Point of Care diagnostics are low-cost methods to diagnose these illnesses, but they are generally only effective when the infection has begun to cause symptoms. Concentrating biological samples has been proven to increase the sensitivity of POC tests and could allow for earlier diagnosis to help monitor the spread of disease. This study reviewed the benefits of freeze concentration over other techniques to process samples in rural areas with limited infrastructure. Additionally, it analyzed the efficiency of thermoelectric devices as the cooling source for freezing. This analysis was conducted reviewing literature on the benefits of concentrating biological samples, software modeling the thermodynamics of a freeze concentrated system, and evaluating a physical model of this process. The software model established trends in altering L/D ratios of sample tubes and thermoelectric temperature. The physical model was successful in increasing the concentration of a red food dye solution through freezing with a thermoelectric device, showing this technique could be beneficial for minimal and efficient concentration devices.

Lab-grown food products of animal cell origin, now becoming popularly coined as, ‘Cellular Agriculture’ is a revolutionary breakthrough technology that has the potential to penetrate the lives of every American or citizen of the world. It is important to recognize that the impetus for developing this technology is fueled by environmental concerns with climate change, rising geopolitical instability, and population growth projections, where farm-grown food has now become a growing national security issue. Notwithstanding its potential, in addition to the necessary technological innovation and economic scalability, the market success of cellular agriculture will depend greatly on regulatory oversight by multiple government agencies without which it can cause undue harm to individuals, populations, and the environment. Thus, it is critical for those appropriate United States governing bodies to ensure that the technology being developed is both safe and of an acceptable quality for human consumption and has no adverse environmental impact. As such, animal foods, derived from farms, previously regulated almost exclusively by the United States Department of Agriculture (USDA) are now being regulated under a joint formal agreement between the US Food and Drug Administration (US FDA) and the USDA if derived from the lab, i.e., lab-grown animal foods. The main reason for joint oversight between the FDA and the USDA is that the FDA has developed the in-house expertise to oversee primary cell harvesting and cell storage, as well as, cell growth and differentiation for the development of 3D-engineered tissues intended for tissue and organ replacement for the emerging field of regenerative medicine. As such, the FDA has been given the authority to oversee the ‘front end’ of lab-grown food processes which relies on the very same processes utilized in engineered human tissues to produce food-grade engineered tissues. Oversight then transitions to the USDA-FSIS (Food Safety and Inspection Service) during the harvesting stage of the cell culture process. The USDA-FSIS then oversees the further production and labeling of these products. Included in the agreement is the understanding that both bodies are responsible for communicating necessary information to each other and collaboratively developing new regulatory actions as needed. However, there currently lacks clarity on some topics regarding certain legal, ethical, and scientific issues. Lab-grown meat products require more extensive regulation than farm-grown animal food products to ensure that they are safe and nutritious for consumption. To do this, CFSAN can create new classes of lab-grown foods, such as ‘lab-grown USDA foods,’ ‘lab-grown non-USDA foods,’ ‘lab-grown extinct foods,’ ‘lab-grown human food tissues,’ and ‘medically activated lab-grown foods.’

This honors thesis explores using machine learning technology to assist a patient's return to activity following a significant injury, specifically an anterior cruciate ligament (ACL) tear. The goal of the project was to determine if a machine learning model trained with ACL reconstruction (ACLR) applicable injury data would be able to correctly predict which phase of return to sport a patient would be classified in when introduced to a new data set.

Regenerative medicine utilizes living cells as therapeutics to replace or repair damaged or diseased tissue, but the manufacturing processes to produce cell-based tissue products require customized biounit operations that do not currently exist as conventional biochemical and biopharma manufacturing processes. Living cells are constantly changing and reacting to their environment, which in the case of cells isolated from their hosts, are utilized as living bioreactor components that, by themselves, are manipulated to biomanufacturer selected tissue products. Therefore, specialized technology is required to assure that cellular products produce the phenotypical tissue characteristics that the final product is designated to have, while also maintaining sterility of the culture. Because of this, FDA guidelines encourage the use of Process Analytical Technology (PAT – see Ref ) to be integrated into manufacturing systems of biologics to ensure quality and safety. To address the need for evaluation of sensor technologies for potential use in PAT, a literature review of both existing sensing technologies and biomarkers was conducted. After a thorough assessment of the sensor technologies that were most applicable to biomanufacturing, spectrophotometry was selected to monitor the metabolic components glucose and lactate of living cells in culture in real time. Initially, spectrophotometric measurements were taken of mock solutions of glucose and lactate solutions at concentrations relevant to human cell culture and physiology. With that data, a mathematical model was developed to predict a solution’s glucose and lactate concentration. This model was then integrated into a Matlab program that was used to continuously monitor and estimate solutions of glucose and lactate concentrations in real time. After testing the accuracy of this program in different solutions, it was determined that calibration curves and models must be made for each media type and estimates of glucose and lactate were found accurate only at higher concentrations. This program was successfully utilized to monitor in real time glucose and lactate production and consumption trends of Mesenchymal Stem Cells (MSCs) in culture, demonstrating proof-of-concept of the proposed bioprocess monitoring schema.

Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to it. A deep learning model is proposed to identify food from an image, where it can help the user manage their carbohydrate counting. This early model has a 68.3% accuracy of identifying 101 different food classes. A more refined model in future work could be deployed into a mobile application to identify food the user is about to consume and log it for easier carbohydrate counting.

The purpose of this study is to collect baseline internal and external pressure data for the three most commonly used pelvic circumferential compression devices (PCCD). Unstable pelvic fractures as a result of automobile accidents, falls, and other traumatic injuries mortality rate [3]. Early use of pelvic circumferential compression devices can mitigate fatal outcomes [4]-[5]. Prolonged eternal pressure above 9.3kPa can result in long-term soft tissue damage and pressure ulcers [7]. This study hypothesizes that the application of the three most commonly used PCCDs would result in the same mean maximum point pressure exertion. To study this, internal and external, both analog and digital, pressure apparati were used to collect data. The results of this data collection demonstrate a discrepancy in the pressure distribution between right and left greater trochanters within each PCCD. Additionally, the results suggest there is an effect of internal packing on the pressure exertion externally at the two greater trochanters within each PCCD. Lastly, the differences in pressure exertion between each PCCD, internally and externally, were inconclusive as some compared metrics resulted in statistically significant results while others did not. The methodologies employed in this study can be improved through fixation of pressure collection instruments, utilization of digital pressure mats, and removal of confounding factors. The results of this study indicate that digitized, discrete data over a fixed time interval may be clinically useful, suggesting that a digital data collection would yield more reliable data. Additionally, internally mounted pressure sensor data will provide more precise results than the analog method employed herein, as well as provide insight towards bone reduction and displacement following the application of PCCDs. Finally, the information gathered from this study can be utilized to improve upon existing technologies to create a more innovative solution.