Diagnosis of bladder cancer at 465 MHz

Current methods for bladder cancer investigation involve cystoscopy, ultrasound scanning, and contrast urography, with additional information provided by cytology. These methods, although having a high detection rate, are expensive, time-consuming, invasive, and uncomfortable. Therefore, there is a need for an inexpensive, non invasive, quick, and simple investigation with a high sensitivity and specificity. In this study we evaluate the use of an in vivo electromagnetic (EM) interaction as a non invasive method for detecting cancer. A clinical trial was designed and completed. The main trial target was the feasibility assessment of the novel method by comparing its results with standard cystoscopy. A physical discussion of the EM interaction with bladder cancer tissue is presented. One hundred and fourteen patients referred for cystoscopy by microscopic or gross haematuria, irritative voiding symptoms, or suspected bladder tumor at ultrasound were first submitted to EM scan by means of the TRIMprob system. Cystoscopy was performed on each patient after the TRIMprob examination. Comparison between EM and cystoscopy results provides a high level of agreement (Cohen's K = 0.77, p < 0.001). The TRIMprob performance in malignant cancer cells detection suggests that this in vivo EM waves method is also worth investigating for routine diagnostic procedures.     

INFLAM – INFLAMmation in Brain and Vessels with Iron Nanoparticles and Cell Trafficking: A Multiscale Approach of Tissue Microenvironment,Iron Nanostructure and Iron Biotransformation

Background

Inflammation is a share process in atherosclerosis and stroke and is thought to be a key player in the evolution of these diseases. Ten years ago, inflammation imaging with magnetic resonance imaging (MRI) was considered very promising for both pre-clinical and clinical studies of atherosclerosis and stroke.

Phylogenetic studies of the genus <Emphasis Type="Italic">Cebus</Emphasis>(Cebidae-Primates) using chromosome painting and G-banding

Background  

Chromosomal painting, using whole chromosome probes from humans and Saguinus oedipus, was used to establish karyotypic divergence among species of the genus Cebus, including C. olivaceus, C. albifrons, C. apella robustus and C. apella paraguayanus. Cytogenetic studies suggested that the species of this genus have conservative karyotypes, with diploid numbers ranging from 2n = 52 to 2n = 54.     

Methods for computing the standard errors of branching points in an evolutionary tree and their application to molecular data from humans and apes

Statistical methods for computing the standard errors of the branching points of an evolutionary tree are developed. These methods are for the unweighted pair-group method-determined (UPGMA) trees reconstructed from molecular data such as amino acid sequences, nucleotide sequences, restriction-sites data, and electrophoretic distances. They were applied to data for the human, chimpanzee, gorilla, orangutan, and gibbon species. Among the four different sets of data used, DNA sequences for an 895-nucleotide segment of mitochondrial DNA (Brown et al. 1982) gave the most reliable tree, whereas electrophoretic data (Bruce and Ayala 1979) gave the least reliable one. The DNA sequence data suggested that the chimpanzee is the closest and that the gorilla is the next closest to the human species. The orangutan and gibbon are more distantly related to man than is the gorilla. This topology of the tree is in agreement with that for the tree obtained from chromosomal studies and DNA-hybridization experiments. However, the difference between the branching point for the human and the chimpanzee species and that for the gorilla species and the human-chimpanzee group is not statistically significant. In addition to this analysis, various factors that affect the accuracy of an estimated tree are discussed.      

Equivalence of multibreed animal models and hierarchical Bayes analysis for maternally influenced traits

Background

It has been argued that multibreed animal models should include a heterogeneous covariance structure. However, the estimation of the (co)variance components is not an easy task, because these parameters can not be factored out from the inverse of the additive genetic covariance matrix. An alternative model, based on the decomposition of the genetic covariance matrix by source of variability, provides a much simpler formulation. In this study, we formalize the equivalence between this alternative model and the one derived from the quantitative genetic theory. Further, we extend the model to include maternal effects and, in order to estimate the (co)variance components, we describe a hierarchical Bayes implementation. Finally, we implement the model to weaning weight data from an Angus × Hereford crossbred experiment.

Methods

Our argument is based on redefining the vectors of breeding values by breed origin such that they do not include individuals with null contributions. Next, we define matrices that retrieve the null-row and the null-column pattern and, by means of appropriate algebraic operations, we demonstrate the equivalence. The extension to include maternal effects and the estimation of the (co)variance components through the hierarchical Bayes analysis are then straightforward. A FORTRAN 90 Gibbs sampler was specifically programmed and executed to estimate the (co)variance components of the Angus × Hereford population.

Results

In general, genetic (co)variance components showed marginal posterior densities with a high degree of symmetry, except for the segregation components. Angus and Hereford breeds contributed with 50.26% and 41.73% of the total direct additive variance, and with 23.59% and 59.65% of the total maternal additive variance. In turn, the contribution of the segregation variance was not significant in either case, which suggests that the allelic frequencies in the two parental breeds were similar.

Conclusion

The multibreed maternal animal model introduced in this study simplifies the problem of estimating (co)variance components in the framework of a hierarchical Bayes analysis. Using this approach, we obtained for the first time estimates of the full set of genetic (co)variance components. It would be interesting to assess the performance of the procedure with field data, especially when interbreed information is limited.