Simulation-based cutaneous surgical-skill training on a chicken-skin bench model in a medical undergraduate program

Background: Because of ethical and medico-legal aspects involved in the training of cutaneous surgical skills on living patients, human cadavers and living animals, it is necessary the search for alternative and effective forms of training simulation. Aims: To propose and describe an alternative methodology for teaching and learning the principles of cutaneous surgery in a medical undergraduate program by using a chicken-skin bench model. Materials and Methods: One instructor for every four students, teaching materials on cutaneous surgical skills, chicken trunks, wings, or thighs, a rigid platform support, needled threads, needle holders, surgical blades with scalpel handles, rat-tooth tweezers, scissors, and marking pens were necessary for training simulation. Results: A proposal for simulation-based training on incision, suture, biopsy, and on reconstruction techniques using a chicken-skin bench model distributed in several sessions and with increasing levels of difficultywas structured. Both feedback and objective evaluations always directed to individual students were also outlined. Conclusion: The teaching of a methodology for the principles of cutaneous surgery using a chicken-skin bench model versatile, portable, easy to assemble, and inexpensive is an alternative and complementary option to the armamentarium of methods based on other bench models described. © Indian Journal of Dermatology 2013.

Java applets para um software educacional distribuído em eletrônica de potência

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A utilização de linguagem VRML na educação à distância em arte

Pós-graduação em Artes - IA

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

EMG amplitude and frequency parameters of muscular activity: Effect of resistance training based on electromyographic fatigue threshold

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Simulation-based ultrasound-guided central venous cannulation training program

PURPOSE: To propose a simulation-based ultrasound-guided central venous cannulation skills' training program, during residency.METHODS: This study describes the strategies for learning the ultrasound-guided central venous cannulation on low-fidelity bench models. The preparation of bench models, educational goals, processes of skill acquisition, feedback and evaluation methods were also outlined. The training program was based on key references to the subject.RESULTS: It was formulated a simulation-based ultrasound-guided central venous cannulation teaching program on low-fidelity bench models.CONCLUSION: A simulation-based inexpensive, low-stress, no-risk learning program on low-fidelity bench models was proposed to facilitate acquisition of ultrasound-guided central venous cannulation skills by residents-in-training before exposure to the living patient.

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

The effects of elastic tubing-based resistance training compared with conventional resistance training in patients with moderate chronic obstructive pulmonary disease: a randomized clinical trial

Objective: To investigate the effects of elastic tubing training compared with conventional resistance training on the improvement of functional exercise capacity, muscle strength, fat-free mass, and systemic inflammation in patients with chronic obstructive pulmonary disease.Design: A prospective, randomized, eight-week clinical trial.Setting: The study was conducted in a university-based, outpatient, physical therapy clinic.Subjects: A total of 49 patients with moderate chronic obstructive pulmonary disease.Interventions: Participants were randomly assigned to perform elastic tubing training or conventional resistance training three times per week for eight weeks.Main measures: The primary outcome measure was functional exercise capacity. The secondary outcome measures were peripheral muscle strength, health-related quality of life assessed by the Chronic Respiratory Disease Questionnaire (CRDQ), fat-free mass, and cytokine profile.Results: After eight weeks, the mean distance covered during six minutes increased by 73 meters (69) in the elastic tubing group and by 42 meters (+/- 59) in the conventional group (p < 0.05). The muscle strength and quality of life improved in both groups (P < 0.05), with no significant differences between the groups. There was a trend toward an improved fat-free mass in both groups (P = 0.05). After the first and last sessions, there was an increase in interleukin 1 (IL-1) and interleukin 10 (IL-10) in both groups, while tumour necrosis factor alpha (TNF-) was stimulated only in the conventional training group.Conclusion: Elastic tubing training had a greater effect on functional exercise capacity than conventional resistance training. Both interventions were equally effective in improving muscle strength and quality of life.

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

Training on synthetic ethylene-vinyl acetate bench model allows novice medical students to acquire suture skills

PURPOSE: To assess the acquisition of suture skills by training on ethylene-vinyl acetate bench model in novice medical students.METHODS: Sixteen medical students without previous surgery experience (novices) were randomly divided into two groups. During one hour group A trained sutures on ethylene-vinyl acetate (EVA) bench model with feedback of instructors, while group B (control) received a faculty-directed training based on books and instructional videos. All students underwent a both pre-and post-tests to perform two-and three-dimensional sutures on ox tongue. All recorded performances were evaluated by two blinded evaluators, using the Global Rating Scale.RESULTS: Although both groups have had a better performance (p<0.05) in the post-test when compared with the pre-test, the analysis of post-test showed that group A (EVA) had a better performance (p<0.05) when compared with group B (control).CONCLUSION: The ethylene vinyl acetate bench model allowed the novice students to acquire suture skills faster when compared to the traditional model of teaching.

Effects of isolated cycle ergometer training on patients with moderate-to-severe chronic obstructive pulmonary disease

Background: Pulmonary rehabilitation (PR) programs are beneficial to patients with chronic obstructive pulmonary disease (COPD), and lower-extremity training is considered a fundamental component of PR. Nevertheless, the isolated effects of each PR component are not well established. Objective: We aimed to evaluate the effects of a cycle ergometry exercise protocol as the only intervention in a group of COPD patients, and to compare these results with a control group. Methods: 25 moderate-to-severe COPD patients were evaluated regarding pulmonary function, respiratory muscle strength, exercise capacity, quality of life and body composition. Patients were allocated to one of two groups: (a) the trained group (TG; n=13; 6 men) was submitted to a protocol of 24 exercise sessions on a cycle ergometer, with training intensity initially set at a heart rate (HR) close to 80% of maximal HR achieved in a maximal test, and load increase based on dyspnea scores, and (b) the control group (CG; n=12; 6 men) with no intervention during the protocol period. Results: TG showed within-group significant improvements in endurance cycling time, 6-min walking distance test, maximal inspiratory pressure and in the domain 'dyspnea' related to quality of life. Despite the within-group changes, no between-group significant differences were observed. Conclusion: In COPD patients, the results of isolated low-to-moderate intensity cycle ergometer training are not comparable to effects of multimodality and high-intensity training programs. Copyright (C) 2004 S. Karger AG, Basel.

High-Intensity Resistance Training with Insufficient Recovery Time Between Bouts Induce Atrophy and Alterations in Myosin Heavy Chain Content in Rat Skeletal Muscle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Maximal lactate steady state in running mice: Effect of exercise training

1. Maximal lactate steady state (MLSS) corresponds to the highest blood lactate concentration (MLSSc) and workload (MLSSw) that can be maintained over time without continual blood lactate accumulation and is considered an important marker of endurance exercise capacity. The present study was undertaken to determine MLSSw and MLSSc in running mice. In addition, we provide an exercise training protocol for mice based on MLSSw.2. Maximal lactate steady state was determined by blood sampling during multiple sessions of constant-load exercise varying from 9 to 21 m/min in adult male C57BL/6J mice. The constant-load test lasted at least 21 min. The blood lactate concentration was analysed at rest and then at 7 min intervals during exercise.3. The MLSSw was found to be 15.1 +/- 0.7 m/min and corresponded to 60 +/- 2% of maximal speed achieved during the incremental exercise testing. Intra- and interobserver variability of MLSSc showed reproducible findings. Exercise training was performed at MLSSw over a period of 8 weeks for 1 h/day and 5 days/week. Exercise training led to resting bradycardia (21%) and increased running performance (28%). of interest, the MLSSw of trained mice was significantly higher than that in sedentary littermates (19.0 +/- 0.5 vs 14.2 +/- 0.5 m/min; P = 0.05), whereas MLSSc remained unchanged (3.0 mmol/L).4. Altogether, we provide a valid and reliable protocol to improve endurance exercise capacity in mice performed at highest workload with predominant aerobic metabolism based on MLSS assessment.