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Making Spinal Cord Injury (SCI) Research Accessible to Everyone. Simplified summaries of the latest research, designed for patients, caregivers and anybody who's interested.

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Bioinformatics Research

Browse the latest research summaries in the field of bioinformatics for spinal cord injury patients and caregivers.

Showing 81-90 of 334 results

Spinal Cord InjuryPain ManagementBioinformatics

Assessing the Feasibility of a Multimodal Approach to Pain Evaluation in Early Stages after Spinal Cord Injury

Int. J. Mol. Sci., 2023 • July 5, 2023

This research evaluates the feasibility of a multimodal pain assessment protocol during rehabilitation following spinal cord injury (SCI). The protocol amalgamates clinical workup (CW), quantitative s...

KEY FINDING: The SCI Pain Instrument (SCIPI) was efficient in distinguishing between nociceptive and neuropathic pain, showing a progressive increase in severity over time.

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GeneticsOrthopedicsBioinformatics

Genetic insights into ossification of the posterior longitudinal ligament of the spine

eLife, 2023 • July 18, 2023

The study conducts a meta-analysis of genome-wide association studies (GWASs) using 22,016 Japanese individuals and identifies 14 significant loci, 8 of which were previously unreported. A Mendelian r...

KEY FINDING: Identified 14 significant loci associated with OPLL, including 8 previously unreported loci.

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Spinal Cord InjuryRegenerative MedicineBioinformatics

Discovery of therapeutic targets for spinal cord injury based on molecular mechanisms of axon regeneration after conditioning lesion

Journal of Translational Medicine, 2023 • July 20, 2023

This study comprehensively maps transcriptional changes in young and old DRGs after injury, identifying hub genes and related drugs affecting axon regeneration. The research pioneers the construction ...

KEY FINDING: Identified 693 and 885 DEGs in old and young mice, respectively, after peripheral nerve injury, with shared DEGs involved in inflammatory and immune responses.

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Spinal Cord InjuryCardiovascular ScienceBioinformatics

Cardiometabolic risk factor clustering in persons with spinal cord injury: A principal component analysis approach

The Journal of Spinal Cord Medicine, 2024 • January 1, 2024

This study used principal component analysis (PCA) to identify how cardiometabolic (CM) risk factors cluster in individuals with spinal cord injury (SCI) compared to non-SCI controls, revealing a six-...

KEY FINDING: Principal component analysis (PCA) identified six factor components (FC) explaining 77% and 82% of the total variance in the SCI and non-SCI cohorts, respectively.

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Spinal Cord InjuryBioinformaticsDermatology

Genomic Biomarkers Can Provide a Deeper Understanding of Recurrent Pressure Injuries

ADV SKIN WOUND CARE, 2023 • October 1, 2023

The study aimed to identify genetic biomarkers predisposing individuals with spinal cord injury (SCI) to recurrent pressure injuries (PIs) through repeated measures of the transcriptome profile. Resul...

KEY FINDING: Whole genome sequencing identified 260 genes with increased single-nucleotide variations in exonic regions among individuals with high intramuscular adipose tissue levels and recurrent PIs.

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Spinal Cord InjuryPatient ExperienceBioinformatics

A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database

Frontiers in Neurology, 2023 • October 12, 2023

This study used cluster analysis to identify five clinically similar subgroups of tSCI patients based on demographics and injury characteristics at baseline. These subgroups showed statistically signi...

KEY FINDING: The study identified five distinct subgroups of tSCI patients based on baseline variables such as age, BMI, injury severity (AIS grade), primary location of injury (PLI), and baseline FIM motor score.

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Spinal Cord InjuryAssistive TechnologyBioinformatics

Evaluation of the improvement of walking ability in patients with spinal cord injury using lower limb rehabilitation robots based on data science

Translational Neuroscience, 2023 • October 7, 2023

This study evaluated the effectiveness of lower limb rehabilitation robots (LLRRs) in improving the walking ability of spinal cord injury (SCI) patients, comparing it to conventional rehabilitation tr...

KEY FINDING: Patients in the LLRR group (Group B) showed significantly higher FAC (functional ambulation category) scores after 10 weeks of training compared to the conventional RT group (Group A).

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NeurologyBioinformatics

Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm

Frontiers in Neuroscience, 2023 • December 13, 2023

This study introduces a novel spatial filter paradigm, adaptive spatial pattern (ASP), which differentiates itself from traditional CSP methods by emphasizing the optimization of energy distribution w...

KEY FINDING: The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively.

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Spinal Cord InjuryBioinformaticsRehabilitation

Development and Validation of an Artificial Intelligence‑Based Motion Analysis System for Upper Extremity Rehabilitation Exercises in Patients with Spinal Cord Injury: A Randomized Controlled Trial

Healthcare, 2024 • December 19, 2023

This study developed an AI‑based real‑time motion feedback system for patients with spinal cord injury (SCI) during rehabilitation, aiming to enhance their interest and motivation. The effectiveness o...

KEY FINDING: The experimental group (using the AI system) showed increased strength in all measured variables, whereas the control group showed constant or reduced results.

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NeurologyBioinformaticsRehabilitation

Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography

Sensors, 2024 • January 19, 2024

The study investigates the use of mechanomyography (MMG) and machine learning (ML) to simplify transcutaneous spinal cord stimulation (tSCS) calibration. It proposes using accelerometers to measure mu...

KEY FINDING: The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients.

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