D-mannose-Coating of Maghemite Nanoparticles Improved Labeling of Neural Stem Cells and Allowed Their Visualization by ex vivo MRI after Transplantation in the Mouse Brain.

Magnetic resonance imaging (MRI) of superparamagnetic iron oxide-labeled cells can be used as a non-invasive technique to track stem cells after transplantation. The aim of this study was to (1) evaluate labeling efficiency of D-mannose-coated maghemite nanoparticles (D-mannose(γ-Fe2O3)) in neural stem cells (NSCs) in comparison to the uncoated nanoparticles, (2) assess nanoparticle utilization as MRI contrast agent to visualize NSCs transplanted into the mouse brain, and (3) test nanoparticle biocompatibility. D-mannose(γ-Fe2O3) labeled the NSCs better than the uncoated nanoparticles. The labeled cells were visualized by ex vivo MRI and their localization subsequently confirmed on histological sections. Although the progenitor properties and differentiation of the NSCs were not affected by labeling, subtle effects on stem cells could be detected depending on dose increase, including changes in cell proliferation, viability, and neurosphere diameter. D-mannose coating of maghemite nanoparticles improved NSC labeling and allowed for NSC tracking by ex vivo MRI in the mouse brain, but further analysis of the eventual side effects might be necessary before translation to the clinic.

Surface coating affects uptake of silver nanoparticles in neural stem cells.

The rapid development and widespread applications of nanotechnology necessitates the design towards safe nanoparticles. Surface structure is among the most important physicochemical characteristics of metallic nanoparticles affecting their mode of action in certain biological or environmental compartments. This study aimed to investigate how different surface coatings affect the cytotoxicity and cellular uptake of silver nanoparticles (AgNPs) in murine neural stem cells (mNSCs). Different AgNPs were prepared by stabilisation with surface coatings encompassing sodium bis(2-ethylhexyl)-sulfosuccinate (AOT), cetyltrimethylammonium bromide (CTAB), poly(vinylpyrrolidone) (PVP), poly-l-lysine (PLL), and bovine serum albumin (BSA). The obtained results revealed that AgNPs stabilized with different surface coating caused different cytotoxicity effects and internalization pattern in mNSCs. Macropinocytosis was determined as the main uptake mechanism in mNSCs for all of the tested AgNP types. These findings contribute to the overall knowledge essential to the safety assessment of novel nanomaterials.

Impact of surface functionalization on the uptake mechanism and toxicity effects of silver nanoparticles in HepG2 cells.

Safe and successful bioapplications of metallic nanoparticles depend on their physicochemical characteristics, in particular their surface properties. This study aimed to investigate how different surface functionalization of silver nanoparticles (AgNP) affect their interaction with mammalian liver cells with regard to cytotoxicity, genotoxicity and mechanism of cellular uptake. Differentially coated AgNP were prepared by surface functionalization using sodium bis(2-ethylhexyl)-sulfosuccinate (AOTAgNP), cetyltrimethylammonium bromide (CTABAgNP), poly(vinylpyrrolidone) (PVPAgNP), poly-l-lysine (PLLAgNP), and bovine serum albumin (BSAAgNP). Data showed varying toxic potential of differentially coated AgNP. All AgNP types demonstrated concentration dependent effects on cytotoxicity and genotoxicity in HepG2 cells. Cytotoxic potential of differentially coated AgNP followed the order of BSAAgNP > PLLAgNP > CTABAgNP > AOTAgNP > PVPAgNP. Exposure of HepG2 cells to non-cytotoxic concentrations (up to 10 mg Ag/L) of AgNP for 24 h induced primary DNA damage as evaluated by alkaline comet assay. The highest increase in both comet tail length and tail intensity was produced by PLLAgNP followed by AOTAgNP, while CTABAgNP appeared to be least damaging. The main uptake mechanisms of AgNP were macropinocytosis and clathrin-mediated endocytosis. The study findings contribute to the criteria that should be considered in evaluating the biocompatibility and safety of novel nanomaterials.

3D surface analysis and classification in neuroimaging segmentation.

This work emphasizes new algorithms for 3D edge and corner detection used in surface extraction and new concept of image segmentation in neuroimaging based on multidimensional shape analysis and classification. We propose using of NifTI standard for describing input data which enables interoperability and enhancement of existing computing tools used widely in neuroimaging research. In methods section we present our newly developed algorithm for 3D edge and corner detection, together with the algorithm for estimating local 3D shape. Surface of estimated shape is analyzed and segmented according to kernel shapes.

Application of novel lossless compression of medical images using prediction and contextual error modeling.

Conduction of tele-3D-computer assisted operations as well as other telemedicine procedures often requires highest possible quality of transmitted medical images and video. Unfortunately, those data types are always associated with high telecommunication and storage costs that sometimes prevent more frequent usage of such procedures. We present a novel algorithm for lossless compression of medical images that is extremely helpful in reducing the telecommunication and storage costs. The algorithm models the image properties around the current, unknown pixel and adjusts itself to the local image region. The main contribution of this work is the enhancement of the well known approach of predictor blends through highly adaptive determination of blending context on a pixel-by-pixel basis using classification technique. We show that this approach is well suited for medical image data compression. Results obtained with the proposed compression method on medical images are very encouraging, beating several well known lossless compression methods. The predictor proposed can also be used in other image processing applications such as segmentation and extraction of image regions.