64 resultados para experimental models


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The diabetic dog represents an excellent model for use in many aspects of diabetic research. The present paper describes, in detail, a reproducible experimental protocol for the successful induction of chemical diabetes in beagles using a combination of the 2 pancreatic beta-cell cytoxic agents alloxan and streptozotocin.

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An experimental model of quinine induced blindness is presented. Electrophysiological, angiographical and morphological examinations were made. The occurrence of blindness and any recovery from blindness was dependent upon the dose of quinine taken. As no evidence of acute retinal ischaemia was found it is concluded that quinine is retinotoxic.

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The present experiments were undertaken to pharmacologically characterize a noninvasive, chronic, experimental dog model of nasal congestion with the overall goal of developing an effective tool for studying the mechanism of action of nasal decongestant drugs.

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Glaucoma is a leading cause of blindness. It is a multifactorial condition, the risk factors for which are increasingly well defined from large-scale epidemiological studies. One risk factor that remains controversial is the presence of diabetes. It has been proposed that diabetic eyes are at greater risk of injury from external stressors, such as elevated intraocular pressure. Alternatively, diabetes may cause ganglion cell loss, which becomes additive to a glaucomatous ganglion cell injury. Several clinical trials have considered whether a link exists between diabetes and glaucoma. In this review, we outline these studies and consider the causes for their lack of concordant findings. We also review the biochemical and cellular similarities between the two conditions. Moreover, we review the available literature that attempts to answer the question of whether the presence of diabetes increases the risk of developing glaucoma. At present, laboratory studies provide robust evidence for an association between diabetes and glaucoma.

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The aim of the study was to use a computational and experimental approach to evaluate, compare and predict the ability of calcium phosphate (CaP) and poly (methyl methacrylate) (PMMA) augmentation cements to restore mechanical stability to traumatically fractured vertebrae, following a vertebroplasty procedure. Traumatic fractures (n = 17) were generated in a series of porcine vertebrae using a drop-weight method. The fractured vertebrae were imaged using μCT and tested under axial compression. Twelve of the fractured vertebrae were randomly selected to undergo a vertebroplasty procedure using either a PMMA (n = 6) or a CaP cement variation (n = 6). The specimens were imaged using μCT and re-tested. Finite element models of the fractured and augmented vertebrae were generated from the μCT data and used to compare the effect of fracture void fill with augmented specimen stiffness. Significant increases (p <0.05) in failure load were found for both of the augmented specimen groups compared to the fractured group. The experimental and computational results indicated that neither the CaP cement nor PMMA cement could completely restore the vertebral mechanical behavior to the intact level. The effectiveness of the procedure appeared to be more influenced by the volume of fracture filled rather than by the mechanical properties of the cement itself.

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Biosorption of Cr(VI) onto date pit biomass has been investigated via kinetic studies as functions of initial Cr(VI) concentration, solution temperature and date pit particle size. Kinetic experiments indicated that chromate ions accumulate onto the date pits and then reduce to less toxic Cr(III) compounds. The López-García, Escudero and Park Cr(VI) biosorption kinetic models, which take into consideration the direct reduction, the passivation process and the follow-on decrease of the active surface area of reaction, were applied to the kinetic data. The models represented the experimental data accurately at low Cr(VI) concentration (0.480 mM) and small particle size (0.11–0.22 mm) at which the Cr(VI) was completely removed from the aqueous solution and completely reduced to Cr(III) after 420 min. Date pit biomass thus offers a green chemical process for the remediation of chromium from wastewater. This investigation will help researchers employ the adsorption-coupled reduction of Cr(VI) models and simplify their application to kinetic experimental data.

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BACKGROUND: Experimental autoimmune encephalomyelitis (EAE) is an animal model of autoimmune inflammatory demyelination that is mediated by Th1 and Th17 cells. The transcription factor interferon regulatory factor 3 (IRF3) is activated by pathogen recognition receptors and induces interferon-beta production.

METHODS: To determine the role of IRF3 in autoimmune inflammation, we immunised wild-type (WT) and irf3-/- mice to induce EAE. Splenocytes from WT and irf3-/- mice were also activated in vitro in Th17-polarising conditions.

RESULTS: Clinical signs of disease were significantly lower in mice lacking IRF3, with reduced Th1 and Th17 cells in the central nervous system. Peripheral T-cell responses were also diminished, including impaired proliferation and Th17 development in irf3-/- mice. Myelin-reactive CD4+ cells lacking IRF3 completely failed to transfer EAE in Th17-polarised models as did WT cells transferred into irf3-/- recipients. Furthermore, IRF3 deficiency in non-CD4+ cells conferred impairment of Th17 development in antigen-activated cultures.

CONCLUSION: These data show that IRF3 plays a crucial role in development of Th17 responses and EAE and warrants investigation in human multiple sclerosis.

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Perifosine is an orally active alkylphospholipid analog, which has shown anti-tumor activity in a variety of cancers by inhibition of AKT phosphorylation. The objective of the current study was to evaluate its efficacy in in vitro models of human endometrial cancer.

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Hydrous cerium oxide (HCO) was synthesized by intercalation of solutions of cerium(III) nitrate and sodium hydroxide and evaluated as an adsorbent for the removal of hexavalent chromium from aqueous solutions. Simple batch experiments and a 25 factorial experimental design were employed to screen the variables affecting Cr(VI) removal efficiency. The effects of the process variables; solution pH, initial Cr(VI) concentration, temperature, adsorbent dose and ionic strength were examined. Using the experimental results, a linear mathematical model representing the influence of the different variables and their interactions was obtained. Analysis of variance (ANOVA) demonstrated that Cr(VI) adsorption significantly increases with decreased solution pH, initial concentration and amount of adsorbent used (dose), but slightly decreased with an increase in temperature and ionic strength. The optimization study indicates 99% as the maximum removal at pH 2, 20 °C, 1.923 mM of metal concentration and a sorbent dose of 4 g/dm3. At these optimal conditions, Langmuir, Freundlich and Redlich–Peterson isotherm models were obtained. The maximum adsorption capacity of Cr(VI) adsorbed by HCO was 0.828 mmol/g, calculated by the Langmuir isotherm model. Desorption of chromium indicated that the HCO adsorbent can be regenerated using NaOH solution 0.1 M (up to 85%). The adsorption interactions between the surface sites of HCO and the Cr(VI) ions were found to be a combined effect of both anion exchange and surface complexation with the formation of an inner-sphere complex.

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A 2D sandbox style experiment was developed to compare the results of numerical modelling to physical testing for saltwater intrusion in homogeneous and heterogeneous aquifers. The sandbox consisted of a thin central viewing chamber filled with glass beads of varying diameters (780μm, 1090μm and 1325μm) under fully saturated conditions. Dyed saltwater (SW) was introduced at the side boundary and a head difference imposed across the porous media. Images of the SW wedge were recorded at intervals in order to assess the suitability of the numerical models predictions of transient SW intrusion. Numerical modelling of the experimental cases were simulated using SUTRA. Two main parameters were chosen to express the condition of the intruding SW wedge at each recorded time step; the toe penetration length (TL) and the width of the mixing zone (WMZ). The WMZ was larger under transient conditions in the heterogeneous case, while the TL was longer for the homogeneous case. The increased variability in the flow field fo the heterogeneous case resulted in increased dispersion, and thus, increased WMZ.

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Extrusion is one of the fundamental production methods in the polymer processing industry and is used in the production of a large number of commodities in a diverse industrial sector. Being an energy intensive production method, process energy efficiency is one of the major concerns and the selection of the most energy efficient processing conditions is a key to reducing operating costs. Usually, extruders consume energy through the drive motor, barrel heaters, cooling fans, cooling water pumps, gear pumps, etc. Typically the drive motor is the largest energy consuming device in an extruder while barrel/die heaters are responsible for the second largest energy demand. This study is focused on investigating the total energy demand of an extrusion plant under various processing conditions while identifying ways to optimise the energy efficiency. Initially, a review was carried out on the monitoring and modelling of the energy consumption in polymer extrusion. Also, the power factor, energy demand and losses of a typical extrusion plant were discussed in detail. The mass throughput, total energy consumption and power factor of an extruder were experimentally observed over different processing conditions and the total extruder energy demand was modelled empirically and also using a commercially available extrusion simulation software. The experimental results show that extruder energy demand is heavily coupled between the machine, material and process parameters. The total power predicted by the simulation software exhibits a lagging offset compared with the experimental measurements. Empirical models are in good agreement with the experimental measurements and hence these can be used in studying process energy behaviour in detail and to identify ways to optimise the process energy efficiency.

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The test of modifications to quantum mechanics aimed at identifying the fundamental reasons behind the unobservability of quantum mechanical superpositions at the macroscale is a crucial goal of modern quantum mechanics. Within the context of collapse models, current proposals based on interferometric techniques for their falsification are far from the experimental state of the art. Here we discuss an alternative approach to the testing of quantum collapse models that, by bypassing the need for the preparation of quantum superposition states might help us addressing nonlinear stochastic mechanisms such as the one at the basis of the continuous spontaneous localization model. 

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Bdellovibrio bacteriovorus is a small, gram-negative, motile bacterium that preys upon other gram-negative bacteria, including several known human pathogens. Its predation efficiency is usually studied in pure cultures containing solely B. bacteriovorus and a suitable prey. However, in natural environments, as well as in any possible biomedical uses as an antimicrobial, Bdellovibrio is predatory in the presence of diverse decoys, including live nonsusceptible bacteria, eukaryotic cells, and cell debris. Here we gathered and mathematically modeled data from three-member cultures containing predator, prey, and nonsusceptible bacterial decoys. Specifically, we studied the rate of predation of planktonic late-log-phase Escherichia coli S17-1 prey by B. bacteriovorus HD100, both in the presence and in the absence of Bacillus subtilis nonsporulating strain 671, which acted as a live bacterial decoy. Interestingly, we found that although addition of the live Bacillus decoy did decrease the rate of Bdellovibrio predation in liquid cultures, this addition also resulted in a partially compensatory enhancement of the availability of prey for predation. This effect resulted in a higher final yield of Bdellovibrio than would be predicted for a simple inert decoy. Our mathematical model accounts for both negative and positive effects of predator-prey-decoy interactions in the closed batch environment. In addition, it informs considerations for predator dosing in any future therapeutic applications and sheds some light on considerations for modeling the massively complex interactions of real mixed bacterial populations in nature.

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Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.

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After the development of a new single-zone meanline modelling technique, benchmarking of the technique and the modelling methods used during its development are presented. The new meanline model had been developed using the results of three automotive turbocharger centrifugal compressors, and single passage CFD models based on their geometry.

The target of the current study was to test the new meanline modelling method on two new centrifugal compressor stages, again from the automotive turbocharger variety. Furthermore the single passage CFD modelling method used in the previous study would be again employed here and also benchmarked.

The benchmarking was twofold; firstly test the overall performance prediction accuracy of the single-zone meanline model. Secondly, test the detailed performance estimation of the CFD model using detailed interstage static pressure tappings.

The final component of this study exposed the weaknesses in the current modelling methods used (explicitly during this study). The non-axisymmetric flow field at the leading and trailing edges for the two compressors was measured and is presented here for the complete compressor map, highlighting the distortion relative to the tongue.