Parallel Metabolomic Profiling of Cerebrospinal Fluid and Serum for Identifying Biomarkers of Injury Severity after Acute Human Spinal Cord Injury
Bibliographic record
Abstract
Suffering an acute spinal cord injury (SCI) can result in catastrophic physical and emotional loss. Efforts to translate novel therapies in acute clinical trials are impeded by the SCI community's singular dependence upon functional outcome measures. Therefore, a compelling rationale exists to establish neurochemical biomarkers for the objective classification of injury severity. In this study, CSF and serum samples were obtained at 3 time points (~24, 48, and 72 hours post-injury) from 30 acute SCI patients (10 AIS A, 12 AIS B, and 8 AIS C). A differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) with a universal metabolome standard (UMS) was applied to the metabolomic profiling of these samples. This method provided enhanced detection of the amine- and phenol-containing submetabolome. Metabolic pathway analysis revealed dysregulations in arginine-proline metabolism following SCI. Six CSF metabolites were identified as potential biomarkers of baseline injury severity, and good classification performance (AUC > 0.869) was achieved by using combinations of these metabolites in pair-wise comparisons of AIS A, B and C patients. Using the UMS strategy, the current data set can be expanded to a larger cohort for biomarker validation, as well as discovering biomarkers for predicting neurologic outcome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".