A New and Fast Technique to Generate Offspring after Germ Cells Transplantation in Adult Fish: The Nile Tilapia (Oreochromis niloticus) Model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Importance of early and continuous use of protein restriction on the progression of adriamycin nephropathy

Three experimental protocols were carried out with the aim of evaluating the role of protein restriction on the progression of the established adriamycin-induced nephropathy, and whether the protective effect of the diet persists after the diet is discontinued. The effect of a low protein diet (LPD) was studied for 6 weeks in protocol 1, 16 weeks in protocol 2 and for 28 weeks in protocol 3. In protocol 3, one group (LL) received LPD and another (NN) was given a normal protein diet (NPD). A third group (LN) received LPD for 16 weeks and then NPD for 12 weeks and a fourth group (NL) was fed NPD for 16 weeks and then LPD for 12 weeks. In protocol I the tubulo- interstitial index (TILl) of rats on LPD (Md = 2, P25 = 0.0; P75 = 3.5) after six weeks, was smaller than that of the animals on NPD (Md = 6.0; P25 = 3.0; P75 = 8.0; p < 0.05). In protocol 2, the group taking LPD presented an area of interstitial fibrosis (IF) (Md= 0.5%, P25 0.2%; P75 = 1.9%) smaller than that of the NPD group (Md = 6.8%; P25 = 5.2%; P75 = 7.1%; P < 0.05). No significant difference in the area of glomerulosclerosis (GSA) was observed between the animals on LPD (Md = 0.0%; P25 = 0.0%, P75 = 0.0%) and NPD (Md = 0.37%; P25 = 02% P75 = 1.25%; p > 0.05). In protocol 3, the group LL showed GSA (Md = 1.3%; P25 0.6%, P75 = 2.5%) and IF (Md = 3.60/0; P25 = 1.6%; P75 = 5.9%) smaller that those of LN (GSA Md = 10.1%; P25 = 6.6%; P75 = 14.8%; IF; Md = 17.3%; P25 = 14.1%; P75 = 24,5%), NL (GSA: Md = 9.1%; P25 = 5,8%; P75 = 11.7%; IF; Md = 25.0%; P25 = 20.4%; P75 = 30%), and NN (GSA: Md = 6. 75%; P25 = 4.9%; P75 = 11.7%; IF: Md = 20.9%; P25 = 16.2%; P75 = 32.4%). In conclusion, in order to be effective, LPD must be introduced early and maintained for a long period of tune.

Increased natural killer activity does not prevent progression of experimental kala-azar

Kala-azar is the visceral form of leishmaniasis and it is caused by intracellular parasites from the complex Leishmania donovani. Golden hamster (Mesocricetus auratus) infected with Leishmania donovani develop a disease very similar to human Kala-azar. There is conspicuous hipergammaglobulinaemia and their T cells do not respond to stimulation with parasite antigens. We used this experimental model to evaluate the natural killer (NK) activity during the initial phase of the disease. Outbred hamsters infected by intravenous route with 5.106 amastigotes of L. donovani 1S showed a concurrent increase in the spleen weight and in the spleen cell number. Using the single cell assay we detected a significant increase in the percentage of NK effector cells on the 4th day of infection. Imprints from spleen and liver showed at days 14 and 28 a significant increase in the parasite burden. These results show that the increased NK activity in the beginning of the infection was not able to restrain the progression of the disease in this experimental model.

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

The role of myofibroblasts and interstitial fibrosis in the progression of membranous nephropathy

Renal interstitial fibrosis has been observed in a large number of nephropathies and contributes to the progressive deterioration of renal function. Myofibroblasts have been implicated in the reparative process of tissue injury, including renal scarring secondary to glomerular diseases. We performed a retrospective study on 28 patients with biopsy-proven primary membranous nephropathy, to determine whether interstitial myofibroblasts and tubulointerstitial lesions correlated with renal function at follow-up. Tubulointerstitial pathology was evaluated by morphometric and semiquantitative methods. Interstitial myofibroblasts were counted; 24-hour urinary protein and serum creatinine at the time of diagnosis and at the end of follow-up were available for all the patients. There were 20 males and 8 females, age 2-67 years (mean 42.3±153), most of them with nephrotic syndrome (78.6%). The final renal function had deteriorated in 16 patients (57.1%) and in 5 patients (17.8%) reached end-stage. The renal outcome was correlated with histological changes. We found a positive correlation between the severity of tubulointerstitial damage and the deterioration of the final serum creatinine (r 2=0.185; p=0.016). Myofibroblasts did not predict impaired renal function at the final follow-up. The current data do not support previous suggestions that myofibroblasts are a useful a predictor of end-stage renal disease.

Progression of liver fibrosis in blood donors infected with hepatitis C virus

Background/Aims. Chronic hepatitis by HCV is progressive towards cirrhosis, with variable rate. We evaluated the rate of fibrosis progression (RFP), risk factors associated with advanced fibrosis (F3 and F4), and estimated the evolution time to cirrhosis. Methods. We transversely selected 142 blood donors infected only with HCV, with a known route of infection, submitted to liver biopsy at admission. RFP= ratio between stage of fibrosis (METAVIR)/estimated duration of infection in years. Non-parametric tests and logistic regression analysis, with significance level of 5% were used. Results. Median RFP was 0.086 U/year (0.05 - 0.142). Ten patients had F4 and 25 had F3. Median RFP values were significantly different (p=0.001) from one age group at contamination to the others and ALT and AST levels. There were no differences in the expected evolution to cirrhosis between intermediate fibrosers (F2) and the rapid fibrosers (F3 and F4). The independent variables associated with advanced fibrosis were ALT (OR 7.2) and GGT (OR 6.4) and age at inclusion (OR 1.12). Conclusion. This study suggests that RFP is extremely variable, it is exponential with age, and mainly influenced by host characteristics, especially age at contamination and possibly ethnical group. These asymptomatic patients had high percentage of fibrosis F2, F3 and F4.

Electrospray ionization mass spectrometry screening of piperidine alkaloids from Senna spectabilis (Fabaceae) extracts: Fast identification of new constituents and co-metabolites

The fragmentation pattern of a homologous series of piperidine alkaloids isolated from S. spectabilis was investigated using electrospray ionization tandem mass spectrometry (ESI-MS/MS). The ESI-MS and ESI-MS/MS analyses of EtOH extracts and fractions from flowers and fruits of S. spectabilis allowed to elucidate the structures of four new compounds. The identification of these co-metabolites, based on the fragmentation patterns of previously isolated compounds, and further confirmed by accurate mass spectrometry defines this technique as a powerful tool to determine the metabolomic profile of species which has pharmacological importance. ©2005 Sociedade Brasileira de Química.

Fast preparative separation of flavones from capitula of Eriocaulon ligulatum (Vell.) L.B.Smith (Eriocaulaceae) by High-speed countercurrent chromatography (HSCCC)

High-speed countercurrent chromatography (HSCCC) is a leading method for the fast separation of natural products from plants. It was used for the preparative isolation of two flavone monoglucosides present in the capitula of Eriocaulon ligulatum (Veil.) L.B.Smith (Eriocaulaceae). This species, known locally as botão-dourado, is exported to Europe, Japan and North America as an ornamental species, constituting an important source of income for the local population of Minas Gerais State, Brazil. The solvent system, optimized in tests prior to the HSCCC run, consisted of the two phases of the mixture ethyl acetate: n-propanol: water (140:8:80, v/v/v), which led to the successful separation of 6-methoxyluteolin-7-O-β-D-allopyranoside and 6-methoxyapigenin-7-O-β-D-allopyranoside in only 3 hours. The two flavonoids were identified by NMR (1-D and 2-D) and ESI-MS, comparing their spectra with published data.

Microwave-induced fast decalcification of rat bone for electron microscopic analysis: An ultrastructural and cytochemical study

Bone decalcification is a time-consuming process. It takes weeks and preservation of the tissue structure depends on the quality and velocity of the demineralization process. In the present study, a decalcification methodology was adapted using microwaving to accelerate the decalcification of rat bone for electron microscopic analysis. The ultrastructure of the bone decalcified by microwave energy was observed. Wistar rats were perfused with paraformaldehyde and maxillary segments were removed and fixed in glutaraldehyde. Half of specimens were decalcified by conventional treatment with immersion in Warshawsky solution at 4oC during 45 days, and the other half of specimens were placed into the beaker with 20 mL of the Warshawsky solution in ice bath and thereafter submitted to irradiation in a domestic microwave oven (700 maximum power) during 20 s/350 W/±37°C. In the first day, the specimens were irradiated 9 times and stored at 40°C overnight. In the second day, the specimens were irradiated 20 times changing the solution and the ice after each bath. After decalcification, some specimens were postfixed in osmium tetroxide and others in osmium tetroxide and potassium pyroantimonate. The specimens were observed under transmission electron microscopy. The results showed an increase in the decalcification rate in the specimens activated by microwaving and a reduction of total experiment time from 45 days in the conventional method to 48 hours in the microwave-aided method.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

Morphological changes in the fast vs slow fiber profiles of the urethras of diabetic pregnant rats

Background: This study was undertaken to test the hypothesis that diabetes and pregnancy detrimentally affect the normal function of urethral striated muscles in rats, providing a model for additional studies related to urinary incontinence. The aim of this study was to evaluate morphological alterations in the urethral striated muscles of diabetic pregnant rats. Materials and Methods: Twenty female Wistar rats were distributed into four experimental groups of five rats as follows: virgin, pregnant, diabetic virgin, and diabetic pregnant. Diabetes was induced using streptozotocin administration (40 mg/kg i.v.). The rats were lethally anesthetized, and the urethra and vagina were extracted as a unit. Cryostat sections (6 μm thick) were cut and stained with hematoxylin-eosin, and immunohistochemical procedures were performed and subjected to morphological and semi quantitative analysis. Results: The urethral striated muscle from the diabetic pregnant rats presented with the following variations: thinning and atrophy, disorganization and disruption associated with the colocalization of fast and slow fibers and a steady decrease in the proportion of fast vs slow fibers. Conclusion: Diabetes and pregnancy impair the urethral striated muscle and alter its fiber type distribution. © Copyright G. Marini et al., 2011.