Chronic pyelonephritis presenting as a renal sinus tumor with retroperitoneal extension: a case report

<p>Abstract</p> <p>Introduction</p> <p>Chronic pyelonephritis is associated with progressive renal scarring and occurs, most of the time, in patients with major anatomical anomalies, including urinary tract obstruction, calculi, renal dysplasia or vesicoureteric reflux. We report the computed tomography imaging findings of a patient with chronic pyelonephritis appearing as a renal sinus mass. To our knowledge, it is the first time that such a case has been published in the literature.</p> <p>Case presentation</p> <p>We present a case of a 68-year-old woman who underwent a computed tomography scan of the abdomen in the work-up for recently diagnosed hypertension. A non-enhancing left renal sinus mass was detected extending to the para-aortic space. The initial diagnosis was that of a tumor of the collecting system. Nephro-ureterectomy was performed and the pathology results revealed changes of chronic pyelonephritis.</p> <p>Conclusion</p> <p>Chronic pyelonephritis presenting as a renal sinus mass is reported for the first time in the literature. This may lead to the conclusion that diagnostic ureteropyeloscopy and biopsy should be performed prior to radical surgery for possible upper tract urothelial tumors.</p

A fuzzy-logic autonomous agent, applied as a supervisory controller in a simulated environment

Thesis (Ph. D.)--University of Washington, 2000An unsupervised learning system, implemented as an autonomous agent is presented. A simulation of a challenging path-planning problem is used to illustrate the agent design and demonstrate its problem solving ability. The agent, dubbed the ORG, employs fuzzy logic and clustering techniques to efficiently represent and retrieve knowledge and uses innovative sensor modeling and attention focus to process a large number of discrete stimuli. Simple initial rules are used to influence behavior and communicate intent to the agent. Self-reflection is utilized so the agent can learn from its environmental constraints and modify its own state. Speculation is utilized in the simulated environment, to produce new rules and fine-tune performance and internal parameters. Several resulting paths of the agent design are shown, and desirable side effects of the agent design are discussed