Browse the latest research summaries in the field of bioinformatics for spinal cord injury patients and caregivers.
Showing 81-90 of 296 results
IBRO Neuroscience Reports, 2021 • February 18, 2021
This pilot study investigated urinary metabolic profiles as potential biomarkers for recovery following spinal cord injury (SCI). Urine samples were collected from male SCI patients at one month and s...
KEY FINDING: Caffeine, 3-hydroxymandelic acid, and L-valine levels in initial urine samples correlated with functional improvement as measured by SCIM scores.
Sensors, 2022 • September 29, 2022
This study developed and validated a methodology for classifying wheelchair-related shoulder-loading activities (SL-ADL) using wearable sensor data and deep learning. A deep learning model, incorporat...
KEY FINDING: The trained deep learning algorithm achieved an overall accuracy of 98% in classifying wheelchair-related shoulder-loading activities (SL-ADL).
Sensors, 2022 • November 3, 2022
This study proposes a traumatic spinal cord injury (TSCI) classification system using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) sig...
KEY FINDING: The CNN shows promise as a classification technique for TSCI compared to traditional machine learning.
Translational Neurodegeneration, 2022 • December 2, 2022
This review focuses on recent research progress on the pathological roles of EVs released from CNS cells in the pathogenesis of NDs. It summarizes findings that identify CNS-derived EV cargos as poten...
KEY FINDING: EVs play a significant role in the pathogenesis of various neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease.
Sensors, 2023 • February 1, 2023
This study developed a machine-learning-based methodology to estimate shoulder load in wheelchair-related activities using wearable sensors. The approach involved collecting data from participants per...
KEY FINDING: A subject-specific biLSTM model trained on a sparse sensor setup (upper arm IMU, WC IMUs, and EMG) yielded the most promising results, with a mean correlation coefficient of 0.74 ± 0.14 and a relative root-mean-squared error of 8.93% ± 2.49%.
Molecular Brain, 2023 • February 5, 2023
This study examined the gut microbiota and serum metabolites in SCI patients compared to healthy individuals to understand their interaction in SCI pathogenesis. We present a comprehensive landscape o...
KEY FINDING: SCI patients have different gut bacteria compared to healthy people, with increases in some bacteria (UBA1819, Anaerostignum, Eggerthella, Enterococcus) and decreases in others (Faecalibacterium, Blautia, Escherichia–Shigella, Agathobacter, Collinsella, Dorea, Ruminococcus, Fusicatenibacter, and Eubacterium).
Sensors, 2023 • February 23, 2023
This paper presents an Assist-as-Needed (AAN) algorithm for controlling an exoskeleton designed for elbow rehabilitation. The algorithm uses FSR sensors and machine learning to personalize assistance....
KEY FINDING: The study provides patients with real-time, visual feedback on their progress by combining range of motion and FSR data to quantify disability levels.
Wearable Technologies, 2022 • December 1, 2022
The study addresses the problem of posture estimation errors in wearable robots due to the compliance of the human-robot interface. A novel algorithm is presented that uses machine learning to correct...
KEY FINDING: The algorithm reduced the estimated thigh segment’s angle RMS error from 6.3° to 2.5°.
Sensors, 2022 • November 24, 2022
This study introduces a novel controller based on a Reinforcement Learning (RL) algorithm for real-time adaptation of the stimulation pattern during FES-cycling. The participant was able to pedal over...
KEY FINDING: The participant with spinal cord injury was able to pedal overground for distances over 3.5 km using the developed system.
International Journal of Molecular Sciences, 2020 • November 27, 2020
This pilot study explored the potential of early CSF biomarkers to predict functional outcomes in spinal cord injury patients. CSF samples were collected within 24 hours of injury, and levels of 38 bi...
KEY FINDING: MIP-1β, MIP-1α, MCP-1, IL-9 and IL-18 were positively associated with neurological level at discharge.