Phenotype of HLA antibodies in patients with antibody mediated rejection

Renal transplantation is the treatment of choice for patients with end stage kidney disease. Not only is there a survival benefit but there is a significant improvement in quality of life when compared to patients that remain on dialysis. Although short term graft survival has improved in the last 60 years, late graft loss remains a problem. Antibody mediated rejection (AMR) is a major determinant of kidney allograft failure, with chronic AMR being the leading cause of late graft loss. Donor specific anti-HLA antibodies (DSA) have been reported to be strongly associated with an increased risk of rejection and allograft failure. Although the presence of anti-HLA DSAs is thought to be one of the most important biomarkers in predicting allograft failure there is no consensus on their pathogenicity and no standardised protocol on how to approach their clinical management. Whilst most DSAs are of immunoglobulin (Ig) G isotype Anti-HLA DSAs can also exist in IgM, IgE and IgA subtypes, although their roles are less well understood. The value of phenotyping these antibodies has not been fully established. The principal aim of this work is to determine whether more sophisticated assays help predict outcomes for those most at risk of allograft rejection. The underlying hypothesis of this work is that phenotyping of DSAs helps stratify the risk of poor outcomes according to the phenotype of rejection. Patients were recruited from Imperial College Renal and Transplant centre. The results are presented in several related studies. 1. IgG anti-HLA donor specific antibody subclass phenotyping in chronic antibody mediated rejection (cAMR) 2. C1q Binding anti-HLA donor specific antibodies in patients with cAMR 3. What is the role of IgM anti-HLA donor specific antibodies in renal transplantation 4. Anti-HLA IgE: good, bad or indifferent?Open Acces

Semantic Web Reasoning by Swarm Intelligence

Abstract. Semantic Web reasoning systems are confronted with the task to process growing amounts of distributed, dynamic resources. This paper presents a novel way of approaching the challenge by RDF graph traversal, exploiting the advantages of swarm intelligence. The natureinspired and index-free methodology is realised by self-organising swarms of autonomous, light-weight entities that traverse RDF graphs by following paths, aiming to instantiate pattern-based inference rules. The method is evaluated on the basis of a series of simulation experiments with regard to desirable properties of Semantic Web reasoning, focussing on anytime behaviour, adaptiveness and scalability.

An event-driven optimization framework for dynamic vehicle routing

International audienceThe real-time operation of a fleet of vehicles introduces challenging optimization problems. In this work, we propose an event-driven framework which anticipates unknown changes arising in the context of dynamic vehicle routing. The framework is intrinsically parallelized to take advantage of modern multi-core and multi-threaded computing architectures. It is also designed to be easily embeddable in decision support systems that cope with a wide range of contexts and side constraints. We illustrate the flexibility of the framework by showing how it can be adapted to tackle the dynamic vehicle routing problem with stochastic demands

CEDAR: The Dutch Historical Censuses as Linked Open Data

In this document we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses, conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. The dataset contains more than 6.8 million statistical observations about the demography, labour and housing of the Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube vocabulary for multidimensional data, uses Open Annotation to express rules of data harmonization, and keeps track of the provenance of every single data point and its transformations using PROV. We link these observations to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC), which in turn link to DBpedia and GeoNames. The two main contributions of the dataset are the improvement of data integration and access for historical research, and the emergence of new historical data hubs, like classifications of historical religions and historical house types, in the Linked Open Data cloud

Interrater reliability of surveillance for ventilator-associated events and pneumonia

OBJECTIVETo compare interrater reliabilities for ventilator-associated event (VAE) surveillance, traditional ventilator-associated pneumonia (VAP) surveillance, and clinical diagnosis of VAP by intensivists.DESIGNA retrospective study nested within a prospective multicenter quality improvement study.SETTINGIntensive care units (ICUs) within 5 hospitals of the Centers for Disease Control and Prevention Epicenters.PATIENTSPatients who underwent mechanical ventilation.METHODSWe selected 150 charts for review, including all VAEs and traditionally defined VAPs identified during the primary study and randomly selected charts of patients without VAEs or VAPs. Each chart was independently reviewed by 2 research assistants (RAs) for VAEs, 2 hospital infection preventionists (IPs) for traditionally defined VAP, and 2 intensivists for any episodes of pulmonary deterioration. We calculated interrater agreement using κ estimates.RESULTSThe 150 selected episodes spanned 2,500 ventilator days. In total, 93–96 VAEs were identified by RAs; 31–49 VAPs were identified by IPs, and 29–35 VAPs were diagnosed by intensivists. Interrater reliability between RAs for VAEs was high (κ, 0.71; 95% CI, 0.59–0.81). Agreement between IPs using traditional VAP criteria was slight (κ, 0.12; 95% CI, −0.05–0.29). Agreement between intensivists was slight regarding episodes of pulmonary deterioration (κ 0.22; 95% CI, 0.05–0.39) and was fair regarding whether episodes of deterioration were attributable to clinically defined VAP (κ, 0.34; 95% CI, 0.17–0.51). The clinical correlation between VAE surveillance and intensivists’ clinical assessments was poor.CONCLUSIONSProspective surveillance using VAE criteria is more reliable than traditional VAP surveillance and clinical VAP diagnosis; the correlation between VAEs and clinically recognized pulmonary deterioration is poor.Infect Control Hosp Epidemiol 2017;38:172–178</jats:sec

Ignition Delay Times of Kerosene (Jet-A)/Air Mixtures

Ignition of Jet-A/air mixtures was studied behind reflected shock waves. Heating of shock tube at temperature of 150 C was used to prepare a homogeneous fuel mixture. Ignition delay times were measured from OH emission at 309 nm and from absorption of He-Ne laser radiation at 3.3922 micrometers. The conditions behind shock waves were calculated by one-dimensional shock wave theory from initial conditions T1, P1, mixture composition and incident shock wave velocity. The ignition delay times were obtained at two fixed pressures 10, 20 atm for lean, stoichiometric and rich mixtures (ER=0.5, 1, 2) at an overall temperature range of 1040-1380 K.Comment: V.P. Zhukov, V.A. Sechenov, and A.Yu. Starikovskii, Ignition Delay Times of Kerosene(Jet-A)/Air Mixtures, 31st Symposium on Combustion, Heidelberg, Germany, August 6-11, 200

Diabetes and male sex are key risk factor correlates of the extent of coronary artery calcification: A Euro-CCAD study.

Background and aimsAlthough much has been written about the conventional cardiovascular risk factor correlates of the extent of coronary artery calcification (CAC), few studies have been carried out on symptomatic patients. This paper assesses the potential ability of risk factors to associate with an increasing CAC score.MethodsFrom the European Calcific Coronary Artery Disease (Euro-CCAD) cohort, we retrospectively investigated 6309 symptomatic patients, 62% male, from Denmark, France, Germany, Italy, Spain and the USA. All had conventional cardiovascular risk factor assessment and CT scanning for CAC scoring.ResultsAmong all patients, male sex (OR = 4.85, p&lt;0.001) and diabetes (OR = 2.36, p&lt;0.001) were the most important risk factors of CAC extent, with age, hypertension, dyslipidemia and smoking also showing a relationship. Among patients with CAC, age, diabetes, hypertension and dyslipidemia were associated with an increasing CAC score in males and females, with diabetes being the strongest dichotomous risk factor (p&lt;0.001 for both). These results were echoed in quantile regression, where diabetes was consistently the most important correlate with CAC extent in every quantile in both males and females. To a lesser extent, hypertension and dyslipidemia were also associated in the high CAC quantiles and the low CAC quantiles respectively.ConclusionIn addition to age and male sex in the total population, diabetes is the most important correlate of CAC extent in both sexes