Characterization of the Early Neuroinflammation After Spinal Cord Injury in Mice
Bibliographic record
Abstract
The occurrence of neuroinflammation after spinal cord injury (SCI) is well established, but its function is debated, with both beneficial and detrimental consequences ascribed. A discriminate of the role of neuroinflammation may be the time period after SCI, and there is evidence to favor early neuroinflammation being undesirable, whereas the later evolving phase may have useful roles. Here, we have focused on the inflammatory response in the first 24 hours of SCI in mice. We found elevation of interleukin (IL)-1beta and other cytokines and chemokines within 15 minutes to 3 hours of injury. The early neuroinflammation in SCI is likely to be CNS-derived and involves microglia, as demonstrated by in situ hybridization for IL-1beta in microglia, by an in vitro model of SCI in which elevation of inflammatory cytokines occurs in the absence of a dynamic source of infiltrating leukocytes, and by the correlation of decreased levels of inflammatory molecules and microglia activity in IL-1beta-null mice. Nonetheless, as there are no specific immunohistochemical markers that clearly differentiate microglia from their peripheral counterparts, macrophages, the latter cannot be definitively excluded as participants in early neuroinflammation in mouse SCI. These results of an instantaneous inflammatory response validate approaches to modulate microglia/macrophage activity to improve recovery from SCI.
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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) | 0.002 | 0.001 |
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".