Date of Award

Fall 2023

Document Type

Open Access Dissertation

Department

Biomedical Engineering

First Advisor

Melissa Moss

Abstract

Inflammation is a body’s physiological response to pathogens, foreign materials, or tissue injury. When immune system is triggered, activated leukocytes produce cytokines and chemokines that promote migration of neutrophils and macrophages to the inflammatory regions. At early stages of injury, acute inflammation occurs immediately to fight off the pathogens and restore tissues to homeostasis. However, if the inflammation-inducing stimulus is not removed and thus cells and tissues do not return to its homeostatic state, the prolonged inflammatory responses become a chronic condition. Recent studies have indicated that the receptor for advanced glycation end-products (RAGE), a key receptor of innate immune response, is upregulated in Alzheimer’s disease (AD), and the RAGE ligand S100B is elevated in AD brain. Activation of RAGE by S100B can both upregulate RAGE expression and induce the release of inflammatory cytokines. Consequently, targeting RAGE to reduce chronic inflammation represents a novel therapeutic strategy for AD. In this study, peptoids have been evaluated as a novel, ideal class of potential AD therapeutics. The peptoids JPT1 and JPT1a demonstrated that they are non-toxic to human macrophage culture and exhibit nanomolar binding affinity for RAGE. In addition, the peptoids significantly attenuated S100B-induced pro-inflammatory responses. Moreover, JPT1 demonstrated a more pronounced ability to reduce inflammatory response, indicating that peptoid helical structure is advantageous. These results implicate peptoids as a potential therapeutic agent for not only AD but also for other inflammation-related illnesses. Because these peptoids also exhibit the ability to modulate amyloid-beta aggregation, they may function as dual-target therapeutics for AD. Inflammatory responses to biomaterial were also observed. This study involved patients who had undergone mesh-implant surgery for pelvic organ prolapse (POP). The relationship between blood cytokines and mesh exposure in patients was investigated via principal component analysis and resulted in finding correlations between important cytokines that played a significant role in predicting mesh exposure. Measured responses were also utilized in supervised machine learning to create predictive models to determine surgical outcome. Retention of predictive power was demonstrated following reduction of the number of cytokines. Furthermore, the previous models were improved in predictability when combination of cytokine and medical record of patients was incorporated, especially in Artificial Neural Network model. Further application of these models to a larger sample size will be pursued to confirm these results. If confirmed, these models could provide an essential tool for surgeons to make more informed recommendations to POP repair surgery candidates.

Rights

© 2024, Mihyun Lim Waugh

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