Role of renal sympathetic nerve activity in volatile anesthesia's effect on renal excretory function

Regulation of fluid balance is pivotal during surgery and anesthesia and affects patient morbidity, mortality, and hospital length of stay. Retention of sodium and water is known to occur during surgery but the mechanisms are poorly defined. In this study, we explore how the volatile anesthetic sevoflurane influences renal function by affecting renal sympathetic nerve activity (RSNA). Our results demonstrate that sevoflurane induces renal sodium and water retention during pediatric anesthesia in association with elevated plasma concentration of renin but not arginine–vasopressin. The mechanisms are further explored in conscious and anesthetized ewes where we show that RSNA is increased by sevoflurane compared with when conscious. This is accompanied by renal sodium and water retention and decreased renal blood flow (RBF). Finally, we demonstrate that renal denervation normalizes renal excretory function and improves RBF during sevoflurane anesthesia in sheep. Taken together, this study describes a novel role of the renal sympathetic nerves in regulating renal function and blood flow during sevoflurane anesthesia

Genetic variants of Anaplasma phagocytophilum from 14 equine granulocytic anaplasmosis cases

<p>Abstract</p> <p>Background</p> <p>Equine Granulocytic Anaplasmosis (EGA) is caused by <it>Anaplasma phagocytophilum</it>, a tick-transmitted, obligate intracellular bacterium. In Europe, it is transmitted by <it>Ixodes ricinus</it>. A large number of genetic variants of <it>A. phagocytophilum </it>circulate in nature and have been found in ticks and different animals. Attempts have been made to assign certain genetic variants to certain host species or pathologies, but have not been successful so far. The purpose of this study was to investigate the causing agent <it>A. phagocytophilum </it>of 14 cases of EGA in naturally infected horses with molecular methods on the basis of 4 partial genes (<it>16S rRNA</it>, <it>groEL</it>, <it>msp2</it>, and <it>msp4</it>).</p> <p>Results</p> <p>All DNA extracts of EDTA-blood samples of the horses gave bands of the correct nucleotide size in all four genotyping PCRs. Sequence analysis revealed 4 different variants in the partial <it>16S rRNA</it>, <it>groEL </it>gene and <it>msp2 </it>genes, and 3 in the <it>msp4 </it>gene. One <it>16S rRNA </it>gene variant involved in 11 of the 14 cases was identical to the "prototype" variant causing disease in humans in the amplified part [GenBank: <ext-link ext-link-id="U02521" ext-link-type="gen">U02521</ext-link>]. Phylogenetic analysis revealed as expected for the <it>groEL </it>gene that sequences from horses clustered separately from roe deer. Sequences of the partial <it>msp2 </it>gene from this study formed a separate cluster from ruminant variants in Europe and from all US variants.</p> <p>Conclusions</p> <p>The results show that more than one variant of <it>A. phagocytophilum </it>seems to be involved in EGA in Germany. The comparative genetic analysis of the variants involved points towards different natural cycles in the epidemiology of <it>A. phagocytophilum</it>, possibly involving different reservoir hosts or host adaptation, rather than a strict species separation.</p

The landscape of a Swedish boat-grave cemetery

This is the published PDF version of an article published in Landscapes© 2010. The definitive version is available at http://www.maneyonline.com/toc/lan/11/1The paper integrates topographical and experiential approaches to the mortuary landscape of a Viking period inhumation-grave excavated in 2005 within the cemetery at Skamby, Kuddy parish, Östergötland province, Sweden. We argue that the landscape context was integral to the performance of the funerary ceremonies and the subsequent monumental presence of the dead in the landscape. We offer a way to move beyond monocausal explanations for burial location based on single-scale analyses. Instead, we suggest that boat-inhumation at Skamby was a commemorative strategy that operated on multiple scales and drew its significance from multiple landscape attributes.British Academ

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P &lt; 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Quality of life and functionality after total hip arthroplasty: a long-term follow-up study

<p>Abstract</p> <p>Background</p> <p>There is a lack of data on the long-term outcome of total hip arthroplasty procedures, as assessed by validated tools.</p> <p>Methods</p> <p>We conducted a follow-up study to evaluate the quality of life and functionality of 250 patients an average of 16 years (range: 11-23 years) after total hip arthroplasty using a validated assessment set including the SF-36 questionnaire, Harris Hip Score, WOMAC score, Functional Comorbidity Index, and a study specific questionnaire. Models of multiple stepwise linear and logistic regression analysis were constructed to evaluate the relationships between several explanatory variables and these functional outcomes.</p> <p>Results</p> <p>The SF-36 physical indexes of these patients compared negatively with the normative values but positively with the results obtained in untreated subjects with severe hip osteoarthritis. Similar results were detected for the Harris Hip Score and WOMAC score. There was a 96% rate of post-surgical satisfaction. Hip functionality and comorbidities were the most important determinants of physical measures on the SF-36.</p> <p>Conclusions</p> <p>Patients who had undergone total hip arthroplasty have impaired long-term self-reported physical quality of life and hip functionality but they still perform physically better than untreated patients with advanced hip osteoarthritis. However, the level of post-surgical satisfaction is high.</p

A graph-search framework for associating gene identifiers with documents

BACKGROUND: One step in the model organism database curation process is to find, for each article, the identifier of every gene discussed in the article. We consider a relaxation of this problem suitable for semi-automated systems, in which each article is associated with a ranked list of possible gene identifiers, and experimentally compare methods for solving this geneId ranking problem. In addition to baseline approaches based on combining named entity recognition (NER) systems with a "soft dictionary" of gene synonyms, we evaluate a graph-based method which combines the outputs of multiple NER systems, as well as other sources of information, and a learning method for reranking the output of the graph-based method. RESULTS: We show that named entity recognition (NER) systems with similar F-measure performance can have significantly different performance when used with a soft dictionary for geneId-ranking. The graph-based approach can outperform any of its component NER systems, even without learning, and learning can further improve the performance of the graph-based ranking approach. CONCLUSION: The utility of a named entity recognition (NER) system for geneId-finding may not be accurately predicted by its entity-level F1 performance, the most common performance measure. GeneId-ranking systems are best implemented by combining several NER systems. With appropriate combination methods, usefully accurate geneId-ranking systems can be constructed based on easily-available resources, without resorting to problem-specific, engineered components

Investigating heterogeneous protein annotations toward cross-corpora utilization

<p>Abstract</p> <p>Background</p> <p>The number of corpora, collections of structured texts, has been increasing, as a result of the growing interest in the application of natural language processing methods to biological texts. Many named entity recognition (NER) systems have been developed based on these corpora. However, in the biomedical community, there is yet no general consensus regarding named entity annotation; thus, the resources are largely incompatible, and it is difficult to compare the performance of systems developed on resources that were divergently annotated. On the other hand, from a practical application perspective, it is desirable to utilize as many existing annotated resources as possible, because annotation is costly. Thus, it becomes a task of interest to integrate the heterogeneous annotations in these resources.</p> <p>Results</p> <p>We explore the potential sources of incompatibility among gene and protein annotations that were made for three common corpora: GENIA, GENETAG and AIMed. To show the inconsistency in the corpora annotations, we first tackle the incompatibility problem caused by corpus integration, and we quantitatively measure the effect of this incompatibility on protein mention recognition. We find that the F-score performance declines tremendously when training with integrated data, instead of training with pure data; in some cases, the performance drops nearly 12%. This degradation may be caused by the newly added heterogeneous annotations, and cannot be fixed without an understanding of the heterogeneities that exist among the corpora. Motivated by the result of this preliminary experiment, we further qualitatively analyze a number of possible sources for these differences, and investigate the factors that would explain the inconsistencies, by performing a series of well-designed experiments. Our analyses indicate that incompatibilities in the gene/protein annotations exist mainly in the following four areas: the boundary annotation conventions, the scope of the entities of interest, the distribution of annotated entities, and the ratio of overlap between annotated entities. We further suggest that almost all of the incompatibilities can be prevented by properly considering the four aspects aforementioned.</p> <p>Conclusion</p> <p>Our analysis covers the key similarities and dissimilarities that exist among the diverse gene/protein corpora. This paper serves to improve our understanding of the differences in the three studied corpora, which can then lead to a better understanding of the performance of protein recognizers that are based on the corpora.</p

BioInfer: a corpus for information extraction in the biomedical domain

BACKGROUND: Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora. RESULTS: We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation. CONCLUSION: We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at