196 resultados para Neural Tube Defects


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Olfactory ensheathing cells, the glial cells of the olfactory nervous system, exhibit unique growth-promoting and migratory properties that make them interesting candidates for cell therapies targeting neuronal injuries such as spinal cord injury. Transplantation of olfactory cells is feasible and safe in humans; however, functional outcomes are highly variable with some studies showing dramatic improvements and some no improvements at all. We propose that the reason for this is that the identity and purity of the cells is different in each individual study. We have shown that olfactory ensheathing cells are not a uniform cell population and that individual subpopulations of OECs are present in different regions of the olfactory nervous system, with strikingly different behaviors. Furthermore, the presence of fibroblasts and other cell types in the transplant can dramatically alter the behavior of the transplanted glial cells. Thus, a thorough characterization of the differences between olfactory ensheathing cell subpopulations and how the behavior of these cells is affected by the presence of other cell types is highly warranted.

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A pilot experiment was performed using the WOMBAT powder diffraction instrument at ANSTO in which the first neutron diffraction peak (Q0) was measured for D2O flowing in a 2 mm internal diameter aluminium tube. Measurements of Q0 were made at -9, 4.3, 6.9, 12, 18.2 and 21.5 °C. The D2O was circulated using a siphon with water in the lower reservoir returned to the upper reservoir using a small pump. This enabled stable flow to be maintained for several hours. For example, if the pump flow increased slightly, the upper reservoir level rose, increasing the siphon flow until it matched the return flow. A neutron wavelength of 2.4 Å was used and data integrated over 60 minutes for each temperature. A jet of nitrogen from a liquid N2 Dewar was directed over the aluminium tube to vary water temperature. After collection of the data, the d spacing of the aluminium peaks was used to calculate the temperature of the aluminium within the neutron beam and therefore was considered to be an accurate measure of water temperature within the beam. Sigmaplot version 12.3 was used to fit a Weibull five parameter peak fit to the first neutron diffraction peak. The values of Q0 obtained in this experiment showed an increase with temperature consistent with data in the literature [1] but were consistently higher than published values for bulk D20. For example at 21.5 °C we obtained a value of 2.008 Å-1 for Q0 compared to a literature value of 1.988 Å-1 for bulk D2O at 20 °C, a difference of 1%. Further experiments are required to see if this difference is real or artifactual.

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The use of the Sengstaken–Blakemore tube as a life-saving treatment for bleeding oesophageal varices is slowly becoming the least preferred method possibly due to the potential complications associated with its placement. Nursing practice pertaining to the care of this patient group appears ad hoc and reliant on local knowledge and experience as opposed to recognised evidence of best practice. Therefore, this paper focuses on the application of Lewin's transitional change theory used to introduce a change in nursing practice with the application of a guideline to enhance the care of patients with a Sengstaken–Blakemore tube in situ within a general intensive care unit. This method identified some of the complexities surrounding the change process including the driving and restraining forces that must be harnessed and minimised in order for the adoption of change to be successful.

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Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.

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Along with the tri-lineage of bone, cartilage and fat, human mesenchymal stem cells (hMSCs) retain neural lineage potential. Multiple factors have been described that influence lineage fate of hMSCs including the extracellular microenvironment or niche. The niche includes the extracellular matrix (ECM) providing structural composition, as well as other associated proteins and growth factors, which collectively influence hMSC stemness and lineage specification. As such, lineage specific differentiation of MSCs is mediated through interactions including cell–cell and cell–matrix, as well as through specific signalling pathways triggering downstream events. Proteoglycans (PGs) are ubiquitous within this microenvironment and can be localised to the cell surface or embedded within the ECM. In addition, the heparan sulfate (HS) and chondroitin sulfate (CS) families of PGs interact directly with a number of growth factors, signalling pathways and ECM components including FGFs, Wnts and fibronectin. With evidence supporting a role for HSPGs and CSPGs in the specification of hMSCs down the osteogenic, chondrogenic and adipogenic lineages, along with the localisation of PGs in development and regeneration, it is conceivable that these important proteins may also play a role in the differentiation of hMSCs toward the neuronal lineage. Here we summarise the current literature and highlight the potential for HSPG directed neural lineage fate specification in hMSCs, which may provide a new model for brain damage repair.

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Background Directed cell migration is essential for normal development. In most of the migratory cell populations that have been analysed in detail to date, all of the cells migrate as a collective from one location to another. However, there are also migratory cell populations that must populate the areas through which they migrate, and thus some cells get left behind while others advance. Very little is known about how individual cells behave to achieve concomitant directional migration and population of the migratory route. We examined the behavior of enteric neural crest-derived cells (ENCCs), which must both advance caudally to reach the anal end and populate each gut region. Results The behaviour of individual ENCCs was examined using live imaging and mice in which ENCCs express a photoconvertible protein. We show that individual ENCCs exhibit very variable directionalities and speed; as the migratory wavefront of ENCCs advances caudally, each gut region is populated primarily by some ENCCs migrating non-directionally. After populating each region, ENCCs remain migratory for at least 24 hours. Endothelin receptor type B (EDNRB) signaling is known to be essential for the normal advance of the ENCC population. We now show that perturbation of EDNRB principally affects individual ENCC speed rather than directionality. The trajectories of solitary ENCCs, which occur transiently at the wavefront, were consistent with an unbiased random walk and so cell-cell contact is essential for directional migration. ENCCs migrate in close association with neurites. We showed that although ENCCs often use neurites as substrates, ENCCs lead the way, neurites are not required for chain formation and neurite growth is more directional than the migration of ENCCs as a whole. Conclusions Each gut region is initially populated by sub-populations of ENCCs migrating non-directionally, rather than stopping. This might provide a mechanism for ensuring a uniform density of ENCCs along the growing gut.

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The application of artificial neural networks (ANN) in finance is relatively new area of research. We employed ANNs that used both fundamental and technical inputs to predict future prices of widely held Australian stocks and used these predicted prices for stock portfolio selection over a 10-year period (2001-2011). We found that the ANNs generally do well in predicting the direction of stock price movements. The stock portfolios selected by the ANNs with median accuracy are able to generate positive alpha over the 10-year period. More importantly, we found that a portfolio based on randomly selected network configuration had zero chance of resulting in a significantly negative alpha but a 27% chance of yielding a significantly positive alpha. This is in stark contrast to the findings of the research on mutual fund performance where active fund managers with negative alphas outnumber those with positive alphas.

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The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

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The transplantation of autologous bone graft as a treatment for large bone defects has the limitation of harvesting co-morbidity and limited availability. This drives the orthopaedic research community to develop bone graft substitutes. Routinely, supra-physiological doses of bone morphogenetic proteins (BMPs) are applied perpetuating concerns over undesired side effects and cost of BMPs. We therefore aimed to design a composite scaffold that allows maintenance of protein bioactivity and enhances growth factor retention at the implantation site. Critical-sized defects in sheep tibiae were treated with the autograft and with two dosages of rhBMP-7, 3.5 mg and 1.75 mg, embedded in a slowly degradable medical grade poly(ε-caprolactone) (PCL) scaffold with β-tricalcium phosphate microparticles (mPCL-TCP). Specimens were characterised by biomechanical testing, microcomputed tomography and histology. Bridging was observed within 3 months for the autograft and both rhBMP-7 treatments. No significant difference was observed between the low and high rhBMP-7 dosages or between any of the rhBMP-7 groups and autograft implantation. Scaffolds alone did not induce comparable levels of bone formation compared to the autograft and rhBMP-7 groups. In summary, the mPCL-TCP scaffold with the lower rhBMP-7 dose led to equivalent results to autograft transplantation or the high BMP dosage. Our data suggest a promising clinical future for BMP application in scaffold-based bone tissue engineering, lowering and optimising the amount of required BMP.

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Adult neural stem cells (NSCs) play important roles in learning and memory and are negatively impacted by neurological disease. It is known that biochemical and genetic factors regulate self-renewal and differentiation, and it has recently been suggested that mechanical and solid-state cues, such as extracellular matrix (ECM) stiffness, can also regulate the functions of NSCs and other stem cell types. However, relatively little is known of the molecular mechanisms through which stem cells transduce mechanical inputs into fate decisions, the extent to which mechanical inputs instruct fate decisions versus select for or against lineage-committed blast populations, or the in vivo relevance of mechanotransductive signaling molecules in native stem cell niches. Here we demonstrate that ECM-derived mechanical signals act through Rho GTPases to activate the cellular contractility machinery in a key early window during differentiation to regulate NSC lineage commitment. Furthermore, culturing NSCs on increasingly stiff ECMs enhances RhoA and Cdc42 activation, increases NSC stiffness, and suppresses neurogenesis. Likewise, inhibiting RhoA and Cdc42 or downstream regulators of cellular contractility rescues NSCs from stiff matrix- and Rho GTPase-induced neurosuppression. Importantly, Rho GTPase expression and ECM stiffness do not alter proliferation or apoptosis rates indicating that an instructive rather than selective mechanism modulates lineage distributions. Finally, in the adult brain, RhoA activation in hippocampal progenitors suppresses neurogenesis, analogous to its effect in vitro. These results establish Rho GTPase-based mechanotransduction and cellular stiffness as biophysical regulators of NSC fate in vitro and RhoA as an important regulatory protein in the hippocampal stem cell niche.

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Background Large segmental defects in bone do not heal well and present clinical challenges. This study investigated modulation of the mechanical environment as a means of improving bone healing in the presence of bone morphogenetic protein (BMP)-2. Although the influence of mechanical forces on the healing of fractures is well established, no previous studies, to our knowledge, have described their influence on the healing of large segmental defects. We hypothesized that bone-healing would be improved by initial, low-stiffness fixation of the defect, followed by high-stiffness fixation during the healing process. We call this reverse dynamization. Methods A rat model of a critical-sized femoral defect was used. External fixators were constructed to provide different degrees of stiffness and, importantly, the ability to change stiffness during the healing process in vivo. Healing of the critical-sized defects was initiated by the implantation of 11 mg of recombinant human BMP (rhBMP)-2 on a collagen sponge. Groups of rats receiving BMP-2 were allowed to heal with low, medium, and high-stiffness fixators, as well as under conditions of reverse dynamization, in which the stiffness was changed from low to high at two weeks. Healing was assessed at eight weeks with use of radiographs, histological analysis, microcomputed tomography, dual x-ray absorptiometry, and mechanical testing. Results Under constant stiffness, the low-stiffness fixator produced the best healing after eight weeks. However, reverse dynamization provided considerable improvement, resulting in a marked acceleration of the healing process by all of the criteria of this study. The histological data suggest that this was the result of intramembranous, rather than endochondral, ossification. Conclusions Reverse dynamization accelerated healing in the presence of BMP-2 in the rat femur and is worthy of further investigation as a means of improving the healing of large segmental bone defects. Clinical Relevance These data provide the basis of a novel, simple, and inexpensive way to improve the healing of critical-sized defects in long bones. Reverse dynamization may also be applicable to other circumstances in which bonehealing is problematic.

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We report a novel technology for the rapid healing of large osseous and chondral defects, based upon the genetic modification of autologous skeletal muscle and fat grafts. These tissues were selected because they not only possess mesenchymal progenitor cells and scaffolding properties, but also can be biopsied, genetically modified and returned to the patient in a single operative session. First generation adenovirus vector carrying cDNA encoding human bone morphogenetic protein-2 (Ad.BMP-2) was used for gene transfer to biopsies of muscle and fat. To assess bone healing, the genetically modified (“gene activated”) tissues were implanted into 5mm-long critical size, mid-diaphyseal, stabilized defects in the femora of Fischer rats. Unlike control defects, those receiving gene-activated muscle underwent rapid healing, with evidence of radiologic bridging as early as 10 days after implantation and restoration of full mechanical strength by 8 weeks. Histologic analysis suggests that the grafts rapidly differentiated into cartilage, followed by efficient endochondral ossification. Fluorescence in situ hybridization detection of Y-chromosomes following the transfer of male donor muscle into female rats demonstrated that at least some of the osteoblasts of the healed bone were derived from donor muscle. Gene activated fat also healed critical sized defects, but less quickly than muscle and with more variability. Anti-adenovirus antibodies were not detected. Pilot studies in a rabbit osteochondral defect model demonstrated the promise of this technology for healing cartilage defects. Further development of these methods should provide ways to heal bone and cartilage more expeditiously, and at lower cost, than is presently possible.

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This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.