Filtering by
- All Subjects: Genetics
This thesis explores the ethical implications of using facial recognition artificial intelligence (AI) technologies in medicine, with a focus on both the opportunities and challenges presented by the use of this technology in the diagnosis and treatment of rare genetic disorders. We highlight the positive outcomes of using AI in medicine, such as accuracy and efficiency in diagnosing rare genetic disorders, while also examining the ethical concerns including bias, misdiagnosis, the issues it may cause within patient-clinician relationships, misuses outside of medicine, and privacy. This paper draws on the opinions of medical providers and other professionals outside of medicine, which finds that while many are excited about the potential of AI to improve medicine, concerns remain about the ethical implications of these technologies. We discuss current legislation controlling the use of AI in healthcare and its ambiguity. Overall, this thesis highlights the need for further research and public discourse to address the ethical implications of using facial recognition and AI technologies in medicine, while also providing recommendations for its future use in medicine.
Evaluating the Heterogeneity of Logistic Regression Models to Predict Coronary Artery Disease Status
Computational and systems biology are rapidly growing fields of academic study, but unfamiliar researchers are impeded by a lack of accessible, programming-optional, modelling tools. To address this gap, I developed BioSSA, a web framework built on JavaScript and D3.js which allows users to explore a small library of curated biophysical models as well as create and simulate their own reaction network. The mathematical foundation of BioSSA is the Stochastic Gillespie Algorithm, which is widely used in mathematical modeling and biology to represent chemical reaction systems. SGA is particularly well-suited as an introductory modelling tool because of its flexibility, broad applicability, and its ability to numerically approximate systems when analytical solutions are not available. BioSSA is freely available to the community and further improvements are planned.
Assessment of DNA methylation was performed on human skeletal muscle and blood using reduced representation bisulfite sequencing (RRBS) for high-throughput identification and pyrosequencing for site-specific confirmation. Sorbin and SH3 homology domain 3 (SORBS3) was identified in skeletal muscle to be increased in methylation (+5.0 to +24.4 %) in the promoter and 5’untranslated region (UTR) in the obese participants (n= 10) compared to lean (n=12), and this finding corresponded with a decrease in gene expression (fold change: -1.9, P=0.0001). Furthermore, SORBS3 was demonstrated in a separate cohort of morbidly obese participants (n=7) undergoing weight-loss induced by surgery, to decrease in methylation (-5.6 to -24.2%) and increase in gene expression (fold change: +1.7; P=0.05) post-surgery. Moreover, SORBS3 promoter methylation was demonstrated in vitro to inhibit transcriptional activity (P=0.000003). The methylation and transcriptional changes for SORBS3 were significantly (P≤0.05) correlated with obesity measures and fasting insulin levels. SORBS3 was not identified in the blood methylation analysis of lean (n=10) and obese (n=10) participants suggesting that it is a muscle specific marker. However, solute carrier family 19 member 1 (SLC19A1) was identified in blood and skeletal muscle to have decreased 5’UTR methylation in obese participants, and this was significantly (P≤0.05) predicted by insulin sensitivity.
These findings suggest SLC19A1 as a potential blood-based biomarker for obese, insulin resistant states. The collective findings of SORBS3 DNA methylation and gene expression present an exciting novel target in skeletal muscle for further understanding obesity and its underlying insulin resistance. Moreover, the dynamic changes to SORBS3 in response to metabolic improvements and weight-loss induced by surgery.