Novel mesoporous TiO2@DNA nanohybrid electrodes, combining covalently encoded DNA with meso-porous TiO2 microbeads using dopamine as a linker, were prepared and characterised for application in supercapacitors. Detailed information about donor density, charge transfer resistance and chemical capacitance, which have an important role in the performance of an electrochemical device, were studied by electrochemical methods. The results indicated the improvement of electrochemical performance of the TiO2 nanohybrid electrode by DNA surface functionalisation. A supercapacitor was constructed from TiO2@DNA nanohybrids with PBS as the electrolyte. From the supercapacitor experiment, it was found that the addition of DNA played an important role in improving the specific capacitance of the TiO2 supercapacitor. The nanohybrid electrodes were shown to be stable over long-term cycling, retaining 95% of their initial specific capacitance after 1500 cycles.
COBISS.SI-ID: 11061076
Peri-implantitis and peri-mucositis pose a severe threat to the success of dental implants. Current research focuses on the development of implant surfaces that inhibit biofilm formation while not inferring with tissue integration. This study compared the adherence of two oral bacterial species, Streptococcus sanguinis and Streptococcus mutans to nanostructured titanium surfaces. The samples included TiO2 nanotubular surfaces formed by anodization of titanium foil of 100, 50 and 15 nm diameter, a nanoporous (15 nm pore diameter) surface and compact TiO2 control. The lowest adhesion of S. sanguinis and S. mutans on TiO2 nanostructured surfaces was observed for small diameter nanoporous surfaces which coincides with the highest osteoblast adhesion on small diameter nanotubular/nanoporous surfaces shown in our previous work. Based on the results of this study it can be concluded that the adherence of oral streptococci to oral titanium implants can be modified by nanostructured surface of titanium implants.
COBISS.SI-ID: 11244628
Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and swallowing problems.
COBISS.SI-ID: 10873940
Automated and semi-automated detection and segmentation of spinal and vertebral structures from computed tomography (CT) images is a challenging task due to a relatively high degree of anatomical complexity, presence of unclear boundaries and articulation of vertebrae with each other, as well as due to insufficient image spatial resolution, partial volume effects, presence of image artifacts, intensity variations and low signal-to-noise ratio. In this paper, we describe a novel framework for automated spine and vertebrae detection and segmentation from 3-D CT images. A novel optimization technique based on interpolation theory is applied to detect the location of the whole spine in the 3-D image and, using the obtained location of the whole spine, to further detect the location of individual vertebrae within the spinal column. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is performed by an improved shape-constrained deformable model approach. The framework was evaluated on two publicly available CT spine image databases of 50 lumbar and 170 thoracolumbar vertebrae. Quantitative comparison against corresponding reference vertebra segmentations yielded an overall mean centroid-to-centroid distance of 1.1 mm and Dice coefficient of 83.6% for vertebra detection, and an overall mean symmetric surface distance of 0.3 mm and Dice coefficient of 94.6% for vertebra segmentation. The results indicate that by applying the proposed automated detection and segmentation framework, vertebrae can be successfully detected and accurately segmented in 3-D from CT spine images.
COBISS.SI-ID: 10904660
Light propagation models often simplify the interface between the optical fiber probe tip and tissue to a laterally uniform boundary with mismatched refractive indices. Such simplification neglects the precise optical properties of the commonly used probe tip materials, e.g. stainless steel or black epoxy. In this paper, we investigate the limitations of the laterally uniform probe-tissue interface in Monte Carlo simulations of diffuse reflectance. In comparison to a realistic probe-tissue interface that accounts for the layout and properties of the probe tip materials, the simplified laterally uniform interface is shown to introduce significant errors into the simulated diffuse reflectance.
COBISS.SI-ID: 11145812