Using 1H NMR Spectroscopy to Compare Urinary Metabolites of Stroke and Spinal Cord Injury Patients Before and After Neurorehabilitation
Bibliographic record
Abstract
Background: Rehabilitation represents the primary approach to promote long-term functional recovery after neurological injury. There is an urgent need to improve the effectiveness of rehabilitation therapies to optimize the potential for recovery in an individual. Precision medicine approaches using modern ‘omics’ techniques represent vital prerequisites in stratifying individual patients with neurological injury to their optimal rehabilitation program. Metabolomics research from our laboratory has demonstrated that metabolite levels in urine detected by NMR spectroscopy serve as reliable prognostic markers for neurological injury. This study aims to determine if a proton NMR-based quantitative metabolic profiling approach can identify novel biomarkers in clinically accessible biofluids that are indicative of both the repair processes and treatment efficacy following neurorehabilitation from stroke and spinal cord injury. The main hypotheses of this study are (a) that each of the neurological conditions will yield changes in the metabolic profiles that can be correlated to the extent of recovery of a patient and (b) that biological pathway analysis will provide an insight into the mechanisms behind the repair process. These techniques will provide a greater understanding of the biochemical processes that mediate neural repair in the central nervous system for clinical application. Methods: Patients with stroke and spinal cord injury (n≥14 per group and sex) were recruited through the Foothills Medical Centre in Calgary, Alberta. Urine samples were collected from patients within 48-72 hours of injury and again at 6-months post-injury, following neurorehabilitation. A 700MHz Bruker Avance III HD NMR spectrometer located at the Canadian Centre for Behavioural Neuroscience was used to acquire the metabolic profiles of urine samples pre- and post-neurorehabilitation. Multivariate statistical and biomarker analysis tests were used to determine changes in the metabolic fingerprint which can potentially be linked to clinical outcomes. Impact: Within the rehabilitation field there is an urgent need to generate evidence-based therapies and validate existing ones. Metabolomic analysis will provide a time- and cost- effective method to identify optimal rehabilitation therapies based on an individual’s impairments, through translational biomarker discovery. This personalized medicine approach offers a new strategy to evaluate and improve the efficacy of neurorehabilitation strategies for stroke and spinal cord injury patients. *Indicates presenter
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".