Endovenous laser ablation: the role of intraluminal blood

AbstractObjectiveIn this histological study, the role of the intraluminal blood during endovenous laser ablation was assessed.MethodsIn 12 goats, 24 lateral saphenous veins were treated with a 1500-nm diode laser. Four goats were treated in an anti-Trendelenburg position (group 1). The next four goats were treated in a Trendelenburg position (group 2) and the remaining four goats in the Trendelenburg position with additional injection of tumescent liquid (group 3). Postoperatively, the veins were removed after 1 week and sent for histological examination. We measured the number of perforations. Vein wall necrosis and the perivenous tissue destruction were quantified using a graded scale.ResultsThe ‘calculated total vein wall destruction’ was significantly higher in the third group (81.83%), as compared with groups one (61.25%) (p < 0.001) and two (65.92%) (p < 0.001). All three groups showed a significant difference in the perivenous tissue destruction scale (p < 0.001) with the lowest score occurring in the third group. Vein wall perforations were significantly more frequent in groups one and two as compared with the third group (T-test respectively p < 0.001, p = 0.02).ConclusionA higher intraluminal blood volume results in reduced total vein wall destruction. Injection of tumescent liquid prevents the perivenous tissue destruction and minimises the number of perforations

BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking

Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data)

Iterative disaggregation for a class of lumpable discrete-time stochastic automata networks

Cataloged from PDF version of article.Stochastic automata networks (SANs) have been developed and used in the last 15 years as a modeling formalism for large systems that can be decomposed into loosely connected components. In this work, we concentrate on the not so much emphasized discrete-time SANs. First, we remodel and extend an SAN that arises in wireless communications. Second, for an SAN with functional transitions, we derive conditions for a special case of ordinary lumpability in which aggregation is done automaton by automaton. Finally, for this class of lumpable discrete-time SANs we devise an efficient aggregation–iterative disaggregation algorithm and demonstrate its performance on the SAN model of interest. © 2002 Elsevier Science B.V. All rights reserved

Lumpable continuous-time stochastic automata networks

Cataloged from PDF version of article.The generator matrix of a continuous-time stochastic automata network (SAN) is a sum of tensor products of smaller matrices, which may have entries that are functions of the global state space. This paper specifies easy to check conditions for a class of ordinarily lumpable partitionings of the generator of a continuous-time SAN in which aggregation is performed automaton by automaton. When there exists a lumpable partitioning induced by the tensor representation of the generator, it is shown that an efficient aggregation-iterative disaggregation algorithm may be employed to compute the steady-state distribution. The results of experiments with two SAN models show that the proposed algorithm performs better than the highly competitive block Gauss-Seidel in terms of both the number of iterations and the time to converge to the solution. © 2002 Elsevier Science B.V. All rights reserved

G-Networks with Adders

Queueing networks are used to model the performance of the Internet, of manufacturing and job-shop systems, supply chains, and other networked systems in transportation or emergency management. Composed of service stations where customers receive service, and then move to another service station till they leave the network, queueing networks are based on probabilistic assumptions concerning service times and customer movement that represent the variability of system workloads. Subject to restrictive assumptions regarding external arrivals, Markovian movement of customers, and service time distributions, such networks can be solved efficiently with “product form solutions” that reduce the need for software simulators requiring lengthy computations. G-networks generalise these models to include the effect of “signals” that re-route customer traffic, or negative customers that reject service requests, and also have a convenient product form solution. This paper extends G-networks by including a new type of signal, that we call an “Adder”, which probabilistically changes the queue length at the service center that it visits, acting as a load regulator. We show that this generalisation of G-networks has a product form solution

High-quality conforming hexahedral meshes of patient-specific abdominal aortic aneurysms including their intraluminal thrombi

In order to perform finite element (FE) analyses of patient-specific abdominal aortic aneurysms, geometries derived from medical images must be meshed with suitable elements. We propose a semi-automatic method for generating conforming hexahedral meshes directly from contours segmented from medical images. Magnetic resonance images are generated using a protocol developed to give the abdominal aorta high contrast against the surrounding soft tissue. These data allow us to distinguish between the different structures of interest. We build novel quadrilateral meshes for each surface of the sectioned geometry and generate conforming hexahedral meshes by combining the quadrilateral meshes. The three-layered morphology of both the arterial wall and thrombus is incorporated using parameters determined from experiments. We demonstrate the quality of our patient-specific meshes using the element Scaled Jacobian. The method efficiently generates high-quality elements suitable for FE analysis, even in the bifurcation region of the aorta into the iliac arteries. For example, hexahedral meshes of up to 125,000 elements are generated in less than 130 s, with 94.8 % of elements well suited for FE analysis. We provide novel input for simulations by independently meshing both the arterial wall and intraluminal thrombus of the aneurysm, and their respective layered morphologies

Editor's Choice - Infective Native Aortic Aneurysms: A Delphi Consensus Document on Terminology, Definition, Classification, Diagnosis, and Reporting Standards.

There is no consensus regarding the terminology, definition, classification, diagnostic criteria, and algorithm, or reporting standards for the disease of infective native aortic aneurysm (INAA), previously known as mycotic aneurysm. The aim of this study was to establish this by performing a consensus study. The Delphi methodology was used. Thirty-seven international experts were invited via mail to participate. Four two week Delphi rounds were performed, using an online questionnaire, initially with 22 statements and nine reporting items. The panellists rated the statements on a five point Likert scale. Comments on statements were analysed, statements revised, and results presented in iterative rounds. Consensus was defined as ≥ 75% of the panel selecting "strongly agree" or "agree" on the Likert scale, and consensus on the final assessment was defined as Cronbach's alpha coefficient &gt; .80. All 38 panellists completed all four rounds, resulting in 100% participation and agreement that this study was necessary, and the term INAA was agreed to be optimal. Three more statements were added based on the results and comments of the panel, resulting in a final 25 statements and nine reporting items. All 25 statements reached an agreement of ≥ 87%, and all nine reporting items reached an agreement of 100%. The Cronbach's alpha increased for each consecutive round (round 1 = .84, round 2 = .87, round 3 = .90, and round 4 = .92). Thus, consensus was reached for all statements and reporting items. This Delphi study established the first consensus document on INAA regarding terminology, definition, classification, diagnostic criteria, and algorithm, as well as reporting standards. The results of this study create essential conditions for scientific research on this disease. The presented consensus will need future amendments in accordance with newly acquired knowledge

Corrigendum to 'Editor's Choice - European Society for Vascular Surgery (ESVS) 2020 Clinical Practice Guidelines on the Management of Vascular Graft and Endograft Infections' [European Journal of Vascular &amp; Endovascular Surgery 59/3 (2020) 339-384]

peer reviewe

Induction of antigen-specific tolerance through hematopoietic stem cell-mediated gene therapy: the future for therapy of autoimmune disease?

Based on the principle that immune ablation followed by HSC-mediated recovery purges disease-causing leukocytes to interrupt autoimmune disease progression, hematopoietic stem cell transplantation (HSCT) has been increasingly used as a treatment for severe autoimmune diseases. Despite clinically-relevant outcomes, HSCT is associated with serious iatrogenic risks and is suitable only for the most serious and intractable diseases. A further limitation of autologous HSCT is that relapse rates can be high, suggesting disease-causing leukocytes are incompletely purged or the environmental and genetic determinants that drive disease remain active. Incorporation of antigen-specific tolerance approaches that synergise with autologous HSCT could reduce or prevent relapse. Further, by reducing the requirement for highly toxic immune-ablation and instead relying on antigen-specific tolerance, the clinical utility of HSCT could be significantly diversified. Substantial progress has been made exploring HSCT-mediated induction of antigen-specific tolerance in animal models but studies have focussed on primarily on prevention of autoimmune diseases. However, as diagnosis of autoimmune disease is often not made until autoimmune disease is well developed and populations of autoantigen-specific pathogenic effector and memory T cells have become well established, immunotherapies must be developed to address effector and memory T-cell responses which have traditionally been considered the key impediment to immunotherapy. Here, focusing on T-cell mediated autoimmune diseases we review progress made in antigen-specific immunotherapy using HSCT-mediated approaches, induction of tolerance in effector and memory T cells and the challenges for progression and clinical application of antigen-specific ‘tolerogenic’ HSCT therapy