Pan-aortic hybrid treatment of mega-aorta syndrome

Hybrid procedures combining traditional open and newer endovascular techniques are increasingly used to treat complex aortic disease. We present a novel approach for total aortic replacement, including hybrid repair of the arch and thoracoabdominal aorta, in a patient with “mega-aorta syndrome.” A two-stage approach using a valve-sparing aortic root replacement, total arch replacement (stage I elephant trunk), and left carotid-axillary bypass was used to treat the root, proximal-mid arch, and left subclavian aneurysmal pathology. This was followed by a hybrid distal arch/Extent II thoracoabdominal aneurysm repair 3 months later. After 15 months follow-up, the patient remains asymptomatic with an intact repair, no endoleak, and normal ventricular and aortic valve function. This case demonstrates a novel “pan-aortic” hybrid approach for repair of extensive thoracic aortic disease

Intrathoracic subclavian artery aneurysm repair in the thoracic endovascular aortic repair era

ObjectiveIntrathoracic subclavian artery aneurysms (SAAs) are rare aneurysms that often occur in association with congenital aortic arch anomalies and/or concomitant thoracic aortic pathology. The advent of thoracic endovascular aortic repair (TEVAR) methods may complement or replace conventional open SAA repair. Herein, we describe our experience with SAA repair in the TEVAR era.MethodsA retrospective review was performed of all intrathoracic SAAs repaired at a single institution since United States Food and Drug Administration approval of TEVAR in 2005.ResultsNineteen patients underwent 20 operations to repair 22 (13 native, nine aberrant) SAAs with an intrathoracic component. Mean SAA diameter was 3.1 cm (range, 1.6-6.0 cm). Mean patient age was 57 years (range, 24-80 years). Twenty-one percent (n = 4) of patients had a connective tissue disorder (two Loeys-Dietz, two Marfan). Thirty-six percent (n = 8) of SAAs were repaired by open techniques and 64% (n = 14) via a TEVAR-based approach. All TEVAR cases required proximal landing zone in the aortic arch (zone 0-2), and revascularization of at least one arch vessel was required in 83% (10/12) of patients. Concomitant repair of associated aortic pathology was performed in 50% (n = 10) of operations. Thirty-day/in-hospital rates of death, stroke, and permanent paraplegia/paraparesis were 5% (n = 1), 5% (n = 1), and 0%, respectively. Over mean (standard deviation) follow-up of 24 (21) months, 16% (n = 3) of patients required reintervention for subclavian artery bypass graft revision (n = 2) or type II endoleak (n = 1).ConclusionsThis is the largest single-institution series to date of TEVAR for SAA repair. Modern endovascular techniques expand SAA repair options with excellent results. The majority of SAAs and nearly all aberrant SAAs (Kommerell's diverticulum) can now be repaired using a TEVAR-based approach without the need for sternotomy or thoracotomy

Risk factors for 1-year mortality after thoracic endovascular aortic repair

ObjectiveThoracic endovascular aortic repair, although physiologically well tolerated, may fail to confer significant survival benefit in some high-risk patients. In an effort to identify patients most likely to benefit from intervention, the present study sought to determine the risk factors for 1-year mortality after thoracic endovascular aortic repair.MethodsA retrospective review was performed on prospectively collected data from all patients undergoing thoracic endovascular aortic repair from 2002 to 2010 at a single institution. Univariate analysis and multivariate Cox proportional hazards regression analysis were used to identify risk factors associated with mortality within 1 year after thoracic endovascular aortic repair.ResultsDuring the study period, 282 patients underwent at least 1 thoracic endovascular aortic repair; index procedures included descending aortic repair (n = 189), hybrid arch repair (n = 55), and hybrid thoracoabdominal repair (n = 38). The 30-day/in-hospital mortality was 7.4% (n = 21) and the overall 1-year mortality was 19% (n = 54). Cardiopulmonary pathologies were the most common cause of nonperioperative 1-year mortality (22%, n = 12). Multivariate modeling demonstrated 3 variables independently associated with 1-year mortality: age older than 75 years (hazard ratio, 2.26; P = .005), aortic diameter greater than 6.5 cm (hazard ratio, 2.20; P = .007), and American Society of Anesthesiologists class 4 (hazard ratio, 1.85; P = .049). A baseline creatinine greater than 1.5 mg/dL (hazard ratio, 1.79; P = .05) and congestive heart failure (hazard ratio, 1.87; P = .08) were also retained in the final model. These 5 variables explained a large proportion of the risk of 1-year mortality (C statistic = 0.74).ConclusionsAge older than 75 years, aortic diameter greater than 6.5 cm, and American Society of Anesthesiologists class 4 are independently associated with 1-year mortality after thoracic endovascular aortic repair. These clinical characteristics may help risk-stratify patients undergoing thoracic endovascular aortic repair and identify those unlikely to derive a long-term survival benefit from the procedure

Virtual coatings application system

A virtual coatings application system has several features to enhance the realism of simulated spray painting. The system generally includes a display screen on which is defined a virtual surface (such as a truck door) that is intended to be virtually painted or coated by the user. The user operates an instrumented spray gun controller that outputs one or more signals representing data as to the status of the controls on the spray gun controller. The system also has a motion tracking system that tracks the position and orientation of the spray gun controller with respect to the virtual surface defined on the display screen. Simulation software generates virtual spray pattern data in response to at least the data from the spray gun controller and the position and orientation data received from the tracking system. Virtual spray pattern images are displayed in real time on the display screen in accordance with the accumulation of virtual spray pattern data at each location on the virtual surface

Incorporating functional inter-relationships into protein function prediction algorithms

<p>Abstract</p> <p>Background</p> <p>Functional classification schemes (e.g. the Gene Ontology) that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches.</p> <p>Results</p> <p>We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the <it>k</it>-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of predictions made over a set of base classes. Finally, we provide qualitative and quantitative evidence that this incorporation of functional inter-relationships enables the discovery of interesting biology in the form of novel functional annotations for several yeast proteins, such as Sna4, Rtn1 and Lin1.</p> <p>Conclusion</p> <p>We implemented and evaluated a methodology for incorporating interrelationships between functional classes into a standard classification-based protein function prediction algorithm. Our results show that this incorporation can help improve the accuracy of such algorithms, and help uncover novel biology in the form of previously unknown functional annotations. The complete source code, a sample data set and the additional files for this paper are available free of charge for non-commercial use at <url>http://www.cs.umn.edu/vk/gaurav/functionalsimilarity/</url>.</p

The ARCH Projects: design and rationale (IAASSG 001)

OBJECTIVE A number of factors limit the effectiveness of current aortic arch studies in assessing optimal neuroprotection strategies, including insufficient patient numbers, heterogenous definitions of clinical variables, multiple technical strategies, inadequate reporting of surgical outcomes and a lack of collaborative effort. We have formed an international coalition of centres to provide more robust investigations into this topic. METHODS High-volume aortic arch centres were identified from the literature and contacted for recruitment. A Research Steering Committee of expert arch surgeons was convened to oversee the direction of the research. RESULTS The International Aortic Arch Surgery Study Group has been formed by 41 arch surgeons from 10 countries to better evaluate patient outcomes after aortic arch surgery. Several projects, including the establishment of a multi-institutional retrospective database, randomized controlled trials and a prospectively collected database, are currently underway. CONCLUSIONS Such a collaborative effort will herald a turning point in the surgical management of aortic arch pathologies and will provide better powered analyses to assess the impact of varying surgical techniques on mortality and morbidity, identify predictors for neurological and operative risk, formulate and validate risk predictor models and review long-term survival outcomes and quality-of-life after arch surger

Directing Experimental Biology: A Case Study in Mitochondrial Biogenesis

Computational approaches have promised to organize collections of functional genomics data into testable predictions of gene and protein involvement in biological processes and pathways. However, few such predictions have been experimentally validated on a large scale, leaving many bioinformatic methods unproven and underutilized in the biology community. Further, it remains unclear what biological concerns should be taken into account when using computational methods to drive real-world experimental efforts. To investigate these concerns and to establish the utility of computational predictions of gene function, we experimentally tested hundreds of predictions generated from an ensemble of three complementary methods for the process of mitochondrial organization and biogenesis in Saccharomyces cerevisiae. The biological data with respect to the mitochondria are presented in a companion manuscript published in PLoS Genetics (doi:10.1371/journal.pgen.1000407). Here we analyze and explore the results of this study that are broadly applicable for computationalists applying gene function prediction techniques, including a new experimental comparison with 48 genes representing the genomic background. Our study leads to several conclusions that are important to consider when driving laboratory investigations using computational prediction approaches. While most genes in yeast are already known to participate in at least one biological process, we confirm that genes with known functions can still be strong candidates for annotation of additional gene functions. We find that different analysis techniques and different underlying data can both greatly affect the types of functional predictions produced by computational methods. This diversity allows an ensemble of techniques to substantially broaden the biological scope and breadth of predictions. We also find that performing prediction and validation steps iteratively allows us to more completely characterize a biological area of interest. While this study focused on a specific functional area in yeast, many of these observations may be useful in the contexts of other processes and organisms

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date