A Targeted Proteomics Analysis of Cerebrospinal Fluid after Acute Human Spinal Cord Injury
Bibliographic record
Abstract
Efforts to validate novel therapies in acute clinical trials for spinal cord injury (SCI) are impeded by the lack of objective quantitative measures that reflect injury severity and accurately predict neurological recovery. Therefore, a strong rationale exists for establishing neurochemical biomarkers that objectively quantify injury severity and predict outcome. Here, we conducted a targeted proteomics analysis of cerebrospinal fluid (CSF) samples derived from 29 acute SCI patients (American Spinal Injury Association Impairment Scale [AIS] A, B, or C) acquired at 24, 48, and 72 h post-injury. From a total of 165 proteins, we identified 27 potential biomarkers of injury severity (baseline AIS A, B, or C), with triosephosphate isomerase having the strongest relationship to AIS grade. The majority of affected proteins (24 of 27) were more abundant in samples from AIS A patients than in those from AIS C patients, suggesting that for the most part, these proteins are released into the CSF more readily with more severe trauma to the spinal cord. We then analyzed the relationship between CSF protein abundance and neurological recovery. For AIS grade improvement over 6 months, we identified 34 proteins that were associated with AIS grade conversion (p < 0.05); however, these associations were not statistically significant after adjusting for multiple comparisons. For total motor score (TMS) recovery over 6 months, after adjusting for baseline neurological injury level, we identified 46 proteins with a statistically significant association with TMS recovery. Twenty-two of these proteins were among the 27 proteins that were related to baseline AIS grade, consistent with the notion that protein markers that reflect a more severe injury also appropriately predict a poorer recovery of motor function. In summary, this study provides a description of the CSF proteome changes that occur after acute human SCI, and reveals a number of protein candidates for further validation as potential biomarkers of injury severity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".