Residual ß-cell function and microvascular complications in type 1 diabetic patients

To determine the influence of residual ß-cell function on retinopathy and microalbuminuria we measured basal C-peptide in 50 type 1 diabetic outpatients aged 24.96 ± 7.14 years, with a duration of diabetes of 9.1 ± 6.2 years. Forty-three patients (86%) with low C-peptide (<0.74 ng/ml) had longer duration of diabetes than 7 patients (14%) with high C-peptide (³0.74 ng/ml) (9 (2-34) vs 3 (1-10) years, P = 0.01) and a tendency to high glycated hemoglobin (HBA1) (8.8 (6-17.9) vs 7.7 (6.9-8.7)%, P = 0.08). Nine patients (18%) had microalbuminuria (two out of three overnight urine samples with an albumin excretion rate (AER) ³20 and <200 µg/min) and 13 (26%) had background retinopathy. No association was found between low C-peptide, microalbuminuria and retinopathy and no difference in basal C-peptide was observed between microalbuminuric and normoalbuminuric patients (0.4 ± 0.5 vs 0.19 ± 0.22 ng/ml, P = 0.61) and between patients with or without retinopathy (0.4 ± 0.6 vs 0.2 ± 0.3 ng/ml, P = 0.43). Multiple regression analysis showed that duration of diabetes (r = 0.30, r2 = 0.09, P = 0.031) followed by HBA1 (r = 0.41, r2 = 0.17, P = 0.01) influenced basal C-peptide, and this duration of diabetes was the only variable affecting AER (r = 0.40, r2 = 0.16, P = 0.004). In our sample of type 1 diabetic patients residual ß-cell function was not associated with microalbuminuria or retinopathy.

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R² = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

Blastocyst-endometrium interaction: intertwining a cytokine network

The successful implantation of the blastocyst depends on adequate interactions between the embryo and the uterus. The development of the embryo begins with the fertilized ovum, a single totipotent cell which undergoes mitosis and gives rise to a multicellular structure named blastocyst. At the same time, increasing concentrations of ovarian steroid hormones initiate a complex signaling cascade that stimulates the differentiation of endometrial stromal cells to decidual cells, preparing the uterus to lodge the embryo. Studies in humans and in other mammals have shown that cytokines and growth factors are produced by the pre-implantation embryo and cells of the reproductive tract; however, the interactions between these factors that converge for successful implantation are not well understood. This review focuses on the actions of interleukin-1, leukemia inhibitory factor, epidermal growth factor, heparin-binding epidermal growth factor, and vascular endothelial growth factor, and on the network of their interactions leading to early embryo development, peri-implantatory endometrial changes, embryo implantation and trophoblast differentiation. We also propose therapeutical approaches based on current knowledge on cytokine interactions.

Stimulated mast cells promote maturation of myocardial microvascular endothelial cell neovessels by modulating the angiopoietin-Tie-2 signaling pathway

Angiopoietin (Ang)-1 and Ang-2 interact in angiogenesis to activate the Tie-2 receptor, which may be involved in new vessel maturation and regression. Mast cells (MCs) are also involved in formation of new blood vessels and angiogenesis. The present study was designed to test whether MCs can mediate angiogenesis in myocardial microvascular endothelial cells (MMVECs). Using a rat MMVEC and MC co-culture system, we observed that Ang-1 protein levels were very low even though its mRNA levels were increased by MCs. Interestingly, MCs were able to enhance migration, proliferation, and capillary-like tube formation, which were associated with suppressed Ang-2 protein expression, but not Tie-2 expression levels. These MCs induced effects that could be reversed by either tryptase inhibitor [N-tosyl-L-lysine chloromethyl ketone (TLCK)] or chymase inhibitor (N-tosyl-L-phenylalanyl chloromethyl ketone), with TLCK showing greater effects. In conclusion, our data indicated that MCs can interrupt neovessel maturation via suppression of the Ang-2/Tie-2 signaling pathway.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

Effect of penehyclidine hydrochloride on β-arrestin-1 expression in lipopolysaccharide-induced human pulmonary microvascular endothelial cells

β-arrestins are expressed proteins that were first described, and are well-known, as negative regulators of G protein-coupled receptor signaling. Penehyclidine hydrochloride (PHC) is a new anti-cholinergic drug that can inhibit biomembrane lipid peroxidation, and decrease cytokines and oxyradicals. However, to date, no reports on the effects of PHC on β-arrestin-1 in cells have been published. The aim of this study was to investigate the effect of PHC on β-arrestin-1 expression in lipopolysaccharide (LPS)-induced human pulmonary microvascular endothelial cells (HPMEC). Cultured HPMEC were pretreated with PHC, followed by LPS treatment. Muscarinic receptor mRNAs were assayed by real-time quantitative PCR. Cell viability was assayed by the methyl thiazolyl tetrazolium (MTT) conversion test. The dose and time effects of PHC on β-arrestin-1 expression in LPS-induced HPMEC were determined by Western blot analysis. Cell malondialdehyde (MDA) level and superoxide dismutase (SOD) activity were measured. It was found that the M3 receptor was the one most highly expressed, and was activated 5 min after LPS challenge. Furthermore, 2 μg/mL PHC significantly upregulated expression of β-arrestin-1 within 10 to 15 min. Compared with the control group, MDA levels in cells were remarkably increased and SOD activities were significantly decreased in LPS pretreated cells, while PHC markedly decreased MDA levels and increased SOD activities. We conclude that PHC attenuated ROS injury by upregulating β-arrestin-1 expression, thereby implicating a mechanism by which PHC may exert its protective effects against LPS-induced pulmonary microvascular endothelial cell injury.

Transduction motif analysis of gastric cancer based on a human signaling network

To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR,MAPK14, BCL2L1, KRT18,PTPN6, CASP3, TGFBR2,AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

Propofol inhibits burn injury-induced hyperpermeability through an apoptotic signal pathway in microvascular endothelial cells

Recent studies have revealed that an intrinsic apoptotic signaling cascade is involved in vascular hyperpermeability and endothelial barrier dysfunction. Propofol (2,6-diisopropylphenol) has also been reported to inhibit apoptotic signaling by regulating mitochondrial permeability transition pore (mPTP) opening and caspase-3 activation. Here, we investigated whether propofol could alleviate burn serum-induced endothelial hyperpermeability through the inhibition of the intrinsic apoptotic signaling cascade. Rat lung microvascular endothelial cells (RLMVECs) were pretreated with propofol at various concentrations, followed by stimulation with burn serum, obtained from burn-injury rats. Monolayer permeability was determined by transendothelial electrical resistance. Mitochondrial release of cytochrome C was measured by ELISA. Bax and Bcl-2 expression and mitochondrial release of second mitochondrial-derived activator of caspases (smac) were detected by Western blotting. Caspase-3 activity was assessed by fluorometric assay; mitochondrial membrane potential (Δψm) was determined with JC-1 (a potential-sensitive fluorescent dye). Intracellular ATP content was assayed using a commercial kit, and reactive oxygen species (ROS) were measured by dichlorodihydrofluorescein diacetate (DCFH-DA). Burn serum significantly increased monolayer permeability (P<0.05), and this effect could be inhibited by propofol (P<0.05). Compared with a sham treatment group, intrinsic apoptotic signaling activation - indicated by Bax overexpression, Bcl-2 downregulation, Δψm reduction, decreased intracellular ATP level, increased cytosolic cytochrome C and smac, and caspase-3 activation - was observed in the vehicle group. Propofol not only attenuated these alterations (P<0.05 for all), but also significantly decreased burn-induced ROS production (P<0.05). Propofol attenuated burn-induced RLMVEC monolayer hyperpermeability by regulating the intrinsic apoptotic signaling pathway.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.