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Many individuals with incomplete spinal cord injury (SCI) exhibit reduced volitional control of trunk muscles, such as impaired voluntary contractions of the erector spinae (ES), due to damage to the neural pathways regulating sensorimotor function. Studies using conventional bipolar electromyography (EMG) showed alterations in the overall, or global, activation of the trunk muscles in people with SCI. However, how activation varied across specific regions within the ES, referred to as regional activation, remains unknown. The aim of the study was to investigate the regional distribution of the ES activity below the level of injury in individuals with incomplete SCI during postural tasks and multidirectional reaching tasks using high-density EMG. Twenty-one individuals with incomplete SCI and age-matched controls were recruited. The EMG amplitude of the thoracic ES and displacement of the arm, trunk, and center of pressure were recorded during the tasks. Activation was more in the lower region of the ES in individuals with SCI than in the controls during the postural tasks. In addition, activation was limited to a small area of the ES during the reaching tasks. The EMG amplitude was greater during reaching forward than returning to the upright posture in the controls; however, this phase-dependent difference in the EMG amplitude was not present in individuals with SCI. Our findings demonstrate changes in regional activation of the thoracic ES during postural and reaching tasks, likely reflecting injury-induced changes in selective neural control to activate residual muscle fibers of the ES for postural control and function after SCI. We demonstrate that individuals with chronic incomplete spinal cord injury (SCI) recruit lower part of the thoracic erector spinae (ES) for postural control of the trunk. We also show that activation was restricted in a smaller part of the ES, and the discrete control of the ES was lost during functional reaching movements in individuals with SCI. Our study provides evidence of alterations in neural control between vertebral levels in individuals with SCI.
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http://dx.doi.org/10.1152/jn.00246.2024 | DOI Listing |
PLoS One
September 2025
Symbiosis Institute of Technology, Symbiosis International University, Pune, India.
With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products.
View Article and Find Full Text PDFJ Pain
September 2025
Cyber-physical Health and Assistive Robotics Technologies Research Group, University of Nottingham, United Kingdom; School of Computer Science, University of Nottingham, Nottingham, United Kingdom.
Neck pain is among the most prevalent musculoskeletal conditions worldwide. The underlying cause mostly remains unidentified, classified as non-specific neck pain. Pain can alter movement patterns and physiological responses, suggesting that certain biomechanical and physiological changes may serve as objective biomarkers for non-specific neck pain.
View Article and Find Full Text PDFEur Spine J
September 2025
Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Purpose: This study aims to address the limitations of radiographic imaging and single-task learning models in adolescent idiopathic scoliosis assessment by developing a noninvasive, radiation-free diagnostic framework.
Methods: A multi-task deep learning model was trained using structured back surface data acquired via fringe projection three-dimensional imaging. The model was designed to simultaneously predict the Cobb angle, curve type (thoracic, lumbar, mixed, none), and curve direction (left, right, none) by learning shared morphological features.
medRxiv
August 2025
The Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Medical Center, NY, USA.
Background: AI agents built on large language models (LLMs) can plan tasks, use external tools, and coordinate with other agents. Unlike standard LLMs, agents can execute multi-step processes, access real-time clinical information, and integrate multiple data sources. There has been interest in using such agents for clinical and administrative tasks, however, there is limited knowledge on their performance and whether multi-agent systems function better than a single agent for healthcare tasks.
View Article and Find Full Text PDFGait Posture
September 2025
School of Health Sciences, University of East Anglia, UK. Electronic address:
Background: International consensus recommends use of kinematic metrics of movement during standardized functional tasks after stroke to ascertain whether rehabilitation is driving behavioral restitution or compensation. Quality of human movement can be characterized by fluency metrics including smoothness and hesitation. Before using these metrics in stroke rehabilitation it is important to find whether 'reference values', from healthy adults, are repeatable.
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