Análise morfométrica de tecido muscular de coelhos submetido a ultra-som pulsado e contínuo de 1 MHz I

Visando estabelecer uma relação entre os efeitos do ultra-som e a variação da área da célula muscular, este estudo analisou os efeitos do ultra-som (US) de 1 MHz, pulsado e contínuo, utilizado com a intensidade máxima de 3 W/cm2, em tecido muscular sadio do músculo vasto lateral de coelhos (Oryctolagus Cuniculus) machos adultos. Utilizaram-se dois grupos, cada um com 5 coelhos, sendo um para o US pulsado e o outro para o US contínuo. Utilizou-se a coxa esquerda dos coelhos como antímero experimental, sendo a direita o controle. Em ambos os grupos foram realizadas 10 sessões consecutivas de US de 1 MHz, com intensidade de 3 W / c m 2 / durante 12 minutos. Ao término das 10 sessões os coelhos foram sacrificados, retirando-se o músculo vasto lateral, bilateralmente, para preparação das lâminas, que foram analisadas histomorfometricamente. Consideraram-se sempre 10 célulasmusculares em cada extremidade da lâmina analisada - superior, inferior,direita e esquerda. Obtiveram-se assim 40 células analisadas, em cadaantímero de cada coelho. Pela análise estatística (teste t-Student) da média das áreas destas células, verificou-se que não houve diferençasignificativa entre o antímero experimental e o controle em ambos os grupos. Conclui-se que, com a metodologia empregada nesta pesquisa, o US não produz efeitos morfométricos no tecido muscular sadio de coelhos.In order to seek a relation between applied ultrasound (US) and variation of muscle cell area, this study analysed the effects of 1 MHz US, in pulsed and continuous mode, at the maximum intensity of 3 W/cm2, in healthy muscle tissue of adult male rabbits' (Oryctolagus Cuniculus) vast lateral muscle. Rabbits' left thighs were tested in the experiment, the right ones being taken as control. Two groups of 5 rabbits each were formed, one for pulsed U S and the other for continuous US. In both groups were applied 10 consecutive sessions of 1 MHz US with intensity of 3 W/cm2 during 12 minutes. At the end of the 10 sessions the rabbits were sacrificed and both thighs lateral muscles were excised, prepared and fixed to slides for microscopic analysis of the cells area. Ten cells were considered at each slide sector (upper, lower, right and left), thus a total of 40 cells were analysed from each thigh. The mean area of cells for each slide sector was compared to control thigh cells area by means of t-Student test. No statistical I ly significant differences were found between the two sides in both groups, leading to the conclusion that, with the described methodology, US does not produce any effect in the cell size of rabbits healthy muscles

Efficiency of threonine utilization in the growing pigs

ABSTRACT Objective. This study aimed to determine the marginal efficiency of threonine utilization in growing pigs by using the nitrogen balance technique. Materials and methods. Twelve castrated pigs with an average live weight of 72±2 kg were housed in metabolic cages maintained in a temperature controlled room of 22±3°C. The treatments consisted of four diets calculated to meet 30, 45, 60 and 70% of the nutritional requirements of standardized ileal digestible threonine. The amino acids others than threonine were maintained at least 15% above threonine requirements, expressed in terms of ideal levels. The amount of feed supplied was calculate to provide 2.6 times the metabolizable energy requirements for maintenance and adjusted daily according to an expected daily gain rate of 0.8 kg. Results. Pigs consumed 1.65 g kg-1 BW0.75 nitrogen daily, on average and 61% was retained and, as a result, 39% was excreted, being 31% through the feces and the remainder (69%) in the urine. The pigs showed a linear retention of threonine in response to the increase of standardized threonine intake. For each gram of standardized threonine intake 0.37 g were lost during the metabolism. Conclusions. It is concluded that for calculating nutritional requirements of standardized threonine of pigs by the factorial method, it is possible to use the utilization marginal efficiency of 0.63. RESUMEN Objetivo. El objetivo de este estudio fue determinar la eficiencia marginal del uso de treonina para cerdos en fase de crecimiento, por medio de la técnica de balance de nitrógeno. Material y métodos. Fueron utilizados doce cerdos machos castrados con peso promedio de 72±2 kg alojados en jaulas metabólicas climatizadas a una temperatura ambiente de 22±3°C. Los tratamientos consistieron en cuatro dietas calculadas para 30, 45, 60 y 70% de las necesidades nutricionales de treonina digestible estandarizada. Los demás aminoácidos se añadieron a las dietas para lograr una proporción de al menos 15% de sus necesidades expresadas con relación a la treonina. La cantidad de alimento proporcionado se calculó para suministrar 2.6 veces la energía metabolizable y ajustada todos los dias, según una tasa de ganancia media diaria de 0.8 kg. Resultados. Los cerdos consumieron 1.65 g kg-1 PV0.75 de nitrógeno por dia. En promedio 61% de nitrógeno ingerido se conservó y 39% fue excretado por el animal. El 31% se excretó en las heces y el resto (69%) en la orina. Los cerdos mostraron una retención lineal de treonina, con relación al aumento en la ingesta de treonina digestible estandarizada. Por cada gramo de treonina digestible estandarizada ingerido 0.37 g se perdieron durante el metabolismo. Conclusiones. Se concluye que, para el cálculo de las necesidades nutricionales de treonina digestible estandarizada para cerdos por método factorial, es posible utilizar la eficiencia marginal del uso de treonina a 0.63.  

A framework for protein structure classification and identification of novel protein structures

BACKGROUND: Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important. RESULTS: In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds. CONCLUSION: We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available a

Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index

BACKGROUND: Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM) approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. RESULTS: Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins. CONCLUSION: Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset

Thimet oligopeptidase (EC 3.4.24.15) key functions suggested by knockout mice phenotype characterization

Thimet oligopeptidase (THOP1) is thought to be involved in neuropeptide metabolism, antigen presentation, neurodegeneration, and cancer. Herein, the generation of THOP1 C57BL/6 knockout mice (THOP1(-/-)) is described showing that they are viable, have estrus cycle, fertility, and a number of puppies per litter similar to C57BL/6 wild type mice (WT). In specific brain regions, THOP1(-/-) exhibit altered mRNA expression of proteasome beta5, serotonin 5HT2a receptor and dopamine D2 receptor, but not of neurolysin (NLN). Peptidomic analysis identifies differences in intracellular peptide ratios between THOP1(-/-) and WT mice, which may affect normal cellular functioning. In an experimental model of multiple sclerosis THOP1(-/-) mice present worse clinical behavior scores compared to WT mice, corroborating its possible involvement in neurodegenerative diseases. THOP1(-/-) mice also exhibit better survival and improved behavior in a sepsis model, but also a greater peripheral pain sensitivity measured in the hot plate test after bradykinin administration in the paw. THOP1(-/-) mice show depressive-like behavior, as well as attention and memory retention deficits. Altogether, these results reveal a role of THOP1 on specific behaviors, immune-stimulated neurodegeneration, and infection-induced inflammation

Automatic structure classification of small proteins using random forest

<p>Abstract</p> <p><b>Background</b></p> <p>Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.</p> <p><b>Result</b>s</p> <p>Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP <it>Class, Fold, Super-family </it>or <it>Family </it>levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.</p> <p>Conclusions</p> <p>The utility of random forest in classifying domains from the place-holder classes of SCOP to the true <it>Class, Fold, Super-family </it>or <it>Family </it>levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.</p

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd