The pathophysiology of eosinophilic esophagitis: Altered mechanisms of antigen detection in the esophagus

Recently, a chronic idiopathic disease of the esophagus has emerged, which is now known as eosinophilic esophagitis (EoE). Incomplete knowledge regarding the pathogenesis of EoE has limited treatment options. EoE is known to be a Th2-type immune-mediated disorder. Based on previous studies in both patients and experimental models, it is possible that an abnormal reaction to antigen mediates the pathophysiology of EoE. In this thesis, symptoms and signs unique to EoE were identified by an age-matched, case-controlled study of 326 patients with EoE and gastroesophageal reflux disease. The molecular mechanisms involved in antigen detection in the esophagus, in relation to EoE were then investigated. Esophageal epithelial cells were found, for the first time, to be capable of acting as non-professional antigen presenting cells, with the ability to engulf, process and present antigen on MHC class II to T helper lymphocytes. Antigen presentation by esophageal epithelial cells was induced by interferon-γ, which is increased in biopsies from patients with EoE. Next, it was discovered that esophageal epithelial cell lines expressed functional toll-like receptor (TLR) 2 and TLR3, but in esophageal mucosal biopsies only infiltrating immune cells (including eosinophils) expressed TLR2 and TLR3. Finally, the potential involvement of IgE in the pathogenesis of esophageal inflammation was investigated. IgE in the esophagus was found to be present on mast cells, which are increased in density in the esophageal mucosae of patients with EoE and especially those with a history of atopy. Mechanisms of antigen detection may mediate the pathophysiology of EoE in the esophagus through antigen presentation by epithelial cells, detection by TLRs on immune cells and detection through IgE on mucosal mast cells. Together, these findings demonstrate that mechanisms of antigen detection may actually contribute to the pathophysiology of EoE. Through increased understanding of the mechanisms of EoE, the results of this thesis may contribute to future therapy.

Communication-efficient decentralized sequential detection

The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.