Clinical performance of the assay was evaluated with sera from 298 patients with untreated Graves’ disease, 220 patients with destructive (painless and subacute) thyroiditis, and 332 healthy volunteers. The optimal cutoff point, which was calculated by receiver operating

characteristic (ROC) analysis with the above subjects, was then used to classify an independent sample set of 80 patients with untreated Graves’ disease, and 152 patients with destructive thyroiditis.\n\nResults: Intraassay coefficient of variation (CV) was 4.24% at 1.85 IU/L and interassay DMXAA molecular weight CV was 10.1% at 1.46 IU/L. All the correlation coefficient values calculated against four commercial assays were larger than 0.85. ROC analysis resulted in a specificity of 99.1% with a sensitivity of 97.0% at a decision limit of 1.86 IU/L from comparison with untreated Graves’ disease and destructive thyroiditis. The cutoff point yielded a sensitivity of 87.5% and specificity of 96.7% with the independent sample set.\n\nConclusion: In spite of the short measuring time of only 27 minutes, the assay showed the same or better results with the existing commercial products. The short measuring time Wnt cancer would contribute to speedy, preconsultation diagnosis of thyroid

disease, especially of Graves’ disease.”
“Purpose: X-ray phase-contrast tomography (PCT) is a rapidly Nutlin-3 price emerging imaging modality for reconstructing estimates of an object’s three-dimensional x-ray refractive index distribution. Unlike conventional x-ray computed tomography methods, the statistical properties of the reconstructed images in PCT remain unexplored. The purpose of this work is

to quantitatively investigate noise propagation in PCT image reconstruction.\n\nMethods: The authors derived explicit expressions for the autocovariance of the reconstructed absorption and refractive index images to characterize noise texture and understand how the noise properties are influenced by the imaging geometry. Concepts from statistical detection theory were employed to understand how the imaging geometry-dependent statistical properties affect the signal detection performance in a signal-known-exactly/background-known-exactly task.\n\nResults: The analytical formulas for the phase and absorption autocovariance functions were implemented numerically and compared to the corresponding empirical values, and excellent agreement was found. They observed that the reconstructed refractive images are highly spatially correlated, while the absorption images are not. The numerical results confirm that the strength of the covariance is scaled by the detector spacing. Signal detection studies were conducted, employing a numerical observer. The detection performance was found to monotonically increase as the detector-plane spacing was increased.