Cryopreservation of bovine spermatozoa from the epididymal tail using conventional and automated methods

Cryopreservation of sperm is important to preserve the gerrnplasm from animals of genetic value, which can die unexpectedly. This study compares conventional and automated methods of cryopreservation of spermatozoa obtained from the epididymis of bulls post-mortem. Twenty-two epididymides were obtained from a commercial slaughterhouse. Spermatozoa were collected from the tail of the epididymis using the retrograde flow technique. Thus, the samples, which were diluted in 10 ml of extender without glycerol (Botubov (R) I, Botupharma, Botucatu, SP, Brazil), were evaluated on motility, sperm vigor, structural and functional (swelling hypoosmotic test) membrane integrity, mitochondrial activity, sperm viability and ADN fragmentation. The samples were divided into two aliquots and diluted in extender with glycerol (Botubov (R) II, Botupharma, Botucatu, SP, Brazil) at a concentration of 50x10(6) motile sperm/0.5 French straws. One sample was frozen by the conventional method (4 hours at 5 degrees C, in a refrigerator and 20 min in nitrogen vapor) and the other by the automated method (Cryogen (R) Dualflex, Neovet, Uberaba, MG, Brazil). The parameters were higher in all the tests of fresh sperm samples, with the exception of the swelling hypoosmotic test, which showed no significant difference when the results were compared with sperm frozen by the conventional method. The average motility of fresh spermatozoa was 74%, and conventional and automated averages were 29 and 25%, respectively. Therefore, although cryopreservation techniques reduce sperm quality parameters, the viability of the sperm is maintained, and these methods can be used to preserve sperm.

A novel automated alternating current biosusceptometry method to characterization of controlled-release magnetic floating tablets of metronidazole

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A reference model as automated process for software adaptation at runtime

The development of self-adaptive software (SaS) has specific characteristics compared to traditional one, since it allows that changes to be incorporated at runtime. Automated processes have been used as a feasible solution to conduct the software adaptation at runtime. In parallel, reference model has been used to aggregate knowledge and architectural artifacts, since capture the systems essence of specific domains. However, there is currently no reference model based on reflection for the development of SaS. Thus, the main contribution of this paper is to present a reference model based on reflection for development of SaS that have a need to adapt at runtime. To present the applicability of this model, a case study was conducted and good perspective to efficiently contribute to the area of SaS has been obtained.

Icodextrin reduces insulin resistance in non-diabetic patients undergoing automated peritoneal dialysis: results of a randomized controlled trial (STARCH)

Insulin resistance is a common risk factor in chronic kidney disease patients contributing to the high cardiovascular burden, even in the absence of diabetes. Glucose-based peritoneal dialysis (PD) solutions are thought to intensify insulin resistance due to the continuous glucose absorption from the peritoneal cavity. The aim of our study was to analyse the effect of the substitution of glucose for icodextrin on insulin resistance in non-diabetic PD patients in a multicentric randomized clinical trial. This was a multicenter, open-label study with balanced randomization (1:1) and two parallel-groups. Inclusion criteria were non-diabetic adult patients on automated peritoneal dialysis (APD) for at least 3 months on therapy prior to randomization. Patients assigned to the intervention group were treated with 2L of icodextrin 7.5%, and the control group with glucose 2.5% during the long dwell and, at night in the cycler, with a prescription of standard glucose-based PD solution only in both groups. The primary end-point was the change in insulin resistance measured by homeostatic model assessment (HOMA) index at 90 days. Sixty patients were included in the intervention (n = 33) or the control (n = 27) groups. There was no difference between groups at baseline. After adjustment for pre-intervention HOMA index levels, the group treated with icodextrin had the lower post-intervention levels at 90 days in both intention to treat [1.49 (95% CI: 1.23-1.74) versus 1.89 (95% CI: 1.62-2.17)], (F = 4.643, P = 0.03, partial η(2) = 0.078); and the treated analysis [1.47 (95% CI: 1.01-1.84) versus 2.18 (95% CI: 1.81-2.55)], (F = 7.488, P = 0.01, partial η(2) = 0.195). The substitution of glucose for icodextrin for the long dwell improved insulin resistance measured by HOMA index in non-diabetic APD patients.

Structural and functional evidence for membrane docking and disruption sites on phospholipase A2-like proteins revealed by complexation with the inhibitor suramin

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automated peritoneal dialysis is associated with better survival rates compared to continuous ambulatory peritoneal dialysis: a propensity score matching analysis

The impact of peritoneal dialysis modality on patient survival and peritonitis rates is not fully understood, and no large-scale randomized clinical trial (RCT) is available. In the absence of a RCT, the use of an advanced matching procedure to reduce selection bias in large cohort studies may be the best approach. The aim of this study is to compare automated peritoneal dialysis (APD) and continuous ambulatory peritoneal dialysis (CAPD) according to peritonitis risk, technique failure and patient survival in a large nation-wide PD cohort. This is a prospective cohort study that included all incident PD patients with at least 90 days of PD recruited in the BRAZPD study. All patients who were treated exclusively with either APD or CAPD were matched for 15 different covariates using a propensity score calculated with the nearest neighbor method. Clinical outcomes analyzed were overall mortality, technique failure and time to first peritonitis. For all analysis we also adjusted the curves for the presence of competing risks with the Fine and Gray analysis. After the matching procedure, 2,890 patients were included in the analysis (1,445 in each group). Baseline characteristics were similar for all covariates including: age, diabetes, BMI, Center-experience, coronary artery disease, cancer, literacy, hypertension, race, previous HD, gender, pre-dialysis care, family income, peripheral artery disease and year of starting PD. Mortality rate was higher in CAPD patients (SHR1.44 CI95%1.21-1.71) compared to APD, but no difference was observed for technique failure (SHR0.83 CI95%0.69-1.02) nor for time till the first peritonitis episode (SHR0.96 CI95%0.93-1.11). In the first large PD cohort study with groups balanced for several covariates using propensity score matching, PD modality was not associated with differences in neither time to first peritonitis nor in technique failure. Nevertheless, patient survival was significantly better in APD patients.

Estudos computacionais de esfingomielinases D: docking, dinâmica molecular e métodos híbridos QM/MM

Pós-graduação em Biofísica Molecular - IBILCE

Estudos computacionais de esfingomielinases D: docking, dinâmica molecular e métodos híbridos QM/MM

Pós-graduação em Biofísica Molecular - IBILCE

Novel aryl beta-aminocarbonyl derivatives as inhibitors of Trypanosoma cruzi trypanothione reductase: binding mode revised by docking and GRIND2-based 3D-QSAR procedures

Trypanothione reductase has long been investigated as a promising target for chemotherapeutic intervention in Chagas disease, since it is an enzyme of a unique metabolic pathway that is exclusively present in the pathogen but not in the human host, which has the analog Glutathione reductase. In spite of the present data-set includes a small number of compounds, a combined use of flexible docking, pharmacophore perception, ligand binding site prediction, and Grid-Independent Descriptors GRIND2-based 3D-Quantitative Structure-Activity Relationships (QSAR) procedures allowed us to rationalize the different biological activities of a series of 11 aryl beta-aminocarbonyl derivatives, which are inhibitors of Trypanosoma cruzi trypanothione reductase (TcTR). Three QSAR models were built and validated using different alignments, which are based on docking with the TcTR crystal structure, pharmacophore, and molecular interaction fields. The high statistical significance of the models thus obtained assures the robustness of this second generation of GRIND descriptors here used, which were able to detect the most important residues of such enzyme for binding the aryl beta-aminocarbonyl derivatives, besides to rationalize distances among them. Finally, a revised binding mode has been proposed for our inhibitors and independently supported by the different methodologies here used, allowing further optimization of the lead compounds with such combined structure- and ligand-based approaches in the fight against the Chagas disease.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Towards automated first-order abduction: the cut-based approach

Traditional abduction imposes as a precondition the restriction that the background information may not derive the goal data. In first-order logic such precondition is, in general, undecidable. To avoid such problem, we present a first-order cut-based abduction method, which has KE-tableaux as its underlying inference system. This inference system allows for the automation of non-analytic proofs in a tableau setting, which permits a generalization of traditional abduction that avoids the undecidable precondition problem. After demonstrating the correctness of the method, we show how this method can be dynamically iterated in a process that leads to the construction of non-analytic first-order proofs and, in some terminating cases, to refutations as well.

Automated analysis of lidocaine and its metabolite in plasma by in-tube solid-phase microextraction coupled with LC-UV for pharmacokinetic study

A sensitive, selective, and reproducible in-tube solid-phase microextraction and liquid chromatographic (in-tube SPME/LC-UV) method for determination of lidocaine and its metabolite monoethylglycinexylidide (MEGX) in human plasma has been developed, validated, and further applied to pharmacokinetic study in pregnant women with gestational diabetes mellitus (GDM) subjected to epidural anesthesia. Important factors in the optimization of in-tube SPME performance are discussed, including the draw/eject sample volume, draw/eject cycle number, draw/eject flow rate, sample pH, and influence of plasma proteins. The limits of quantification of the in-tube SPME/LC method were 50 ng/mL for both metabolite and lidocaine. The interday and intraday precision had coefficients of variation lower than 8%, and accuracy ranged from 95 to 117%. The response of the in-tube SPME/LC method for analytes was linear over a dynamic range from 50 to 5000 ng/mL, with correlation coefficients higher than 0.9976. The developed in-tube SPME/LC method was successfully used to analyze lidocaine and its metabolite in plasma samples from pregnant women with GDM subjected to epidural anesthesia for pharmacokinetic study.

Synthesis, anti-inflammatory activity and molecular docking studies of 2,5-diarylfuran amino acid derivatives

A series of 2,5-diaryl substituted furans functionalized with several amino acids were synthesized and evaluated as the cyclooxygenases COX-1 and COX-2 enzymes inhibitors. The proline-substituted compound inhibited PGE(2) secretion by LPS-stimulated neutrophils, suggesting selectivity for COX-2. Molecular docking studies in the binding site of COX-2 were performed. (C) 2011 Elsevier Masson SAS. All rights reserved.

Comparison of 3 modes of automated weaning from mechanical ventilation: A bench study

Purpose: Automated weaning modes are available in some mechanical ventilators, but no studies compared them hitherto. We compared the performance of 3 automated modes under standard and challenging situations. Methods: We used a lung simulator to compare 3 automated modes, adaptive support ventilation (ASV), mandatory rate ventilation (MRV), and Smartcare, in 6 situations, weaning success, weaning failure, weaning success with extreme anxiety, weaning success with Cheyne-Stokes, weaning success with irregular breathing, and weaning failure with ineffective efforts. Results: The 3 modes correctly recognized the situations of weaning success and failure, even when anxiety or irregular breathing were present but incorrectly recognized weaning success with Cheyne-Stokes. MRV incorrectly recognized weaning failure with ineffective efforts. Time to pressure support (PS) stabilization was shorter for ASV (1-2 minutes for all situations) and MRV (1-7 minutes) than for Smartcare (8-78 minutes). ASV had higher rates of PS oscillations per 5 minutes (4-15), compared with Smartcare (0-1) and MRV (0-12), except when extreme anxiety was present. Conclusions: Smartcare, ASV, and MRV were equally able to recognize weaning success and failure, despite the presence of anxiety or irregular breathing but performed incorrectly in the presence of Cheyne-Stokes. PS behavior over the time differs among modes, with ASV showing larger and more frequent PS oscillations over the time. Clinical studies are needed to confirm our results. (C) 2012 Elsevier Inc. All rights reserved.

EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.