Acute Immunological Phenotypes in Individuals with Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Acutely after traumatic spinal cord injury (SCI), the immune system responds with an inflammatory cascade that promotes secondary damage to the spinal cord and systemic inflammation, which promotes persistent medical consequences. Here, we combined clinical and research data to evaluate cellular and molecular changes in the systemic immune system of individuals with SCI (SCI, N = 36) within 0-4 days after injury compared to uninjured individuals (CTL, N = 36). Analyzing blood samples by bulk-RNA Seq, 4752 differentially expressed (DE) gene transcripts were identified in SCI compared with CTLs, including increased expression of genes associated with inflammation and innate immunity (e.g., Neutrophil degranulation, Toll-Like Receptor signaling). Most participants with SCI had complete blood count data available, of whom 36% had elevated white blood cell and neutrophil counts, 24% had elevated monocytes, and 36% had lymphopenia. Significantly reduced expression of canonical natural killer (NK) cell, T cell and dendritic cell (DC) genes were identified, consistent with reduced frequencies of these cell types, determined by flow cytometry. Some molecular changes appeared to be influenced by motor completeness of injury. C-reactive protein, a validated clinical biomarker of inflammation, was significantly elevated after SCI, with levels higher in motor complete compared to motor incomplete injuries. This was also apparent for several other proinflammatory cytokines (e.g., High Mobility Group Box 1 protein, IL-6, IL-8). These data confirm and extend prior observations of cellular and molecular immunological changes, that may serve as potential biomarkers of injury severity, or as future therapeutic targets to improve health.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".