989 resultados para fixed path methods
Resumo:
High performance liquid chromatographic (HPLC) and UV derivative spectrophotometric (UVDS) methods were developed and validated for the quantitative determination of sotalol hydrochloride in tablets. The HPLC method was performed on a C18 column with fluorescence detection. The excitation and emission wavelengths were 235 and 310nm, respectively. The mobile phase was composed of acetonitrile-water containing 0.1% trietylamine (7:93v/v) and pH adjusted to 4.6 with formic acid. The UVDS method was performed taking a signal at 239.1nm in the first derivative. The correlation coefficients (r) obtained were 0.9998 and 0.9997 for HPLC and UVDS methods, respectively. The proposed methods are simple and adaptable to routine analysis.
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Chlorpheniramine maleate (CLOR) enantiomers were quantified by ultraviolet spectroscopy and partial least squares regression. The CLOR enantiomers were prepared as inclusion complexes with beta-cyclodextrin and 1-butanol with mole fractions in the range from 50 to 100%. For the multivariate calibration the outliers were detected and excluded and variable selection was performed by interval partial least squares and a genetic algorithm. Figures of merit showed results for accuracy of 3.63 and 2.83% (S)-CLOR for root mean square errors of calibration and prediction, respectively. The ellipse confidence region included the point for the intercept and the slope of 1 and 0, respectively. Precision and analytical sensitivity were 0.57 and 0.50% (S)-CLOR, respectively. The sensitivity, selectivity, adjustment, and signal-to-noise ratio were also determined. The model was validated by a paired t test with the results obtained by high-performance liquid chromatography proposed by the European pharmacopoeia and circular dichroism spectroscopy. The results showed there was no significant difference between the methods at the 95% confidence level, indicating that the proposed method can be used as an alternative to standard procedures for chiral analysis.
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PLA microparticles containing 17-beta-estradiol valerate were prepared by an emulsion/evaporation method in order to sustain drug release. This system was characterized concerning particle size, particle morphology and the influence of formulation and processing parameters on drug encapsulation and in vitro drug release. The biodegradation of the microparticles was observed by tissue histological analysis. Scanning electron microscopy and particle size analysis showed that the microparticles were spherical, presenting non-aggregated homogeneous surface and had diameters in the range of 718-880 nm (inert microparticles) and 3-4 mu m (drug loaded microparticles). The encapsulation efficiency was similar to 80%. Hormone released from microparticles was sustained. An in vivo degradation experiment confirmed that microparticles are biodegradable. The preparation method was shown to be suitable, since the morphological characteristics and efficiency yield were satisfactory. Thus, the method of developed microparticles seems to be a promising system for sustained release of 17-beta-estradiol.
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Background/purpose: Vitamins C and its derivatives, mainly due to their antioxidant properties, are being used in cosmetic products to protect and to reduce the signs of ageing. However, there are no studies comparing the effects of vitamin C [ascorbic acid (AA)] and its derivatives, magnesium ascorbyl phosphate (MAP) and ascorbyl tetra-isopalmitate (ATIP), when vehiculated in topical formulations, mainly using objective measurements, which are an important tool in clinical efficacy studies. Thus, the objective of this study was to determine the in vitro antioxidant activity of AA and its derivatives, MAP and ATIP, as well as their in vivo efficacy on human skin, when vehiculated in topical formulations. Methods: The study of antioxidant activity in vitro was performed with an aqueous and a lipid system. The in vivo methodology consisted of the application of these formulations on human volunteers` forearm skin and the analysis of the skin conditions after 4-week period daily applications in terms of transepidermal water loss (TEWL), stratum corneum moisture content and viscoelasticity using a Tewameter (R), Corneometer (R) and Cutometer (R), respectively. Results: In vitro experiments demonstrated that in an aqueous system, AA had the best antioxidant potential, and MAP was more effective than ATIP, whereas in the lipid system ATIP was more effective than MAP. In in vivo studies, all formulations enhanced stratum corneum moisture content after a 4-week period daily applications when compared with baseline values; however, only the formulation containing AA caused alterations in TEWL values. The formulations containing MAP caused alterations in the viscoelastic-to-elastic ratio, which suggested its action in the deeper layers of the skin. Conclusion: AA and its derivates presented an in vitro antioxidant activity but AA had the best antioxidant effect. In in vivo efficacy studies, only the formulation containing AA caused alterations in TEWL values and the formulation containing MAP caused alterations in the viscoelastic-to-elastic ratio. This way, vitamin C derivatives did not present the same effects of AA on human skin; however, MAP showed other significant effect-improving skin hydration, which is very important for the normal cutaneous metabolism and also to prevent skin alterations and early ageing.
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Hydrodynamic studies were conducted in a semi-cylindrical spouted bed column of diameter 150 mm, height 1000 mm, conical base included angle of 60 degrees and inlet orifice diameter 25 mm. Pressure transducers at several axial positions were used to obtain pressure fluctuation time series with 1.2 and 2.4 mm glass beads at U/U-ms from 0.3 to 1.6, and static bed depths from 150 to 600 mm. The conditions covered several flow regimes (fixed bed, incipient spouting, stable spouting, pulsating spouting, slugging, bubble spouting and fluidization). Images of the system dynamics were also acquired through the transparent walls with a digital camera. The data were analyzed via statistical, mutual information theory, spectral and Hurst`s Rescaled Range methods to assess the potential of these methods to characterize the spouting quality. The results indicate that these methods have potential for monitoring spouted bed operation.
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P>Typing methods to evaluate isolates in relation to their phenotypical and molecular characteristics are essential in epidemiological studies. In this study, Candida albicans biotypes were determined before and after storage in order to verify their stability. Twenty C. albicans isolates were typed by Randomly Amplified Polymorphic DNA (RAPD), production of phospholipase and proteinase exoenzymes (enzymotyping) and morphotyping before and after 180 days of storage in Sabouraud dextrose agar (SDA) and sterilised distilled water. Before the storage, 19 RAPD patterns, two enzymotypes and eight morphotypes were identified. The fragment patterns obtained by RAPD, on the one hand, were not significantly altered after storage. On the other hand, the majority of the isolates changed their enzymotype and morphotype after storage. RAPD typing provided the better discriminatory index (DI) among isolates (DI = 0.995) and maintained the profile identified, thereby confirming its utility in epidemiological surveys. Based on the low reproducibility observed after storage in SDA and distilled water by morphotyping (DI = 0.853) and enzymotyping (DI = 0.521), the use of these techniques is not recommended on stored isolates.
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The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.
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This paper critically assesses several loss allocation methods based on the type of competition each method promotes. This understanding assists in determining which method will promote more efficient network operations when implemented in deregulated electricity industries. The methods addressed in this paper include the pro rata [1], proportional sharing [2], loss formula [3], incremental [4], and a new method proposed by the authors of this paper, which is loop-based [5]. These methods are tested on a modified Nordic 32-bus network, where different case studies of different operating points are investigated. The varying results obtained for each allocation method at different operating points make it possible to distinguish methods that promote unhealthy competition from those that encourage better system operation.
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We propose quadrature rules for the approximation of line integrals possessing logarithmic singularities and show their convergence. In some instances a superconvergence rate is demonstrated.
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The generalized Gibbs sampler (GGS) is a recently developed Markov chain Monte Carlo (MCMC) technique that enables Gibbs-like sampling of state spaces that lack a convenient representation in terms of a fixed coordinate system. This paper describes a new sampler, called the tree sampler, which uses the GGS to sample from a state space consisting of phylogenetic trees. The tree sampler is useful for a wide range of phylogenetic applications, including Bayesian, maximum likelihood, and maximum parsimony methods. A fast new algorithm to search for a maximum parsimony phylogeny is presented, using the tree sampler in the context of simulated annealing. The mathematics underlying the algorithm is explained and its time complexity is analyzed. The method is tested on two large data sets consisting of 123 sequences and 500 sequences, respectively. The new algorithm is shown to compare very favorably in terms of speed and accuracy to the program DNAPARS from the PHYLIP package.
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Rate expression for enzyme poisoning which are consistent with a Michaelis-Menten main reaction are used to analyze the performance of a fixed bed reactor containing immobilized enzyme. When enzyme deactivation results from the irreversible bonding of a product molecule to an existing substrate-enzyme complex, it is shown that minimum enzyme activity can occur in the interior of the bed, well away from the ends. This suggests that bed sectioning techniques may enable direct evaluation of fundamental poisoning mechanisms.
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The long performance of an isothermal fixed bed reactor undergoing catalyst poisoning is theoretically analyzed using the dispersion model. First order reaction with dth order deactivation is assumed and the model equations are solved by matched asymptotic expansions for large Peclet number. Simple closed-form solutions, uniformly valid in time, are obtained.
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There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.