For the evaluation of anti-human bladder cancer immunotoxin approaches, the orthotopic nude murine model that mimics the human counterpart is essential for preclinical evaluation of new treatment modalities. The objective of this study was to develop and characterize such a model. To accomplish this, the established human bladder transitional cell carcinoma (TCC) cell line, BIU-87, was transplanted orthotopically into immunodeficient nude mice. BIU-87 TCC cells were grown in monolayer cell culture and instilled intravesically as single cell suspensions into bladders that had been conditioned with mild acid washing. Tumor growth was assessed weekly by subjecting the mice to magnetic resonance imaging (MRI). At intervals following implantation and MRI tumor detection, the animals were sacrificed for necropsy, histological examination and immunocytochemical studies. The overall tumor establishment was 93% (52/56 mice) at 7-36 days, while in a subgroup of animals sacrificed at 12-13 days, 40 out of 42 animals (95%) developed TCC, the majority of which was superficial. Tumor stage was assessed by gross pathology and light microscopy. Histological examination of the tumor specimens confirmed the presence of grade II-III TCC. Immunocytochemistry confirmed that the tumor model maintained the features of BIU-87 cells. The changes seen on MRI correlated well with the extent of tumor invasion identified histologically. Carcinoma in situ could be detected histologically 7-9 days post-inoculation, and progressed to papillary tumor or invasive disease thereafter. The orthotopic BIU-87 TCC model is highly reproducible and is ideal for preclinical studies on experimental intravesical therapies.

Abstract. This paper investigates the fault detection problem for Discrete Event Systems which can be modeled by Partially Observed Petri Nets (POPN). To overcome the shortage of the low diagnosability of the currently POPN online fault diagnoser, we propose an improved on-line fault diagnosis algorithm that integrates Generalized Mutual Exclusion Constraints(GMEC) and Integer Linear Programming (ILP). We assume that the POPN structure and its initial markings are known, the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. The ILP problem of POPN is solved for elementary diagnosis for the system behavior. While the system is diagnosed that some faults may have happened, we use the GMEC for the further diagnosis. Finally, a real discrete event system is taken as an example, we model and analyse the discrete event system, the proposed algorithm increases the diagnosablity remarkably.