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
In the present work we investigate the key factors involved in the interaction of small-sized charged proteins with TiO2 nanostructures, i.e. albumin (negatively charged), histone (positively charged). We examine anodic nanotubes with specific morphology (simultaneous control over diameter and length, e.g. diameter - 15, 50 or 100 nm, length – 250 nm up to 10 μm) and nanopores. The nanostructures surface area has a direct influence on the amount of bound protein, nonetheless the protein physical properties as electric charge and size (in relation to nanotopography and biomaterial's electric charge) are crucial too. The highest quantity of adsorbed protein is registered for histone, for 100 nm diameter nanotubes (10μm length) while higher values are registered for 15 nm diameter nanotubes when normalizing protein adsorption to nanostructures' surface unit area (evaluated from dye desorption measurements) - consistent with theoretical considerations. The proteins presence on the nanostructures is evaluated by XPS and ToF-SIMS; additionally, we qualitatively assess their presence along the nanostructures length by ToF-SIMS depth profiles, with decreasing concentration towards the bottom.
COBISS.SI-ID: 29709095
Pathological conditions that cause instability of the spine are commonly treated by vertebral fixation involving pedicle screw placement surgery. However, existing methods for preoperative planning are based only on geometrical properties of vertebral structures (i.e., shape) without taking into account their structural properties (i.e., appearance). We propose a novel automated method for computer-assisted preoperative planning of the thoracic pedicle screw size and insertion trajectory. The proposed method extracts geometrical properties of vertebral structures by parametric modeling of vertebral bodies and pedicles in three dimensions (3D), and combines them with structural properties, evaluated through underlying image intensities in computed tomography (CT) images while considering the guidelines for pedicle screw design. The method was evaluated on 81 pedicles, obtained from 3D CT images of 11 patients that were appointed for pedicle screw placement surgery.
COBISS.SI-ID: 11250260
Accurate characterization of white-matter lesions from magnetic resonance (MR) images has increasing importance for diagnosis and management of treatment of certain neurological diseases, and can be performed in an objective and effective way by automated lesion segmentation. This usually involves modeling the whole-brain MR intensity distribution, however, capturing various sources of MR intensity variability and lesion heterogeneity results in highly complex whole-brain MR intensity models, thus their robust estimation on a large set of MR images presents a huge challenge. We propose a novel approach employing stratified mixture modeling, where the main premise is that the otherwise complex whole-brain model can be reduced to a tractable parametric form in small brain subregions. We show on MR images of multiple sclerosis (MS) patients with different lesion loads that robust estimators enable accurate mixture modeling of MR intensity in small brain subregions even in the presence of lesions. Recombination of the mixture models across strata provided an accurate whole-brain MR intensity model. Increasing the number of subregions and, thereby, the model complexity, consistently improved the accuracy of whole-brain MR intensity modeling and segmentation of normal structures. The proposed approach was incorporated into three unsupervised lesion segmentation methods and, compared to original and three other state-of-the-art methods, the proposed modeling approach significantly improved lesion segmentation according to increased Dice similarity indices and lower number of false positives on real MR images of 30 patients with MS.
COBISS.SI-ID: 11198292
Computerized segmentation of pathological structures in medical images is challenging, as, in addition to unclear image boundaries, image artifacts and traces of surgical activities, the shape of pathological structures may be very different from the shape of normal structures. Even if a sufficient number of pathological training samples are collected, statistical shape modeling cannot always capture shape features of pathological samples as they may be suppressed by shape features of a considerably larger number of healthy samples. At the same time, landmarking can be efficient in analyzing pathological structures but often lacks robustness. In this paper, we combine the advantages of landmark detection and deformable models into a novel supervised multi-energy segmentation framework that can efficiently segment structures with pathological shape. The framework adopts the theory of Laplacian shape editing that was introduced in the field of computer graphics, so that the limitations of statistical shape modeling are avoided. The performance of the proposed framework was validated by segmenting fractured lumbar vertebrae from three-dimensional (3D) computed tomography (CT) images, atrophic corpora callosa from two-dimensional (2D) magnetic resonance (MR) crosssections and cancerous prostates from 3D MR images.
COBISS.SI-ID: 11684692