Beneficial effects of secretory leukocyte protease inhibitor after spinal cord injury
Bibliographic record
Abstract
Secretory leukocyte protease inhibitor is a serine protease inhibitor produced by various cell types, including neutrophils and activated macrophages, and has anti-inflammatory properties. It has been shown to promote wound healing in the skin and other non-neural tissues, however, its role in central nervous system injury was not known. We now report a beneficial role for secretory leukocyte protease inhibitor after spinal cord injury. After spinal cord contusion injury in mice, secretory leukocyte protease inhibitor is expressed primarily by astrocytes and neutrophils but not macrophages. We show, using transgenic mice over-expressing secretory leukocyte protease inhibitor, that this molecule has an early protective effect after spinal cord contusion injury. Furthermore, wild-type mice treated for the first week after spinal cord contusion injury with recombinant secretory leukocyte protease inhibitor exhibit sustained improvement in locomotor control and reduced secondary tissue damage. Recombinant secretory leukocyte protease inhibitor injected intraperitoneally localizes to the nucleus of circulating leukocytes, is detected in the injured spinal cord, reduces activation of nuclear factor-kappaB and expression of tumour necrosis factor-alpha. Administration of recombinant secretory leukocyte protease inhibitor might therefore be useful for the treatment of acute spinal cord injury.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".