In Vivo Genotoxicity Assessment of Titanium Dioxide Nanoparticles by Allium cepa Root Tip Assay at High Exposure Concentrations

The industrial production and commercial applications of titanium dioxide nanoparticles have increased considerably in recent times, which has increased the probability of environmental contamination with these agents and their adverse effects on living systems. This study was designed to assess the genotoxicity potential of TiO2 NPs at high exposure concentrations, its bio-uptake, and the oxidative stress it generated, a recognised cause of genotoxicity. Allium cepa root tips were treated with TiO2 NP dispersions at four different concentrations (12.5, 25, 50, 100 mu g/mL). A dose dependant decrease in the mitotic index (69 to 21) and an increase in the number of distinctive chromosomal aberrations were observed. Optical, fluorescence and confocal laser scanning microscopy revealed chromosomal aberrations, including chromosomal breaks and sticky, multipolar, and laggard chromosomes, and micronucleus formation. The chromosomal aberrations and DNA damage were also validated by the comet assay. The bio-uptake of TiO2 in particulate form was the key cause of reactive oxygen species generation, which in turn was probably the cause of the DNA aberrations and genotoxicity observed in this study.

LDPC Code Designs Based on root I Matrices

LDPC codes can be constructed by tiling permutation matrices that belong to the square root of identity type and similar algebraic structures. We investigate into the properties of such codes. We also present code structures that are amenable for efficient encoding.

Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidate pixel is classified as belonging to the lesion boundary, lesion interior or normal tissue based on its descriptor value. The tissue transitions are modeled using a Markov chain to estimate the likelihood of a candidate lesion region. Experimental evaluation on a clinical dataset of 135 images show that the proposed approach can achieve high sensitivity (95 %) with modest (3) false positives per image. The approach achieves very similar results (94 % for 3 false positives) on a completely different clinical dataset of 159 images without retraining, highlighting the robustness of the approach.