Characterization of Cerebrospinal Fluid Ubiquitin C-Terminal Hydrolase L1 as a Biomarker of Human Acute Traumatic Spinal Cord Injury
Bibliographic record
Abstract
A major obstacle for translational research in acute spinal cord injury (SCI) is the lack of biomarkers that can objectively stratify injury severity and predict outcome. Ubiquitin C-terminal hydrolase L1 (UCH-L1) is a neuron-specific enzyme that shows promise as a diagnostic biomarker in traumatic brain injury (TBI), but has not been studied in SCI. In this study, cerebrospinal fluid (CSF) and serum samples were collected over the first 72-96 h post-injury from 32 acute SCI patients who were followed prospectively to determine neurological outcomes at 6 months post-injury. UCH-L1 concentration was measured using the Quanterix Simoa platform (Quanterix, Billerica, MA) and correlated to injury severity, time, and neurological recovery. We found that CSF UCH-L1 was significantly elevated by 10- to 100-fold over laminectomy controls in an injury severity- and time-dependent manner. Twenty-four-hour post-injury CSF UCH-L1 concentrations distinguished between American Spinal Injury Association Impairment Scale (AIS) A and AIS B, and AIS A and AIS C patients in the acute setting, and predicted who would remain "motor complete" (AIS A/B) at 6 months with a sensitivity of 100% and a specificity of 86%. AIS A patients who did not improve their AIS grade at 6 months post-injury were characterized by sustained elevations in CSF UCH-L1 up to 96 h. Similarly, the failure to gain >8 points on the total motor score at 6 months post-injury was associated with higher 24-h CSF UCH-L1. Unfortunately, serum UCH-L1 levels were not informative about injury severity or outcome. In conclusion, CSF UCH-L1 in acute SCI shows promise as a biomarker to reflect injury severity and predict outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".