O marcapasso definitivo como alternativa terapêutica na doença aterosclerótica coronariana com bloqueio AV total e síncope. Apresentaçao de caso

Homem com 52 anos de idade, portador de diabetes mellitus, hipertensao arterial sistêmica e doença aterosclerótica coronariana. Apresentava episódios de síncope precedidos por angina, durante os quais o eletrocardiograma mostrava ritmo sinusal e bloqueio AV total, com corrente de lesao subepicárdica. A cineangiocoronariografia revelou lesoes ateroscleróticas significativas nas três artérias coronárias. A terapêutica proposta foi a cirurgia de revascularizaçao do miocárdio, recusada pelo paciente. As opçoes farmacológicas foram exploradas sem sucesso. O implante de marca passo definitivo tipo VVI foi realizado como a única e razoável opçao terapêutica. No seguimento de um ano o paciente nao mais apresentou síncopes

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

The Development of a Universal <i>In Silico</i> Predictor of Protein-Protein Interactions

<div><p>Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal <i>In Silico</i> Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation.</p></div

Graphics of classification of the test sets using the F and F′ Normal combined models.

<p>The score represents the classification of the instances as PPI. The instances were classified as PPIs or no-PPIs, and no-PPIs classification scores were converted to interaction scores. “A”, classification of the F test set using the F Normal combined model; “B”, classification of the F′ test set using the F′ Normal combined model; Red, PPIs instances; Gray, no-PPIs instances.</p

Synthesis of several decision trees generated during the ML training.

<p>The aa at the top of each box is relative to the attributes specified at the first and second level of the trees. A, B, C and D indicate the low, moderate, high and very high bins, respectively (for more details, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065587#pone-0065587-t003" target="_blank">Table 3</a> and the “<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065587#s4" target="_blank">Materials and Methods</a>” section). Green boxes, a combination that classifies an instance as a PPI; Red boxes, a combination that classifies an instance as a no-PPI; Brown boxes, a combination that classifies an instance as a PPI or no-PPI; “?”, a combination for which an instance can not be classified, requiring classification at the next level of the tree.</p

Predictive performance of the machine learning using the Normal training datasets.

<p>The letters F, C and F+C indicate that the Normal training datasets originated from the feature descriptors “frequency”, “composition” and “frequency” plus “composition”, respectively. The prime symbol indicates the Normal training datasets formed using the symmetrical attributes of the previously mentioned datasets (details in the “Material and Methods” section). The numbers at the bottom of the boxes are the medians for each dataset.</p