Complex regional pain syndrome (CRPS) is thought to be characterized by cognitive deficits affecting patients' ability to represent, perceive, and use their affected limb as well as its surrounding space. This has been tested, among others, by straight-ahead tasks testing oneself's egocentric representation, but such experiments lead to inconsistent results. Because spatial cognitive abilities encompass various processes, we completed such evaluations by varying the sensory inputs used to perform the task. Complex regional pain syndrome and matched control participants were asked to assess their own body midline either visually (ie, by means of a moving visual cue) or manually (ie, by straight-ahead pointing with one of their upper limbs) and to reach and point to visual targets at different spatial locations. Although the 2 former tasks only required one single sensory input to be performed (ie, either visual or proprioceptive), the latter task was based on the ability to coordinate perception of the position of one's own limb with visuospatial perception. However, in this latter task, limb position could only be estimated by proprioception, as vision of the limb was prevented. Whereas in the 2 former tasks CRPS participants' performance was not different from that of controls, they made significantly more deviations errors during the visuospatial task, regardless of the limb used to point or the direction of pointing. Results suggest that CRPS patients are not specifically characterized by difficulties in representing their body but, more particularly, in integrating somatic information (ie, proprioception) during visually guided movements of the limb.
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http://dx.doi.org/10.1097/j.pain.0000000000002068 | DOI Listing |
JAMA Cardiol
January 2025
National Amyloidosis Centre, Division of Medicine, University College London, Royal Free Hospital, London, United Kingdom.
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Objective: To characterize the clinical phenotype and natural history of asymptomatic patients with ATTR cardiac amyloid infiltration.
Design, Setting, And Participants: This cohort study analyzed data of all patients at 12 international centers for amyloidosis from January 1, 2008, through December 31, 2023.
Environ Sci Pollut Res Int
January 2025
Earth Sciences, Engineering Faculty, Autonomous University of San Luis Potosi, Av. Manuel Nava 8, San Luis Potosí, SLP, Mexico.
Ecosystems such as wetlands have karst groundwater as their primary source of preserving their services and functions. Karst systems are complex hydrogeological systems that are difficult to study because of their complicated functioning mechanism, which requires an interdisciplinary effort based on hydrodynamic assessment and characterization of the hydrogeology of the system. The study area is the Ramsar wetland Ciénaga de Tamasopo (Mexico), which is dependent on the discharge of karst groundwater that is affected by water extraction of extensive sugarcane agriculture and is also the main water source for the rural towns.
View Article and Find Full Text PDFCurr Nutr Rep
January 2025
Faculty of Pharmaceutical Science, Assam down town University, Sankar Madhab Path, Gandhi Nagar, Panikhaiti, Guwahati, Assam, India, PIN - 781026.
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View Article and Find Full Text PDFNeuroinformatics
January 2025
Department of Information Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, 600089, India.
Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost.
View Article and Find Full Text PDFEnviron Manage
January 2025
Department of Geoecology, Institute of Geosciences and Geography, Martin Luther University, Halle-Wittenberg, Halle (Saale), Germany.
In the face of unabated urban expansion, understanding the intrinsic characteristics of landscape structure is pertinent to preserving ecological diversity and managing the supply of ecosystem services. This study integrates machine-learning-based geospatial and landscape ecological techniques to assess the dynamics of landscape structure in cities of the rainforest (Akure and Owerri) and Guinea savanna (Makurdi and Minna) ecological regions of Nigeria between 1986 and 2022. Supervised classification using the random forest (RF) machine-learning classifier was performed on Landsat images on the Google Earth Engine (GEE) platform, and landscape metrics were calculated with FRAGSTATS to assess landscape composition, configuration, and connectivity.
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