Short-term phytoestrogen supplementation alters insulin-like growth factor profile but not lipid or antioxidant status

Phytoestrogens are plant compounds that have been proposed to have a variety of health benefits. The aim of this study was to assess the effects of these compounds on a number of physiological endpoints. Subjects were given a single intake of a phytoestrogen-rich (80 mg total phytoestrogens) supplement containing soy, rye and linseed (Phase 1), followed by a week-long intervention using the same supplement (Phase 2) (80 mg total phytoestrogens daily). A number of biochemical endpoints were assessed including urinary phytoestrogen metabolites, lipids, antioxidant status, DNA damage and insulin-like growth factor-1 (IGF-1) and IGF binding protein-1 (IGFBP-1) and -3 (IGFBP-3). Ten healthy female subjects took part in the study. Excretion of the isoflavones genistein, daidzein and equol in urine increased in both phases of the study. No other endpoint was altered in Phase 1. However, in Phase 2, concentrations of IGF-1 and IGFBP-3 were increased by phytoestrogen supplementation [IGF-1, median (IQ range), baseline 155 (123, 258), postweek 265 (228, 360) ng/ml, P

Effect of red clover-derived isoflavone supplementation on insulin-like growth factor, lipid and antioxidant status in healthy female volunteers: a pilot study

Background: Isoflavones are estrogen-like plant compounds that may protect against cardiovascular disease and endocrine-responsive cancer. Isoflavones may, because of their ability to act as selective estrogen receptor modulators, alter insulin-like growth factor (IGF) status.

In vitro isoflavone supplementation reduces hydrogen peroxide-induced DNA damage in sperm

Isoflavones are plant compounds, proposed to have health benefits in a variety of human diseases, including coronary heart disease and endocrine-responsive cancers. Their physiological effects include possible antioxidant activity, therefore suggesting a role for isoflavones in the prevention of male infertility. The aim of this study was to test the antioxidant effects of the isoflavones genistein and equol on sperm DNA integrity, assessed in vitro after hydrogen peroxide-mediated damage, using the cornet assay. Pre-treatment with genistein or equol at doses of 0.01-100 mumol/l significantly protected sperm DNA against oxidative damage. Both ascorbic acid (10-600 mumol/l) and alpha-tocopherol (1-100 mumol/l) also protected. Compared with ascorbic acid and alpha-tocopherol, added at physiological concentrations, genistein was the most potent antioxidant, followed by equol, ascorbic acid, and alpha-tocopherol. Genistein and equol added in combination were more protective than when added singly. Based on these preliminary data, which are similar to those observed previously in lymphocytes, these compounds may have a role to play in antioxidant protection against male infertility.

IGF status is altered by tamoxifen in patients with breast cancer

Aims-An increased concentration of insulin-like growth factor 1 (IGF-1) is an independent risk factor for premenopausal breast cancer. Tamoxifen is thought initially to reduce concentrations of IGF-1 and increase concentrations of the IGF binding proteins. The aim of this study was to compare concentrations of IGF-1, IGF binding protein 1 (IGF-BP1), and IGF-BP3 in patients with breast cancer (n = 14) with those seen in control subjects (n = 23) and to assess the effect of tamoxifen on IGF status in these patients.

Application of Nonlinear PCA for Fault Detection in Polymer Extrusion Processes

This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusion

Polymer extrusion is one of the major methods of processing polymer materials and advanced process monitoring is important to ensure good product quality. However, commonly used process monitoring devices, e.g. temperature and pressure sensors, are limited in providing information on process dynamics inside an extruder barrel. Screw load torque dynamics, which may occur due to changes in solids conveying, melting, mixing, melt conveying, etc., are believed to be a useful indicator of process fluctuations inside the extruder barrel. However, practical measurement of the screw load torque is difficult to achieve. In this work, inferential monitoring of the screw load torque signal in an extruder was shown to be possible by monitoring the motor current (armature and/or field) and simulation studies were used to check the accuracy of the proposed method. The ability of this signal to aid identification and diagnosis of process issues was explored through an experimental investigation. Power spectral density and wavelet frequency analysis were implemented together with a covariance analysis. It was shown that the torque signal is dominated by the solid friction in the extruder and hence it did not correlate well with melting fluctuations. However, it is useful for online identification of solids conveying issues.

Extruder Melt Temperature Control with Fuzzy Logic

In polymer extrusion, the delivery of a melt which is homogenous in composition and temperature is paramount for achieving high quality extruded products. However, advancements in process control are required to reduce temperature variations across the melt flow which can result in poor product quality. The majority of thermal monitoring methods provide only low accuracy point/bulk melt temperature measurements and cause poor controller performance. Furthermore, the most common conventional proportional-integral-derivative controllers seem to be incapable of performing well over the nonlinear operating region. This paper presents a model-based fuzzy control approach to reduce the die melt temperature variations across the melt flow while achieving desired average die melt temperature. Simulation results confirm the efficacy of the proposed controller.

A new model based approach for the prediction and optimisation of thermal homogeneity in single screw extrusion

In polymer extrusion, delivery of a melt which is homogenous in composition and temperature is important for good product quality. However, the process is inherently prone to temperature fluctuations which are difficult to monitor and control via single point based conventional thermo- couples. In this work, the die melt temperature profile was monitored by a thermocouple mesh and the data obtained was used to generate a model to predict the die melt temperature profile. A novel nonlinear model was then proposed which was demonstrated to be in good agreement with training and unseen data. Furthermore, the proposed model was used to select optimum process settings to achieve the desired average melt temperature across the die while improving the temperature homogeneity. The simulation results indicate a reduction in melt temperature variations of up to 60%.