Balance algorithm for cluster randomized trials

<p>Abstract</p> <p>Background</p> <p>Within cluster randomized trials no algorithms exist to generate a full enumeration of a block randomization, balancing for covariates across treatment arms. Furthermore, often for practical reasons multiple blocks are required to fully randomize a study, which may not have been well balanced within blocks.</p> <p>Results</p> <p>We present a convenient and easy to use randomization tool to undertake allocation concealed block randomization. Our algorithm highlights allocations that minimize imbalance between treatment groups across multiple baseline covariates.</p> <p>We demonstrate the algorithm using a cluster randomized trial in primary care (the PRE-EMPT Study) and show that the software incorporates a trade off between independent random allocations that were likely to be imbalanced, and predictable deterministic approaches that would minimise imbalance. We extend the methodology of single block randomization to allocate to multiple blocks conditioning on previous allocations.</p> <p>Conclusion</p> <p>The algorithm is included as Additional file <supplr sid="S1">1</supplr> and we advocate its use for robust randomization within cluster randomized trials.</p> <suppl id="S1"> <title> <p>Additional File 1</p> </title> <text> <p><b>Cluster randomization allocation algorithm version 1.</b> Algorithms scripted in R to provide robust cluster randomization.</p> </text> <file name="1471-2288-8-65-S1.zip"> <p>Click here for file</p> </file> </suppl

Worldwide Esophageal Cancer Collaboration : clinical staging data

Peer reviewe

Fluid flow in the osteocyte mechanical environment : a fluid-structure interaction approach

Osteocytes are believed to be the primary sensor of mechanical stimuli in bone, which orchestrate osteoblasts and osteoclasts to adapt bone structure and composition to meet physiological loading demands. Experimental studies to quantify the mechanical environment surrounding bone cells are challenging, and as such, computational and theoretical approaches have modelled either the solid or fluid environment of osteocytes to predict how these cells are stimulated in vivo. Osteocytes are an elastic cellular structure that deforms in response to the external fluid flow imposed by mechanical loading. This represents a most challenging multi-physics problem in which fluid and solid domains interact, and as such, no previous study has accounted for this complex behaviour. The objective of this study is to employ fluid–structure interaction (FSI) modelling to investigate the complex mechanical environment of osteocytes in vivo. Fluorescent staining of osteocytes was performed in order to visualise their native environment and develop geometrically accurate models of the osteocyte in vivo. By simulating loading levels representative of vigorous physiological activity (3,000με compression and 300 Pa pressure gradient), we predict average interstitial fluid velocities (∼60.5μ m/s ) and average maximum shear stresses (∼11 Pa ) surrounding osteocytes in vivo. Interestingly, these values occur in the canaliculi around the osteocyte cell processes and are within the range of stimuli known to stimulate osteogenic responses by osteoblastic cells in vitro. Significantly our results suggest that the greatest mechanical stimulation of the osteocyte occurs in the cell processes, which, cell culture studies have indicated, is the most mechanosensitive area of the cell. These are the first computational FSI models to simulate the complex multi-physics mechanical environment of osteocyte in vivo and provide a deeper understanding of bone mechanobiology

IGF-1, IGFBP-1, and IGFBP-3 Polymorphisms Predict Circulating IGF Levels but Not Breast Cancer Risk: Findings from the Breast and Prostate Cancer Cohort Consortium (BPC3)

IGF-1 has been shown to promote proliferation of normal epithelial breast cells, and the IGF pathway has also been linked to mammary carcinogenesis in animal models. We comprehensively examined the association between common genetic variation in the IGF1, IGFBP1, and IGFBP3 genes in relation to circulating IGF-I and IGFBP-3 levels and breast cancer risk within the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). This analysis included 6,912 breast cancer cases and 8,891 matched controls (n = 6,410 for circulating IGF-I and 6,275 for circulating IGFBP-3 analyses) comprised primarily of Caucasian women drawn from six large cohorts. Linkage disequilibrium and haplotype patterns were characterized in the regions surrounding IGF1 and the genes coding for two of its binding proteins, IGFBP1 and IGFBP3. In total, thirty haplotype-tagging single nucleotide polymorphisms (htSNP) were selected to provide high coverage of common haplotypes; the haplotype structure was defined across four haplotype blocks for IGF1 and three for IGFBP1 and IGFBP3. Specific IGF1 SNPs individually accounted for up to 5% change in circulating IGF-I levels and individual IGFBP3 SNPs were associated up to 12% change in circulating IGFBP-3 levels, but no associations were observed between these polymorphisms and breast cancer risk. Logistic regression analyses found no associations between breast cancer and any htSNPs or haplotypes in IGF1, IGFBP1, or IGFBP3. No effect modification was observed in analyses stratified by menopausal status, family history of breast cancer, body mass index, or postmenopausal hormone therapy, or for analyses stratified by stage at diagnosis or hormone receptor status. In summary, the impact of genetic variation in IGF1 and IGFBP3 on circulating IGF levels does not appear to substantially influence breast cancer risk substantially among primarily Caucasian postmenopausal women

Diffuse glioma growth: a guerilla war

In contrast to almost all other brain tumors, diffuse gliomas infiltrate extensively in the neuropil. This growth pattern is a major factor in therapeutic failure. Diffuse infiltrative glioma cells show some similarities with guerilla warriors. Histopathologically, the tumor cells tend to invade individually or in small groups in between the dense network of neuronal and glial cell processes. Meanwhile, in large areas of diffuse gliomas the tumor cells abuse pre-existent “supply lines” for oxygen and nutrients rather than constructing their own. Radiological visualization of the invasive front of diffuse gliomas is difficult. Although the knowledge about migration of (tumor)cells is rapidly increasing, the exact molecular mechanisms underlying infiltration of glioma cells in the neuropil have not yet been elucidated. As the efficacy of conventional methods to fight diffuse infiltrative glioma cells is limited, a more targeted (“search & destroy”) tactic may be needed for these tumors. Hopefully, the study of original human glioma tissue and of genotypically and phenotypically relevant glioma models will soon provide information about the Achilles heel of diffuse infiltrative glioma cells that can be used for more effective therapeutic strategies