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Using robotic devices might improve recovery post-stroke, but the optimal way to apply robotic assistance has yet to be determined. The current study aimed to investigate whether training under the robotic active-assisted mode improves bimanual motor skill learning (biMSkL) more than training under the active mode in stroke patients. Twenty-six healthy individuals (HI) and 23 chronic hemiparetic stroke patients with a detectable lesion on MRI or CT scan, who demonstrated motor deficits in the upper limb, were randomly allocated to two parallel groups. The protocol included a two-day training on a new bimanual cooperative task, LIFT-THE-TRAY, under either the active or active-assisted modes (where assistance decreased in a pre-determined stepwise fashion) with the bimanual version of the REAplan® robotic device. The hypothesis was that the active-assisted mode would result in greater biMSkL than the active mode. The biMSkL was quantified by a speed-accuracy trade-off (SAT) before (T1) and immediately after (T2) training on days 1 and 2 (T3 and T4). The change in SAT after 2 days of training (T4/T1) indicated that both HI and stroke patients learned and retained the bimanual cooperative task. After 2 days of training, the active-assisted mode did not improve biMSkL more than the active mode (T4/T1) in HI nor stroke patients. Whereas HI generalized the learned bimanual skill to different execution speeds in both the active and active-assisted subgroups, the stroke patients generalized the learned skill only in the active subgroup. Taken together, the active-assisted mode, applied in a pre-determined stepwise decreasing fashion, did not improve biMSkL more than the active mode in HI and stroke subjects. Stroke subjects might benefit more from robotic assistance when applied "as-needed." This study was approved by the local ethical committee (Comité d'éthique médicale, CHU UCL Namur, Mont-Godinne, Yvoir, Belgium; Internal number: 54/2010, EudraCT number: NUB B039201317382) on July 14, 2016 and was registered with ClinicalTrials.gov (Identifier: NCT03974750) on June 5, 2019.
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http://dx.doi.org/10.4103/1673-5374.301030 | DOI Listing |
J Clin Ultrasound
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Hebei General Hospital, Shijiazhuang, China.
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Division of Neurosurgery, Children's Hospital Los Angeles, Los Angeles, CA, USA.
It has become evident from decades of clinical trials that multimodal therapeutic approaches with focus on cell intrinsic and microenvironmental cues are needed to improve understanding and treat the rare, inoperable, and ultimately fatal diffuse intrinsic pontine glioma (DIPG), now categorized as a diffuse midline glioma. In this study we report the development and characterization of an in vitro system utilizing 3D Tumor Tissue Analogs (TTA), designed to replicate the intricate DIPG microenvironment. The innate ability of fluorescently labeled human brain endothelial cells, microglia, and patient-derived DIPG cell lines to self-assemble has been exploited to generate multicellular 3D TTAs that mimic tissue-like microstructures, enabling an in- depth exploration of the spatio-temporal dynamics between neoplastic and stromal cells.
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Irish National Audit of Stroke Care, National Office of Clinical Audit, Dublin, Ireland; St Vincent's University Hospital, Dublin, Ireland.
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Neurophysiopathology, Careggi University Hospital, Florence, Italy.
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View Article and Find Full Text PDFRadiography (Lond)
September 2025
Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China; School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China; Jiangsu Provincial Engineering Research Center for Medical Imaging and Digital Medicine, Xuzhou, Jiangs
Introduction: Carotid artery disease is a major cause of stroke and is frequently evaluated using Carotid CT Angiography (CTA). However, the associated radiation exposure and contrast agent use raise concerns, particularly for high-risk patients. Recent advances in Deep Learning Image Reconstruction (DLIR) offer new potential to enhance image quality under low-dose conditions.
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