test, and cost per test. SalivaDirect simplified the diagnostic process by collecting samples via saliva and skipping the nucleic acid extraction and purification, and did it in a way that resulted in a highly sensitive limit of detection of 6-12 SARS-CoV-2 copies/μL with a minimal decrease in positive test agreement.

The ASU Biodesign Clinical Testing Laboratory began in March 2020 after the severe acute respiratory syndrome, coronavirus 2, began spreading throughout the world. ASU worked towards implementing  its own efficient way of testing for the virus, in order to assist the university but also keep the communities around it safe. By developing its own strategy for COVID-19 testing, ASU was on the forefront of research by developing new ways to test for the virus. This process began when research labs at ASU were quickly converted into clinical testing laboratories, which used saliva testing to develop swift COVID-19 diagnostic tests for the Arizona community. The lab developed more accurate and time efficient results, while also converting Nasopharyngeal tests to saliva tests. Not only did this allow for fewer amounts of resources required, but more individuals were able to get tested at faster rates. The ASU Biodesign Clinical Testing Laboratory (ABCTL) was able to accomplish this through the adaptation of previous machines and personnel to fit the testing needs of the community. In the future, the ABCTL will continue to adapt to the ever-changing needs of the community in regards to the unprecedented COVID-19 pandemic. The research collected throughout the past year following the breakout of the COVID-19 pandemic is a reflection of the impressive strategy ASU has created to keep its communities safe, while continuously working towards improving not only the testing sites and functions, but also the ways in which an institution approaches and manages an unfortunate impact on diverse communities.

This thesis project is part of a larger collaboration documenting the history of the ASU Biodesign Clinical Testing Laboratory (ABCTL). There are many different aspects that need to be considered when transforming to a clinical testing laboratory. This includes the different types of tests performed in the laboratory. In addition to the diagnostic polymerase chain reaction (PCR) test that is performed detecting the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), antibody testing is also performed in clinical laboratories. Antibody testing is used to detect a previous infection. Antibodies are produced as part of the immune response against SARS-CoV-2. There are many different forms of antibody tests and their sensitives and specificities have been examined and reviewed in the literature. Antibody testing can be used to determine the seroprevalence of the disease which can inform policy decisions regarding public health strategies. The results from antibody testing can also be used for creating new therapeutics like vaccines. The ABCTL recognizes the shifting need of the community to begin testing for previous infections of SARS-CoV-2 and is developing new forms of antibody testing that can meet them.

The COVID-19 pandemic has resulted in preventative measures and has led to extensive changes in lifestyle for the vast majority of the American population. As the pandemic progresses, a growing amount of evidence shows that minority groups, such as the Deaf community, are often disproportionately and uniquely affected. Deaf people are directly affected in their ability to personally socialize and continue with daily routines. More specifically, this can constitute their ability to meet new people, connect with friends/family, and to perform in their work or learning environment. It also may result in further mental health changes and an increased reliance on technology. The impact of COVID-19 on the Deaf community in clinical settings must also be considered. This includes changes in policies for in-person interpreters and a rise in telehealth. Often, these effects can be representative of the pre-existing low health literacy, frequency of miscommunication, poor treatment, and the inconvenience felt by Deaf people when trying to access healthcare. Ultimately, these effects on the Deaf community must be taken into account when attempting to create a full picture of the societal shift caused by COVID-19.

The Founders lab is a year-long program that gives its students an opportunity to participate in a unique team-based, experiential Barrett honors thesis project to design and apply marketing and sales strategies, as well as business and financial models to start up and launch a new business. This honors thesis project focuses on increasing the rate of vaccination outcomes in a country where people are increasingly busy (less time) and unwilling to get a needle through a new business venture that provides a service that brings vaccinations straight to businesses, making them available for their employees. Through our work with the Founders Lab, our team was able to create this pitch deck.

Traumatic brain injury (TBI) is a widespread health issue that affects approximately 1.7 million lives per year. The effects of TBI go past the incident of primary injury, as chronic damage can follow for years and cause irreversible neurodegeneration. A potential strategy for repair that has been studied is cell transplantation, as neural stem cells improve neurological function. While promising, neural stem cell transplantation presents challenges due to a relatively low survival rate post-implantation and issues with determining the optimal method of transplantation. Shear-thinning hydrogels are a type of hydrogel whose linkages break when under shear stress, exhibiting viscous flow, but reform and recover upon relaxation. Such properties allow them to be easily injected for minimally invasive delivery, while also shielding encapsulated cells from high shear forces, which would normally degrade the function and viability of such cells. As such, it is salient to research whether shear-thinning hydrogels are feasible candidates in neural cell transplantation applications for neuroregenerative medicine. In this honors thesis, shear-thinning hydrogels were formed through guest-host interactions of adamantane modified HA (guest ad-HA) and beta-cyclodextrin modified HA (host CD-HA). The purpose of the study was to characterize the injection force profile of different weight percentages of the HA shear-thinning hydrogel. The break force and average glide force were also compared between the differing weight percentages. By understanding the force exerted on the hydrogel when being injected, we could characterize how neural cells may respond to encapsulation and injection within HA shear-thinning hydrogels. We identified that 5% weight HA hydrogel required greater injection force than 4% weight HA hydrogel to be fully delivered. Such contexts are valuable, as this implies that higher weight percentage gels impart higher shear forces on encapsulated cells than lower weight gels. Further study is required to optimize our injection force system’s sensitivity and to investigate if cell encapsulation increases the force required for injection.

There are many challenges in designing neuroprostheses and one of them is to maintain proper axon selectivity in all situations. This project is based on an electrode that is implanted into a fascicle in a peripheral nerve and used to provide tactile sensory feedback of a prosthetic arm. This fascicle can undergo mechanical deformation during every day motion. This work aims to characterize the effect of fascicle deformation on axon selectivity and recruitment when electrically stimulated using hybrid modeling. The main framework consists of combining finite element modeling (FEM) and simulation environment NEURON. A suite of programs was developed to first populate a fascicle with axons followed by deforming the fascicle and rearranging axons accordingly. A model of the fascicle with an implanted electrode is simulated to find the electrical potential profile through FEM. The potential profile is then used to compare which axons are activated in the two conformations of the fascicle using NERUON.

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.

This analysis explores what the time needed to harden, and time needed to degrade is of a PLGA bead, as well as whether the size of the needle injecting the bead and the addition of a drug (Vismodegib) may affect these variables. Polymer degradation and hardening are critical to understand for the polymer’s use in clinical settings, as these factors help determine the patients’ and healthcare providers’ use of the drug and estimated treatment time. Based on the literature, it is expected that the natural logarithmic polymer mass degradation forms a linear relationship to time. Polymer hardening was tested by taking video recordings of gelatin plates as they are injected with microneedles and performing RGB analysis on the polymer “beads” created. Our results for the polymer degradation experiments showed that the polymer hardened for all solutions and trials within approximately 1 minute, presenting a small amount of time in which the patient would have to remain motionless in the affected area. Both polymer bead size and drug concentration may have had a modest impact on the hardening time experiments, while bead size may affect the time required for the polymer to degrade. Based on the results, the polymer degradation is expected to last multiple weeks, which may allow for the polymer to be used as a long-term drug delivery system in treatment of basal cell carcinoma.

The goal of this research project is to create a Mathcad template file capable of statistically modelling the effects of mean and standard deviation on a microparticle batch characterized by the log normal distribution model. Such a file can be applied during manufacturing to explore tolerances and increase cost and time effectiveness. Theoretical data for the time to 60% drug release and the slope and intercept of the log-log plot were collected and subjected to statistical analysis in JMP. Since the scope of this project focuses on microparticle surface degradation drug release with no drug diffusion, the characteristic variables relating to the slope (n = diffusional release exponent) and the intercept (k = kinetic constant) do not directly apply to the distribution model within the scope of the research. However, these variables are useful for analysis when the Mathcad template is applied to other types of drug release models.