BAFF and MyD88 signals promote a lupuslike disease independent of T cells

Systemic lupus erythernatosus (SLE) is a systemic autoimmune disease characterized by the production of autoantibodies. However, the underlying cause of disease appears to relate to defects in T cell tolerance or T cell help to 13 cells. Transgenic (Tg) mice over-expressing the cytokine 13 cell-activating factor of the tumor necrosis factor family (BAFF) develop an autoimmune disorder similar to SLE and show impaired B cell tolerance and altered T cell differentiation. We generated BAFF Tg mice that were completely deficient in T cells, and, surprisingly, these mice developed an SLE-like disease indistinguishable from that of BAFF Tg mice. Autoimmunity in BAFF Tg mice did, however, require 13 cell-intrinsic signals through the Toll-like receptor (TLR)-associated signaling adaptor MyD88, which controlled the production of proinflammatory autoantibody isotypes. TLR7/9 activation strongly up-regulated expression of transmembrane activator and calcium modulator and cyclophilin ligand interactor (TACI), which is a receptor for BAFF involved in 13 cell responses to T cell-independent antigens. Moreover, BAFF enhanced TLR7/9 expression on 13 cells and TLR-mediated production of autoantibodies. Therefore, autoirnmunity in BAFF Tg mice results from altered 13 cell tolerance, but requires TLR signaling and is independent of T cell help. It is possible that SLE patients with elevated levels of BAFF show a similar basis for disease

Applying the 3C Model to FLOSS communities

Publicado em "Collaboration and technology: 22nd International Conference, CRIWG 2016, Kanazawa, Japan, September 14-16, 2016, proceedings". ISBN 978-3-319-44798-8How learning occurs within Free/Libre Open Source (FLOSS) communities and what is the dynamics such projects (e.g. the life cycle of such projects) are very relevant questions when considering the use of FLOSS projects in a formal education setting. This paper introduces an approach based on the 3C collaboration model (communication, coordination and cooperation) to represent the collaborative learning dynamics within FLOSS communities. To explore the collaborative learning potential of FLOSS communities a number of questionnaires and interviews to selected FLOSS contributors were run. From this study a 3C collaborative model applicable to FLOSS communities was designed and discussed.Programa Operacional da Região Norte, NORTE2020, in the context of project NORTE-01-0145-FEDER-000037FCT under grant SFRH/BSAB/113890/201

New trends for metal complexes with anticancer activity

Medicinal inorganic chemistry can exploit the unique properties of metal ions for the design of new drugs. This has, for instance, led to the clinical application of chemotherapeutic agents for cancer treatment, such as cisplatin. The use of cisplatin is, however, severely limited by its toxic side-effects. This has spurred chemists to employ different strategies in the development of new metal-based anticancer agents with different mechanisms of action. Recent trends in the field are discussed in this review. These include the more selective delivery and/or activation of cisplatin-related prodrugs and the discovery of new non-covalent interactions with the classical target, DNA. The use of the metal as scaffold rather than reactive centre and the departure from the cisplatin paradigm of activity towards a more targeted, cancer cell-specific approach, a major trend, are discussed as well. All this, together with the observation that some of the new drugs are organometallic complexes, illustrates that exciting times lie ahead for those interested in ‘metals in medicine

Reductions in cardiovascular, cerebrovascular, and respiratory mortality following the national Irish smoking ban: Interrupted time-series analysis

Copyright @ 2013 Stallings-Smith et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.Background: Previous studies have shown decreases in cardiovascular mortality following the implementation of comprehensive smoking bans. It is not known whether cerebrovascular or respiratory mortality decreases post-ban. On March 29, 2004, the Republic of Ireland became the first country in the world to implement a national workplace smoking ban. The aim of this study was to assess the effect of this policy on all-cause and cause-specific, non-trauma mortality. Methods: A time-series epidemiologic assessment was conducted, utilizing Poisson regression to examine weekly age and gender-standardized rates for 215,878 non-trauma deaths in the Irish population, ages ≥35 years. The study period was from January 1, 2000, to December 31, 2007, with a post-ban follow-up of 3.75 years. All models were adjusted for time trend, season, influenza, and smoking prevalence. Results: Following ban implementation, an immediate 13% decrease in all-cause mortality (RR: 0.87; 95% CI: 0.76-0.99), a 26% reduction in ischemic heart disease (IHD) (RR: 0.74; 95% CI: 0.63-0.88), a 32% reduction in stroke (RR: 0.68; 95% CI: 0.54-0.85), and a 38% reduction in chronic obstructive pulmonary disease (COPD) (RR: 0.62; 95% CI: 0.46-0.83) mortality was observed. Post-ban reductions in IHD, stroke, and COPD mortalities were seen in ages ≥65 years, but not in ages 35-64 years. COPD mortality reductions were found only in females (RR: 0.47; 95% CI: 0.32-0.70). Post-ban annual trend reductions were not detected for any smoking-related causes of death. Unadjusted estimates indicate that 3,726 (95% CI: 2,305-4,629) smoking-related deaths were likely prevented post-ban. Mortality decreases were primarily due to reductions in passive smoking. Conclusions: The national Irish smoking ban was associated with immediate reductions in early mortality. Importantly, post-ban risk differences did not change with a longer follow-up period. This study corroborates previous evidence for cardiovascular causes, and is the first to demonstrate reductions in cerebrovascular and respiratory causes

Derivation and Validation of a 10-Year Risk Score for Symptomatic Abdominal Aortic Aneurysm: Cohort Study of Nearly 500 000 Individuals

Background: Abdominal aortic aneurysm (AAA) can occur in patients who are ineligible for routine ultrasound screening. A simple AAA risk score was derived and compared with current guidelines used for ultrasound screening of AAA. Methods: United Kingdom Biobank participants without previous AAA were split into a derivation cohort (n=401 820, 54.6% women, mean age 56.4 years, 95.5% White race) and validation cohort (n=83 816). Incident AAA was defined as first hospital inpatient diagnosis of AAA, death from AAA, or an AAA-related surgical procedure. A multivariable Cox model was developed in the derivation cohort into an AAA risk score that did not require blood biomarkers. To illustrate the sensitivity and specificity of the risk score for AAA, a theoretical threshold to refer patients for ultrasound at 0.25% 10-year risk was modeled. Discrimination of the risk score was compared with a model of US Preventive Services Task Force (USPSTF) AAA screening guidelines. Results: In the derivation cohort, there were 1570 (0.40%) cases of AAA over a median 11.3 years of follow-up. Components of the AAA risk score were age (stratified by smoking status), weight (stratified by smoking status), antihypertensive and cholesterol-lowering medication use, height, diastolic blood pressure, baseline cardiovascular disease, and diabetes. In the validation cohort, over 10 years of follow-up, the C-index for the model of the USPSTF guidelines was 0.705 (95% CI, 0.678-0.733). The C-index of the risk score as a continuous variable was 0.856 (95% CI, 0.837-0.878). In the validation cohort, the USPSTF model yielded sensitivity 63.9% and specificity 71.3%. At the 0.25% 10-year risk threshold, the risk score yielded sensitivity 82.1% and specificity 70.7% while also improving the net reclassification index compared with the USPSTF model +0.176 (95% CI, 0.120-0.232). A combined model, whereby risk scoring was combined with the USPSTF model, also improved prediction compared with USPSTF alone (net reclassification index +0.101 [95% CI, 0.055-0.147]). Conclusions: In an asymptomatic general population, a risk score based on patient age, height, weight, and medical history may improve identification of asymptomatic patients at risk for clinical events from AAA. Further development and validation of risk scores to detect asymptomatic AAA are needed

An Investigation into Healthcare-Data Patterns

Visualising complex data facilitates a more comprehensive stage for conveying knowledge. Within the medical data domain, there is an increasing requirement for valuable and accurate information. Patients need to be confident that their data is being stored safely and securely. As such, it is now becoming necessary to visualise data patterns and trends in real-time to identify erratic and anomalous network access behaviours. In this paper, an investigation into modelling data flow within healthcare infrastructures is presented; where a dataset from a Liverpool-based (UK) hospital is employed for the case study. Specifically, a visualisation of transmission control protocol (TCP) socket connections is put forward, as an investigation into the data complexity and user interaction events within healthcare networks. In addition, a filtering algorithm is proposed for noise reduction in the TCP dataset. Positive results from using this algorithm are apparent on visual inspection, where noise is reduced by up to 89.84%

Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordLOD 2019: Fifth International Conference on Machine Learning, Optimization, and Data Science, 10-13 September 2019, Siena, ItalyGaussian processes (GPs) belong to a class of probabilistic techniques that have been successfully used in different domains of machine learning and optimization. They are popular because they provide uncertainties in predictions, which sets them apart from other modelling methods providing only point predictions. The uncertainty is particularly useful for decision making as we can gauge how reliable a prediction is. One of the fundamental challenges in using GPs is that the efficacy of a model is conferred by selecting an appropriate kernel and the associated hyperparameter values for a given problem. Furthermore, the training of GPs, that is optimizing the hyperparameters using a data set is traditionally performed using a cost function that is a weighted sum of data fit and model complexity, and the underlying trade-off is completely ignored. Addressing these challenges and shortcomings, in this article, we propose the following automated training scheme. Firstly, we use a weighted product of multiple kernels with a view to relieve the users from choosing an appropriate kernel for the problem at hand without any domain specific knowledge. Secondly, for the first time, we modify GP training by using a multi-objective optimizer to tune the hyperparameters and weights of multiple kernels and extract an approximation of the complete trade-off front between data-fit and model complexity. We then propose to use a novel solution selection strategy based on mean standardized log loss (MSLL) to select a solution from the estimated trade-off front and finalise training of a GP model. The results on three data sets and comparison with the standard approach clearly show the potential benefit of the proposed approach of using multi-objective optimization with multiple kernels.Natural Environment Research Council (NERC

Lack of association between HLA antigen DR3 and α<inf>1</inf> deficiency in liver transplant recipients

The relationship between α1-antitrypsin deficiency (α-ATD) and the HLA antigen system was studied in 32 liver transplant recipients. Despite previous reports of an association of HLA antigen DR3 with homozygosity for α-AT ZZ, no such association was seen in this population of α-ATD homozygous ZZ patients with advanced hepatic disease. Thus, the reported association of HLA class II antigens and homozygosity for the Z allele for α-AT may be an artifact of either a small study population or geographic inbreeding and a coincidental association of certain HLA antigens with the presence of homozygosity for the Z allele of α-AT. © 1993 Plenum Publishing Corporation