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Introduction. Renal transplantation is the best treatment for end-stage renal disease. In the last years, we have seen improvements in immunosuppressive treatment, which have allowed patients to experience a better quality of life and graft survival. Nevertheless, surgical complications remain important problems that increase morbidity, mortality, costs, and hospitalization. Our purpose was to evaluate surgical complications among a large series of 2000 renal transplantations. Patients and Methods. We retrospectively analyzed all surgical complications among 2000 renal transplants performed between June 1980 and March 2010 in our department. Results. Among 318 (15.9%) surgical complications, 4.8% of patients had urologic problems. Ureteral stenosis and fistula, stent obstruction, and ureteral necrosis occurred in 2.7%, 1.8%, 0.1%, and 0.2% of patients, respectively. Vascular complications reported in 2.7% of patients included arterial or venous thrombosis (1.0% or 0.4%), both arterial and venous thrombosis (0.1%), renal infarction (0.1%), renal artery aneurysm (0.1%) as well as arterial stenosis (0.5%), kinking (0.4%), or dissection (0.1%). Other complications, not specifically related with transplantation surgery, occurred in 4.4% of patients. Conclusion. Renal transplantation is a safe surgery by experienced teams. Our rates of surgical complications were within those reported by other series. A meticulous surgical technique is mandatory to prevent them. Prompt diagnosis and management are required to prevent graft damage and patient morbidity.

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We present a strategy for the automatic generation of test cases from parametrised use case templates that capture control flow, state, input and output. Our approach allows test scenario selection based on particular traces or states of the model. The templates are internally represented as CSP processes with explicit input and output alphabets, and test generation is expressed as counter-examples of refinement checking, mechanised using the FDR tool. Soundness is addressed through an input–output conformance relation formally defined in the CSP traces model. This purely process algebraic characterisation of testing has some potential advantages, mainly an easy automation of conformance verification and test case generation via model checking, without the need to develop any explicit algorithm.