1,424 research outputs found

    Front-line management of indolent non-Hodgkin lymphoma in Australia. Part 2: mantle cell lymphoma and marginal zone lymphoma

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    Mantle cell lymphoma (MCL) and the marginal zone lymphoma (MZL) subtypes (nodal MZL, extra-nodal MZL of mucosa-associated lymphoid tissue (MALT lymphoma) and splenic MZL) are uncommon lymphoma subtypes, accounting for less than 5-10% of all non-Hodgkin lymphoma. The evidence base for therapy is therefore limited and enrolment into clinical trials is preferred. Outcomes for patients with MCL have been steadily improving mainly due to the adoption of more intense strategies in younger patients, the use of rituximab maintenance and the recent introduction of bendamustine in older patients. MZL is a more heterogeneous group of cancer with both nodal, extra-nodal and splenic subtypes. Extranodal MZL may be associated with autoimmune or infectious aetiologies, and can respond to eradication of the causative pathogen. Proton pump inhibitor plus dual antibiotics in Helicobacter pylori positive gastric MALT lymphoma is curative in many patients. Watchful waiting is appropriate in most patients with asymptomatic advanced stage disease, which tends to behave in a particularly indolent manner. Other options for symptomatic disease include splenectomy, chemoimmunotherapy with rituximab and, more recently, targeted therapies

    Pulmonary Arterial Stiffness: An Early and Pervasive Driver of Pulmonary Arterial Hypertension

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    Pulmonary arterial hypertension (PAH) is a historically neglected and highly morbid vascular disease that leads to right heart failure and, in some cases, death. The molecular origins of this disease have been poorly defined, and as such, current pulmonary vasodilator therapies do not cure or reverse this disease. Although extracellular matrix (ECM) remodeling and pulmonary arterial stiffening have long been associated with end-stage PAH, recent studies have reported that such vascular stiffening can occur early in pathogenesis. Furthermore, there is emerging evidence that ECM stiffening may represent a key first step in pathogenic reprogramming and molecular crosstalk among endothelial, smooth muscle, and fibroblast cells in the remodeled pulmonary vessel. Such processes represent the convergence of activation of a number of specific mechanoactivated signaling pathways, microRNAs, and metabolic pathways in pulmonary vasculature. In this review, we summarize the contemporary understanding of vascular stiffening as a driver of PAH, its mechanisms, potential therapeutic targets and clinical perspectives. Of note, early intervention targeting arterial stiffness may break the vicious cycle of PAH progression, leading to outcome improvement which has not been demonstrated by current vasodilator therapy

    An Integrated Content and Metadata based Retrieval System for Art

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    In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type

    Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device

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    Objective: Freezing of Gait (FOG) often described as the sensation of “the feet being glued to the ground” is prevalent in people with Parkinson's disease (PD) and severely disturbs mobility. In addition to tracking disease progression, precise detection of the exact boundaries for each FOG episode may enable new technologies capable of “breaking” FOG in real time. This study investigates the limits of sensitivity and performance for automatic device-based FOG detection. Methods: Eight machine-learning classifiers (including Neural Networks, Ensemble & Support Vector Machine) were developed using (i) accelerometer and (ii) accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed a walking and mobility task designed to elicit FOG. Two clinicians independently annotated the precise FOG episodes using synchronized video according to international guidelines, which were incorporated into a flowchart algorithm developed for this study. Device-detected FOG episodes were compared to the annotated FOG episodes using 10-fold cross-validation to determine accuracy and with Interclass Correlation Coefficients (ICC) to assess level of agreement. Results: Development used 50,962 windows of data representing over 10 hours of data and annotated activities. Very strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity and ICC 1,1 = 0.97 for total FOG duration). Device-based performance varied by method, complexity and cost matrix. The Neural Network that used only 67 accelerometer features provided a good balance between high sensitivity to FOG (89% sensitivity, 81% specificity and ICC 1,1 = 0.83) and solution stability (validation loss ≤ 5%). Conclusion: The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The superior agreement between clinicians indicates there is room to improve future device-based FOG detection by using larger and more varied data sets. Significance: This study has clinical implications with regard to improving PD care by reducing reliance on clinical FOG assessments and time-consuming visual inspection. It shows high sensitivity to automatically detect FOG is possible

    Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device

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    Objective: Freezing of Gait (FOG) often described as the sensation of “the feet being glued to the ground” is prevalent in people with Parkinson's disease (PD) and severely disturbs mobility. In addition to tracking disease progression, precise detection of the exact boundaries for each FOG episode may enable new technologies capable of “breaking” FOG in real time. This study investigates the limits of sensitivity and performance for automatic device-based FOG detection. Methods: Eight machine-learning classifiers (including Neural Networks, Ensemble & Support Vector Machine) were developed using (i) accelerometer and (ii) accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed a walking and mobility task designed to elicit FOG. Two clinicians independently annotated the precise FOG episodes using synchronized video according to international guidelines, which were incorporated into a flowchart algorithm developed for this study. Device-detected FOG episodes were compared to the annotated FOG episodes using 10-fold cross-validation to determine accuracy and with Interclass Correlation Coefficients (ICC) to assess level of agreement. Results: Development used 50,962 windows of data representing over 10 hours of data and annotated activities. Very strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity and ICC 1,1 = 0.97 for total FOG duration). Device-based performance varied by method, complexity and cost matrix. The Neural Network that used only 67 accelerometer features provided a good balance between high sensitivity to FOG (89% sensitivity, 81% specificity and ICC 1,1 = 0.83) and solution stability (validation loss ≤ 5%). Conclusion: The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The superior agreement between clinicians indicates there is room to improve future device-based FOG detection by using larger and more varied data sets. Significance: This study has clinical implications with regard to improving PD care by reducing reliance on clinical FOG assessments and time-consuming visual inspection. It shows high sensitivity to automatically detect FOG is possible
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