Regenerative Rehabilitation for Spinal Cord Injury

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Regenerative Engineering".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 603

Special Issue Editors

Center for Neural Development and Repair, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
Interests: spinal cord injury; brain and spinal cord stimulation; spasticity; epidural stimulation; rehabilitation; sensorimotor recovery

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Guest Editor
Shriners Hospitals Pediatric Research Center (Center for Neural Repair and Rehabilitation), Department of Neural Sciences, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19122, USA
Interests: spinal cord injury; neurotrophins; gene therapy; axonal regeneration; rehabilitation; sensorimotor recovery
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Guest Editor
Department of Neural Sciences, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19122, USA
Interests: spinal cord injury; motor control; chemogenetics; forelimb kinematics; rehabilitation; motor recovery

Special Issue Information

Dear Colleagues,

This Special Issue of Bioengineering focuses on the emerging field of regenerative rehabilitation for spinal cord injury (SCI), an area at the intersection of tissue engineering, cellular therapies, and rehabilitation science. The goal is to highlight innovative strategies that combine biological repair with functional recovery, bridging gaps between regenerative medicine and physical therapy.

Spinal cord injury presents complex challenges that go beyond structural damage, often involving chronic inflammation, scarring, and functional loss. While regenerative approaches such as stem cell transplantation, biomaterials, and neurotrophic factors show promise in repairing or replacing damaged tissue, their full potential depends on integrated rehabilitation strategies that guide, reinforce, and sustain recovery.

This issue invites contributions that explore mechanistic studies, preclinical models, clinical trials, and technological advances that integrate regeneration with rehabilitation. Topics may include neuroplasticity, activity-based therapies, biomaterials designed for functional integration, and data-driven tools for personalized recovery. We are especially interested in research that demonstrates synergy between biological repair and functional outcomes.

By bringing together interdisciplinary work, this issue aims to advance the understanding and clinical translation of regenerative rehabilitation for SCI, with the long-term goal of improving quality of life and functional independence for individuals living with spinal cord injury.

Dr. Ajay Pal
Prof. Dr. George Smith
Dr. Andrew J. Spence
Guest Editors

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Keywords

  • spinal cord injury
  • rehabilitation
  • neuromodulation
  • neural regeneration
  • neuroprotection
  • neuroplasticity
  • functional recovery
  • activity-based therapy
  • tissue engineering
  • neurotrophic factors

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Published Papers (1 paper)

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22 pages, 3299 KiB  
Article
Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors
by Alexandru Bogdan Ilies, Cornel Cheregi, Hassan Hassan Thowayeb, Jan Reinald Wendt, Maur Sebastian Horgos and Liviu Lazar
Bioengineering 2025, 12(7), 752; https://doi.org/10.3390/bioengineering12070752 - 10 Jul 2025
Viewed by 413
Abstract
Background: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This study analyzed data from 29 SCI patients undergoing [...] Read more.
Background: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This study analyzed data from 29 SCI patients undergoing Lokomat-based rehabilitation. A dataset of 46 variables including range of motion (L-ROM), joint stiffness (L-STIFF), and muscular force (L-FORCE) was examined using statistical methods (paired t-test, ANOVA, and ordinary least squares regression), clustering techniques (k-means), dimensionality reduction (t-SNE), and anomaly detection (Isolation Forest). Predictive modeling was applied to assess the influence of age, speed, body weight, body weight support, and exercise duration on biomechanical outcomes. Results: No statistically significant asymmetries were found between left and right limb measurements, indicating functional symmetry post-treatment (p > 0.05). Clustering analysis revealed a weak structure among patient groups (Silhouette score ≈ 0.31). Isolation Forest identified minimal anomalies in stiffness data, supporting treatment consistency. Regression models showed that body weight and body weight support significantly influenced joint stiffness (p < 0.01), explaining up to 60% of the variance in outcomes. Conclusions: Lokomat-assisted robotic rehabilitation demonstrates high functional symmetry and biomechanical consistency in SCI patients. Machine learning methods provided meaningful insight into the structure and predictability of outcomes, highlighting the clinical value of weight and support parameters in tailoring recovery protocols. Full article
(This article belongs to the Special Issue Regenerative Rehabilitation for Spinal Cord Injury)
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