Lipid metabolism in autoimmune rheumatic disease: implications for modern and conventional therapies

Suppressing inflammation has been the primary focus of therapies in autoimmune rheumatic diseases (AIRDs), including rheumatoid arthritis and systemic lupus erythematosus. However, conventional therapies with low target specificity can have effects on cell metabolism that are less predictable. A key example is lipid metabolism; current therapies can improve or exacerbate dyslipidemia. Many conventional drugs also require in vivo metabolism for their conversion into therapeutically beneficial products; however, drug metabolism often involves the additional formation of toxic by-products, and rates of drug metabolism can be heterogeneous between patients. New therapeutic technologies and research have highlighted alternative metabolic pathways that can be more specifically targeted to reduce inflammation but also to prevent undesirable off-target metabolic consequences of conventional antiinflammatory therapies. This Review highlights the role of lipid metabolism in inflammation and in the mechanisms of action of AIRD therapeutics. Opportunities for cotherapies targeting lipid metabolism that could reduce immunometabolic complications and potential increased cardiovascular disease risk in patients with AIRDs are discussed

PialNN: A fast deep learning framework for cortical pial surface reconstruction

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by 30% compared to state-of-the-art deep learning approaches. The codes are publicly available at https://github.com/m-qiang/PialNN

Metabolomics defines complex patterns of dyslipidaemia in juvenile-sle patients associated with inflammation and potential cardiovascular disease risk

Cardiovascular disease (CVD) is a leading cause of mortality in patients with juvenile-onset systemic lupus erythematosus (JSLE) associated with atherosclerosis. The interplay between dyslipidaemia and inflammation—mechanisms that drive atherosclerosis—were investigated retro-spectively in adolescent JSLE patients using lipoprotein-based serum metabolomics in patients with active and inactive disease, compared to healthy controls (HCs). Data was analysed using machine learning, logistic regression, and linear regression. Dyslipidaemia in JSLE patients was characterised by lower levels of small atheroprotective high-density lipoprotein subsets compared to HCs. These changes were exacerbated by active disease and additionally associated with significantly higher atherogenic very-low-density lipoproteins (VLDL) compared to patients with low disease activity. Atherogenic lipoprotein subset expression correlated positively with clinical and serological markers of JSLE disease activity/inflammation and was associated with disturbed liver function, and elevated expression of T-cell and B-cell lipid rafts (cell signalling platforms mediating immune cell activa-tion). Finally, exposing VLDL/LDL from patients with active disease to HC lymphocytes induced a significant increase in lymphocyte lipid raft activation compared to VLDL/LDL from inactive patients. Thus, metabolomic analysis identified complex patterns of atherogenic dyslipidaemia in JSLE patients associated with inflammation. This could inform lipid-targeted therapies in JSLE to improve cardiovascular outcomes

Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks

Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work, we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer’s Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework can estimate realistic deformations, following the known course of Alzheimer’s disease, that clearly differentiate between healthy and demented patterns of ageing. This suggests the framework has potential to be incorporated into explainable models of disease, for the exploration of interventions and counterfactual examples

Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex

The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously

Plant-based dietary changes may improve symptoms in patients with systemic lupus erythematosus

INTRODUCTION: Previous studies have reported that patients affected by systemic lupus erythematosus (SLE) are interested in using diet to treat fatigue, cardiovascular disease and other symptoms. However, to date, there is insufficient information regarding the ways for patients to modify their diet to improve SLE symptoms. We investigated the relationship between the eating patterns of SLE patients and their self-reported disease symptoms and general aspects of health. METHODS: A UK-based, online survey was developed, in which patients with SLE were asked about their attitudes and experiences regarding their SLE symptoms and diet. RESULTS: The majority (>80%) of respondents that undertook new eating patterns with increased vegetable intake and/or decreased intake of processed food, sugar, gluten, dairy and carbohydrates reported benefiting from their dietary change. Symptom severity ratings after these dietary changes were significantly lower than before (21.3% decrease, p<0.0001). The greatest decreases in symptom severity were provided by low/no dairy (27.1% decrease), low/no processed foods (26.6% decrease) and vegan (26% decrease) eating patterns (p<0.0001). Weight loss, fatigue, joint/muscle pain and mood were the most cited symptoms that improved with dietary change. CONCLUSION: SLE patients who changed their eating patterns to incorporate more plant-based foods while limiting processed foods and animal products reported improvements in their disease symptoms. Thus, our findings show promises in using nutrition interventions for the management of SLE symptoms, setting the scene for future clinical trials in this area. Randomised studies are needed to further test whether certain dietary changes are effective for improving specific symptoms of SLE

Manipulation of visual biofeedback during gait with a time delayed adaptive Virtual Mirror Box.

A mirror placed in the mid-sagittal plane of the body has been used to reduce phantom limb pain and improve movement function in medical conditions characterised by asymmetrical movement control. The mirrored illusion of unimpaired limb movement during gait might enhance the effect, but a physical mirror is only capable of showing parallel movement of limbs in real time typically while sitting. We aimed to overcome the limitations of physical mirrors by developing and evaluating a Virtual Mirror Box which delays the mirrored image of limbs during gait to ensure temporal congruency with the impaired physical limb

Sex hormones drive changes in lipoprotein metabolism

Summary Women have a reduced cardiovascular disease (CVD) risk compared to men which could be partially driven by sex hormones influencing lipid levels post-puberty. The interrelationship between sex hormones and lipids was explored in pre-pubertal children, young post-pubertal cis-men/women, and transgender individuals on cross-sex-hormone treatment (trans-men/women) using serum metabolomics assessing 149 lipids. High-density lipoproteins (HDL, typically atheroprotective) were significantly increased and very-low- and low-density lipoproteins (typically atherogenic) were significantly decreased in post-pubertal cis-women compared to cis-men. These differences were not observed pre-puberty and were induced appropriately by cross-sex-hormone treatment in transgender individuals, supporting that sex hormones regulate lipid metabolism in vivo. Only atheroprotective apolipoprotein (Apo)A1 expressing lipoproteins (HDL) were differentially expressed between all hormonally unique comparisons. Thus, oestradiol drives a typically atheroprotective lipid profile through upregulation of HDL/ApoA1 which could contribute to the sexual dimorphism observed in CVD risk post-puberty. Together, this could inform sex-specific therapeutic strategies for CVD management

European tourism policy: Its evolution and structure

© 2015 Elsevier Ltd. This article reviews the procedural complexity of tourism policy-making by the European Commission leading up to the 2010 Communication. Initially, the European Commission had to present interventions affecting tourism as a community action or measure; intended to assist in the implementation of the Internal Market. Later, integration of the sustainable development principle into European Treaties established a framework for governance and a foundation for tourism policy, and the Lisbon Treaty in 2007 established a European policy that explicitly related to tourism, albeit a complementary competence in character. This article highlights a lack of leadership from the Member States throughout the process and contrasts this with the self-serving, driving force of the Commission in making tourism policy that focuses primarily on promotional actions. Consequently, the Commission has not created a robust, dynamic, flexible European model for tourism, designed in a way to best serve the needs of the Member States