The Relationship between Localized Subarachnoid Inflammation and Parenchymal Pathophysiology after Spinal Cord Injury
Bibliographic record
Abstract
Subarachnoid inflammation following spinal cord injury (SCI) can lead to the formation of localized subarachnoid scarring and the development of post-traumatic syringomyelia (PTS). While PTS is a devastating complication of SCI, its relative rarity (occurring symptomatically in about 5% of clinical cases), and lack of fundamental physiological insights, have led us to examine an animal model of traumatic SCI with induced arachnoiditis. We hypothesized that arachnoiditis associated with SCI would potentiate early parenchymal pathophysiology. To test this theory, we examined early spatial pathophysiology in four groups: (1) sham (non-injured controls), (2) arachnoiditis (intrathecal injection of kaolin), (3) SCI (35-g clip contusion/compression injury), and (4) PTS (intrathecal kaolin+SCI). Overall, there was greater parenchymal inflammation and scarring in the PTS group relative to the SCI group. This was demonstrated by significant increases in cytokine (IL-1α and IL-1β) and chemokine (MCP-1, GRO/KC, and MIP-1α) production, MPO activity, blood-spinal cord barrier (BSCB) permeability, and MMP-9 activity. However, parenchymal inflammatory mediator production (acute IL-1α and IL-1β, subacute chemokines), BSCB permeability, and fibrous scarring in the PTS group were larger than the sum of the SCI group and arachnoiditis group combined, suggesting that arachnoiditis does indeed potentiate parenchymal pathophysiology. Accordingly, these findings suggest that the development of arachnoiditis associated with SCI can lead to an exacerbation of the parenchymal injury, potentially impacting the outcome of this devastating condition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".