Expression of inflammatory cytokines following acute spinal cord injury in a rodent model
Bibliographic record
Abstract
Many therapies that have been developed for acute spinal cord injury (SCI) either influence or are influenced by posttraumatic inflammation. Many such therapies have reportedly produced promising neurologic benefits in animal models of SCI, but demonstrating convincing efficacy in human clinical trials has remained elusive. This discrepancy may be related in part to differences in the inflammatory response to SCI between human patients and the widely studied rodent models. Our objectives were, therefore, to establish the time course of inflammatory cytokine release in the spinal cord of rats after a thoracic contusion, to determine whether the cytokine release was injury dependent, and to correlate these findings with those that we have recently reported for the cerebrospinal fluid (CSF) of human SCI patients. After rodent SCI, GRO (the rat equivalent of IL-8), IL-6, IL-1α, IL-1β, IL-13, MCP-1, MIP1α, RANTES, and TNFα were elevated within the spinal cord, whereas IL-12p70 was decreased. In human SCI, IL-6, IL-8, and MCP-1 were also elevated within the cerebrospinal fluid but at later times than those observed in the rodent spinal cord. IL-6, IL-8, and MCP-1 were released in an injury-dependent manner in both the rodent model of SCI and the human condition. In this regard, similar patterns of expression were observed for a number of inflammatory cytokines after SCI in rodent spinal cords and in human CSF. Such proteins may therefore have potential utility as biomarkers and surrogate outcome measures for evaluating biological response to therapeutic interventions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".