Dialysis water treated by reverse osmosis decreases the levels of C-reactive protein in uremic patients

Atherosclerosis is a major complication of chronic renal failure. Microinflammation is involved in atherogenesis and is associated with uremia and dialysis. The role of dialysate water contamination in inducing inflammation has been debated. Our aim was to study inflammatory markers in patients on chronic dialysis, before and 3 to 6 months after switching the water purification system from deionization to reverse osmosis. Patients had demographic, clinical and nutritional information collected and blood drawn for determination of albumin, ferritin, C-reactive protein (CRP), interleukin-6, and tumor necrosis factor-alpha in both situations. Acceptable levels of water purity were less than 200 colony-forming units of bacteria and less than 1 ng/ml of endotoxin. Sixteen patients died. They had higher median CRP (26.6 vs 11.2 mg/dl, P = 0.007) and lower median albumin levels (3.1 vs 3.9 g/l, P < 0.05) compared to the 31 survivors. Eight patients were excluded because of obvious inflammatory conditions. From the 23 remaining patients (mean age ± SD: 51.3 ± 13.9 years), 18 had a decrease in CRP after the water treatment system was changed. Overall, median CRP was lower with reverse osmosis than with deionization (13.2 vs 4.5 mg/l, P = 0.022, N = 23). There was no difference in albumin, cytokines, subjective global evaluation, or clinical and biochemical parameters. In conclusion, uremic patients presented a clinically significant reduction in CRP levels when dialysate water purification system switched from deionization to reverse osmosis. It is possible that better water treatments induce less inflammation and eventually less atherosclerosis in hemodialysis patients.

Changes in cell shape and desmin intermediate filament distribution are associated with down-regulation of desmin expression in C2C12 myoblasts grown in the absence of extracellular Ca2+

Desmin is the main intermediate filament (IF) protein of muscle cells. In skeletal muscle, desmin IFs form a scaffold that interconnects the entire contractile apparatus with the subsarcolemmal cytoskeleton and cytoplasmic organelles. The interaction between desmin and the sarcolemma is mediated by a number of membrane proteins, many of which are Ca2+-sensitive. In the present study, we analyzed the effects of the Ca2+ chelator EGTA (1.75 mM) on the expression and distribution of desmin in C2C12 myoblasts grown in culture. We used indirect immunofluorescence microscopy and reverse transcription polymerase chain reaction (RT-PCR) to analyze desmin distribution and expression in C2C12 cells grown in the presence or absence of EGTA. Control C2C12 myoblasts showed a well-spread morphology after a few hours in culture and became bipolar when grown for 24 h in the presence of EGTA. Control C2C12 cells showed a dense network of desmin from the perinuclear region to the cell periphery, whereas EGTA-treated cells showed desmin aggregates in the cytoplasm. RT-PCR analysis revealed a down-regulation of desmin expression in EGTA-treated C2C12 cells compared to untreated cells. The present results suggest that extracellular Ca2+ availability plays a role in the regulation of desmin expression and in the spatial distribution of desmin IFs in myoblasts, and is involved in the generation and maintenance of myoblast cell shape.

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.

Partial baroreceptor dysfunction and low plasma nitric oxide bioavailability as determinants of salt-sensitive hypertension: a reverse translational rat study

This study determined whether clinical salt-sensitive hypertension (cSSHT) results from the interaction between partial arterial baroreceptor impairment and a high-sodium (HNa) diet. In three series (S-I, S-II, S-III), mean arterial pressure (MAP) of conscious male Wistar ChR003 rats was measured once before (pdMAP) and twice after either sham (SHM) or bilateral aortic denervation (AD), following 7 days on a low-sodium (LNa) diet (LNaMAP) and then 21 days on a HNa diet (HNaMAP). The roles of plasma nitric oxide bioavailability (pNOB), renal medullary superoxide anion production (RMSAP), and mRNA expression of NAD(P)H oxidase and superoxide dismutase were also assessed. In SHM (n=11) and AD (n=15) groups of S-I, LNaMAP-pdMAP was 10.5±2.1 vs 23±2.1 mmHg (P<0.001), and the salt-sensitivity index (SSi; HNaMAP−LNaMAP) was 6.0±1.9 vs 12.7±1.9 mmHg (P=0.03), respectively. In the SHM group, all rats were normotensive, and 36% were salt sensitive (SSi≥10 mmHg), whereas in the AD group ∼50% showed cSSHT. A 45% reduction in pNOB (P≤0.004) was observed in both groups in dietary transit. RMSAP increased in the AD group on both diets but more so on the HNa diet (S-II, P<0.03) than on the LNa diet (S-III, P<0.04). MAP modeling in rats without a renal hypertensive genotype indicated that the AD*HNa diet interaction (P=0.008) increases the likelihood of developing cSSHT. Translationally, these findings help to explain why subjects with clinical salt-sensitive normotension may transition to cSSHT.

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.

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.

Concentration of pineapple juice by reverse osmosis: physicochemical characteristics and consumer acceptance

Reverse osmosis has been used for the concentration of fruit juices with promising considering the quality of the obtained products. The objective of this study was to concentrate single strength pineapple juice by reverse osmosis. The concentration was carried out with polyamide composite membranes in a 0.65 m² plate and frame module at 60 bar transmembrane pressure at 20 °C. The permeate flux was 17 L.hm-2. The total soluble solid content of the juice increased from 11 to 31 °Brix corresponding to a Volumetric Concentration Factor (VCF) of 2.9. The concentration of soluble solids, total solids, and total acidity increased proportionally to FCV. The concentrated juice and three commercial concentrated pineapple juices were evaluated regarding preference and purchase intention by 79 pineapple juice consumers. The concentrated juice by reverse osmosis was the preferred among consumers. It can be concluded that this process may be considered an alternative to the pre-concentration of fruit juices.

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.