Urinary biomarkers indicative of recovery from spinal cord injury: A pilot study
Bibliographic record
Abstract
Current assessments of recovery following spinal cord injury (SCI) focus on clinical outcome measures. These assessments bear an inherent risk of bias, emphasizing the need for more reliable prognostic biomarkers to measure SCI severity. This study evaluated fluid biomarkers as an objective tool to aid with prognosticating outcomes following SCI. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolomics approach of urine samples, the objectives were to determine (a) if alterations in metabolic profiles reflect the extent of recovery of individual SCI patients, (b) whether changes in urine metabolites correlate to patient outcomes, and (c) whether biological pathway analysis reflects mechanisms of neural damage and repair. An inception cohort exploratory pilot study collected morning urine samples from male SCI patients (n=6) following injury and again at 6-months post-injury. A 700 MHz Bruker Avance III HD NMR spectrometer was used to acquire the metabolic signatures of urine samples, which were used to derive metabolic pathways. Multivariate statistical analyses were used to identify changes in metabolic signatures, which were correlated to clinical outcomes in the Spinal Cord Independence Measure (SCIM). Among SCI-induced metabolic changes, biomarkers which significantly correlated to patient SCIM scores included caffeine (R = -0.76, p < 0.01), 3-hydroxymandelic acid (R= -0.85, p < 0.001), L-valine (R = 0.90, p < 0.001; R = -0.64, p < 0.05), and N-methylhydantoin (R = -0.90, p < 0.001). The most affected pathway was purine metabolism. These findings indicate that urinary metabolites reflect SCI lesion severity and recovery and provide potentially prognostic biomarkers of SCI outcome in precision medicine approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".