MicroRNA Biomarkers in Cerebrospinal Fluid and Serum Reflect Injury Severity in Human Acute Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Spinal cord injury (SCI) is a devastating condition with variability in injury mechanisms and neurologic recovery. Spinal cord impairment after SCI is measured and classified by a widely accepted standard neurological examination. In the very acute stages post-injury, however, this examination is extremely challenging (and often impossible) to conduct and has modest prognostic value in terms of neurological recovery. The lack of objective tools to classify injury severity and predict outcome is a barrier for clinical trials and thwarts development of therapies for those with SCI. Biological markers (biomarkers) represent a promising, complementary approach to these challenges because they represent an unbiased approach to classify injury severity and predict neurological outcome. Identification of a suitable panel of molecular biomarkers would comprise a fundamental shift in how patients with acute SCI are evaluated, stratified, and treated in clinical trials. MicroRNA are attractive biomarker candidates in neurological disorders for several reasons, including their stability in biological fluids, their conservation between humans and model mammals, and their tissue specificity. In this study, we used next-generation sequencing to identify microRNA associated with injury severity within the cerebrospinal fluid (CSF) and serum of human patients with acute SCI. The CSF and serum samples were obtained 1-5 days post-injury from 39 patients with acute SCI (24 American Spinal Injury Association Impairment Scale [AIS] A, 8 AIS B, 7 AIS C) and from five non-SCI controls. We identified a severity-dependent pattern of change in microRNA expression in CSF and identified a set of microRNA that are diagnostic of baseline AIS classification and prognostic of neurological outcome six months post-injury. The data presented here provide a comprehensive description of the CSF and serum microRNA expression changes that occur after acute human SCI. This data set reveals microRNA candidates that warrant further evaluation as biomarkers of injury severity after SCI and as key regulators in other neurological disorders.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".