Relating the physicochemical characteristics and dispersion of multiwalled carbon nanotubes in different suspension media to their oxidative reactivity in vitro and inflammation in vivo

Reactive oxygen species (ROS) production is important in the toxicity of pathogenic particles such as fibres. We examined the oxidative potential of straight (50 microm and 10 microm) and tangled carbon nanotubes in a cell free assay, in vitro and in vivo using different dispersants. The cell free oxidative potential of tangled nanotubes was higher than for the straight fibres. In cultured macrophages tangled tubes exhibited significantly more ROS at 30 min, while straight tubes increased ROS at 4 h. ROS was significantly higher in bronchoalveolar lavage cells of animals instilled with tangled and 10 mum straight fibres, whereas the number of neutrophils increased only in animals treated with the long tubes. Addition of dispersants in the suspension media lead to enhanced ROS detection by entangled tubes in the cell-free system. Tangled fibres generated more ROS in a cell-free system and in cultured cells, while straight fibres generated a slower but more prolonged effect in animals.

Comparative assessment of conventional and self-ligating appliances on the effect of mandibular intermolar distance in adolescent nonextraction patients: a single-center randomized controlled trial

Our aim in this study was to compare intermolar widths after alignment of crowded mandibular dental arches in nonextraction adolescent patients between conventional and self-ligating brackets.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.