Quartz crystal microbalance monitoring of density changes in mesoporous TiO2 phytate films during redox and ion exchange processes

Nanofilm deposits of TiO2 nanoparticle phytates are formed on gold electrode surfaces by 'directed assembly' methods. Alternate exposure of a 3-mercapto-propionic acid modified gold surface to (i) a TiO2 sol and (ii) an aqueous phytic acid solution (pH 3) results in layer-by-layer formation of a mesoporous film. Ru(NH3)(6)(3+) is shown to strongly adsorb/accumulate into the mesoporous structure whilst remaining electrochemically active. Scanning the electrode potential into a sufficiently negative potential range allows the Ru(NH3)(6)(3+) complex to be reduced to Ru(NH3)(6)(2+) which undergoes immediate desorption. When applied to a gold coated quartz crystal microbalance (QCM) sensor, electrochemically driven adsorption and desorption processes in the mesoporous structure become directly detectable as a frequency response, which corresponds directly to a mass or density change in the membrane. The frequency response (at least for thin films) is proportional to the thickness of the mass-responsive film, which suggests good mechanical coupling between electrode and film. Based on this observation, a method for the amplified QCM detection of small mass/density changes is proposed by conducting measurements in rigid mesoporous structures. (C) 2003 Elsevier Science B.V. All rights reserved.

Effect of oxidized low-density lipoprotein on differential gene expression in primary human endothelial cells

Oxidative modification of low-density lipoprotein (LDL) plays an important role in the initiation and progression of atherosclerosis. It has been proposed that the biological action of oxidized LDL (ox-LDL) may be partially attributed to its effect on a shift of the pattern of gene expression in endothelial cells. To examine the transcriptional response to ox-LDL, we applied cDNA array technology to cultured primary human endothelial cells challenged with oxidized human LDL. A twofold or greater difference in the expression of a particular gene was considered a significant difference in transcript abundance. Seventy-eight of the 588 genes analyzed were differentially expressed in response to the treatment. Ox-LDL significantly affected the expression of genes encoding for transcription factors, cell receptors, growth factors, adhesion molecules, extracellular matrix proteins, and enzymes involved in cholesterol metabolism. The alteration of the expression pattern of several genes was substantiated post hoc using RT-PCR. The experimental strategy identified several novel ox-LDL-sensitive genes associated with a "response to injury" providing a conceptual background to be utilized for future studies addressing the molecular basis of the early stages of atherogenesis.

Effect of circulating forms of soy isoflavones on the oxidation of low density lipoprotein

Soy isoflavones are thought to have a cardioprotective effect that is partly mediated by an inhibitory influence on the oxidation of low density lipoprotein (LDL). However, the aglycone forms investigated in many previous studies do not circulate in appreciable quantities because they are metabolised in the gut and liver. We investigated effects of various isoflavone metabolites, including for the first time the sulphated conjugates formed in the liver and the mucosa of the small intestine, on copper-induced LDL oxidation. The parent aglycones inhibited oxidation, although only 5% as well as quercetin. Metabolism increased or decreased their effectiveness. Equol inhibited 2.65-fold better than its parent compound daidzein and 8-hydroxydaidzein, not previously assessed, was 12.5-fold better than daidzein. However, monosulphated conjugates of genistein, daidzein and equol were much less effective and disulphates completely ineffective. Since almost all isoflavones circulate as conjugates, these data suggest that despite the increased potency produced by some metabolic changes, isoflavones may not be effective antioxidants in vivo unless they are deconjugated again.

Mathematical modelling of competitive LDL/VLDL binding and uptake by hepatocytes

Elevated levels of low-density-lipoprotein cholesterol (LDL-C) in the plasma are a well-established risk factor for the development of coronary heart disease. Plasma LDL-C levels are in part determined by the rate at which LDL particles are removed from the bloodstream by hepatic uptake. The uptake of LDL by mammalian liver cells occurs mainly via receptor-mediated endocytosis, a process which entails the binding of these particles to specific receptors in specialised areas of the cell surface, the subsequent internalization of the receptor-lipoprotein complex, and ultimately the degradation and release of the ingested lipoproteins' constituent parts. We formulate a mathematical model to study the binding and internalization (endocytosis) of LDL and VLDL particles by hepatocytes in culture. The system of ordinary differential equations, which includes a cholesterol-dependent pit production term representing feedback regulation of surface receptors in response to intracellular cholesterol levels, is analysed using numerical simulations and steady-state analysis. Our numerical results show good agreement with in vitro experimental data describing LDL uptake by cultured hepatocytes following delivery of a single bolus of lipoprotein. Our model is adapted in order to reflect the in vivo situation, in which lipoproteins are continuously delivered to the hepatocyte. In this case, our model suggests that the competition between the LDL and VLDL particles for binding to the pits on the cell surface affects the intracellular cholesterol concentration. In particular, we predict that when there is continuous delivery of low levels of lipoproteins to the cell surface, more VLDL than LDL occupies the pit, since VLDL are better competitors for receptor binding. VLDL have a cholesterol content comparable to LDL particles; however, due to the larger size of VLDL, one pit-bound VLDL particle blocks binding of several LDLs, and there is a resultant drop in the intracellular cholesterol level. When there is continuous delivery of lipoprotein at high levels to the hepatocytes, VLDL particles still out-compete LDL particles for receptor binding, and consequently more VLDL than LDL particles occupy the pit. Although the maximum intracellular cholesterol level is similar for high and low levels of lipoprotein delivery, the maximum is reached more rapidly when the lipoprotein delivery rates are high. The implications of these results for the design of in vitro experiments is discussed.

Effects of oils rich in eicosapentaenoic and docosahexaenoic acids on the oxidizability and thrombogenicity of low-density lipoprotein

Consumption of oily fish and fish oils is associated with protection against cardiovascular disease. Paradoxically, long-chain polyunsaturated fatty acids present in low-density lipoprotein (LDL) are suggested to be susceptible to oxidation. It is not clear whether eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have similar effects on the susceptibility of LDL to oxidation or whether they affect the thrombogenicity of oxidized LDL. This study examined the influence of highly purified preparations of EPA and DHA on LDL oxidizability and LDL-supported thrombin generation in healthy human volunteers. Forty-two healthy volunteers were randomly assigned to receive olive oil (placebo), an EPA-rich oil or a DHA-rich oil for 4 weeks at a dose of 9 g oil/day. EPA and DHA were incorporated into LDL phospholipids and cholesteryl esters during the supplementation period, but were progressively lost during ex vivo copper-mediated oxidation. Following supplementation, the EPA treatment significantly increased the formation of conjugated dienes during LDL oxidation compared with baseline, whereas the DHA treatment had no effect. Neither treatment significantly affected the lag time for oxidation, oxidation rate during the propagation phase or maximum diene production. Neither EPA nor DHA significantly affected the thrombotic tendency of oxidized LDL compared with the placebo, although DHA tended to decrease it. In conclusion, there are subtle differences in the effects of EPA and DHA on the oxidizability and thrombogenicity of LDL. DHA does not appear to increase the susceptibility of LDL to oxidation to the same degree as EPA and has a tendency to decrease LDL-supported thrombin generation. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

Degree of oxidation of low density lipoprotein affects expression of CD36 and PPAR gamma, but not cytokine production, by human monocyte-macrophages

Oxidized low-density lipoprotein (oxLDL) exhibits many atherogenic effects, including the promotion of monocyte recruitment to the arterial endothelium and the induction of scavenger receptor expression. However, while atherosclerosis involves chronic inflammation within the arterial intima, it is unclear whether oxLDL alone provides a direct inflammatory stimulus for monocyte-macrophages. Furthermore, oxLDL is not a single, well-defined entity, but has structural and physical properties which vary according to the degree of oxidation. We tested the hypothesis that the biological effects of oxLDL will vary according to its degree of oxidation and that some species of oxLDL will have atherogenic properties, while other species may be responsible for its inflammatory activity. The atherogenic and inflammatory properties of LDL oxidized to predetermined degrees (mild, moderate and extensive oxidation) were investigated in a single system using human monocyte-derived macrophages. Expression of CD36 mRNA was up-regulated by mildly- and moderately-oxLDL, but not highly-oxLDL. The expression of the transcription factor, proliferator-activated receptor-gamma (PPARgamma), which has been proposed to positively regulate the expression of CD36, was increased to the greatest degree by highly-oxLDL. However, the DNA binding activity of PPARgamma was increased only by mildly- and moderately-oxLDL. None of the oxLDL species appeared to be pro-inflammatory towards monocytes, either directly or indirectly through mediators derived from lymphocytes, regardless of the degree of oxidation. (C) 2003 Published by Elsevier Science Ireland Ltd.

Atopostipes suicloacale gen. nov., sp. nov., isolated from an underground swine manure storage pit

Phenotypic and molecular genetic studies were performed on an unknown facultative anaerobic, catalase-negative, non-spore-forming, rod-shaped bacterium isolated from a pig manure storage pit. The unknown bacterium was nutritionally fastidious with growth enhanced by the addition of rumen fluid and was phenotypically initially identified as an Eubacterium species. Comparative 16S rRNA gene sequencing studies, however, revealed that the unknown bacterium was phylogenetically distant from Eubacterium limosum (the type species of the genus Eubacterium) and related organisms. Phylogenetically, the unknown species displayed a close association with an uncultured organism from human subgingival plaque and formed an unknown sub-line within a cluster of organisms which includes Alloioccoccus otitis, Alkalibacterium olivoapovliticus, Allofustis seminis, Dolosigranulum pigrum, and related organisms, within the low mol% G + C Gram-positive bacteria. Sequence divergence values of > 8% with all known taxonomically recognised taxa, however, clearly indicates the novel bacterium represents a hitherto unknown genus. Based on both phenotypic and phylogenetic considerations, it is proposed that the unknown bacterium from pig manure be classified in a new genus and species, as Atopostipes suicloacale gen. nov., sp. nov. The type strain of Atopostipes suicloacale is PPC79(T) = NRRL 23919(T) = DSM 15692(T). Crown Copyright (C) 2004 Published by Elsevier Ltd. All rights reserved.

Pseudo-neglect in Huntington's disease correlates with decreased angular gyrus density

Visuospatial attentional bias was examined in Huntington's disease (HID) patients with mild disease, asymptomatic gene-positive patients and controls. No group differences were found on the grey scales task (which is a non-motor task of visuospatial attentional bias), although patients' trinucleotide (CAG) repeat length correlated with increasing leftward bias. On the line bisection task, symptomatic patients made significantly larger leftward bisection errors relative to controls, who showed the normal slight degree of leftward error (pseudo-neglect). The asymptomatic group showed a trend for greater leftward error than controls. A subset of participants went on to have structural MRI, which showed a correlation between increased leftward error on the line bisection task and reduced density in the angular gyrus area (BA39) bilaterally. This finding is consistent with recent literature suggesting a critical role for the angular gyrus in the lateralization of visuospatial attention.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Kernel density construction using orthogonal forward regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

An orthogonal forward regression technique for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKID estimate with comparable accuracy to that of the full-sample optimised PW density estimate. (c) 2007 Elsevier B.V. All rights reserved.

A forward-constrained regression algorithm for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward-constrained regression (FCR) manner. The proposed algorithm selects significant kernels one at a time, while the leave-one-out (LOO) test score is minimized subject to a simple positivity constraint in each forward stage. The model parameter estimation in each forward stage is simply the solution of jackknife parameter estimator for a single parameter, subject to the same positivity constraint check. For each selected kernels, the associated kernel width is updated via the Gauss-Newton method with the model parameter estimate fixed. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate the efficacy of the proposed approach.