Application of fluorescence in situ hybridization in the detection of bladder transitional-cell carcinoma: A multi-center clinical study based on Chinese population

Objective:To evaluate the diagnostic value of fluorescence in situ hybridization(FISH)in bladder cancer.Methods:We enrolled healthy volunteers and patients who were clinically suspected to have bladder cancer and conducted FISH tests and cytology examinations from August 2007 to December 2008.Receiver operating characteristic(ROC)curve analysis was performed and the area under curve(AUC)values were calculated for both the FISH and urine cytology tests.Results:A cohort of 988 healthy volunteers was enrolled to establish a reference range for the normal population.A total of 4807 patients with hematuria were prospectively,randomly enrolled for the simultaneous analysis of urine cytology,FISH testing,and a final diagnosis as determined by the pathologic findings of a biopsy or a surgically-excised specimen.Overall,the sensitivity of FISH in detecting transitional-cell carcinoma was 82.7%,while that of cytology was 33.4%(p<0.001).The sensitivity values of FISH for non-muscle invasive and muscle invasive bladder transitional-cell carcinoma were 81.7%and 89.6%,respectively(p=0.004).The sensitivity values of FISH for low and high grade bladder cancer were 82.6%and 90.1%,respectively(p=0.002).Conclusion:FISH is significantly more sensitive than voided urine cytology for detecting bladder cancer in patients evaluated for gross hematuria at all cancer grades and stages.Higher sensitivity using FISH was obtained in high grade and muscle invasive tumors.

Accelerating the discontinuous Galerkin method for seismic wave propagation simulations using multiple GPUs with CUDA and MPI

We have successfully ported an arbitrary highorder discontinuous Galerkin method for solving the threedimensional isotropic elastic wave equation on unstructured tetrahedral meshes to multiple Graphic Processing Units （GPUs） using the Compute Unified Device Architecture （CUDA） of NVIDIA and Message Passing Interface （MPI） and obtained a speedup factor of about 28.3 for the single-precision version of our codes and a speedup factor of about 14.9 for the double-precision version. The GPU used in the comparisons is NVIDIA Tesla C2070 Fermi, and the CPU used is Intel Xeon W5660. To effectively overlap inter-process communication with computation, we separate the elements on each subdomain into inner and outer elements and complete the computation on outer elements and fill the MPI buffer first. While the MPI messages travel across the network, the GPU performs computation on inner elements, and all other calculations that do not use information of outer elements from neighboring subdomains. A significant portion of the speedup also comes from a customized matrix-matrix multiplication kernel, which is used extensively throughout our program. Preliminary performance analysis on our parallel GPU codes shows favorable strong and weak scalabilities.