Comparison of established and emerging biodosimetry assays

Rapid biodosimetry tools are required to assist with triage in the case of a large-scale radiation incident. Here, we aimed to determine the dose-assessment accuracy of the well-established dicentric chromosome assay (DCA) and cytokinesis-block micronucleus assay (CBMN) in comparison to the emerging γ-H2AX foci and gene expression assays for triage mode biodosimetry and radiation injury assessment. Coded blood samples exposed to 10 X-ray doses (240 kVp, 1 Gy/min) of up to 6.4 Gy were sent to participants for dose estimation. Report times were documented for each laboratory and assay. The mean absolute difference (MAD) of estimated doses relative to the true doses was calculated. We also merged doses into binary dose categories of clinical relevance and examined accuracy, sensitivity and specificity of the assays. Dose estimates were reported by the first laboratories within 0.3-0.4 days of receipt of samples for the γ-H2AX and gene expression assays compared to 2.4 and 4 days for the DCA and CBMN assays, respectively. Irrespective of the assay we found a 2.5-4-fold variation of interlaboratory accuracy per assay and lowest MAD values for the DCA assay (0.16 Gy) followed by CBMN (0.34 Gy), gene expression (0.34 Gy) and γ-H2AX (0.45 Gy) foci assay. Binary categories of dose estimates could be discriminated with equal efficiency for all assays, but at doses ≥1.5 Gy a 10% decrease in efficiency was observed for the foci assay, which was still comparable to the CBMN assay. In conclusion, the DCA has been confirmed as the gold standard biodosimetry method, but in situations where speed and throughput are more important than ultimate accuracy, the emerging rapid molecular assays have the potential to become useful triage tools

Down-Regulation of Yes Associated Protein 1 Expression Reduces Cell Proliferation and Clonogenicity of Pancreatic Cancer Cells

BACKGROUND: The Hippo pathway regulates organ size by inhibiting cell proliferation and promoting cell apoptosis upon its activation. The Yes Associated Protein 1 (YAP1) is a nuclear effector of the Hippo pathway that promotes cell growth as a transcription co-activator. In human cancer, the YAP1 gene was reported as amplified and over-expressed in several tumor types. METHODS: Immunohistochemical staining of YAP1 protein was used to assess the expression of YAP1 in pancreatic tumor tissues. siRNA oligonucleotides were used to knockdown the expression of YAP1 and their effects on pancreatic cancer cells were investigated using cell proliferation, apoptosis, and anchorage-independent growth assays. The Wilcoxon signed-rank, Pearson correlation coefficient, Kendall's Tau, Spearman's Rho, and an independent two-sample t (two-tailed) test were used to determine the statistical significance of the data. RESULTS: Immunohistochemistry studies in pancreatic tumor tissues revealed YAP1 staining intensities were moderate to strong in the nucleus and cytoplasm of the tumor cells, whereas the adjacent normal epithelial showed negative to weak staining. In cultured cells, YAP1 expression and localization was modulated by cell density. YAP1 total protein expression increased in the nuclear fractions in BxPC-3 and PANC-1, while it declined in HPDE6 as cell density increased. Additionally, treatment of pancreatic cancer cell lines, BxPC-3 and PANC-1, with YAP1-targeting siRNA oligonucleotides significantly reduced their proliferation in vitro. Furthermore, treatment with YAP1 siRNA oligonucleotides diminished the anchorage-independent growth on soft agar of pancreatic cancer cells, suggesting a role of YAP1 in pancreatic cancer tumorigenesis. CONCLUSIONS: YAP1 is overexpressed in pancreatic cancer tissues and potentially plays an important role in the clonogenicity and growth of pancreatic cancer cells

Characterization and simulation of cDNA microarray spots using a novel mathematical model

<p>Abstract</p> <p>Background</p> <p>The quality of cDNA microarray data is crucial for expanding its application to other research areas, such as the study of gene regulatory networks. Despite the fact that a number of algorithms have been suggested to increase the accuracy of microarray gene expression data, it is necessary to obtain reliable microarray images by improving wet-lab experiments. As the first step of a cDNA microarray experiment, spotting cDNA probes is critical to determining the quality of spot images.</p> <p>Results</p> <p>We developed a governing equation of cDNA deposition during evaporation of a drop in the microarray spotting process. The governing equation included four parameters: the surface site density on the support, the extrapolated equilibrium constant for the binding of cDNA molecules with surface sites on glass slides, the macromolecular interaction factor, and the volume constant of a drop of cDNA solution. We simulated cDNA deposition from the single model equation by varying the value of the parameters. The morphology of the resulting cDNA deposit can be classified into three types: a doughnut shape, a peak shape, and a volcano shape. The spot morphology can be changed into a flat shape by varying the experimental conditions while considering the parameters of the governing equation of cDNA deposition. The four parameters were estimated by fitting the governing equation to the real microarray images. With the results of the simulation and the parameter estimation, the phenomenon of the formation of cDNA deposits in each type was investigated.</p> <p>Conclusion</p> <p>This study explains how various spot shapes can exist and suggests which parameters are to be adjusted for obtaining a good spot. This system is able to explore the cDNA microarray spotting process in a predictable, manageable and descriptive manner. We hope it can provide a way to predict the incidents that can occur during a real cDNA microarray experiment, and produce useful data for several research applications involving cDNA microarrays.</p

A constant-time algorithm for erosions/dilations with applications to morphological texture feature computation

A computationally efficient algorithm for computing erosions and dilations by one-dimensional grayscale structuring elements with constant slope is proposed. The computational complexity of this algorithm is independent of the size of the support of the structuring function. This is a generalization of the method proposed by Van Herk for the case of erosion and dilation by flat one-dimensional structuring elements. By appropriate combinations of these structuring elements, it is possible to approximate many useful structuring elements. This enables efficient computation of granulometries where the number of operations depends linearly on the number of openings. Theoretical and experimental results comparing the complexity of this algorithm with other standard techniques is presented. Two memory efficient algorithms are then presented. Several implementation issues in computing a granulometry and moments of the associated morphological pattern spectrum are then addressed. An efficient implementation of granulometries for large images on machines with limited memory, by dividing the image into smaller rectangular patches is then discussed. The optimum size of these patches is a function of the specific hardware and has been obtained experimentally for three different hardware platforms. Finally, parallel implementation of the different algorithms on two multiprocessor machines is discussed