Estrogen treatment decreases matrix metalloproteinase (MMP)-9 in autoimmune demyelinating disease through estrogen receptor alpha (ERalpha).

Matrix metalloproteinases (MMPs) have a crucial function in migration of inflammatory cells into the central nervous system (CNS). Levels of MMP-9 are elevated in multiple sclerosis (MS) and predict the occurrence of new active lesions on magnetic resonance imaging (MRI). This translational study aims to determine whether in vivo treatment with the pregnancy hormone estriol affects MMP-9 levels from immune cells in patients with MS and mice with experimental autoimmune encephalomyelitis (EAE). Peripheral blood mononuclear cells (PBMCs) collected from three female MS patients treated with estriol and splenocytes from EAE mice treated with estriol, estrogen receptor (ER) alpha ligand, ERbeta ligand or vehicle were stimulated ex vivo and analyzed for levels of MMP-9. Markers of CNS infiltration were assessed using MRI in patients and immunohistochemistry in mice. Supernatants from PBMCs obtained during estriol treatment in female MS patients showed significantly decreased MMP-9 compared with pretreatment. Decreases in MMP-9 coincided with a decrease in enhancing lesion volume on MRI. Estriol treatment of mice with EAE reduced MMP-9 in supernatants from autoantigen-stimulated splenocytes, coinciding with decreased CNS infiltration by T cells and monocytes. Experiments with selective ER ligands showed that this effect was mediated through ERalpha. In conclusion, estriol acting through ERalpha to reduce MMP-9 from immune cells is one mechanism potentially underlying the estriol-mediated reduction in enhancing lesions in MS and inflammatory lesions in EAE

Characterization of the CD4+ T Cell Response to Epstein-Barr Virus during Primary and Persistent Infection

The CD8+ T cell response to Epstein-Barr virus (EBV) is well characterized. Much less is known about the evolution of the CD4+ T cell response. Here we show that EBV stimulates a primary burst of effector CD4+ T cells and this is followed by a period of down-regulation. A small population of EBV-specific effector CD4+ T cells survives during the lifelong persistent phase of infection. The EBV-specific effector CD4+ T cells accumulate within a CD27+ CD28+ differentiation compartment during primary infection and remain enriched within this compartment throughout the persistent phase of infection. Analysis of CD4+ T cell responses to individual epitopes from EBV latent and lytic cycle proteins confirms the observation that the majority of the effector cells express both CD27 and CD28, although CD4+ T cells specific for lytic cycle antigens have a greater tendency to express CD45RA than those specific for the latent antigens. In clear contrast, effector CD4+ T cells specific for cytomegalovirus (CMV) accumulate within the CD27− CD28+ and CD27− CD28− compartments. There are striking parallels in terms of the differentiation of CD8+ T cells specific for EBV and CMV. The results challenge current ideas on the definition of memory subsets

Collaborative International Research in Clinical and Longitudinal Experience Study in NMOSD.

Objective: To develop a resource of systematically collected, longitudinal clinical data and biospecimens for assisting in the investigation into neuromyelitis optica spectrum disorder (NMOSD) epidemiology, pathogenesis, and treatment. Methods: To illustrate its research-enabling purpose, epidemiologic patterns and disease phenotypes were assessed among enrolled subjects, including age at disease onset, annualized relapse rate (ARR), and time between the first and second attacks. Results: As of December 2017, the Collaborative International Research in Clinical and Longitudinal Experience Study (CIRCLES) had enrolled more than 1,000 participants, of whom 77.5% of the NMOSD cases and 71.7% of the controls continue in active follow-up. Consanguineous relatives of patients with NMOSD represented 43.6% of the control cohort. Of the 599 active cases with complete data, 84% were female, and 76% were anti-AQP4 seropositive. The majority were white/Caucasian (52.6%), whereas blacks/African Americans accounted for 23.5%, Hispanics/Latinos 12.4%, and Asians accounted for 9.0%. The median age at disease onset was 38.4 years, with a median ARR of 0.5. Seropositive cases were older at disease onset, more likely to be black/African American or Hispanic/Latino, and more likely to be female. Conclusions: Collectively, the CIRCLES experience to date demonstrates this study to be a useful and readily accessible resource to facilitate accelerating solutions for patients with NMOSD

W.: Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm

Abstract. We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms. We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute.

Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm

We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms.We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute

W.: Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm

Abstract. We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms. We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute.

Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm

We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms.We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute

Electronic prescriptions and disruptions to the jurisdiction of community pharmacists

The practice of community pharmacists is being challenged by the appearance of electronic prescription (e-Rx) technology. This article examines the disruptions caused by e-Rx technology to the jurisdiction of community pharmacists based on a model developed from work by Abbott (1988). The main disruptions to professional activities were investigated by qualitative methods in a series of interviews with pharmacists and physicians separated in two groups: practitioners who tested a typical e-Rx technology and stakeholders involved in the implementation of this large-scale e-Rx project in Quebec, Canada. The findings suggest that the technology may disrupt the jurisdiction of community pharmacists, mainly by changing the distribution of information among physicians and community pharmacists. More specifically, the technology represents both a threat to community pharmacists - by supporting the dominant position held by physicians if it gives them access to information held exclusively by pharmacists - and an opportunity - by redistributing information to the pharmacists' benefit, allowing them to improve the quality of their inferences about medication. However, it would appear that the opportunities offered by the technology generate concerns and tensions, both between physicians and pharmacists and between the pharmacists themselves. This phenomenon may well work against the implementation and use of available tools.Canada Electronic prescription e-Rx technology Jurisdiction Community pharmacist Primary care physician Clinical data exchange Professions

A.W.: Anisotropic Laplace-Beltrami eigenmaps: Bridging Reeb graphs and skeletons

In this paper we propose a novel approach of computing skeletons of robust topology for simply connected surfaces with boundary by constructing Reeb graphs from the eigenfunctions of an anisotropic Laplace-Beltrami operator. Our work brings together the idea of Reeb graphs and skeletons by incorporating a flux-based weight function into the Laplace-Beltrami operator. Based on the intrinsic geometry of the surface, the resulting Reeb graph is pose independent and captures the global profile of surface geometry. Our algorithm is very efficient and it only takes several seconds to compute on neuroanatomical structures such as the cingulate gyrus and corpus callosum. In our experiments, we show that the Reeb graphs serve well as an approximate skeleton with consistent topology while following the main body of conventional skeletons quite accurately. 1