Drug Hepatotoxicity: Environmental Factors

Drug-induced liver injury presents as various forms of acute and chronic liver disease. There is wide geographic variation in the most commonly implicated agents. Smoking can induce cytochrome P450 enzymes but this does not necessarily translate into clinically relevant drug-induced liver injury. Excessive alcohol consumption is a clear risk factor for intrinsic hepatotoxicity from acetaminophen and may predispose to injury from antituberculosis medications. Understanding of the role of infection, proinflammatory states, disorders of coagulation, and the hepatic clock in predisposing patients to drug-induced liver injury is evolving. More study focusing specifically on environmental risk factors predisposing patients to drug-induced liver injury is needed

Liver Stiffness Measurements in Patients with Non‐cirrhotic Portal Hypertension – The Devil is In the Details

Non‐cirrhotic portal hypertension (NCPH) is often a diagnostic challenge due to signs and symptoms of portal hypertension that overlap with cirrhosis. The etiology of NCPH is broadly classified as prehepatic, hepatic (pre‐sinusoidal and sinusoidal) and post‐hepatic.1 Some common etiologies of NCPH encountered in clinical practice include portal vein thrombosis (prehepatic) and nodular regenerative hyperplasia (NRH) (hepatic)

Optimization Of Network Parameters And Semi-supervision In Gaussian Art Architectures

In this thesis we extensively experiment with two ART (adaptive resonance theory) architectures called Gaussian ARTMAP (GAM) and Distributed Gaussian ARTMAP (dGAM). Both of these classifiers have been successfully used in the past on a variety of applications. One of our contributions in this thesis is extensively experiments with the GAM and dGAM network parameters and appropriately identifying ranges for these parameters for which these architectures attain good performance (good classification performance and small network size). Furthermore, we have implemented novel modifications of these architectures, called semi-supervised GAM and dGAM architectures. Semi-supervision is a concept that has been used effectively before with the FAM and EAM architectures and in this thesis we are answering the question of whether semi-supervision has the same beneficial effect on the GAM architectures too. Finally, we compared the performance of GAM, dGAM, EAM, FAM and their semi-supervised versions on a number of datasets (simulated and real datasets). These experiments allowed us to draw appropriate conclusions regarding the comparative performance of these architectures

Quality Improvements in Extruded Meshes Using Topologically Adaptive Generalized Elements

In this dissertation, a novel method to extrude near-body meshes from surface meshes of arbitrary topology that exploits topologically adaptive generalized elements to improve mesh quality is presented. Specifically, an advancing layer algorithm to generate near-body meshes which are appropriate for viscous fluid flows is discussed. First, an orthogonal two-layer algebraic reference mesh is generated. The reference mesh is then smoothed using a locally three-dimensional Poisson-type mesh generation equation that is generalized to smooth extruded meshes of arbitrary surface topology. Local quality improvement operations such as edge collapse, face refinement, and local reconnection are performed in each layer to drive the mesh toward isotropy. An automatic marching thickness reduction algorithm is used to extrude from multiple geometries in close proximity. A global face refinement algorithm is used to improve the transition from the extruded mesh to the voidilling tetrahedral mesh. A few example meshes along with quality plots are presented to demonstrate the efficacy of the algorithms developed